日志

xiaotingwang@macbookpro-ttt cloud-bot-sr2 %  lsof -ti :8020 | xargs kill -9 2>/dev/null; sleep 2; uv run uvicorn src.cc_bot.main:app --host 0.0.0.0 --port 8020   
    --reload --reload-dir src --reload-dir static    
Loaded config: http_timeout=600s, max_retries=2
INFO:     Started server process [6283]
INFO:     Waiting for application startup.
2026-04-17 14:56:26,513 - cc_bot.router.lifespan - WARNING - Found stale lock file (age: 1959s > 600s), likely from crashed process. Attempting cleanup...
2026-04-17 14:56:26,513 - cc_bot.router.lifespan - INFO - Stale lock file removed successfully
2026-04-17 14:56:26,513 - cc_bot.router.lifespan - INFO - Acquired lifespan initialization lock (this worker is the leader)
2026-04-17 14:56:26,513 - cc_bot.router.lifespan - INFO - Worker startup time recorded: 2026-04-17T14:56:26.513718
2026-04-17 14:56:26,593 - cc_bot.services.claude_init - INFO - Removed existing .claude-sandbox: /Users/xiaotingwang/code/cloud-bot-sr2/.claude-sandbox
2026-04-17 14:56:26,593 - cc_bot.services.claude_init - INFO - Created .claude-sandbox: /Users/xiaotingwang/code/cloud-bot-sr2/.claude-sandbox
2026-04-17 14:56:26,820 - cc_bot.services.claude_init - INFO - Copied claude_template to .claude-sandbox/.claude: /Users/xiaotingwang/code/cloud-bot-sr2/.claude-sandbox/.claude
2026-04-17 14:56:26,872 - cc_bot.services.claude_init - INFO - Domain replacement: 21 files modified, 0 files skipped
2026-04-17 14:56:26,873 - cc_bot.services.claude_init - INFO - Created generic settings.json in .claude-sandbox/.claude
2026-04-17 14:56:26,873 - cc_bot.services.claude_init - INFO - Global .claude-sandbox template initialized successfully
2026-04-17 14:56:26,876 - cc_bot.services.proxy - INFO - Proxy server started on 0.0.0.0:58088 (allowed_domains: ['*'])
2026-04-17 14:56:26,876 - cc_bot.router.lifespan - INFO - HTTP proxy server started on 0.0.0.0:58088
2026-04-17 14:56:27 [INFO] proxy_llm.py:292 - LLM proxy listening on 0.0.0.0:8021 -> https://llm-gateway.momenta.works
2026-04-17 14:56:27,372 - cc_bot.services.config_service - INFO - ConfigService connected to Redis at 10.10.2.238:6379
2026-04-17 14:56:27,515 - cc_bot.services.registry - INFO - Connected to Redis at 10.10.2.238:6379
2026-04-17 14:56:27,544 - cc_bot.services.registry - INFO - No redirect users configured
2026-04-17 14:56:27,544 - cc_bot.router.lifespan - INFO - Not in K8s, Feishu token refresh skipped
2026-04-17 14:56:27,544 - cc_bot.router.lifespan - INFO - Leader worker initialization completed
2026-04-17 14:56:27,992 - cc_bot.models.log_service - INFO - LogService connected to MySQL 10.10.3.41:3306/data_gpt_test
INFO:     Application startup complete.
INFO:     Uvicorn running on http://0.0.0.0:8020 (Press CTRL+C to quit)
2026-04-17 14:57:32,387 - cc_bot.api.tasks - INFO - Stream task 2393f3ac-a257-419e-ae70-7d738db55591 started for session cloud-bot-debug_ou_55e18beb2bc179306d121ae91d1f736b (user: qihong.tang)
INFO:     127.0.0.1:57061 - "POST /api/query/stream?async_reply_callback_url=http://localhost:8010/api/v2/stream-callback?msg_id=om_x100b5129d36e6080b4a7f28482d4310 HTTP/1.1" 200 OK
2026-04-17 14:57:32,790 - cc_bot.services.claude - INFO - Sandbox already initialized at /tmp/cc-sandbox/88c39178-6fe2-8ca7-9174-74f8768874e4, skipping setup
2026-04-17 14:57:32,892 - cc_bot.services.claude - INFO - [_sync_template_dirs] synced 21 files → /tmp/cc-sandbox/88c39178-6fe2-8ca7-9174-74f8768874e4/.claude
2026-04-17 14:57:32,893 - cc_bot.services.claude_memory - INFO - [update_auto_memory_directory] Set autoMemoryDirectory: /tmp/cc-sandbox/auto-memory/qihong_tang/
2026-04-17 14:57:32,894 - cc_bot.services.claude - INFO - session 88c39178-6fe2-8ca7-9174-74f8768874e4: resume=True (file=False, private=True, env=True)
2026-04-17 14:57:32,894 - cc_bot.services.claude - INFO - [execute_stream] cwd=/tmp/cc-sandbox/88c39178-6fe2-8ca7-9174-74f8768874e4 cmd=npx -y @anthropic-ai/claude-code@2.1.77 -p --output-format stream-json --verbose --dangerously-skip-permissions --resume 88c39178-6fe2-8ca7-9174-74f8768874e4 --append-system-prompt '你是 Data SPA Bot 问答机器人，负责理解用户意图并路由到合适的 subagent或skill，同时协调跨 subagent或skill 的数据流转。
> 注意skills/agents 可能随时增加和更新，如找不到agent/skill, 请自动加载最新的能力清单

## Subagent 和 Skill 调用规则（重要）

### 业务型 Subagent：统一使用 Agent 工具

所有业务 subagent（ftl-agent / cfc-agent / tos-agent 等）通过 **Agent 工具**调用，不使用 Skill 工具，不使用 ToolSearch：

```
Agent(
  subagent_type="ftl-agent",
  description="一句话说明本次调用目的",
  prompt="【用户原始请求】{完整原始问题，不改写}

【本次任务】{说明该 subagent 需完成的具体子任务}

【上下文】{前置步骤的输出结果，没有则省略此段}"
)
```

prompt 三要素（缺一不可）：
1. 用户原始请求：完整复制，不改写
2. 本次任务：该 subagent 在本轮需要完成的具体事项
3. 上下文（按需）：前置步骤的输出、依赖的中间数据

### 工具型 Skill：使用 Skill 工具

cron / date / feishu / knowledge-base 等轻量工具型 Skill 仍用 Skill(name, args="...") 调用。

### ToolSearch 说明

- 本环境 ToolSearch 仅支持 `select:<ToolName>` 格式（如 `select:Skill`），**不支持自由文本搜索**
- 业务 subagent 调用无需 ToolSearch，直接用 Agent 工具即可
- **禁止**用自然语言做 ToolSearch（必然失败，浪费时间）
- **禁止** `select:Agent`（Agent 工具不在 ToolSearch 注册表中，必然返回空）

### knowledge-base 优先调用规则
当用户询问平台的**使用方法、操作步骤、API文档、配置说明**等细节问题时（包含"怎么"、"如何"、"步骤"、"教程"、"文档"、"配置"等触发词），**优先**调用 `Skill(knowledge-base)`，不要直接路由到业务 subagent。
- "FTL 怎么导出 dataset？" → `Skill(knowledge-base)` ✅（不是 ftl-agent）
- "CFC 算子任务怎么创建？" → `Skill(knowledge-base)` ✅（不是 cfc-agent）
- "帮我导出 dataset" → ftl-agent ✅（这是操作请求，不是文档查询）

## 多 Subagent 协同编排

### 并行（任务相互独立）

在同一轮中同时发起多个 Agent 调用，不要顺序等待。

### 串行（后步骤依赖前步骤结果）

在后续 Agent 的 prompt【上下文】段落中包含前一步输出。

示例：搜数据 → 导出 dataset → 提交 Flyte
  步骤1：Agent(ftl-agent,  prompt="【用户原始请求】...
【本次任务】搜索天马山远光灯数据")
  步骤2：Agent(ftl-agent,  prompt="【用户原始请求】...
【本次任务】导出为dataset
【上下文】步骤1结果：{dataset_name}")
  步骤3：Agent(tos-flyte-agent, prompt="【用户原始请求】...
【本次任务】提交Flyte任务
【上下文】目标dataset：{dataset_name}")

### 主 Agent 职责边界

- ✅ 意图分解、任务分发、结果汇总
- ❌ 禁止自行调用 HTTP API（curl、wget 等）绕过 subagent
- ❌ 禁止 subagent 失败后擅自切换到其他 subagent 或自行实现
- ❌ 禁止用自由文本做 ToolSearch

## Fallback 策略
如果无法匹配到合适的 subagent或skill：
1. 先尝试 skill(feishu) 搜索公司内部文档
2. 再尝试 mcp__searxng 进行 web search
3. 最后尝试 mcp__fetch-mcp 读取 web url 内容

## Skill 调用规则
调用 Skill 工具时，**必须**通过 args 参数传递用户的具体问题或请求。
示例：Skill(skill="TOS-Flyte", args="查询 dev 环境 dpi 项目的执行状态")
不要只传 skill 名称而不传 args，否则 fork 模式的 skill 无法获取用户意图。

## skill http 接口调用规则

### 调用http请求时的 HTTP Headers
1. 绝大部分skill接口都有HTTP接口，使用curl调用
2. 执行curl 请求时，统一把以下环境变量添加到 headers 中(不用检查环境变量是否存在,可为空)：

-H "x-username: $SESSION_USERNAME" -H "x-msgid: $SESSION_MSGID" -H "x-platform: $SESSION_PLATFORM" -H "x-quote-userlist: $SESSION_QUOTE_USERLIST"

### HTTP 请求配置
- 超时时间: 600s（超时则返回具体错误信息给用户）
- **可重试错误**（超时、HTTP 5xx）：最多重试 1 次，超出则直接报错
- **不可重试错误**（HTTP 4xx、连接拒绝、响应体为 HTML）：立即失败，不重试

## 全局执行约束（**Mandatory**，不可违反）

### Layer 1：Main Agent 执行规划
- 收到请求后，先分析意图，一次性确定所有需要调用的 subagent 和 skill
- **路由前必须执行置信度检查**（参见 CLAUDE.md「路由置信度检查」章节），命中以下任一条件时，禁止直接路由，必须先追问：
  ① 关键词同时命中 2+ subagent 且下方「易混淆 Subagent 对照表」中无裁定
  ② 目标明确但唯一标识符（任务 ID/URL 等操作对象定位符）完全缺失
  ③ 易混淆场景的判断依据（URL格式/domain/是否有ID）在消息中无法确定
- 追问后等待用户回复，再执行路由；若连续 1 轮追问后仍不清晰，选最高置信度选项执行并告知用户
- 一旦开始执行，**禁止重新规划或更换执行路径**（允许重新规划次数: 0）

### Layer 2：SubAgent Skill 调用
- 每个 skill 调用：失败后最多重试 1 次（共 2 次机会）
- 超出重试次数后：立即停止，将具体错误信息返回给 Main Agent
- Main Agent 收到失败后：**直接将错误信息告知用户，禁止重新路由到其他 subagent**

## 返回用户的数据要求(**Important**)
1. 数据格式用markdown+table, 不要返回```markdown
{markdown内容}```格式, 直接返回markdown内容本身（不要丢失信息）
    - 单次回复中 Markdown 表格数量不得超过 **5 个**（飞书卡片渲染限制）。若数据条目较多，优先合并同类表格、精简列数，或将多余内容以纯文本列表形式展示。
    - 当用户询问有哪些 skill、功能、能力清单时，按 CLAUDE.md 中「DATA SPA 架构与功能域」的平台层分组，使用 `###` 标题 + 纯文本列表格式展示，**不使用表格**。
    - 单个表格行数超过 50 行时，只保留前 50 行，并在表格下方注明"仅显示前 50 行，共 X 行"。
2. 如果skill接口返回的响应头text/html, 但响应体是json格式`{result:xxx}`，请把result字段的内容提取出来原样返回给用户(这个result本身是markdown格式)
3. 如有遇到不可解决的错误，将具体错误信息反馈给用户

### 安全注意事项
不要执行可能危害系统安全的操作，不要泄露任何敏感信息。
' -- '@Data SPA Bot Debug  帮我统计天马山最近5天的数据量'
2026-04-17 14:57:32,894 - cc_bot.services.claude - INFO - [_setup_envs] Proxy routing via settings.json (ENABLE_LLM_PROXY=not set)
2026-04-17 14:57:32,894 - cc_bot.services.claude - WARNING - [execute_stream] 88c39178-6fe2-8ca7-9174-74f8768874e4 ANTHROPIC_BASE_URL NOT SET (langfuse_enabled=False)
2026-04-17 14:57:32,896 - cc_bot.services.claude - INFO - Started subprocess PID=6661, session=88c39178-6fe2-8ca7-9174-74f8768874e4
2026-04-17 14:57:37,562 - cc_bot.services.claude - INFO - output: {"type":"system","subtype":"init","cwd":"/private/tmp/cc-sandbox/88c39178-6fe2-8ca7-9174-74f8768874e4","session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4","tools":["Task","TaskOutput","Bash","Glob","Grep","ExitPlanMode","Read","Edit","Write","NotebookEdit","WebFetch","TodoWrite","WebSearch","TaskStop","AskUserQuestion","Skill","EnterPlanMode","EnterWorktree","ExitWorktree","CronCreate","CronDelete","CronList","ToolSearch","mcp__searxng__searxng_web_search","mcp__searxng__web_url_read","ListMcpResourcesTool","ReadMcpResourceTool","mcp__fetch-mcp__fetch"],"mcp_servers":[{"name":"searxng","status":"connected"},{"name":"fetch-mcp","status":"connected"}],"model":"claude-sonnet-4.6","permissionMode":"bypassPermissions","slash_commands":["update-config","debug","simplify","batch","loop","claude-api","CAS-Import","CFC","Common-Dct","Common-Utils","DATE","DCS","DCT","DIS","DMP","DRM","DSS","DW","Dataflow","Devops-Coder","FMP","FTL","Flowchain","NSP","PRT","SIM","SMP","STATS","TDP","TOS","TOS-Flyte","Toolkits","USER","about-me","autodct-checker-config-helper","bag-topic","checkpoint","cron","feishu","follow-up","import-badcase-by-msg","knowledge-base","log-analyzer","markdown-to-feishu-card","prompt-check","regression-test","skill-optimization","valid-bag","compact","context","cost","heapdump","init","pr-comments","release-notes","review","security-review","insights"],"apiKeySource":"none","claude_code_version":"2.1.77","output_style":"default","agents":["general-purpose","statusline-setup","Explore","Plan","adp-agent","dss-agent","dw-agent","smp-agent","ftl-agent","devops-coder-agent","sim-agent","cas-import-agent","prt-agent","tos-agent","drm-agent","common-dct-agent","dmp-agent","bag-topic-agent","dct-agent","cfc-agent","common-utils-agent","dis-agent","tos-flyte-agent","valid-bag-agent","tdp-agent","fmp-agent","user-agent","nsp-agent","dcs-agent","stats-agent","dataflow-agent","flowchain-agent"],"skills":["update-config","debug","simplify","batch","loop","claude-api","CAS-Import","CFC","Common-Dct","Common-Utils","DATE","DCS","DCT","DIS","DMP","DRM","DSS","DW","Dataflow","Devops-Coder","FMP","FTL","Flowchain","NSP","PRT","SIM","SMP","STATS","TDP","TOS","TOS-Flyte","Toolkits","USER","about-me","autodct-checker-config-helper","bag-topic","checkpoint","cron","feishu","follow-up","import-badcase-by-msg","knowledge-base","log-analyzer","markdown-to-feishu-card","prompt-check","regression-test","skill-optimization","valid-bag"],"plugins":[],"uuid":"a6e353a5-b489-47a1-ba2f-853c25034593","fast_mode_state":"off"}
2026-04-17 14:57:37,563 - cc_bot.services.claude - INFO - Parsing stream line: type=system
2026-04-17 14:57:37,563 - cc_bot.services.claude - INFO - [execute_stream] 88c39178-6fe2-8ca7-9174-74f8768874e4 system_init: system_init(tools, mcp_servers, skills, plugins)
INFO:     127.0.0.1:57073 - "POST /api/log/hook-langfuse HTTP/1.1" 200 OK
INFO:     127.0.0.1:57074 - "POST /api/log/hook HTTP/1.1" 200 OK
2026-04-17 14:57:43,210 - cc_bot.services.claude - INFO - output: {"type":"assistant","message":{"model":"claude-sonnet-4-6","id":"msg_bdrk_011f595ComHEzzEZNDhBR9gG","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"用户想统计天马山最近5天的数据量。今天是2026-04-17。\n\n最近5天：\n- start = 2026-04-13 00:00:00\n- end = 2026-04-17 23:59:59\n\n直接路由到 dss-agent。","signature":"EtYCCkgIDBABGAIqQO7GyMrAzRb4feg2c7aEGZMFyFzWpZfUIPP81XW6OqmsPyNdlcm0PaSxoJvyZIkZFcbYCjhRqsk9i9fQrUXA9d8SDFjzFwzW8/PmmYUb9RoM98sXJgDRO0HM8f/qIjDWxbO89E2DtKbMaFs6GuPLbyLZEY2LCNavbqzWuSts/xK0qIzqGDccWDVy4GQMMFYquwH9RhOQomgXLMmUNQSM9NtOiA/KsvGnWE8LfG4Y+lKFLDFwgZkT4sSxKRvd6MmCTASHlWwMaI2s+88fm9mg3wFwVyhTjXKa2/hSX5lm6vZRsRoHXsmoJRnom4G/TCgja8uImDXaqAQbZMbuhMxkcRPK1pd8CMzgRBsBUOl5qQXXjo062bXsRlMF2jvs4RIuomJtIIyLOR17l9FvEib5sdmxPabuUk6PRUP1I36XuvFQcJb2ghAP5kxA/ovmGAE="}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":10,"cache_creation_input_tokens":22325,"cache_read_input_tokens":6552,"cache_creation":{"ephemeral_5m_input_tokens":22325,"ephemeral_1h_input_tokens":0},"output_tokens":3},"context_management":null},"parent_tool_use_id":null,"session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4","uuid":"2f7ee525-eaf6-4d60-900a-038f8c6b8023"}
2026-04-17 14:57:43,211 - cc_bot.services.claude - INFO - Parsing stream line: type=assistant
2026-04-17 14:57:43,211 - cc_bot.services.claude - INFO - [execute_stream] 88c39178-6fe2-8ca7-9174-74f8768874e4 text: [Thinking]
用户想统计天马山最近5天的数据量。今天是2026-04-17。

最近5天：
- start = 2026-04-13 00:00:00
- end = 2026-04-17 23:59:59

直接路由到 dss-agent。

2026-04-17 14:57:43,578 - cc_bot.services.claude - INFO - output: {"type":"assistant","message":{"model":"claude-sonnet-4-6","id":"msg_bdrk_011f595ComHEzzEZNDhBR9gG","type":"message","role":"assistant","content":[{"type":"text","text":"好的，帮你统计天马山最近5天（2026-04-13 至 2026-04-17）的数据量。"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":10,"cache_creation_input_tokens":22325,"cache_read_input_tokens":6552,"cache_creation":{"ephemeral_5m_input_tokens":22325,"ephemeral_1h_input_tokens":0},"output_tokens":3},"context_management":null},"parent_tool_use_id":null,"session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4","uuid":"0b96a55a-d4e7-4101-9099-655b6f470f81"}
2026-04-17 14:57:43,578 - cc_bot.services.claude - INFO - Parsing stream line: type=assistant
2026-04-17 14:57:43,578 - cc_bot.services.claude - INFO - [execute_stream] 88c39178-6fe2-8ca7-9174-74f8768874e4 text: 好的，帮你统计天马山最近5天（2026-04-13 至 2026-04-17）的数据量。
2026-04-17 14:57:46,883 - cc_bot.services.claude - INFO - output: {"type":"assistant","message":{"model":"claude-sonnet-4-6","id":"msg_bdrk_011f595ComHEzzEZNDhBR9gG","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_bdrk_0158X6K1CpSxPakUEMiksfiR","name":"Agent","input":{"subagent_type":"dss-agent","description":"统计天马山最近5天的数据量","prompt":"【用户原始请求】帮我统计天马山最近5天的数据量\n\n【本次任务】统计天马山（tianshanshan / 天马山）最近5天的数据量，时间范围：2026-04-13 00:00:00 至 2026-04-17 23:59:59，按日期分组返回数据统计结果（数量+大小）。"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":10,"cache_creation_input_tokens":22325,"cache_read_input_tokens":6552,"cache_creation":{"ephemeral_5m_input_tokens":22325,"ephemeral_1h_input_tokens":0},"output_tokens":3},"context_management":null},"parent_tool_use_id":null,"session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4","uuid":"b02c5889-ab5c-4760-9db9-0fd9dc548f23"}
2026-04-17 14:57:46,883 - cc_bot.services.claude - INFO - Parsing stream line: type=assistant
2026-04-17 14:57:46,883 - cc_bot.services.claude - INFO - Unknown content block type: tool_use
2026-04-17 14:57:46,983 - cc_bot.services.claude - INFO - output: {"type":"system","subtype":"task_started","task_id":"a9f42b8fbe4a66059","tool_use_id":"toolu_bdrk_0158X6K1CpSxPakUEMiksfiR","description":"统计天马山最近5天的数据量","task_type":"local_agent","prompt":"【用户原始请求】帮我统计天马山最近5天的数据量\n\n【本次任务】统计天马山（tianshanshan / 天马山）最近5天的数据量，时间范围：2026-04-13 00:00:00 至 2026-04-17 23:59:59，按日期分组返回数据统计结果（数量+大小）。","uuid":"bdaad27e-55d0-44a4-bb06-9d13b144b353","session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4"}
2026-04-17 14:57:46,984 - cc_bot.services.claude - INFO - Parsing stream line: type=system
2026-04-17 14:57:46,984 - cc_bot.services.claude - INFO - Unrecognized system message in stream: data={'type': 'system', 'subtype': 'task_started', 'task_id': 'a9f42b8fbe4a66059', 'tool_use_id': 'toolu_bdrk_0158X6K1CpSxPakUEMiksfiR', 'description': '统计天马山最近5天的数据量', 'task_type': 'local_agent', 'prompt': '【用户原始请求】帮我统计天马山最近5天的数据量\n\n【本次任务】统计天马山（tianshanshan / 天马山）最近5天的数据量，时间范围：2026-04-13 00:00:00 至 2026-04-17 23:59:59，按日期分组返回数据统计结果（数量+大小）。', 'uuid': 'bdaad27e-55d0-44a4-bb06-9d13b144b353', 'session_id': '88c39178-6fe2-8ca7-9174-74f8768874e4'}
2026-04-17 14:57:46,984 - cc_bot.services.claude - INFO - output: {"type":"user","message":{"role":"user","content":[{"type":"text","text":"【用户原始请求】帮我统计天马山最近5天的数据量\n\n【本次任务】统计天马山（tianshanshan / 天马山）最近5天的数据量，时间范围：2026-04-13 00:00:00 至 2026-04-17 23:59:59，按日期分组返回数据统计结果（数量+大小）。"}]},"parent_tool_use_id":"toolu_bdrk_0158X6K1CpSxPakUEMiksfiR","session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4","uuid":"336ab796-f419-4ef5-a7f4-69e22a203b65","timestamp":"2026-04-17T06:57:46.978Z"}
2026-04-17 14:57:46,984 - cc_bot.services.claude - INFO - Parsing stream line: type=user
2026-04-17 14:57:46,984 - cc_bot.services.claude - INFO - [execute_stream] 88c39178-6fe2-8ca7-9174-74f8768874e4 user: user.message:text:
INFO:     127.0.0.1:57087 - "POST /api/log/hook HTTP/1.1" 200 OK
INFO:     127.0.0.1:57086 - "POST /api/log/hook-langfuse HTTP/1.1" 200 OK
INFO:     127.0.0.1:57088 - "POST /api/log/hook-langfuse HTTP/1.1" 200 OK
INFO:     127.0.0.1:57089 - "POST /api/log/hook HTTP/1.1" 200 OK
2026-04-17 14:57:50,993 - cc_bot.services.claude - INFO - output: {"type":"system","subtype":"task_progress","task_id":"a9f42b8fbe4a66059","tool_use_id":"toolu_bdrk_0158X6K1CpSxPakUEMiksfiR","description":"统计天马山最近5天的数据量","usage":{"total_tokens":15151,"tool_uses":1,"duration_ms":4012},"last_tool_name":"ToolSearch","uuid":"3dcd9b54-0400-45b5-a758-caf84e97a645","session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4"}
2026-04-17 14:57:50,993 - cc_bot.services.claude - INFO - Parsing stream line: type=system
2026-04-17 14:57:50,994 - cc_bot.services.claude - INFO - Unrecognized system message in stream: data={'type': 'system', 'subtype': 'task_progress', 'task_id': 'a9f42b8fbe4a66059', 'tool_use_id': 'toolu_bdrk_0158X6K1CpSxPakUEMiksfiR', 'description': '统计天马山最近5天的数据量', 'usage': {'total_tokens': 15151, 'tool_uses': 1, 'duration_ms': 4012}, 'last_tool_name': 'ToolSearch', 'uuid': '3dcd9b54-0400-45b5-a758-caf84e97a645', 'session_id': '88c39178-6fe2-8ca7-9174-74f8768874e4'}
2026-04-17 14:57:50,994 - cc_bot.services.claude - INFO - output: {"type":"assistant","message":{"model":"claude-sonnet-4-6","id":"msg_bdrk_01DXyqVesgv5cZKg29qmvYYu","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_bdrk_01YGdKSsoQc8p5hmN5fkarve","name":"ToolSearch","input":{"query":"select:Read","max_results":1}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":15147,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":15147,"ephemeral_1h_input_tokens":0},"output_tokens":1},"context_management":null},"parent_tool_use_id":"toolu_bdrk_0158X6K1CpSxPakUEMiksfiR","session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4","uuid":"cccbe1d0-eb58-4e1b-86dd-5035296d2e30"}
2026-04-17 14:57:50,994 - cc_bot.services.claude - INFO - Parsing stream line: type=assistant
2026-04-17 14:57:50,994 - cc_bot.services.claude - INFO - Unknown content block type: tool_use
2026-04-17 14:57:51,013 - cc_bot.services.claude - INFO - output: {"type":"user","message":{"role":"user","content":[{"type":"tool_result","tool_use_id":"toolu_bdrk_01YGdKSsoQc8p5hmN5fkarve","content":[{"type":"tool_reference","tool_name":"Read"}]}]},"parent_tool_use_id":"toolu_bdrk_0158X6K1CpSxPakUEMiksfiR","session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4","uuid":"c0af9f18-328a-44fd-aa0a-209ed5660890","timestamp":"2026-04-17T06:57:51.010Z"}
2026-04-17 14:57:51,013 - cc_bot.services.claude - INFO - Parsing stream line: type=user
2026-04-17 14:57:51,013 - cc_bot.services.claude - INFO - [execute_stream] 88c39178-6fe2-8ca7-9174-74f8768874e4 user: user.message:tool_result:[{'type': 'tool_reference', 'tool_name': 'Read'}]
INFO:     127.0.0.1:57093 - "POST /api/log/hook-langfuse HTTP/1.1" 200 OK
INFO:     127.0.0.1:57092 - "POST /api/log/hook HTTP/1.1" 200 OK
INFO:     127.0.0.1:57095 - "POST /api/log/hook-langfuse HTTP/1.1" 200 OK
INFO:     127.0.0.1:57094 - "POST /api/log/hook HTTP/1.1" 200 OK
2026-04-17 14:57:54,762 - cc_bot.services.claude - INFO - output: {"type":"system","subtype":"task_progress","task_id":"a9f42b8fbe4a66059","tool_use_id":"toolu_bdrk_0158X6K1CpSxPakUEMiksfiR","description":"Reading /tmp/cc-sandbox/88c39178-6fe2-8ca7-9174-74f8768874e4/.claude/skills/DSS/references/statistics.md","usage":{"total_tokens":15952,"tool_uses":2,"duration_ms":7781},"last_tool_name":"Read","uuid":"771ce899-2067-4137-b31f-8aec12dba8ef","session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4"}
2026-04-17 14:57:54,762 - cc_bot.services.claude - INFO - Parsing stream line: type=system
2026-04-17 14:57:54,762 - cc_bot.services.claude - INFO - Unrecognized system message in stream: data={'type': 'system', 'subtype': 'task_progress', 'task_id': 'a9f42b8fbe4a66059', 'tool_use_id': 'toolu_bdrk_0158X6K1CpSxPakUEMiksfiR', 'description': 'Reading /tmp/cc-sandbox/88c39178-6fe2-8ca7-9174-74f8768874e4/.claude/skills/DSS/references/statistics.md', 'usage': {'total_tokens': 15952, 'tool_uses': 2, 'duration_ms': 7781}, 'last_tool_name': 'Read', 'uuid': '771ce899-2067-4137-b31f-8aec12dba8ef', 'session_id': '88c39178-6fe2-8ca7-9174-74f8768874e4'}
2026-04-17 14:57:54,763 - cc_bot.services.claude - INFO - output: {"type":"assistant","message":{"model":"claude-sonnet-4-6","id":"msg_bdrk_01KCZftNjyscmt9Nhd2bFD6P","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_bdrk_01FbKPp1ydqhHB6zY8ZA4Ukg","name":"Read","input":{"file_path":"/tmp/cc-sandbox/88c39178-6fe2-8ca7-9174-74f8768874e4/.claude/skills/DSS/references/statistics.md"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":15922,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":15922,"ephemeral_1h_input_tokens":0},"output_tokens":25},"context_management":null},"parent_tool_use_id":"toolu_bdrk_0158X6K1CpSxPakUEMiksfiR","session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4","uuid":"e74110a2-a441-4572-98fb-5cd3e3dfeb84"}
2026-04-17 14:57:54,763 - cc_bot.services.claude - INFO - Parsing stream line: type=assistant
2026-04-17 14:57:54,763 - cc_bot.services.claude - INFO - Unknown content block type: tool_use
2026-04-17 14:57:54,967 - cc_bot.services.claude - INFO - output: {"type":"system","subtype":"task_progress","task_id":"a9f42b8fbe4a66059","tool_use_id":"toolu_bdrk_0158X6K1CpSxPakUEMiksfiR","description":"Reading /tmp/cc-sandbox/88c39178-6fe2-8ca7-9174-74f8768874e4/.claude/skills/DSS/references/field_mapping.md","usage":{"total_tokens":15977,"tool_uses":3,"duration_ms":7986},"last_tool_name":"Read","uuid":"3f8e22a7-4bf3-4ea4-ba85-a68f14ffeb93","session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4"}
2026-04-17 14:57:54,967 - cc_bot.services.claude - INFO - Parsing stream line: type=system
2026-04-17 14:57:54,967 - cc_bot.services.claude - INFO - Unrecognized system message in stream: data={'type': 'system', 'subtype': 'task_progress', 'task_id': 'a9f42b8fbe4a66059', 'tool_use_id': 'toolu_bdrk_0158X6K1CpSxPakUEMiksfiR', 'description': 'Reading /tmp/cc-sandbox/88c39178-6fe2-8ca7-9174-74f8768874e4/.claude/skills/DSS/references/field_mapping.md', 'usage': {'total_tokens': 15977, 'tool_uses': 3, 'duration_ms': 7986}, 'last_tool_name': 'Read', 'uuid': '3f8e22a7-4bf3-4ea4-ba85-a68f14ffeb93', 'session_id': '88c39178-6fe2-8ca7-9174-74f8768874e4'}
2026-04-17 14:57:54,967 - cc_bot.services.claude - INFO - output: {"type":"assistant","message":{"model":"claude-sonnet-4-6","id":"msg_bdrk_01KCZftNjyscmt9Nhd2bFD6P","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_bdrk_01TRxKNZLoHiriRNybUrSwan","name":"Read","input":{"file_path":"/tmp/cc-sandbox/88c39178-6fe2-8ca7-9174-74f8768874e4/.claude/skills/DSS/references/field_mapping.md"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":15922,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":15922,"ephemeral_1h_input_tokens":0},"output_tokens":25},"context_management":null},"parent_tool_use_id":"toolu_bdrk_0158X6K1CpSxPakUEMiksfiR","session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4","uuid":"ed34699d-e1a5-4c5c-a030-ae58361eec10"}
2026-04-17 14:57:54,967 - cc_bot.services.claude - INFO - Parsing stream line: type=assistant
2026-04-17 14:57:54,967 - cc_bot.services.claude - INFO - Unknown content block type: tool_use
2026-04-17 14:57:55,001 - cc_bot.services.claude - INFO - output: {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_bdrk_01TRxKNZLoHiriRNybUrSwan","type":"tool_result","content":"     1→# DSS 字段映射与查询转换指南\n     2→\n     3→本文档供 agent 将用户自然语言转换为 DSS ES query 时参考，分为三个部分：\n     4→- **Part 1**：字段映射速查表（字段名、类型、说明）\n     5→- **Part 2**：字段识别与格式化规则（输入识别、格式校验、语义转换）\n     6→- **Part 3**：查询构造规则（优先级、时间处理、排除条件、特殊标记转换）\n     7→\n     8→---\n     9→\n    10→# Part 1: 字段映射速查表\n    11→\n    12→## 基础字段\n    13→\n    14→| 用户表达 | ES 字段名 | 类型 | 说明 |\n    15→|----------|-----------|------|------|\n    16→| 回流时间 | `create_time` | date | 格式 `\"2021-03-26T02:46:52.723Z\"`（北京时间，非 UTC） |\n    17→| 采集时间 | `collected_time` | date | 同上 |\n    18→| 车牌号 | `plate_number` | string | 如 `PLEBR9537` |\n    19→| hostname | `fdi.fmp.car_info.device_box_id` | string | 如 `LC6-EAT3708` |\n    20→| 匿名ID、VIN码 | `custom_meta.mass_production.vin` | string | 40位匿名ID / 17位VIN / 19位byd格式 |\n    21→| 硬盘序列号 | `hard_disk_sn` | string | S开头15位，如 `S5G9NC0RA01196E` |\n    22→| MD5 | `md5` | string | 32位小写hex |\n    23→| 数据名、bag名 | `name` | string | 如 `xxx.bag` |\n    24→| 项目名、山头 | `asset.project` | string | 如 `天马山`、`Harz`、`Rocky` |\n    25→| 数据源环境 | `custom_meta.data_source_env` | string | `SOP` / `TEST` |\n    26→| 车型 | `fleet_meta.car_type` | string | 量产研采统一字段 |\n    27→| 数据类型 | `type` | string | `bag`（默认）/ `json` / `gz` |\n    28→| FDI类型、量产/研采 | `asset.fdi_type` | string | `CFDI`（量产）/ `DFDI`（研采） |\n    29→| 版本号 | `fleet_meta.packages_version` | string | 量产研采统一字段 |\n    30→| 数据大小 | `size` | long | 单位字节 |\n    31→| 筛选器/事件名 | `fleet_meta.event_statistics_name` | list[string] | 车端筛选器名称（量产研采统一字段） |\n    32→| 上传用户 | `user` | string | 如 `xiaolong.li` |\n    33→| 采集类型 | `collect_type` | string | |\n    34→| UUID | `fleet_meta.uuid` | string | |\n    35→| 标签、tag | `tag` | list[string] | 含车辆类型标签、相机故障标签等 |\n    36→| topic | `baginfo.topics.topic` | string | bag 包含的 ROS topic 名称，精确匹配 |\n    37→\n    38→## 关联数据字段\n    39→\n    40→bag 的关联文件信息存储在 `relative.*` 命名空间下。\n    41→\n    42→**[CRITICAL] 查询规则**：\n    43→- `meta_type` 默认为 `\"bag\"`，禁止改为 mflog、landmark 等（唯一例外：foreverlog 用 `\"gz\"`，见下方「独立数据类型」）\n    44→- 使用 `exists` 查询筛选有关联数据的 bag，示例：`{\"bool\": {\"must\": [{\"exists\": \"relative.landmark\"}]}}`\n    45→- 关联数据的链接通过 `relative.*` 字段值构造：`https://cla.momenta.works/cdi/data/{relative_md5}`\n    46→- 查询关联数据时需将对应 `relative.*` 字段加入 `save_data`，用 `get_field()` 获取值构造链接\n    47→\n    48→| 用户表达 | ES 字段名 | 类型 | 说明 |\n    49→|----------|-----------|------|------|\n    50→| landmark | `relative.landmark` | string | bag 关联的 landmark md5 |\n    51→| landmark 对应的 bag | `relative.landmark_source` | string | landmark 对应的源 bag md5 |\n    52→| mflog、日志、日志文件 | `relative.mflog` | string | bag 的默认日志文件 md5 |\n    53→| msfbag、小bag | `relative.msfbag` | string | bag 关联的小 bag md5 |\n    54→| coredump、崩溃、崩溃文件 | `relative.coredump` | string | bag 的 coredump 文件 md5 |\n    55→\n    56→## 独立数据类型（非关联数据）\n    57→\n    58→以下数据类型有独立的 meta 记录，搜索时需改变 `meta_type`，不走 `relative.*` 路径。\n    59→\n    60→| 用户表达 | meta_type | 附加查询条件 | 说明 |\n    61→|----------|-----------|-------------|------|\n    62→| foreverlog / forever_log / 永久日志 | `\"gz\"` | `{\"fleet_meta.event_statistic_name\": \"forever_log\"}` | 永久日志，独立 gz 文件 |\n    63→\n    64→---\n    65→\n    66→## features 子字段\n    67→\n    68→features 为 meta 中的嵌套对象，查询时字段路径为 `features.xxx`。\n    69→\n    70→| 用户表达 | ES 字段路径 | 类型 | 说明 |\n    71→|----------|-------------|------|------|\n    72→| 碰撞风险 | `features.crash_risk` | bool | |\n    73→| RB碰撞风险 | `features.RB_crash` | bool | |\n    74→| CP安全分数 | `features.cp_safety_score` | float[] | 0~1 |\n    75→| UNP安全分数 | `features.unp_safety_score` | float[] | 0~1 |\n    76→| 降级标志 | `features.degradation.flag` | bool | |\n    77→| 降级功能 | `features.degradation.degradation_functions` | string[] | |\n    78→| 降级触发功能 | `features.degradation.degradation_functions_trigger` | string[] | |\n    79→| 故障码 | `features.degradation.error_code` | string[] | `0x` 开头6位 |\n    80→| 降级触发故障码 | `features.degradation.error_code_trigger` | string[] | |\n    81→| 智驾状态 | `features.function_active` | string[] | 见 Part 2 完整枚举 |\n    82→| ACC接管 | `features.takeover.ACC` | bool | |\n    83→| CP接管 | `features.takeover.CP` | bool | |\n    84→| HNP接管 | `features.takeover.HNP` | bool | |\n    85→| UNP接管 | `features.takeover.UNP` | bool | |\n    86→| 最大速度 | `features.velocity_max` | float | 单位 km/h |\n    87→| 最小速度 | `features.velocity_min` | float | 单位 km/h |\n    88→| 视觉碰撞面积 | `features.dsc_collision.vision_od` | float | |\n    89→| 视觉碰撞TTC | `features.dsc_collision.vision_od_ttc` | float | |\n    90→| 雷达碰撞面积 | `features.dsc_collision.lidar_od` | float | |\n    91→| 雷达碰撞TTC | `features.dsc_collision.lidar_od_ttc` | float | |\n    92→| mpilot抑制adas | `features.suppress.mpilot_suppress_adas` | string[] | 见下方 suppress 章节 |\n    93→| adas抑制mpilot | `features.suppress.adas_suppress_mpilot` | string[] | 见下方 suppress 章节 |\n    94→| 红绿灯 | `features.red_light_flag` | bool | |\n    95→| 天色 | `features.time_of_day` | string | day/night/dusk/dawn |\n    96→| 云端筛选器触发 | `features.cloud_filters` | string[] | 云端筛选器触发名称 |\n    97→| 施工标志数量 | `features.static_barrier.construction_sign_numbers` | int | |\n    98→| A型标志牌数量 | `features.static_barrier.aframe_sign_numbers` | int | |\n    99→| 防撞桶数量 | `features.static_barrier.anticrash_bucket_numbers` | int | |\n   100→| 隔离石数量 | `features.static_barrier.barrier_stone_numbers` | int | |\n   101→| 防撞柱套数量 | `features.static_barrier.bollard_sleeve_numbers` | int | |\n   102→| 锥桶数量 | `features.static_barrier.cone_numbers` | int | |\n   103→| 垃圾桶数量 | `features.static_barrier.dustbin_numbers` | int | |\n   104→| 车位锁数量 | `features.static_barrier.parking_lock_numbers` | int | |\n   105→| 车位挡板数量 | `features.static_barrier.parking_stopper_numbers` | int | |\n   106→| 警示三角架数量 | `features.static_barrier.warning_triangle_numbers` | int | |\n   107→| 水马数量 | `features.static_barrier.water_barrier_numbers` | int | |\n   108→| 动物数量 | `features.vrus.animal` | int | |\n   109→| 自行车数量 | `features.vrus.bicycle` | int | |\n   110→| 行人数量 | `features.vrus.pedestrian` | int | |\n   111→| 三轮车数量 | `features.vrus.tricycle` | int | |\n   112→| 手推车数量 | `features.vrus.trolley` | int | |\n   113→\n   114→---\n   115→\n   116→## 车辆类型标签映射\n   117→\n   118→车辆类型标签存储在 `tag` 字段中，查询时直接使用 `{\"tag\": \"VEHICLE_SUB_TYPE_xxx\"}`。\n   119→\n   120→| 用户表达 | 标签值 |\n   121→|----------|--------|\n   122→| 未知车辆 | `VEHICLE_SUB_TYPE_UNKNOWN_VEHICLE` |\n   123→| 车灯车辆、夜间车辆 | `VEHICLE_SUB_TYPE_LAMP_UNKNOWN_VEHICLE` |\n   124→| 车轮车辆 | `VEHICLE_SUB_TYPE_WHEEL_UNKNOWN_VEHICLE` |\n   125→| 小汽车、乘用车 | `VEHICLE_SUB_TYPE_UNKNOWN_CAR` |\n   126→| 轿车 | `VEHICLE_SUB_TYPE_SEDAN_CAR` |\n   127→| SUV | `VEHICLE_SUB_TYPE_SUV_CAR` |\n   128→| 皮卡 | `VEHICLE_SUB_TYPE_PICKUP_CAR` |\n   129→| 警车 | `VEHICLE_SUB_TYPE_POLICE_CAR` |\n   130→| MPV | `VEHICLE_SUB_TYPE_MPV_CAR` |\n   131→| 卡车、货车 | `VEHICLE_SUB_TYPE_UNKNOWN_TRUCK` |\n   132→| 厢式卡车 | `VEHICLE_SUB_TYPE_VAN_TRUCK` |\n   133→| 平板卡车 | `VEHICLE_SUB_TYPE_PLATE_TRUCK` |\n   134→| 带侧防护的平板卡车 | `VEHICLE_SUB_TYPE_PLATE_W_SG_TRUCK` |\n   135→| 无侧防护的平板卡车 | `VEHICLE_SUB_TYPE_PLATE_WO_SG_TRUCK` |\n   136→| 自卸卡车 | `VEHICLE_SUB_TYPE_DUMP_TRUCK` |\n   137→| 罐式卡车 | `VEHICLE_SUB_TYPE_TANK_TRUCK` |\n   138→| 危险品运输车、油罐车 | `VEHICLE_SUB_TYPE_DANGER_TRANSPORT_TRUCK` |\n   139→| 拖挂车 | `VEHICLE_SUB_TYPE_TRAILER_TRUCK` |\n   140→| 车辆运输车 | `VEHICLE_SUB_TYPE_VEHICLE_CARRIER_TRUCK` |\n   141→| 工程车 | `VEHICLE_SUB_TYPE_ENGINEERING_TRUCK` |\n   142→| 救护车 | `VEHICLE_SUB_TYPE_AMBULANCE_TRUCK` |\n   143→| 消防车 | `VEHICLE_SUB_TYPE_FIRE_ENGINE_TRUCK` |\n   144→| 超长卡车 | `VEHICLE_SUB_TYPE_EXTREMELY_LONG_TRUCK` |\n   145→| 清扫车 | `VEHICLE_SUB_TYPE_SWEEPER_TRUCK` |\n   146→| 叉车 | `VEHICLE_SUB_TYPE_FORK_LIFT_TRUCK` |\n   147→| 巴士、客车 | `VEHICLE_SUB_TYPE_UNKNOWN_BUS` |\n   148→| 长途客运巴士 | `VEHICLE_SUB_TYPE_PASSENGER_BUS` |\n   149→| 校车 | `VEHICLE_SUB_TYPE_SCHOOL_BUS` |\n   150→| 城市公交车 | `VEHICLE_SUB_TYPE_CITY_BUS` |\n   151→| 铰接巴士 | `VEHICLE_SUB_TYPE_ARTICULATED_BUS` |\n   152→| 无轨电车 | `VEHICLE_SUB_TYPE_TROLLEY_BUS` |\n   153→| 面包车、小型巴士 | `VEHICLE_SUB_TYPE_MINI_BUS` |\n   154→| 房车 | `VEHICLE_SUB_TYPE_RV_BUS` |\n   155→\n   156→---\n   157→\n   158→## 相机故障标签映射\n   159→\n   160→相机故障标签存储在 `tag` 字段中，查询时直接使用 `{\"tag\": \"FS_xxx\"}`。\n   161→\n   162→| 用户表达 | 标签值 |\n   163→|----------|--------|\n   164→| 其他相机故障 | `FS_OTHERS-Front30` |\n   165→| 相机挂水 | `FS_WATER_BLOCKAGE-Front30` |\n   166→| 相机涂抹 | `FS_SMEARED_HALOS-Front30` |\n   167→| 相机炫光 | `FS_DIRTY_BLOCKAGE-Front30` |\n   168→| 相机模糊 | `FS_HEAVY_BLUR-Front30` |\n   169→| 相机过亮 | `FS_OVER_BRIGHTNESS-Front30` |\n   170→| 相机黑图 | `FS_FULL_BLOCKAGE-Front30` |\n   171→| 相机过暗 | `FS_OVER_DARKNESS-Front30` |\n   172→| 左侧patch故障 | `FS_PATCH_0-Front30` |\n   173→| 中间patch故障 | `FS_PATCH_1-Front30` |\n   174→| 右侧patch故障 | `FS_PATCH_2-Front30` |\n   175→\n   176→---\n   177→\n   178→# Part 2: 字段识别与格式化规则\n   179→\n   180→## 2.1 标识符格式校验\n   181→\n   182→### plate_number（车牌号）\n   183→\n   184→**合法格式**：\n   185→- PL 车牌：PL前缀 + 字母数字组合，如 `PLEBR9537`、`PLEAT3708` → 可直接搜 `plate_number` 字段\n   186→- 物理车牌号：中文 + 字母数字组合，如 `京AC81006` → **不能直接搜此字段**，需先通过 FMP skill 转化为 hostname + anonymous_id 后构造 OR 查询（详见 `search.md`「车牌号处理规则」）\n   187→\n   188→**强制排除规则**（以下不得识别为车牌号）：\n   189→- hostname 格式（含连字符，如 `LC6-EAT3708`）→ 应匹配 `fdi.fmp.car_info.device_box_id`\n   190→- 纯英文字母串（如 `imdev`、`IMDevcar`）→ `IMDevcar` 应识别为版本号\n   191→- 超长字符串（> 15字符）\n   192→- S开头 + 数字字母混合长串（如 `S6P4NX0RB01813J`）→ 这是 `hard_disk_sn`\n   193→\n   194→PL 车牌用 `in` 查询：`{\"in\": {\"plate_number\": [\"PLEBR9537\", \"PLEAT3708\"]}}`\n   195→\n   196→### fdi.fmp.car_info.device_box_id（hostname）\n   197→\n   198→**合法格式**：\n   199→- 两段字符串用连字符连接，长度 < 15字符，如 `LC6-EAT3708`\n   200→\n   201→多个 hostname 用 `in` 查询：`{\"in\": {\"fdi.fmp.car_info.device_box_id\": [\"LC6-EAT3708\", \"LC6-EBR9537\"]}}`\n   202→\n   203→### custom_meta.mass_production.vin（匿名ID/VIN码）\n   204→\n   205→**三种合法格式**：\n   206→- 40位匿名ID：数字+大写字母，如 `3E02A3B699B241F3DE0D3EE1EFF71CA40628EA65`\n   207→- 17位标准VIN码：如 `LC0DD4C42R7073134`\n   208→- 19位byd格式：`byd` 开头，如 `byd9BCC0D7BD04AC056`\n   209→\n   210→**排除规则**：其他长度的字符串均不能识别为 vin。\n   211→\n   212→### md5\n   213→\n   214→**格式**：32位小写hex字符串。\n   215→\n   216→**禁止提取规则**：\n   217→- 输入包含 `file_key`、`file_v3` 时，**禁止**提取 md5\n   218→- 类似 `file_v3_00mu_94790da4-0932-4835-88d1-6256e8c4a62g` 格式的字符串**禁止**提取 md5\n   219→- 输入 dataset/dataview 时，不得将其中任何部分匹配到 md5\n   220→\n   221→**从链接提取**：如 `https://cla.momenta.works/cdi/data/769ebb543074d25c014affa74932724f` → 提取 `769ebb543074d25c014affa74932724f`\n   222→\n   223→### name（数据名）\n   224→\n   225→**禁止提取规则**：\n   226→- `file_v3` 开头的字符串**禁止**提取 name\n   227→- `file_v3_00mu_94790da4-...` 格式**禁止**提取 name\n   228→- \"这个文件\"、\"该文件\"等模糊描述不能填入 name\n   229→\n   230→**注意**：产物包名（如 `HarzQNX-V5.0.16-ST35_V660_6093safety_2275-20250228.14910.tgz`）应识别为版本号，不是 `name`。\n   231→\n   232→### fleet_meta.packages_version（版本号/产物包）\n   233→\n   234→- `IMDevcar` 必须识别为版本号，**无论用户输入什么大小写**，统一传入 `IMDevcar`\n   235→- `IMDevcar` 绝不能识别为车牌号/hostname\n   236→- **前缀搜索规则**：当用户输入以 `IMDevcar` 开头的版本号（如 `IMDevcar-V3.0`），或明确要求搜索 `IMDevcar` 开头/前缀的版本时，使用 `prefix` 查询：\n   237→  ```json\n   238→  {\"prefix\": {\"fleet_meta.packages_version\": \"IMDevcar\"}}\n   239→  ```\n   240→  若用户给出完整精确值（如 `IMDevcar-V3.0.16-xxx`），仍使用默认的精确匹配查询\n   241→\n   242→---\n   243→\n   244→## 2.2 语义到标准值映射\n   245→\n   246→### asset.fdi_type（FDI类型）\n   247→\n   248→| 用户表达 | 标准值 |\n   249→|----------|--------|\n   250→| DFDI、dfdi、研采 | `DFDI` |\n   251→| CFDI、cfdi、量产、customer、fleet_type=custom | `CFDI` |\n   252→\n   253→**特殊规则**：用户同时要查量产和研采时，**不设置** `asset.fdi_type` 字段（不能设为 `CFDI,DFDI`）。\n   254→- \"研采车\"、\"量产车\" → 匹配 `asset.fdi_type`，**不匹配** `custom_meta.data_source_env`\n   255→\n   256→### custom_meta.data_source_env（数据源环境）\n   257→\n   258→| 用户表达 | 标准值 |\n   259→|----------|--------|\n   260→| SOP、sop | `SOP` |\n   261→| TEST、test、测试 | `TEST` |\n   262→\n   263→### fleet_meta.event_statistics_name（筛选器/事件名）\n   264→\n   265→量产和研采数据统一使用 `fleet_meta.event_statistics_name` 字段，无需区分。\n   266→\n   267→**高频筛选器固定映射**（无需进一步匹配，直接使用以下值）：\n   268→\n   269→| 用户表达 | 传入值（数组） |\n   270→|----------|---------------|\n   271→| 远光灯 | `[\"high_highbeam_on\", \"mid_highbeam_on\", \"light_recording\", \"hotupdate_filter_highbeam_on\"]` |\n   272→| 全量筛选器 | `[\"all_time_event\"]` |\n   273→| 手动录制 | `[\"manual_recording\"]` |\n   274→| 人工接管 | `[\"dbw_disabled_takeover\"]` |\n   275→\n   276→**重要规则**：\n   277→- 用户说\"全量\"（不是\"全量筛选器\"）时，**不能**匹配到筛选器字段\n   278→- UNP/HNP/NP/ACC/CP/MNP/APA/RADS/LPNP 是**智驾状态**，**不能**识别为筛选器 → 应匹配 `features.function_active`\n   279→- **严禁**捏造不存在的筛选器名\n   280→\n   281→**查询示例**：\n   282→\n   283→单个筛选器：\n   284→```json\n   285→{\"in\": {\"fleet_meta.event_statistics_name\": [\"manual_recording\"]}}\n   286→```\n   287→\n   288→多个筛选器（远光灯）：\n   289→```json\n   290→{\"in\": {\"fleet_meta.event_statistics_name\": [\"high_highbeam_on\", \"mid_highbeam_on\", \"light_recording\", \"hotupdate_filter_highbeam_on\"]}}\n   291→```\n   292→\n   293→---\n   294→\n   295→## 2.3 features 字段详细语义\n   296→\n   297→### function_active（智驾状态）\n   298→\n   299→**完整枚举值**：\n   300→`UNP`、`HNP`、`NP`、`ACC`、`CP`、`MNP`、`APA`、`RADS`、`LPNP`、`ELK`、`LKA`、`ESS`、`AES`、`ALL_AUTOMATIC`、`ALL_MANUAL`、`MANUAL`、`AUTOMATIC`、`L3`、`L4`、`AEB`、`AEBEH`、`AEBLIM`、`AEBEBA`、`AEBFB`、`AEBPB`、`MRM`\n   301→\n   302→**特殊子状态**：\n   303→- ELK 可细分为：`ELK_ONCOMING`、`ELK_OVERTAKING`、`ELK_ROADEDGE`\n   304→- MRM 可细分为：`MRM_EmergencyPullOver`、`MRM_PullOver`、`MRM_InLaneBrake`、`MRM_BlindBrake`\n   305→\n   306→**语义映射**：\n   307→\n   308→| 用户表达 | 值 |\n   309→|----------|-----|\n   310→| UNP场景、进入UNP | `UNP` |\n   311→| 始终处于智驾状态 | `ALL_AUTOMATIC` |\n   312→| 始终处于人工驾驶状态 | `ALL_MANUAL` |\n   313→| 车辆运行过程中包含人工驾驶 | `MANUAL` |\n   314→| 车辆运行过程中包含智能驾驶 | `AUTOMATIC` |\n   315→\n   316→多个状态用 `bool.should` 实现 OR 查询：`{\"bool\": {\"should\": [{\"features.function_active\": \"UNP\"}, {\"features.function_active\": \"HNP\"}], \"minimum_should_match\": 1}}`\n   317→\n   318→### takeover（接管）\n   319→\n   320→用户提到\"XX接管\"时，映射到对应字段：\n   321→\n   322→| 用户表达 | ES query |\n   323→|----------|----------|\n   324→| ACC接管 | `{\"features.takeover.ACC\": true}` |\n   325→| CP接管 | `{\"features.takeover.CP\": true}` |\n   326→| HNP接管 | `{\"features.takeover.HNP\": true}` |\n   327→| UNP接管 | `{\"features.takeover.UNP\": true}` |\n   328→\n   329→泛指\"接管\"时，根据上下文确定具体类型。\n   330→\n   331→### velocity_max / velocity_min（速度）\n   332→\n   333→**单位**：ES 中存储为 km/h。如用户输入 m/s，需转换：`速度(km/h) = 速度(m/s) × 3.6`\n   334→\n   335→**语义严格区分**：\n   336→\n   337→| 用户表达 | ES query |\n   338→|----------|----------|\n   339→| 最大速度大于80 | `{\"features.velocity_max\": {\"gte\": 80.0}}` |\n   340→| 最大速度小于120 | `{\"features.velocity_max\": {\"lt\": 120.0}}` |\n   341→| 最小速度大于10 | `{\"features.velocity_min\": {\"gte\": 10.0}}` |\n   342→| 最小速度小于20 | `{\"features.velocity_min\": {\"lt\": 20.0}}` |\n   343→| 最小车速在10~20，最大车速在80~90 | `{\"features.velocity_min\": {\"gte\": 10.0, \"lt\": 20.0}}` + `{\"features.velocity_max\": {\"gte\": 80.0, \"lt\": 90.0}}` |\n   344→| 速度在60到120之间 | `{\"features.velocity_min\": {\"gte\": 60.0}}` + `{\"features.velocity_max\": {\"lt\": 120.0}}` |\n   345→\n   346→**关键规则**：\n   347→- \"最大速度大于X\" → **必须**匹配 `velocity_max`，**绝不能**匹配 `velocity_min`\n   348→- \"最小速度大于X\" → **必须**匹配 `velocity_min`，**绝不能**匹配 `velocity_max`\n   349→- \"速度在X到Y之间\" → **同时**设置 `velocity_min` 和 `velocity_max`\n   350→\n   351→### static_barrier / vrus（数量类字段格式化）\n   352→\n   353→这两类字段的 ES query 格式化规则完全一致：\n   354→\n   355→| 用户表达 | ES query 格式 |\n   356→|----------|---------------|\n   357→| 具体数字（如\"锥桶3个\"） | `{\"features.static_barrier.cone_numbers\": 3}` |\n   358→| \"存在\"、\"有\" | `{\"features.static_barrier.cone_numbers\": {\"gt\": 0}}` |\n   359→| \"不存在\"、\"没有\" | `{\"features.static_barrier.cone_numbers\": 0}` |\n   360→| 范围（如\"2到5个\"） | `{\"features.static_barrier.cone_numbers\": {\"gte\": 2, \"lt\": 6}}` |\n   361→| 多值（如\"数量为1或2或3\"） | `{\"in\": {\"features.static_barrier.cone_numbers\": [1, 2, 3]}}` |\n   362→\n   363→> 注意：范围查询的上界使用 `lt`（不含），所以\"2到5个\"是 `{\"gte\": 2, \"lt\": 6}`。\n   364→\n   365→### suppress（抑制关系）\n   366→\n   367→抑制关系拆分为两个独立字段，值格式为 `{抑制方功能}_suppress_{被抑制方功能}`：\n   368→\n   369→- **mpilot 功能**：acc, cp, np\n   370→- **adas 功能**：elk_ot, ldw, lka\n   371→\n   372→| 用户表达 | ES 查询 |\n   373→|----------|---------|\n   374→| acc 抑制 lka | `{\"features.suppress.mpilot_suppress_adas\": \"acc_suppress_lka\"}` |\n   375→| lka 抑制 acc | `{\"features.suppress.adas_suppress_mpilot\": \"lka_suppress_acc\"}` |\n   376→| cp 抑制 ldw | `{\"features.suppress.mpilot_suppress_adas\": \"cp_suppress_ldw\"}` |\n   377→| 有 mpilot 抑制 adas 的数据 | `{\"exists\": \"features.suppress.mpilot_suppress_adas\"}` |\n   378→\n   379→### relative（关联数据）\n   380→\n   381→搜索关联数据时，`meta_type` 为 `\"bag\"`，通过 `exists` 查询筛选有关联文件的 bag。\n   382→\n   383→| 用户表达 | ES 查询 |\n   384→|----------|---------|\n   385→| 找有 landmark 的 bag | `{\"exists\": \"relative.landmark\"}` |\n   386→| 找没有 landmark 的 bag | bool.must_not: `{\"exists\": \"relative.landmark\"}` |\n   387→| 找有 mflog/日志/日志文件 的 bag | `{\"exists\": \"relative.mflog\"}` |\n   388→| 找有 msfbag/小bag 的 bag | `{\"exists\": \"relative.msfbag\"}` |\n   389→| 找有 coredump/崩溃/崩溃文件 的 bag | `{\"exists\": \"relative.coredump\"}` |\n   390→\n   391→**结果中获取关联数据链接**：\n   392→- `save_data` 中加入对应字段（如 `\"relative.landmark\"`）\n   393→- 用 `get_field(item, \"relative.landmark\")` 获取关联 md5 值\n   394→- 链接：`https://cla.momenta.works/cdi/data/{relative_md5}`\n   395→\n   396→**完整查询示例**（找2025年7月15日天马山有landmark的bag）：\n   397→```json\n   398→{\n   399→  \"bool\": {\n   400→    \"must\": [\n   401→      {\"collected_time\": {\"gte\": \"2025-07-15T00:00:00.000Z\", \"lte\": \"2025-07-15T23:59:59.999Z\"}},\n   402→      {\"in\": {\"asset.project\": [\"天马山\", \"tianmashan\"]}},\n   403→      {\"exists\": \"relative.landmark\"}\n   404→    ]\n   405→  }\n   406→}\n   407→```\n   408→`save_data` 需加入 `\"relative.landmark\"` 以获取 landmark 链接。\n   409→\n   410→### dsc_collision（碰撞信息）\n   411→\n   412→所有子字段使用范围查询，如 `{\"features.dsc_collision.vision_od\": {\"gte\": 0.3, \"lt\": 0.8}}`。\n   413→\n   414→| 用户表达 | 字段 |\n   415→|----------|------|\n   416→| 视觉碰撞面积 | `features.dsc_collision.vision_od` |\n   417→| 视觉碰撞TTC | `features.dsc_collision.vision_od_ttc` |\n   418→| 雷达碰撞面积 | `features.dsc_collision.lidar_od` |\n   419→| 雷达碰撞TTC | `features.dsc_collision.lidar_od_ttc` |\n   420→\n   421→### time_of_day（天色）\n   422→\n   423→| 用户表达 | 值 |\n   424→|----------|-----|\n   425→| 白天 | `day` |\n   426→| 黑夜、夜晚 | `night` |\n   427→| 黄昏 | `dusk` |\n   428→| 黎明 | `dawn` |\n   429→\n   430→### red_light_flag（红绿灯）\n   431→\n   432→- 有红绿灯：`{\"features.red_light_flag\": true}`，没有：`false`\n   433→- **重要**：红绿灯相关描述**必须**匹配到 `features.red_light_flag`，**不能**匹配到 fleet_meta.event_statistics_name\n   434→\n   435→### cloud_filters（云端筛选器触发）\n   436→\n   437→用户查找\"触发了某个云端筛选器\"的数据时使用。\n   438→\n   439→---\n   440→\n   441→## 2.4 数据大小格式化\n   442→\n   443→**单位转换**：用户输入 GB/KB/TB 等需先转换为字节。\n   444→\n   445→**格式化规则**：\n   446→- 精确值：直接传入字节数\n   447→- 不精确值（如\"2G\"）：扩展左右 100MB → `{\"size\": {\"gte\": 2040109466, \"lt\": 2254857830}}`\n   448→- 范围值：`{\"size\": {\"gte\": 0, \"lt\": 50000}}`\n   449→\n   450→---\n   451→\n   452→# Part 3: 查询构造规则\n   453→\n   454→## 3.1 场景语义匹配优先级\n   455→\n   456→当用户描述的场景可能匹配多个字段时，**严格按以下优先级执行**：\n   457→\n   458→```\n   459→features（最高）> fleet_meta.event_statistics_name > 其他基础字段（最低）\n   460→```\n   461→\n   462→1. **最高优先级**：`features` 字段匹配 — 优先尝试匹配 features 下的子字段\n   463→2. **中等优先级**：`fleet_meta.event_statistics_name` 字段匹配 — features 无法匹配时，尝试 fleet_meta.event_statistics_name\n   464→3. **最低优先级**：其他基础字段\n   465→\n   466→**核心规则**：\n   467→- 一旦高优先级字段成功匹配，**立即停止**，不再检查低优先级字段\n   468→- 能匹配到非筛选器字段的信息，**直接字段填充**，不利用业务知识填充 fleet_meta.event_statistics_name\n   469→- 例：用户要找\"有碰撞风险的数据\" → 设置 `features.crash_risk: true`，**不**匹配 fleet_meta.event_statistics_name\n   470→\n   471→## 3.2 时间处理规则\n   472→\n   473→### 时间字段选择\n   474→- 用户未指定具体时间字段时，默认使用**采集时间** `collected_time`\n   475→- 用户明确提到\"回流\"时，使用 `create_time`\n   476→\n   477→### 时间格式\n   478→ES query 中时间格式为：`\"2025-03-26T02:46:52.723Z\"`（北京时间，非 UTC）\n   479→\n   480→### 年份推断规则\n   481→- 用户未指定年份时：\n   482→  - 若日期 <= 当前日期 → 使用当前年份\n   483→  - 若日期 > 当前日期 → 使用前一年\n   484→\n   485→### 不精确时间自动扩展\n   486→当用户给出的时间不是精确范围（如\"14点5分左右\"），**自动扩展前后10分钟**：\n   487→```\n   488→\"11月5日14点5分左右\" → {\"gte\": \"2025-11-05T13:55:00.000Z\", \"lt\": \"2025-11-05T14:15:00.000Z\"}\n   489→```\n   490→\n   491→### \"最近N天\"计算\n   492→包含今天在内共N个自然日：`start = today - (N-1)天 00:00:00`，`end = 明天 00:00:00`\n   493→\n   494→```\n   495→当前 2026-03-30，\"最近7天\" → {\"gte\": \"2026-03-24T00:00:00.000Z\", \"lt\": \"2026-03-31T00:00:00.000Z\"}\n   496→```\n   497→\n   498→### 时间段口语化表达\n   499→\n   500→| 用户表达 | 时间范围 |\n   501→|----------|----------|\n   502→| 早上/上午 | 00:00 ~ 12:00 |\n   503→| 下午 | 12:00 ~ 18:00 |\n   504→| 晚上 | 18:00 ~ 次日00:00 |\n   505→\n   506→### 多时间段查询\n   507→多时间段使用 `bool.should` + `minimum_should_match: 1` 组合（语法见 `search.md` Bool 组合查询章节）。\n   508→\n   509→## 3.3 排除条件的 ES query 转换\n   510→\n   511→用户表达\"排除\"、\"不要\"、\"非\"等语义时，使用 `bool.must_not` 构造查询。\n   512→\n   513→### 排除条件映射表\n   514→\n   515→| 用户表达 | ES query（放入 `bool.must_not`） |\n   516→|----------|----------------------------------|\n   517→| 非landmark数据、不是landmark | `{\"tag\": \"landmark_bag_data\"}` |\n   518→| 排除某匿名ID | `{\"custom_meta.mass_production.vin\": \"xxx\"}` |\n   519→| 排除某MD5 | `{\"md5\": \"xxx\"}` 或 `{\"in\": {\"md5\": [\"xxx\", \"yyy\"]}}` |\n   520→| 排除某UUID | `{\"fleet_meta.uuid\": \"xxx\"}` |\n   521→| 排除某采集类型 | `{\"collect_type\": \"xxx\"}` |\n   522→| 排除某标签 | `{\"tag\": \"xxx\"}` |\n   523→\n   524→排除条件的完整示例见 `search.md` 示例 2。\n   525→\n   526→## 3.4 特殊标记的 tag 转换\n   527→\n   528→| 用户表达 | tag 值 |\n   529→|----------|--------|\n   530→| valid bag、有效bag、合格bag | `fst_cla_valid` |\n   531→| invalid bag、无效bag、不合格bag | `fst_cla_invalid` |\n   532→| 原始数据、原始bag、original data | `original_data` |\n   533→\n   534→## 3.5 asset.project 字段规则\n   535→\n   536→**合法项目名列表**（仅允许匹配以下值或以\"山\"字结尾的关键字）：\n   537→天马山、莲花山、白云山、雾灵山、天目山、武岳山、筑波山、大金山、Rocky、Harz、多瑙、Pike、筑莲山、莫干山、白波山、御岳山、庙香山、五华山、Rossberg、Taunus、武当山、赤城山、汉拿山、北竿山、秦望山、小昆山、越秀山、歌乐山、大明山\n   538→\n   539→- 英文项目名**忽略大小写**匹配\n   540→- 地名（非项目名）**不得**匹配到 `asset.project`\n   541→\n   542→## 3.6 标识符混淆防范\n   543→\n   544→当输入中同时出现多种标识符时，需严格按格式区分：\n   545→\n   546→| 特征 | 匹配字段 |\n   547→|------|----------|\n   548→| 32位小写hex | `md5` |\n   549→| 32位hex（可能含大写） | `fleet_meta.uuid`（用户需明确提到 uuid） |\n   550→| 40位大写字母+数字 | `custom_meta.mass_production.vin`（匿名ID） |\n   551→| 17位字母数字 | `custom_meta.mass_production.vin`（VIN码） |\n   552→| 19位 byd 开头 | `custom_meta.mass_production.vin`（byd格式） |\n   553→| S开头15位字母数字 | `hard_disk_sn` |\n   554→| PL前缀 + 字母数字（如 `PLEBR9537`） | `plate_number`（车牌号） |\n   555→| 含连字符的短字符串（如 `LC6-EAT3708`） | `fdi.fmp.car_info.device_box_id`（hostname） |\n   556→| 含 `file_key`/`file_v3` | **禁止**提取 md5 和 name |\n   557→\n   558→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"}]},"parent_tool_use_id":"toolu_bdrk_0158X6K1CpSxPakUEMiksfiR","session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4","uuid":"c579bb80-a66e-4be6-9857-bccd06c3eb7e","timestamp":"2026-04-17T06:57:54.983Z"}
2026-04-17 14:57:55,003 - cc_bot.services.claude - INFO - Parsing stream line: type=user
2026-04-17 14:57:55,003 - cc_bot.services.claude - INFO - [execute_stream] 88c39178-6fe2-8ca7-9174-74f8768874e4 user: user.message:tool_result:     1→# DSS 字段映射与查询转换指南
     2→
     3→本文档供 agent 将用户自然语言转换为 DSS ES query 时参考，分为三个部分：
     4→- **Part 1**：字段映射速查表（字段名、类型、说明）
     5→- **Part 2**：字段识别与格式化规则（输入识别、格式校验、语义转换）
  
2026-04-17 14:57:55,004 - cc_bot.services.claude - INFO - output: {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_bdrk_01FbKPp1ydqhHB6zY8ZA4Ukg","type":"tool_result","content":"     1→# DSS 数据统计功能文档\n     2→\n     3→本文档供 agent 执行数据聚合统计任务时参考。\n     4→\n     5→- **通用脚本规范**（开发流程、输出格式、异常处理）：见 `references/script_guide.md`\n     6→- 本文档仅包含数据统计的**专有内容**：统计原理、SDK 方法、常用聚合字段、脚本模板\n     7→\n     8→---\n     9→\n    10→## 核心思路\n    11→\n    12→agent 根据用户语义构造 ES aggs DSL，通过 `StatisticsClient.aggregate(query, aggs)` 透传到 `/api/v2/aggregate/meta` 执行聚合。**SDK 不做任何字段映射或结果转换，agent 直接使用 ES 真实字段名构造 aggs**。\n    13→\n    14→**与搜索的关系**：\n    15→- **搜索**（`SearchClient`）：获取具体的数据列表（md5、meta 信息）\n    16→- **统计**（`StatisticsClient`）：获取聚合统计结果（数量、分布、趋势）\n    17→\n    18→两者共享相同的 ES query 语法（参考 `references/search.md` 和 `references/field_mapping.md`）。\n    19→\n    20→---\n    21→\n    22→## SDK 方法\n    23→\n    24→SDK 文件路径：`skills/DSS/scripts/dss_sdk.py`\n    25→\n    26→| 方法 | 参数 | 返回值 | 说明 |\n    27→|------|------|--------|------|\n    28→| `StatisticsClient(endpoint, token)` | endpoint（可选）、token（可选） | StatisticsClient 实例 | 构造客户端 |\n    29→| `aggregate(query, aggs)` | query: ES query dict<br>aggs: ES aggs DSL dict | `{\"total\": int, \"aggregations\": dict}` | 透传 ES 聚合请求 |\n    30→\n    31→**异常类型**：`DSSClientError`\n    32→\n    33→---\n    34→\n    35→## 常用聚合字段\n    36→\n    37→agent 根据用户语义选择对应的 ES 真实字段构造 aggs：\n    38→\n    39→| 用户语义 | ES 真实字段 | 聚合类型 | 说明 |\n    40→|---------|------------|---------|------|\n    41→| 按采集时间（每天/每月/每年） | `collected_time` | `date_histogram` | 默认时间字段 |\n    42→| 按回流时间 | `create_time` | `date_histogram` | |\n    43→| 按项目/山头 | `asset.project` | `terms` | |\n    44→| 按车型 | `fleet_meta.car_type` | `terms` | |\n    45→| 按筛选器 | `record_name` | `terms` | 研采筛选器 |\n    46→| 按版本 | `fleet_meta.packages_version` | `terms` | |\n    47→| 按 FDI 类型（研采/量产） | `asset.fdi_type` | `terms` | **默认聚合维度** |\n    48→| 按数据源环境 | `custom_meta.data_source_env` | `terms` | SOP/TEST |\n    49→| 按采集类型 | `collect_type` | `terms` | |\n    50→| 总大小 | `size` | `sum` | 通常作为子聚合 |\n    51→\n    52→**默认行为**：用户未指定聚合维度时，默认按 `asset.fdi_type`（研采/量产）聚合。\n    53→\n    54→---\n    55→\n    56→## aggs DSL 构造规则\n    57→\n    58→### terms 聚合（按字段分组）\n    59→\n    60→```json\n    61→{\n    62→    \"group_by_fdi_type\": {\n    63→        \"terms\": {\"field\": \"asset.fdi_type\", \"size\": 100},\n    64→        \"aggs\": {\"total_size\": {\"sum\": {\"field\": \"size\"}}}\n    65→    }\n    66→}\n    67→```\n    68→\n    69→### date_histogram 聚合（按时间分组）\n    70→\n    71→`calendar_interval` 值：`hour`、`day`、`week`、`month`、`year`\n    72→\n    73→```json\n    74→{\n    75→    \"group_by_time\": {\n    76→        \"date_histogram\": {\"field\": \"collected_time\", \"calendar_interval\": \"day\"},\n    77→        \"aggs\": {\"total_size\": {\"sum\": {\"field\": \"size\"}}}\n    78→    }\n    79→}\n    80→```\n    81→\n    82→### 嵌套聚合（多维度）\n    83→\n    84→外层先按时间，内层再按字段分组：\n    85→\n    86→```json\n    87→{\n    88→    \"group_by_time\": {\n    89→        \"date_histogram\": {\"field\": \"collected_time\", \"calendar_interval\": \"day\"},\n    90→        \"aggs\": {\n    91→            \"group_by_fdi_type\": {\n    92→                \"terms\": {\"field\": \"asset.fdi_type\", \"size\": 100},\n    93→                \"aggs\": {\"total_size\": {\"sum\": {\"field\": \"size\"}}}\n    94→            },\n    95→            \"total_size\": {\"sum\": {\"field\": \"size\"}}\n    96→        }\n    97→    }\n    98→}\n    99→```\n   100→\n   101→### cardinality 聚合（去重计数）\n   102→\n   103→统计某字段有多少个不同值：\n   104→\n   105→```json\n   106→{\n   107→    \"unique_count\": {\n   108→        \"cardinality\": {\"field\": \"fleet_meta.car_type\"}\n   109→    }\n   110→}\n   111→```\n   112→\n   113→---\n   114→\n   115→## 脚本模板\n   116→\n   117→### 基础统计（仅总数 + 总大小）\n   118→\n   119→```python\n   120→import json\n   121→import sys\n   122→sys.path.insert(0, \".\")\n   123→from dss_sdk import StatisticsClient\n   124→\n   125→def main():\n   126→    client = StatisticsClient()\n   127→    try:\n   128→        query = {\n   129→            \"bool\": {\n   130→                \"must\": [\n   131→                    {\"asset.project\": \"天马山\"},\n   132→                    {\"collected_time\": {\"gte\": \"2026-03-24T00:00:00.000Z\", \"lt\": \"2026-03-31T00:00:00.000Z\"}}\n   133→                ]\n   134→            }\n   135→        }\n   136→\n   137→        # 默认按 fdi_type 聚合 + 总大小\n   138→        aggs = {\n   139→            \"group_by_fdi_type\": {\n   140→                \"terms\": {\"field\": \"asset.fdi_type\", \"size\": 100},\n   141→                \"aggs\": {\"total_size\": {\"sum\": {\"field\": \"size\"}}}\n   142→            },\n   143→            \"overall_size\": {\"sum\": {\"field\": \"size\"}}\n   144→        }\n   145→        result = client.aggregate(query=query, aggs=aggs)\n   146→\n   147→        total = result[\"total\"]\n   148→        overall_size = result[\"aggregations\"].get(\"overall_size\", {}).get(\"value\", 0)\n   149→\n   150→        # 解析 fdi_type 分组\n   151→        buckets = result[\"aggregations\"].get(\"group_by_fdi_type\", {}).get(\"buckets\", [])\n   152→        lines = []\n   153→        for b in buckets:\n   154→            lines.append(f\"- {b['key']}：{b['doc_count']} 条（{format_size(b.get('total_size', {}).get('value', 0))}）\")\n   155→        detail = \"\\n\".join(lines) if lines else \"无分组数据\"\n   156→\n   157→        message = f\"共 **{total}** 条数据，总大小 **{format_size(overall_size)}**\\n\\n{detail}\"\n   158→        print(json.dumps({\"success\": True, \"message\": message}, ensure_ascii=False))\n   159→    except Exception as e:\n   160→        print(json.dumps({\"success\": False, \"error\": str(e)}, ensure_ascii=False))\n   161→\n   162→def format_size(size_bytes):\n   163→    if not size_bytes or size_bytes <= 0:\n   164→        return \"0 B\"\n   165→    units = [\"B\", \"KB\", \"MB\", \"GB\", \"TB\", \"PB\"]\n   166→    idx = 0\n   167→    val = float(size_bytes)\n   168→    while val >= 1024 and idx < len(units) - 1:\n   169→        val /= 1024\n   170→        idx += 1\n   171→    return f\"{val:.2f} {units[idx]}\"\n   172→\n   173→if __name__ == \"__main__\":\n   174→    main()\n   175→```\n   176→\n   177→### 按时间 + 维度聚合\n   178→\n   179→```python\n   180→import json\n   181→import sys\n   182→from datetime import datetime, timedelta\n   183→sys.path.insert(0, \".\")\n   184→from dss_sdk import StatisticsClient\n   185→\n   186→def main():\n   187→    client = StatisticsClient()\n   188→    try:\n   189→        query = {\n   190→            \"bool\": {\n   191→                \"must\": [\n   192→                    {\"asset.project\": \"天马山\"},\n   193→                    {\"collected_time\": {\"gte\": \"2026-03-24T00:00:00.000Z\", \"lt\": \"2026-03-31T00:00:00.000Z\"}}\n   194→                ]\n   195→            }\n   196→        }\n   197→\n   198→        # 按天 + 按车型聚合\n   199→        aggs = {\n   200→            \"group_by_time\": {\n   201→                \"date_histogram\": {\"field\": \"collected_time\", \"calendar_interval\": \"day\"},\n   202→                \"aggs\": {\n   203→                    \"group_by_car_type\": {\n   204→                        \"terms\": {\"field\": \"fleet_meta.car_type\", \"size\": 100},\n   205→                        \"aggs\": {\"total_size\": {\"sum\": {\"field\": \"size\"}}}\n   206→                    },\n   207→                    \"total_size\": {\"sum\": {\"field\": \"size\"}}\n   208→                }\n   209→            },\n   210→            \"overall_size\": {\"sum\": {\"field\": \"size\"}}\n   211→        }\n   212→        result = client.aggregate(query=query, aggs=aggs)\n   213→\n   214→        # 解析嵌套 buckets 为扁平行\n   215→        rows = []\n   216→        time_buckets = result[\"aggregations\"].get(\"group_by_time\", {}).get(\"buckets\", [])\n   217→        for tb in time_buckets:\n   218→            day = format_time_key(tb[\"key\"])\n   219→            sub_buckets = tb.get(\"group_by_car_type\", {}).get(\"buckets\", [])\n   220→            if sub_buckets:\n   221→                for sb in sub_buckets:\n   222→                    rows.append((day, sb[\"key\"], sb[\"doc_count\"], sb.get(\"total_size\", {}).get(\"value\", 0)))\n   223→            else:\n   224→                rows.append((day, \"-\", tb[\"doc_count\"], tb.get(\"total_size\", {}).get(\"value\", 0)))\n   225→\n   226→        if not rows:\n   227→            print(json.dumps({\"success\": True, \"message\": \"统计结果为空。\"}, ensure_ascii=False))\n   228→            return\n   229→\n   230→        # 构造表格\n   231→        header = \"| 日期 | 车型 | 数量 | 大小 |\"\n   232→        separator = \"|---|---|---|---|\"\n   233→        body = \"\\n\".join(f\"| {r[0]} | {r[1]} | {r[2]} | {format_size(r[3])} |\" for r in rows)\n   234→        overall_size = result[\"aggregations\"].get(\"overall_size\", {}).get(\"value\", 0)\n   235→\n   236→        message = f\"统计完成，共 **{result['total']}** 条数据（总大小 {format_size(overall_size)}）\\n\\n{header}\\n{separator}\\n{body}\"\n   237→        print(json.dumps({\"success\": True, \"message\": message}, ensure_ascii=False))\n   238→    except Exception as e:\n   239→        print(json.dumps({\"success\": False, \"error\": str(e)}, ensure_ascii=False))\n   240→\n   241→def format_time_key(timestamp):\n   242→    try:\n   243→        ts = float(timestamp)\n   244→        if ts > 1e12:\n   245→            ts /= 1000\n   246→        return datetime.fromtimestamp(ts).strftime(\"%Y-%m-%d\")\n   247→    except Exception:\n   248→        return str(timestamp)\n   249→\n   250→def format_size(size_bytes):\n   251→    if not size_bytes or size_bytes <= 0:\n   252→        return \"0 B\"\n   253→    units = [\"B\", \"KB\", \"MB\", \"GB\", \"TB\", \"PB\"]\n   254→    idx = 0\n   255→    val = float(size_bytes)\n   256→    while val >= 1024 and idx < len(units) - 1:\n   257→        val /= 1024\n   258→        idx += 1\n   259→    return f\"{val:.2f} {units[idx]}\"\n   260→\n   261→if __name__ == \"__main__\":\n   262→    main()\n   263→```\n   264→\n   265→### 去重计数（有多少个 xxx）\n   266→\n   267→```python\n   268→import json\n   269→import sys\n   270→sys.path.insert(0, \".\")\n   271→from dss_sdk import StatisticsClient\n   272→\n   273→def main():\n   274→    client = StatisticsClient()\n   275→    try:\n   276→        query = {\n   277→            \"bool\": {\n   278→                \"must\": [\n   279→                    {\"asset.project\": \"天马山\"},\n   280→                    {\"collected_time\": {\"gte\": \"2026-03-24T00:00:00.000Z\", \"lt\": \"2026-03-31T00:00:00.000Z\"}}\n   281→                ]\n   282→            }\n   283→        }\n   284→\n   285→        aggs = {\n   286→            \"unique_car_types\": {\n   287→                \"cardinality\": {\"field\": \"fleet_meta.car_type\"}\n   288→            }\n   289→        }\n   290→        result = client.aggregate(query=query, aggs=aggs)\n   291→\n   292→        count = result[\"aggregations\"].get(\"unique_car_types\", {}).get(\"value\", 0)\n   293→        message = f\"当前条件下，共有 **{count}** 个车型\"\n   294→        print(json.dumps({\"success\": True, \"message\": message}, ensure_ascii=False))\n   295→    except Exception as e:\n   296→        print(json.dumps({\"success\": False, \"error\": str(e)}, ensure_ascii=False))\n   297→\n   298→if __name__ == \"__main__\":\n   299→    main()\n   300→```\n   301→\n   302→---\n   303→\n   304→## 统计专有约束\n   305→\n   306→除 `script_guide.md` 中的通用约束外，统计操作还需遵守：\n   307→\n   308→1. **使用 ES 真实字段名**：aggs 中直接使用 `field_mapping.md` 中的 ES 真实字段名（如 `asset.fdi_type`、`fleet_meta.car_type`），不存在简写名\n   309→2. **默认聚合维度**：用户未指定按什么维度聚合时，默认按 `asset.fdi_type`（研采/量产）聚合\n   310→3. **不要将时间范围误识别为时间聚合**：用户说\"最近7天\"是查询条件中的时间范围，不是按天聚合；用户说\"每天\"才是按天聚合\n   311→4. **query 语法一致**：统计的 query 与搜索完全相同，参考 `references/search.md` 和 `references/field_mapping.md`\n   312→5. **\"每\"表示聚合维度**：用户说\"每天每个车型有多少\"，\"每天\"和\"每个车型\"是聚合维度，前面的筛选条件（如\"天马山最近7天\"）是 query 条件\n   313→6. **不要把\"天马山\"识别为\"每天\"**：项目名中包含\"天\"字，不要误识别为时间间隔\n   314→7. **聚合维度顺序**：嵌套聚合中，时间字段（`date_histogram`）放最外层，其他字段按用户提及顺序嵌套\n   315→8. **始终带 size 子聚合**：每个分组聚合内都加 `\"total_size\": {\"sum\": {\"field\": \"size\"}}` 以便展示数据大小\n   316→9. **结果可视化**：脚本执行成功后，调用 `Skill(markdown-to-feishu-card)` 将统计结果转换为飞书图表卡片输出\n   317→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"}]},"parent_tool_use_id":"toolu_bdrk_0158X6K1CpSxPakUEMiksfiR","session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4","uuid":"4133f2ec-2b23-4bd2-9dec-6fa9b52bba0d","timestamp":"2026-04-17T06:57:54.986Z"}
2026-04-17 14:57:55,006 - cc_bot.services.claude - INFO - Parsing stream line: type=user
2026-04-17 14:57:55,006 - cc_bot.services.claude - INFO - [execute_stream] 88c39178-6fe2-8ca7-9174-74f8768874e4 user: user.message:tool_result:     1→# DSS 数据统计功能文档
     2→
     3→本文档供 agent 执行数据聚合统计任务时参考。
     4→
     5→- **通用脚本规范**（开发流程、输出格式、异常处理）：见 `references/script_guide.md`
     6→- 本文档仅包含数据统计的**专有内容**：统计原理、SDK
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2026-04-17 14:58:01,958 - cc_bot.services.claude - INFO - output: {"type":"system","subtype":"task_progress","task_id":"a9f42b8fbe4a66059","tool_use_id":"toolu_bdrk_0158X6K1CpSxPakUEMiksfiR","description":"Reading /tmp/cc-sandbox/88c39178-6fe2-8ca7-9174-74f8768874e4/.claude/skills/DSS/references/script_guide.md","usage":{"total_tokens":33854,"tool_uses":4,"duration_ms":14978},"last_tool_name":"Read","uuid":"d6d14e66-aa94-42ef-9a80-232d9ebfbdc2","session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4"}
2026-04-17 14:58:01,959 - cc_bot.services.claude - INFO - Parsing stream line: type=system
2026-04-17 14:58:01,959 - cc_bot.services.claude - INFO - Unrecognized system message in stream: data={'type': 'system', 'subtype': 'task_progress', 'task_id': 'a9f42b8fbe4a66059', 'tool_use_id': 'toolu_bdrk_0158X6K1CpSxPakUEMiksfiR', 'description': 'Reading /tmp/cc-sandbox/88c39178-6fe2-8ca7-9174-74f8768874e4/.claude/skills/DSS/references/script_guide.md', 'usage': {'total_tokens': 33854, 'tool_uses': 4, 'duration_ms': 14978}, 'last_tool_name': 'Read', 'uuid': 'd6d14e66-aa94-42ef-9a80-232d9ebfbdc2', 'session_id': '88c39178-6fe2-8ca7-9174-74f8768874e4'}
2026-04-17 14:58:01,959 - cc_bot.services.claude - INFO - output: {"type":"assistant","message":{"model":"claude-sonnet-4-6","id":"msg_bdrk_01EEJjCCgTot1CNcjJZDQs93","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_bdrk_01KbPP4qwamYDTBjUP5VtQpp","name":"Read","input":{"file_path":"/tmp/cc-sandbox/88c39178-6fe2-8ca7-9174-74f8768874e4/.claude/skills/DSS/references/script_guide.md"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":12608,"cache_creation_input_tokens":5270,"cache_read_input_tokens":15922,"cache_creation":{"ephemeral_5m_input_tokens":5270,"ephemeral_1h_input_tokens":0},"output_tokens":1},"context_management":null},"parent_tool_use_id":"toolu_bdrk_0158X6K1CpSxPakUEMiksfiR","session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4","uuid":"eefa5ba7-e60c-4bd2-a250-b412d11d60e8"}
2026-04-17 14:58:01,959 - cc_bot.services.claude - INFO - Parsing stream line: type=assistant
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2026-04-17 14:58:02,608 - cc_bot.services.claude - INFO - output: {"type":"system","subtype":"task_progress","task_id":"a9f42b8fbe4a66059","tool_use_id":"toolu_bdrk_0158X6K1CpSxPakUEMiksfiR","description":"Reading /tmp/cc-sandbox/88c39178-6fe2-8ca7-9174-74f8768874e4/.claude/skills/DSS/scripts/dss_sdk.py","usage":{"total_tokens":33855,"tool_uses":5,"duration_ms":15627},"last_tool_name":"Read","uuid":"6de545fe-fb01-4fb9-b15b-1cd956b686d1","session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4"}
2026-04-17 14:58:02,608 - cc_bot.services.claude - INFO - Parsing stream line: type=system
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2026-04-17 14:58:02,608 - cc_bot.services.claude - INFO - output: {"type":"assistant","message":{"model":"claude-sonnet-4-6","id":"msg_bdrk_01EEJjCCgTot1CNcjJZDQs93","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_bdrk_01CCR1nDxzycFQ8duhoCRDgR","name":"Read","input":{"file_path":"/tmp/cc-sandbox/88c39178-6fe2-8ca7-9174-74f8768874e4/.claude/skills/DSS/scripts/dss_sdk.py"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":12608,"cache_creation_input_tokens":5270,"cache_read_input_tokens":15922,"cache_creation":{"ephemeral_5m_input_tokens":5270,"ephemeral_1h_input_tokens":0},"output_tokens":1},"context_management":null},"parent_tool_use_id":"toolu_bdrk_0158X6K1CpSxPakUEMiksfiR","session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4","uuid":"ecffb806-4c0d-4cde-9dd2-271c7b2a2891"}
2026-04-17 14:58:02,608 - cc_bot.services.claude - INFO - Parsing stream line: type=assistant
2026-04-17 14:58:02,608 - cc_bot.services.claude - INFO - Unknown content block type: tool_use
2026-04-17 14:58:02,625 - cc_bot.services.claude - INFO - output: {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_bdrk_01KbPP4qwamYDTBjUP5VtQpp","type":"tool_result","content":"     1→# DSS 脚本通用开发规范\n     2→\n     3→本文档定义所有 DSS 功能域（搜索、Dataset 管理等）共享的脚本开发流程、输出格式和异常处理策略。各功能域文档通过引用本文档来避免重复。\n     4→\n     5→---\n     6→\n     7→## 脚本开发流程\n     8→\n     9→### 第一步：编写脚本\n    10→\n    11→将脚本保存到 `skills/DSS/scripts/task_<描述>.py`，必须遵循以下骨架：\n    12→\n    13→```python\n    14→import json\n    15→import sys\n    16→sys.path.insert(0, \".\")\n    17→from dss_sdk import XxxClient  # 按功能域选择对应 Client\n    18→\n    19→def main():\n    20→    client = XxxClient()\n    21→    try:\n    22→        # 1. 前置校验（如有）\n    23→        # 2. 核心业务逻辑\n    24→        # 3. 构造 message（面向用户的完整回复，markdown 格式）\n    25→        print(json.dumps({\n    26→            \"success\": True,\n    27→            \"message\": \"面向用户的结果摘要\"\n    28→        }, ensure_ascii=False))\n    29→    except Exception as e:\n    30→        print(json.dumps({\"success\": False, \"error\": str(e)}, ensure_ascii=False))\n    31→\n    32→if __name__ == \"__main__\":\n    33→    main()\n    34→```\n    35→\n    36→### 第二步：执行脚本\n    37→\n    38→```bash\n    39→cd skills/DSS/scripts && cat task_<描述>.py && timeout 300 python task_<描述>.py\n    40→```\n    41→\n    42→- 先 `cat` 打印脚本内容，便于排查问题\n    43→- `timeout 300`（5 分钟）防止脚本挂起\n    44→\n    45→### 第三步：处理结果\n    46→\n    47→读取 stdout 输出的 JSON，根据 `success` 字段判断：\n    48→- `success: true` → **直接将 `message` 返回用户**（无需二次加工）\n    49→- `success: false` → 进入异常处理流程（见下方）\n    50→\n    51→### 第四步：清理脚本\n    52→\n    53→任务结束后（无论成功或失败），**必须删除**生成的临时脚本文件：\n    54→\n    55→```bash\n    56→rm -f skills/DSS/scripts/task_<描述>.py\n    57→```\n    58→\n    59→- `dss_sdk.py` 是永久保留的 SDK 模块，**不要删除**\n    60→- `task_*.py` 是一次性临时脚本，执行完必须清理\n    61→\n    62→---\n    63→\n    64→## 输出规范\n    65→\n    66→stdout 仅输出**单行 JSON**，格式固定两种：\n    67→\n    68→**成功**：`{\"success\": true, \"message\": \"...\"}`\n    69→- `message`：**脚本负责生成的、面向用户的完整回复文本**，使用 markdown 格式\n    70→- agent 拿到 message 后**直接返回用户**，无需二次加工\n    71→\n    72→**失败**：`{\"success\": false, \"error\": \"...\"}`\n    73→- `error`：错误描述，用于 agent 判断异常类型并决定后续处理\n    74→\n    75→---\n    76→\n    77→## 通用编写约束\n    78→\n    79→1. **数据不进上下文**：禁止将完整数据列表（md5 列表、搜索结果等）输出到 message，message 只包含统计数字和关键摘要\n    80→2. **大数据写文件**：如需输出明细数据，写入文件后在 message 中告知用户文件路径\n    81→3. **单行 JSON**：stdout 仅输出单行 JSON，不要 print 调试信息或原始 traceback\n    82→4. **message 自包含**：脚本的 message 应是用户可直接阅读的完整回复，包含所有必要信息（统计数据、链接、建议等），agent 无需补充\n    83→5. **时间格式**：DSS 时间字段使用 `\"2025-03-26T02:46:52.723Z\"` 格式（北京时间，非 UTC）\n    84→6. **limit 默认 10**：搜索类操作默认返回 10 条，如用户需要更多可调整至最大 100\n    85→\n    86→---\n    87→\n    88→## 异常处理策略\n    89→\n    90→### 执行超时\n    91→\n    92→脚本执行超过 `timeout` 限制时，`timeout` 命令会自动终止进程。此时：\n    93→1. 向用户说明操作因耗时过长被终止\n    94→2. 建议用户添加更多过滤条件或缩小数据范围后重试\n    95→\n    96→### 执行失败\n    97→\n    98→根据 stdout 输出的 JSON 中 `error` 字段判断错误类型：\n    99→\n   100→| 错误特征 | 类型 | 处理方式 |\n   101→|----------|------|---------|\n   102→| `HTTP 5xx`、`ConnectionError`、`Timeout` | 网络/服务端问题 | **重试 1 次**（重新执行同一脚本），仍失败则报错给用户 |\n   103→| `HTTP 4xx`、`不存在` | 参数/业务错误 | **不重试**，直接将错误信息返回用户 |\n   104→| `超过.*限制`、`DatasetTooLargeError` | 数据量超限 | **不重试**，将错误信息返回用户，建议缩小数据范围 |\n   105→| `ImportError`、`SyntaxError`、`KeyError`、`TypeError` | 脚本代码问题 | **修复脚本**后重新执行，最多修复 2 次，仍失败则报错给用户 |\n   106→| 无 JSON 输出（stderr 有 traceback） | 脚本未捕获异常 | 读取 stderr 内容，**修复脚本**的异常处理后重新执行 |\n   107→\n   108→### 关键原则\n   109→\n   110→- 网络类错误：最多重试 1 次，不要反复重试\n   111→- 脚本类错误：分析 traceback 修复代码，最多修复 2 轮\n   112→- 所有失败最终都要向用户返回具体错误信息，不要静默吞掉\n   113→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"}]},"parent_tool_use_id":"toolu_bdrk_0158X6K1CpSxPakUEMiksfiR","session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4","uuid":"403441f2-f5a6-4981-953c-8424f3d32f26","timestamp":"2026-04-17T06:58:02.622Z"}
2026-04-17 14:58:02,626 - cc_bot.services.claude - INFO - Parsing stream line: type=user
2026-04-17 14:58:02,626 - cc_bot.services.claude - INFO - [execute_stream] 88c39178-6fe2-8ca7-9174-74f8768874e4 user: user.message:tool_result:     1→# DSS 脚本通用开发规范
     2→
     3→本文档定义所有 DSS 功能域（搜索、Dataset 管理等）共享的脚本开发流程、输出格式和异常处理策略。各功能域文档通过引用本文档来避免重复。
     4→
     5→---
     6→
     7→## 脚本开发流程
     8→
     9→### 第一
INFO:     127.0.0.1:57115 - "POST /api/log/hook-langfuse HTTP/1.1" 200 OK
INFO:     127.0.0.1:57116 - "POST /api/log/hook HTTP/1.1" 200 OK
INFO:     127.0.0.1:57117 - "POST /api/log/hook HTTP/1.1" 200 OK
INFO:     127.0.0.1:57118 - "POST /api/log/hook-langfuse HTTP/1.1" 200 OK
INFO:     127.0.0.1:57119 - "POST /api/log/hook-langfuse HTTP/1.1" 200 OK
INFO:     127.0.0.1:57120 - "POST /api/log/hook HTTP/1.1" 200 OK
2026-04-17 14:58:03,314 - cc_bot.services.claude - INFO - output: {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_bdrk_01CCR1nDxzycFQ8duhoCRDgR","type":"tool_result","content":"<persisted-output>\nOutput too large (64.8KB). Full output saved to: /tmp/cc-sandbox/88c39178-6fe2-8ca7-9174-74f8768874e4/.claude/projects/-private-tmp-cc-sandbox-88c39178-6fe2-8ca7-9174-74f8768874e4/88c39178-6fe2-8ca7-9174-74f8768874e4/tool-results/toolu_bdrk_01CCR1nDxzycFQ8duhoCRDgR.txt\n\nPreview (first 2KB):\n     1→\"\"\"\n     2→DSS SDK - 轻量级 DSS HTTP 客户端\n     3→\n     4→提供六个 Client + 两个独立函数：\n     5→    - BaseClient：公用 HTTP 基础能力（endpoint/token/请求/响应）\n     6→    - DatasetClient(BaseClient)：Dataset 管理（DSS/DDLD 的获取、创建、数量校验）\n     7→    - SearchClient(BaseClient)：数据搜索（ES query 搜索 + save_query 链接生成）\n     8→    - ESSClient：ESS 操作（event set / scenario set 的读写，独立于 BaseClient）\n     9→    - ExportClient(BaseClient)：数据导出（组合 DatasetClient + ESSClient）\n    10→    - StatisticsClient(BaseClient)：数据统计（聚合统计）\n    11→    - get_subscribe_list()：获取用户订阅的车辆/硬盘列表\n    12→    - get_field()：通过 dotted path 获取嵌套字段值\n    13→\n    14→ESSClient 提供四者互转能力（Dataset ↔ Event Set ↔ Scenario Set ↔ ClipSet）：\n    15→    - 基础层：get_event_set_ids / get_scenario_set_ids / get_clip_set_clip_ids → ID 列表\n    16→    - 转换层：convert_event_ids_to_md5s / convert_clip_ids_to_md5s / convert_clip_ids_to_event_ids\n    17→    - 便捷层：get_*_md5s / get_*_event_ids → 一步到位\n    18→    - 管理层：get_or_create_scenario_set / create_clip_set\n    19→    - 导入层：import_scenario_set* / import_clip_set*\n    20→\n    21→使用方式：\n    22→    from dss_sdk import DatasetClient, SearchClient, ESSClient, ExportClient, StatisticsClient, get_subscribe_list, get_field\n    23→\n    24→    # Dataset 操作\n    25→    ds_client = DatasetClient()\n    26→    ds = ds_client.get_dataset(\"ds_A\")\n    27→\n    28→    # 搜索操作\n    29→    search_client = SearchClient()\n    30→    result = search_client.search_meta({\"project\": \"天马山\"})\n    31→\n    32→    # ESS 操作 - 基础层：只获取 event_id\n    33→    ess_client = ESSClient()\n    34→    event_ids = ess_client.get_event_set_ids(\"event_set_123\")\n    35→\n    36→    # ESS 操作 - 转换层：event_id → md5\n    37→    md5s = ess_client.convert_event_ids_to_md5s(event_ids)\n    38→\n    39→    # ESS 操作 - 便捷层：一步到位获取 md5（向后兼容）\n    40→    md5s = ess_client.get_event_set_md5s(\"event_set_123\")\n    41→\n    42→    # 导出操作（统一入口，内部委托 DatasetClient/ESSClient）\n    43→    export_client = ExportClient()\n    44→    export_client.export_md5_file(md5s)\n    45→\n    46→    # 统计操作\n...\n</persisted-output>"}]},"parent_tool_use_id":"toolu_bdrk_0158X6K1CpSxPakUEMiksfiR","session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4","uuid":"caf18ff4-a6bc-421f-8a51-a86d3ce00ee5","timestamp":"2026-04-17T06:58:03.310Z"}
2026-04-17 14:58:03,315 - cc_bot.services.claude - INFO - Parsing stream line: type=user
2026-04-17 14:58:03,315 - cc_bot.services.claude - INFO - [execute_stream] 88c39178-6fe2-8ca7-9174-74f8768874e4 user: user.message:tool_result:<persisted-output>
Output too large (64.8KB). Full output saved to: /tmp/cc-sandbox/88c39178-6fe2-8ca7-9174-74f8768874e4/.claude/projects/-private-tmp-cc-sandbox-88c39178-6fe2
INFO:     127.0.0.1:57122 - "POST /api/log/hook-langfuse HTTP/1.1" 200 OK
INFO:     127.0.0.1:57123 - "POST /api/log/hook HTTP/1.1" 200 OK
2026-04-17 14:58:08,755 - cc_bot.services.claude - INFO - output: {"type":"system","subtype":"task_progress","task_id":"a9f42b8fbe4a66059","tool_use_id":"toolu_bdrk_0158X6K1CpSxPakUEMiksfiR","description":"统计天马山最近5天的数据量","usage":{"total_tokens":37313,"tool_uses":6,"duration_ms":21775},"last_tool_name":"ToolSearch","uuid":"01563217-0b91-416e-9c73-f4129f365031","session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4"}
2026-04-17 14:58:08,755 - cc_bot.services.claude - INFO - Parsing stream line: type=system
2026-04-17 14:58:08,755 - cc_bot.services.claude - INFO - Unrecognized system message in stream: data={'type': 'system', 'subtype': 'task_progress', 'task_id': 'a9f42b8fbe4a66059', 'tool_use_id': 'toolu_bdrk_0158X6K1CpSxPakUEMiksfiR', 'description': '统计天马山最近5天的数据量', 'usage': {'total_tokens': 37313, 'tool_uses': 6, 'duration_ms': 21775}, 'last_tool_name': 'ToolSearch', 'uuid': '01563217-0b91-416e-9c73-f4129f365031', 'session_id': '88c39178-6fe2-8ca7-9174-74f8768874e4'}
2026-04-17 14:58:08,756 - cc_bot.services.claude - INFO - output: {"type":"assistant","message":{"model":"claude-sonnet-4-6","id":"msg_bdrk_0182CPm7U7UTYP1RxmN14aLj","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_bdrk_01VGsWj4GPabz1gEahFxm9ZM","name":"ToolSearch","input":{"query":"select:Write,Bash","max_results":2}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":16063,"cache_read_input_tokens":21192,"cache_creation":{"ephemeral_5m_input_tokens":16063,"ephemeral_1h_input_tokens":0},"output_tokens":1},"context_management":null},"parent_tool_use_id":"toolu_bdrk_0158X6K1CpSxPakUEMiksfiR","session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4","uuid":"4d68bb94-88f8-4c2b-aaa0-c45fda951c17"}
2026-04-17 14:58:08,756 - cc_bot.services.claude - INFO - Parsing stream line: type=assistant
2026-04-17 14:58:08,756 - cc_bot.services.claude - INFO - Unknown content block type: tool_use
2026-04-17 14:58:08,761 - cc_bot.services.claude - INFO - output: {"type":"user","message":{"role":"user","content":[{"type":"tool_result","tool_use_id":"toolu_bdrk_01VGsWj4GPabz1gEahFxm9ZM","content":[{"type":"tool_reference","tool_name":"Write"},{"type":"tool_reference","tool_name":"Bash"}]}]},"parent_tool_use_id":"toolu_bdrk_0158X6K1CpSxPakUEMiksfiR","session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4","uuid":"1a192275-f1ec-40de-a44a-1226af5f868d","timestamp":"2026-04-17T06:58:08.759Z"}
2026-04-17 14:58:08,761 - cc_bot.services.claude - INFO - Parsing stream line: type=user
2026-04-17 14:58:08,761 - cc_bot.services.claude - INFO - [execute_stream] 88c39178-6fe2-8ca7-9174-74f8768874e4 user: user.message:tool_result:[{'type': 'tool_reference', 'tool_name': 'Write'}, {'type': 'tool_reference', 'tool_name': 'Bash'}]
INFO:     127.0.0.1:57133 - "POST /api/log/hook HTTP/1.1" 200 OK
INFO:     127.0.0.1:57132 - "POST /api/log/hook-langfuse HTTP/1.1" 200 OK
INFO:     127.0.0.1:57134 - "POST /api/log/hook-langfuse HTTP/1.1" 200 OK
INFO:     127.0.0.1:57135 - "POST /api/log/hook HTTP/1.1" 200 OK
2026-04-17 14:58:41,465 - cc_bot.services.claude - INFO - output: {"type":"system","subtype":"task_progress","task_id":"a9f42b8fbe4a66059","tool_use_id":"toolu_bdrk_0158X6K1CpSxPakUEMiksfiR","description":"Writing /tmp/cc-sandbox/88c39178-6fe2-8ca7-9174-74f8768874e4/.claude/skills/DSS/scripts/task_tianmashan_stats_5days.py","usage":{"total_tokens":40912,"tool_uses":7,"duration_ms":54484},"last_tool_name":"Write","uuid":"456ef2f2-3916-48f1-a3c2-153c4ddf3fc7","session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4"}
2026-04-17 14:58:41,465 - cc_bot.services.claude - INFO - Parsing stream line: type=system
2026-04-17 14:58:41,465 - cc_bot.services.claude - INFO - Unrecognized system message in stream: data={'type': 'system', 'subtype': 'task_progress', 'task_id': 'a9f42b8fbe4a66059', 'tool_use_id': 'toolu_bdrk_0158X6K1CpSxPakUEMiksfiR', 'description': 'Writing /tmp/cc-sandbox/88c39178-6fe2-8ca7-9174-74f8768874e4/.claude/skills/DSS/scripts/task_tianmashan_stats_5days.py', 'usage': {'total_tokens': 40912, 'tool_uses': 7, 'duration_ms': 54484}, 'last_tool_name': 'Write', 'uuid': '456ef2f2-3916-48f1-a3c2-153c4ddf3fc7', 'session_id': '88c39178-6fe2-8ca7-9174-74f8768874e4'}
2026-04-17 14:58:41,466 - cc_bot.services.claude - INFO - output: {"type":"assistant","message":{"model":"claude-sonnet-4-6","id":"msg_bdrk_01URpAcTqtPpqHpFGCRrqpPq","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_bdrk_014KYqBwUXrAj9JkDogcfTu8","name":"Write","input":{"file_path":"/tmp/cc-sandbox/88c39178-6fe2-8ca7-9174-74f8768874e4/.claude/skills/DSS/scripts/task_tianmashan_stats_5days.py","content":"import json\nimport sys\nfrom datetime import datetime\nsys.path.insert(0, \".\")\nfrom dss_sdk import StatisticsClient\n\ndef format_size(size_bytes):\n    if not size_bytes or size_bytes <= 0:\n        return \"0 B\"\n    units = [\"B\", \"KB\", \"MB\", \"GB\", \"TB\", \"PB\"]\n    idx = 0\n    val = float(size_bytes)\n    while val >= 1024 and idx < len(units) - 1:\n        val /= 1024\n        idx += 1\n    return f\"{val:.2f} {units[idx]}\"\n\ndef format_time_key(timestamp):\n    try:\n        ts = float(timestamp)\n        if ts > 1e12:\n            ts /= 1000\n        return datetime.utcfromtimestamp(ts).strftime(\"%Y-%m-%d\")\n    except Exception:\n        return str(timestamp)\n\ndef main():\n    client = StatisticsClient()\n    try:\n        query = {\n            \"bool\": {\n                \"must\": [\n                    {\"asset.project\": \"天马山\"},\n                    {\"collected_time\": {\"gte\": \"2026-04-13T00:00:00.000Z\", \"lt\": \"2026-04-18T00:00:00.000Z\"}}\n                ]\n            }\n        }\n\n        # 按天聚合，子聚合按 fdi_type 分组，并统计每天总大小\n        aggs = {\n            \"group_by_time\": {\n                \"date_histogram\": {\"field\": \"collected_time\", \"calendar_interval\": \"day\"},\n                \"aggs\": {\n                    \"group_by_fdi_type\": {\n                        \"terms\": {\"field\": \"asset.fdi_type\", \"size\": 100},\n                        \"aggs\": {\"total_size\": {\"sum\": {\"field\": \"size\"}}}\n                    },\n                    \"total_size\": {\"sum\": {\"field\": \"size\"}}\n                }\n            },\n            \"overall_size\": {\"sum\": {\"field\": \"size\"}}\n        }\n\n        result = client.aggregate(query=query, aggs=aggs)\n\n        total = result[\"total\"]\n        overall_size = result[\"aggregations\"].get(\"overall_size\", {}).get(\"value\", 0)\n\n        time_buckets = result[\"aggregations\"].get(\"group_by_time\", {}).get(\"buckets\", [])\n\n        if not time_buckets:\n            message = f\"天马山最近5天（2026-04-13 至 2026-04-17）数据量为 **0** 条，无数据。\"\n            print(json.dumps({\"success\": True, \"message\": message}, ensure_ascii=False))\n            return\n\n        # 构造每日汇总表格\n        header = \"| 日期 | FDI类型 | 数量 | 大小 |\"\n        separator = \"|---|---|---|---|\"\n        rows = []\n        daily_summary = []\n\n        for tb in time_buckets:\n            day = format_time_key(tb[\"key\"])\n            day_count = tb[\"doc_count\"]\n            day_size = tb.get(\"total_size\", {}).get(\"value\", 0)\n            daily_summary.append((day, day_count, day_size))\n\n            sub_buckets = tb.get(\"group_by_fdi_type\", {}).get(\"buckets\", [])\n            if sub_buckets:\n                for i, sb in enumerate(sub_buckets):\n                    fdi_type = sb[\"key\"]\n                    count = sb[\"doc_count\"]\n                    size = sb.get(\"total_size\", {}).get(\"value\", 0)\n                    if i == 0:\n                        rows.append(f\"| {day} | {fdi_type} | {count} | {format_size(size)} |\")\n                    else:\n                        rows.append(f\"| | {fdi_type} | {count} | {format_size(size)} |\")\n                # 每天小计\n                rows.append(f\"| **{day} 小计** | - | **{day_count}** | **{format_size(day_size)}** |\")\n            else:\n                rows.append(f\"| {day} | - | {day_count} | {format_size(day_size)} |\")\n\n        body = \"\\n\".join(rows)\n\n        message = (\n            f\"天马山最近5天（2026-04-13 至 2026-04-17）数据统计\\n\\n\"\n            f\"共 **{total}** 条数据，总大小 **{format_size(overall_size)}**\\n\\n\"\n            f\"{header}\\n{separator}\\n{body}\\n\\n\"\n            f\"| **合计** | - | **{total}** | **{format_size(overall_size)}** |\"\n        )\n\n        print(json.dumps({\"success\": True, \"message\": message}, ensure_ascii=False))\n\n    except Exception as e:\n        print(json.dumps({\"success\": False, \"error\": str(e)}, ensure_ascii=False))\n\nif __name__ == \"__main__\":\n    main()\n"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":3595,"cache_read_input_tokens":37255,"cache_creation":{"ephemeral_5m_input_tokens":3595,"ephemeral_1h_input_tokens":0},"output_tokens":1},"context_management":null},"parent_tool_use_id":"toolu_bdrk_0158X6K1CpSxPakUEMiksfiR","session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4","uuid":"a5ded913-f70d-456f-a52c-6eae3676d9e0"}
2026-04-17 14:58:41,466 - cc_bot.services.claude - INFO - Parsing stream line: type=assistant
2026-04-17 14:58:41,466 - cc_bot.services.claude - INFO - Unknown content block type: tool_use
2026-04-17 14:58:41,477 - cc_bot.services.claude - INFO - output: {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_bdrk_014KYqBwUXrAj9JkDogcfTu8","type":"tool_result","content":"File created successfully at: /tmp/cc-sandbox/88c39178-6fe2-8ca7-9174-74f8768874e4/.claude/skills/DSS/scripts/task_tianmashan_stats_5days.py"}]},"parent_tool_use_id":"toolu_bdrk_0158X6K1CpSxPakUEMiksfiR","session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4","uuid":"ec731925-8f27-417d-813b-8943f1f1c73d","timestamp":"2026-04-17T06:58:41.474Z"}
2026-04-17 14:58:41,478 - cc_bot.services.claude - INFO - Parsing stream line: type=user
2026-04-17 14:58:41,478 - cc_bot.services.claude - INFO - [execute_stream] 88c39178-6fe2-8ca7-9174-74f8768874e4 user: user.message:tool_result:File created successfully at: /tmp/cc-sandbox/88c39178-6fe2-8ca7-9174-74f8768874e4/.claude/skills/DSS/scripts/task_tianmashan_stats_5days.py
INFO:     127.0.0.1:57172 - "POST /api/log/hook-langfuse HTTP/1.1" 200 OK
INFO:     127.0.0.1:57171 - "POST /api/log/hook HTTP/1.1" 200 OK
INFO:     127.0.0.1:57173 - "POST /api/log/hook HTTP/1.1" 200 OK
INFO:     127.0.0.1:57175 - "POST /api/log/hook-langfuse HTTP/1.1" 200 OK
INFO:     127.0.0.1:57176 - "POST /api/log/hook HTTP/1.1" 200 OK
INFO:     127.0.0.1:57177 - "POST /api/log/hook-langfuse HTTP/1.1" 200 OK
2026-04-17 14:58:44,874 - cc_bot.services.claude - INFO - output: {"type":"system","subtype":"task_progress","task_id":"a9f42b8fbe4a66059","tool_use_id":"toolu_bdrk_0158X6K1CpSxPakUEMiksfiR","description":"Running 打印脚本内容并执行统计","usage":{"total_tokens":42586,"tool_uses":8,"duration_ms":57893},"last_tool_name":"Bash","uuid":"153479e8-3f9d-4ae7-b3dd-a6dabaef3a0e","session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4"}
2026-04-17 14:58:44,874 - cc_bot.services.claude - INFO - Parsing stream line: type=system
2026-04-17 14:58:44,874 - cc_bot.services.claude - INFO - Unrecognized system message in stream: data={'type': 'system', 'subtype': 'task_progress', 'task_id': 'a9f42b8fbe4a66059', 'tool_use_id': 'toolu_bdrk_0158X6K1CpSxPakUEMiksfiR', 'description': 'Running 打印脚本内容并执行统计', 'usage': {'total_tokens': 42586, 'tool_uses': 8, 'duration_ms': 57893}, 'last_tool_name': 'Bash', 'uuid': '153479e8-3f9d-4ae7-b3dd-a6dabaef3a0e', 'session_id': '88c39178-6fe2-8ca7-9174-74f8768874e4'}
2026-04-17 14:58:44,874 - cc_bot.services.claude - INFO - output: {"type":"assistant","message":{"model":"claude-sonnet-4-6","id":"msg_bdrk_01Kf9FntkRvS2pdcHNRzndM4","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_bdrk_01J7WHuuJC65JDLYBBexqMJ9","name":"Bash","input":{"command":"cd /tmp/cc-sandbox/88c39178-6fe2-8ca7-9174-74f8768874e4/.claude/skills/DSS/scripts && cat task_tianmashan_stats_5days.py && timeout 300 python task_tianmashan_stats_5days.py","description":"打印脚本内容并执行统计"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1650,"cache_read_input_tokens":40850,"cache_creation":{"ephemeral_5m_input_tokens":1650,"ephemeral_1h_input_tokens":0},"output_tokens":26},"context_management":null},"parent_tool_use_id":"toolu_bdrk_0158X6K1CpSxPakUEMiksfiR","session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4","uuid":"ff13a5dd-effc-48e3-be95-8a56f4b68611"}
2026-04-17 14:58:44,874 - cc_bot.services.claude - INFO - Parsing stream line: type=assistant
2026-04-17 14:58:44,874 - cc_bot.services.claude - INFO - Unknown content block type: tool_use
2026-04-17 14:58:44,983 - cc_bot.services.claude - INFO - output: {"type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 127\nimport json\nimport sys\nfrom datetime import datetime\nsys.path.insert(0, \".\")\nfrom dss_sdk import StatisticsClient\n\ndef format_size(size_bytes):\n    if not size_bytes or size_bytes <= 0:\n        return \"0 B\"\n    units = [\"B\", \"KB\", \"MB\", \"GB\", \"TB\", \"PB\"]\n    idx = 0\n    val = float(size_bytes)\n    while val >= 1024 and idx < len(units) - 1:\n        val /= 1024\n        idx += 1\n    return f\"{val:.2f} {units[idx]}\"\n\ndef format_time_key(timestamp):\n    try:\n        ts = float(timestamp)\n        if ts > 1e12:\n            ts /= 1000\n        return datetime.utcfromtimestamp(ts).strftime(\"%Y-%m-%d\")\n    except Exception:\n        return str(timestamp)\n\ndef main():\n    client = StatisticsClient()\n    try:\n        query = {\n            \"bool\": {\n                \"must\": [\n                    {\"asset.project\": \"天马山\"},\n                    {\"collected_time\": {\"gte\": \"2026-04-13T00:00:00.000Z\", \"lt\": \"2026-04-18T00:00:00.000Z\"}}\n                ]\n            }\n        }\n\n        # 按天聚合，子聚合按 fdi_type 分组，并统计每天总大小\n        aggs = {\n            \"group_by_time\": {\n                \"date_histogram\": {\"field\": \"collected_time\", \"calendar_interval\": \"day\"},\n                \"aggs\": {\n                    \"group_by_fdi_type\": {\n                        \"terms\": {\"field\": \"asset.fdi_type\", \"size\": 100},\n                        \"aggs\": {\"total_size\": {\"sum\": {\"field\": \"size\"}}}\n                    },\n                    \"total_size\": {\"sum\": {\"field\": \"size\"}}\n                }\n            },\n            \"overall_size\": {\"sum\": {\"field\": \"size\"}}\n        }\n\n        result = client.aggregate(query=query, aggs=aggs)\n\n        total = result[\"total\"]\n        overall_size = result[\"aggregations\"].get(\"overall_size\", {}).get(\"value\", 0)\n\n        time_buckets = result[\"aggregations\"].get(\"group_by_time\", {}).get(\"buckets\", [])\n\n        if not time_buckets:\n            message = f\"天马山最近5天（2026-04-13 至 2026-04-17）数据量为 **0** 条，无数据。\"\n            print(json.dumps({\"success\": True, \"message\": message}, ensure_ascii=False))\n            return\n\n        # 构造每日汇总表格\n        header = \"| 日期 | FDI类型 | 数量 | 大小 |\"\n        separator = \"|---|---|---|---|\"\n        rows = []\n        daily_summary = []\n\n        for tb in time_buckets:\n            day = format_time_key(tb[\"key\"])\n            day_count = tb[\"doc_count\"]\n            day_size = tb.get(\"total_size\", {}).get(\"value\", 0)\n            daily_summary.append((day, day_count, day_size))\n\n            sub_buckets = tb.get(\"group_by_fdi_type\", {}).get(\"buckets\", [])\n            if sub_buckets:\n                for i, sb in enumerate(sub_buckets):\n                    fdi_type = sb[\"key\"]\n                    count = sb[\"doc_count\"]\n                    size = sb.get(\"total_size\", {}).get(\"value\", 0)\n                    if i == 0:\n                        rows.append(f\"| {day} | {fdi_type} | {count} | {format_size(size)} |\")\n                    else:\n                        rows.append(f\"| | {fdi_type} | {count} | {format_size(size)} |\")\n                # 每天小计\n                rows.append(f\"| **{day} 小计** | - | **{day_count}** | **{format_size(day_size)}** |\")\n            else:\n                rows.append(f\"| {day} | - | {day_count} | {format_size(day_size)} |\")\n\n        body = \"\\n\".join(rows)\n\n        message = (\n            f\"天马山最近5天（2026-04-13 至 2026-04-17）数据统计\\n\\n\"\n            f\"共 **{total}** 条数据，总大小 **{format_size(overall_size)}**\\n\\n\"\n            f\"{header}\\n{separator}\\n{body}\\n\\n\"\n            f\"| **合计** | - | **{total}** | **{format_size(overall_size)}** |\"\n        )\n\n        print(json.dumps({\"success\": True, \"message\": message}, ensure_ascii=False))\n\n    except Exception as e:\n        print(json.dumps({\"success\": False, \"error\": str(e)}, ensure_ascii=False))\n\nif __name__ == \"__main__\":\n    main()\n(eval):1: command not found: timeout","is_error":true,"tool_use_id":"toolu_bdrk_01J7WHuuJC65JDLYBBexqMJ9"}]},"parent_tool_use_id":"toolu_bdrk_0158X6K1CpSxPakUEMiksfiR","session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4","uuid":"0fa5f666-7f37-4fb4-9013-883beab39422","timestamp":"2026-04-17T06:58:44.976Z","tool_use_result":"Error: Exit code 127\nimport json\nimport sys\nfrom datetime import datetime\nsys.path.insert(0, \".\")\nfrom dss_sdk import StatisticsClient\n\ndef format_size(size_bytes):\n    if not size_bytes or size_bytes <= 0:\n        return \"0 B\"\n    units = [\"B\", \"KB\", \"MB\", \"GB\", \"TB\", \"PB\"]\n    idx = 0\n    val = float(size_bytes)\n    while val >= 1024 and idx < len(units) - 1:\n        val /= 1024\n        idx += 1\n    return f\"{val:.2f} {units[idx]}\"\n\ndef format_time_key(timestamp):\n    try:\n        ts = float(timestamp)\n        if ts > 1e12:\n            ts /= 1000\n        return datetime.utcfromtimestamp(ts).strftime(\"%Y-%m-%d\")\n    except Exception:\n        return str(timestamp)\n\ndef main():\n    client = StatisticsClient()\n    try:\n        query = {\n            \"bool\": {\n                \"must\": [\n                    {\"asset.project\": \"天马山\"},\n                    {\"collected_time\": {\"gte\": \"2026-04-13T00:00:00.000Z\", \"lt\": \"2026-04-18T00:00:00.000Z\"}}\n                ]\n            }\n        }\n\n        # 按天聚合，子聚合按 fdi_type 分组，并统计每天总大小\n        aggs = {\n            \"group_by_time\": {\n                \"date_histogram\": {\"field\": \"collected_time\", \"calendar_interval\": \"day\"},\n                \"aggs\": {\n                    \"group_by_fdi_type\": {\n                        \"terms\": {\"field\": \"asset.fdi_type\", \"size\": 100},\n                        \"aggs\": {\"total_size\": {\"sum\": {\"field\": \"size\"}}}\n                    },\n                    \"total_size\": {\"sum\": {\"field\": \"size\"}}\n                }\n            },\n            \"overall_size\": {\"sum\": {\"field\": \"size\"}}\n        }\n\n        result = client.aggregate(query=query, aggs=aggs)\n\n        total = result[\"total\"]\n        overall_size = result[\"aggregations\"].get(\"overall_size\", {}).get(\"value\", 0)\n\n        time_buckets = result[\"aggregations\"].get(\"group_by_time\", {}).get(\"buckets\", [])\n\n        if not time_buckets:\n            message = f\"天马山最近5天（2026-04-13 至 2026-04-17）数据量为 **0** 条，无数据。\"\n            print(json.dumps({\"success\": True, \"message\": message}, ensure_ascii=False))\n            return\n\n        # 构造每日汇总表格\n        header = \"| 日期 | FDI类型 | 数量 | 大小 |\"\n        separator = \"|---|---|---|---|\"\n        rows = []\n        daily_summary = []\n\n        for tb in time_buckets:\n            day = format_time_key(tb[\"key\"])\n            day_count = tb[\"doc_count\"]\n            day_size = tb.get(\"total_size\", {}).get(\"value\", 0)\n            daily_summary.append((day, day_count, day_size))\n\n            sub_buckets = tb.get(\"group_by_fdi_type\", {}).get(\"buckets\", [])\n            if sub_buckets:\n                for i, sb in enumerate(sub_buckets):\n                    fdi_type = sb[\"key\"]\n                    count = sb[\"doc_count\"]\n                    size = sb.get(\"total_size\", {}).get(\"value\", 0)\n                    if i == 0:\n                        rows.append(f\"| {day} | {fdi_type} | {count} | {format_size(size)} |\")\n                    else:\n                        rows.append(f\"| | {fdi_type} | {count} | {format_size(size)} |\")\n                # 每天小计\n                rows.append(f\"| **{day} 小计** | - | **{day_count}** | **{format_size(day_size)}** |\")\n            else:\n                rows.append(f\"| {day} | - | {day_count} | {format_size(day_size)} |\")\n\n        body = \"\\n\".join(rows)\n\n        message = (\n            f\"天马山最近5天（2026-04-13 至 2026-04-17）数据统计\\n\\n\"\n            f\"共 **{total}** 条数据，总大小 **{format_size(overall_size)}**\\n\\n\"\n            f\"{header}\\n{separator}\\n{body}\\n\\n\"\n            f\"| **合计** | - | **{total}** | **{format_size(overall_size)}** |\"\n        )\n\n        print(json.dumps({\"success\": True, \"message\": message}, ensure_ascii=False))\n\n    except Exception as e:\n        print(json.dumps({\"success\": False, \"error\": str(e)}, ensure_ascii=False))\n\nif __name__ == \"__main__\":\n    main()\n(eval):1: command not found: timeout"}
2026-04-17 14:58:44,984 - cc_bot.services.claude - INFO - Parsing stream line: type=user
2026-04-17 14:58:44,984 - cc_bot.services.claude - INFO - [execute_stream] 88c39178-6fe2-8ca7-9174-74f8768874e4 user: user.message:tool_result:Exit code 127
import json
import sys
from datetime import datetime
sys.path.insert(0, ".")
from dss_sdk import StatisticsClient

def format_size(size_bytes):
    if not size_b
INFO:     127.0.0.1:57179 - "POST /api/log/hook HTTP/1.1" 200 OK
INFO:     127.0.0.1:57180 - "POST /api/log/hook-langfuse HTTP/1.1" 200 OK
INFO:     127.0.0.1:57181 - "POST /api/log/hook-langfuse HTTP/1.1" 200 OK
INFO:     127.0.0.1:57182 - "POST /api/log/hook HTTP/1.1" 200 OK
2026-04-17 14:58:51,188 - cc_bot.services.claude - INFO - output: {"type":"system","subtype":"task_progress","task_id":"a9f42b8fbe4a66059","tool_use_id":"toolu_bdrk_0158X6K1CpSxPakUEMiksfiR","description":"Running 执行天马山最近5天数据统计脚本","usage":{"total_tokens":51262,"tool_uses":9,"duration_ms":64207},"last_tool_name":"Bash","uuid":"78503d4d-854e-4773-a5e6-0029277ef38e","session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4"}
2026-04-17 14:58:51,188 - cc_bot.services.claude - INFO - Parsing stream line: type=system
2026-04-17 14:58:51,188 - cc_bot.services.claude - INFO - Unrecognized system message in stream: data={'type': 'system', 'subtype': 'task_progress', 'task_id': 'a9f42b8fbe4a66059', 'tool_use_id': 'toolu_bdrk_0158X6K1CpSxPakUEMiksfiR', 'description': 'Running 执行天马山最近5天数据统计脚本', 'usage': {'total_tokens': 51262, 'tool_uses': 9, 'duration_ms': 64207}, 'last_tool_name': 'Bash', 'uuid': '78503d4d-854e-4773-a5e6-0029277ef38e', 'session_id': '88c39178-6fe2-8ca7-9174-74f8768874e4'}
2026-04-17 14:58:51,188 - cc_bot.services.claude - INFO - output: {"type":"assistant","message":{"model":"claude-sonnet-4-6","id":"msg_bdrk_014HEAW9wzPiE4F9tx7n4SQn","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_bdrk_01VDvbF8FtsfTC3pgkq19bUD","name":"Bash","input":{"command":"cd /tmp/cc-sandbox/88c39178-6fe2-8ca7-9174-74f8768874e4/.claude/skills/DSS/scripts && python3 task_tianmashan_stats_5days.py","description":"执行天马山最近5天数据统计脚本","timeout":300000}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":8674,"cache_read_input_tokens":42500,"cache_creation":{"ephemeral_5m_input_tokens":8674,"ephemeral_1h_input_tokens":0},"output_tokens":1},"context_management":null},"parent_tool_use_id":"toolu_bdrk_0158X6K1CpSxPakUEMiksfiR","session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4","uuid":"68c11a6f-0d27-46f5-bee0-969765b5fe15"}
2026-04-17 14:58:51,188 - cc_bot.services.claude - INFO - Parsing stream line: type=assistant
2026-04-17 14:58:51,188 - cc_bot.services.claude - INFO - Unknown content block type: tool_use
INFO:     127.0.0.1:57188 - "POST /api/log/hook-langfuse HTTP/1.1" 200 OK
INFO:     127.0.0.1:57187 - "POST /api/log/hook HTTP/1.1" 200 OK
2026-04-17 14:58:52,377 - cc_bot.services.claude - INFO - output: {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_bdrk_01VDvbF8FtsfTC3pgkq19bUD","type":"tool_result","content":"/private/tmp/cc-sandbox/88c39178-6fe2-8ca7-9174-74f8768874e4/.claude/skills/DSS/scripts/task_tianmashan_stats_5days.py:23: DeprecationWarning: datetime.datetime.utcfromtimestamp() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.fromtimestamp(timestamp, datetime.UTC).\n  return datetime.utcfromtimestamp(ts).strftime(\"%Y-%m-%d\")\n{\"success\": true, \"message\": \"天马山最近5天（2026-04-13 至 2026-04-17）数据统计\\n\\n共 **959866** 条数据，总大小 **548.58 TB**\\n\\n| 日期 | FDI类型 | 数量 | 大小 |\\n|---|---|---|---|\\n| 2026-04-13 | DFDI | 222590 | 126.57 TB |\\n| **2026-04-13 小计** | - | **222590** | **126.57 TB** |\\n| 2026-04-14 | DFDI | 254953 | 147.45 TB |\\n| **2026-04-14 小计** | - | **254953** | **147.45 TB** |\\n| 2026-04-15 | DFDI | 233481 | 133.55 TB |\\n| **2026-04-15 小计** | - | **233481** | **133.55 TB** |\\n| 2026-04-16 | DFDI | 227785 | 128.08 TB |\\n| **2026-04-16 小计** | - | **227785** | **128.08 TB** |\\n| 2026-04-17 | DFDI | 21057 | 12.93 TB |\\n| **2026-04-17 小计** | - | **21057** | **12.93 TB** |\\n\\n| **合计** | - | **959866** | **548.58 TB** |\"}","is_error":false}]},"parent_tool_use_id":"toolu_bdrk_0158X6K1CpSxPakUEMiksfiR","session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4","uuid":"dd2371ba-53ce-414e-91cd-3378342d910b","timestamp":"2026-04-17T06:58:52.374Z"}
2026-04-17 14:58:52,377 - cc_bot.services.claude - INFO - Parsing stream line: type=user
2026-04-17 14:58:52,377 - cc_bot.services.claude - INFO - [execute_stream] 88c39178-6fe2-8ca7-9174-74f8768874e4 user: user.message:tool_result:/private/tmp/cc-sandbox/88c39178-6fe2-8ca7-9174-74f8768874e4/.claude/skills/DSS/scripts/task_tianmashan_stats_5days.py:23: DeprecationWarning: datetime.datetime.utcfromtimesta
INFO:     127.0.0.1:57192 - "POST /api/log/hook-langfuse HTTP/1.1" 200 OK
INFO:     127.0.0.1:57193 - "POST /api/log/hook HTTP/1.1" 200 OK
2026-04-17 14:58:56,857 - cc_bot.services.claude - INFO - output: {"type":"system","subtype":"task_progress","task_id":"a9f42b8fbe4a66059","tool_use_id":"toolu_bdrk_0158X6K1CpSxPakUEMiksfiR","description":"Running 删除临时统计脚本","usage":{"total_tokens":52025,"tool_uses":10,"duration_ms":69876},"last_tool_name":"Bash","uuid":"073bd561-674e-4a37-8376-14dada00c01a","session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4"}
2026-04-17 14:58:56,857 - cc_bot.services.claude - INFO - Parsing stream line: type=system
2026-04-17 14:58:56,857 - cc_bot.services.claude - INFO - Unrecognized system message in stream: data={'type': 'system', 'subtype': 'task_progress', 'task_id': 'a9f42b8fbe4a66059', 'tool_use_id': 'toolu_bdrk_0158X6K1CpSxPakUEMiksfiR', 'description': 'Running 删除临时统计脚本', 'usage': {'total_tokens': 52025, 'tool_uses': 10, 'duration_ms': 69876}, 'last_tool_name': 'Bash', 'uuid': '073bd561-674e-4a37-8376-14dada00c01a', 'session_id': '88c39178-6fe2-8ca7-9174-74f8768874e4'}
2026-04-17 14:58:56,857 - cc_bot.services.claude - INFO - output: {"type":"assistant","message":{"model":"claude-sonnet-4-6","id":"msg_bdrk_013qcexWhXhsJWKWdEwiYguK","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_bdrk_01YCZW9dFBg6ixWznpMebn1K","name":"Bash","input":{"command":"rm -f /tmp/cc-sandbox/88c39178-6fe2-8ca7-9174-74f8768874e4/.claude/skills/DSS/scripts/task_tianmashan_stats_5days.py","description":"删除临时统计脚本"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":747,"cache_read_input_tokens":51174,"cache_creation":{"ephemeral_5m_input_tokens":747,"ephemeral_1h_input_tokens":0},"output_tokens":8},"context_management":null},"parent_tool_use_id":"toolu_bdrk_0158X6K1CpSxPakUEMiksfiR","session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4","uuid":"2866947c-961c-4e32-b70b-c7999e9b4067"}
2026-04-17 14:58:56,858 - cc_bot.services.claude - INFO - Parsing stream line: type=assistant
2026-04-17 14:58:56,858 - cc_bot.services.claude - INFO - Unknown content block type: tool_use
2026-04-17 14:58:56,886 - cc_bot.services.claude - INFO - output: {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_bdrk_01YCZW9dFBg6ixWznpMebn1K","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"parent_tool_use_id":"toolu_bdrk_0158X6K1CpSxPakUEMiksfiR","session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4","uuid":"9c000d8e-232a-48dd-b67a-c50c4ff87c08","timestamp":"2026-04-17T06:58:56.884Z"}
2026-04-17 14:58:56,887 - cc_bot.services.claude - INFO - Parsing stream line: type=user
2026-04-17 14:58:56,887 - cc_bot.services.claude - INFO - [execute_stream] 88c39178-6fe2-8ca7-9174-74f8768874e4 user: user.message:tool_result:(Bash completed with no output)
INFO:     127.0.0.1:57197 - "POST /api/log/hook-langfuse HTTP/1.1" 200 OK
INFO:     127.0.0.1:57198 - "POST /api/log/hook HTTP/1.1" 200 OK
INFO:     127.0.0.1:57199 - "POST /api/log/hook-langfuse HTTP/1.1" 200 OK
INFO:     127.0.0.1:57200 - "POST /api/log/hook HTTP/1.1" 200 OK
INFO:     127.0.0.1:57202 - "POST /api/log/hook HTTP/1.1" 200 OK
INFO:     127.0.0.1:57203 - "POST /api/log/hook-langfuse HTTP/1.1" 200 OK
2026-04-17 14:59:03,440 - cc_bot.services.claude - INFO - output: {"type":"system","subtype":"task_notification","task_id":"a9f42b8fbe4a66059","tool_use_id":"toolu_bdrk_0158X6K1CpSxPakUEMiksfiR","status":"completed","output_file":"","summary":"统计天马山最近5天的数据量","usage":{"total_tokens":52192,"tool_uses":10,"duration_ms":76458},"uuid":"73b97760-b809-4457-b5ae-508a840765ee","session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4"}
2026-04-17 14:59:03,441 - cc_bot.services.claude - INFO - Parsing stream line: type=system
2026-04-17 14:59:03,441 - cc_bot.services.claude - INFO - Unrecognized system message in stream: data={'type': 'system', 'subtype': 'task_notification', 'task_id': 'a9f42b8fbe4a66059', 'tool_use_id': 'toolu_bdrk_0158X6K1CpSxPakUEMiksfiR', 'status': 'completed', 'output_file': '', 'summary': '统计天马山最近5天的数据量', 'usage': {'total_tokens': 52192, 'tool_uses': 10, 'duration_ms': 76458}, 'uuid': '73b97760-b809-4457-b5ae-508a840765ee', 'session_id': '88c39178-6fe2-8ca7-9174-74f8768874e4'}
2026-04-17 14:59:03,441 - cc_bot.services.claude - INFO - output: {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_bdrk_0158X6K1CpSxPakUEMiksfiR","type":"tool_result","content":[{"type":"text","text":"天马山最近5天（2026-04-13 至 2026-04-17）数据统计\n\n共 **959866** 条数据，总大小 **548.58 TB**\n\n| 日期 | FDI类型 | 数量 | 大小 |\n|---|---|---|---|\n| 2026-04-13 | DFDI | 222590 | 126.57 TB |\n| **2026-04-13 小计** | - | **222590** | **126.57 TB** |\n| 2026-04-14 | DFDI | 254953 | 147.45 TB |\n| **2026-04-14 小计** | - | **254953** | **147.45 TB** |\n| 2026-04-15 | DFDI | 233481 | 133.55 TB |\n| **2026-04-15 小计** | - | **233481** | **133.55 TB** |\n| 2026-04-16 | DFDI | 227785 | 128.08 TB |\n| **2026-04-16 小计** | - | **227785** | **128.08 TB** |\n| 2026-04-17 | DFDI | 21057 | 12.93 TB |\n| **2026-04-17 小计** | - | **21057** | **12.93 TB** |\n\n| **合计** | - | **959866** | **548.58 TB** |\n\n说明：5天数据全部为研采（DFDI）数据；4月17日（今天）数据量明显偏少，属于当天数据尚未完全回流的正常现象。"},{"type":"text","text":"agentId: a9f42b8fbe4a66059 (use SendMessage with to: 'a9f42b8fbe4a66059' to continue this agent)\n<usage>total_tokens: 52531\ntool_uses: 10\nduration_ms: 76461</usage>"}]}]},"parent_tool_use_id":null,"session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4","uuid":"d0f44844-2666-4792-b291-94eba51a72e8","timestamp":"2026-04-17T06:59:03.439Z","tool_use_result":{"status":"completed","prompt":"【用户原始请求】帮我统计天马山最近5天的数据量\n\n【本次任务】统计天马山（tianshanshan / 天马山）最近5天的数据量，时间范围：2026-04-13 00:00:00 至 2026-04-17 23:59:59，按日期分组返回数据统计结果（数量+大小）。","agentId":"a9f42b8fbe4a66059","content":[{"type":"text","text":"天马山最近5天（2026-04-13 至 2026-04-17）数据统计\n\n共 **959866** 条数据，总大小 **548.58 TB**\n\n| 日期 | FDI类型 | 数量 | 大小 |\n|---|---|---|---|\n| 2026-04-13 | DFDI | 222590 | 126.57 TB |\n| **2026-04-13 小计** | - | **222590** | **126.57 TB** |\n| 2026-04-14 | DFDI | 254953 | 147.45 TB |\n| **2026-04-14 小计** | - | **254953** | **147.45 TB** |\n| 2026-04-15 | DFDI | 233481 | 133.55 TB |\n| **2026-04-15 小计** | - | **233481** | **133.55 TB** |\n| 2026-04-16 | DFDI | 227785 | 128.08 TB |\n| **2026-04-16 小计** | - | **227785** | **128.08 TB** |\n| 2026-04-17 | DFDI | 21057 | 12.93 TB |\n| **2026-04-17 小计** | - | **21057** | **12.93 TB** |\n\n| **合计** | - | **959866** | **548.58 TB** |\n\n说明：5天数据全部为研采（DFDI）数据；4月17日（今天）数据量明显偏少，属于当天数据尚未完全回流的正常现象。"}],"totalDurationMs":76461,"totalTokens":52531,"totalToolUseCount":10,"usage":{"input_tokens":1,"cache_creation_input_tokens":166,"cache_read_input_tokens":51921,"output_tokens":443,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":166},"inference_geo":"","iterations":[],"speed":"standard"}}}
2026-04-17 14:59:03,441 - cc_bot.services.claude - INFO - Parsing stream line: type=user
2026-04-17 14:59:03,441 - cc_bot.services.claude - INFO - [execute_stream] 88c39178-6fe2-8ca7-9174-74f8768874e4 user: user.message:tool_result:[{'type': 'text', 'text': '天马山最近5天（2026-04-13 至 2026-04-17）数据统计\n\n共 **959866** 条数据，总大小 **548.58 TB**\n\n| 日期 | FDI类型 | 数量 | 大小 |\n|---|---|---|---|\n| 2026-04-13 | DFDI | 222
INFO:     127.0.0.1:57210 - "POST /api/log/hook-langfuse HTTP/1.1" 200 OK
INFO:     127.0.0.1:57211 - "POST /api/log/hook HTTP/1.1" 200 OK
INFO:     127.0.0.1:57212 - "POST /api/log/hook-langfuse HTTP/1.1" 200 OK
INFO:     127.0.0.1:57213 - "POST /api/log/hook HTTP/1.1" 200 OK
2026-04-17 14:59:26,400 - cc_bot.services.claude - INFO - output: {"type":"assistant","message":{"model":"claude-sonnet-4-6","id":"msg_bdrk_01VNBJ9zCTH596TA3nfXkSBV","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"The user didn't send a new message. This appears to be just the system reminder being displayed again without any new user content. But wait, looking at the conversation structure again, the previous assistant turn should have shown the results. Let me re-read...\n\nActually, looking at the conversation:\n1. First user message: \"@Data SPA Bot Debug  帮我统计天马山最近2天的数据量\"\n2. Assistant responded with results\n3. Second user message: \"@Data SPA Bot Debug  帮我统计天马山最近5天的数据量\"\n4. Assistant responded with results for 5 days\n5. Now there's a third Human turn with just the system-reminder and no actual user message\n\n\n6. Since there's no actual new request from the user, I should simply acknowledge that I'm ready for the next request or wait for further input. I'm noticing the conversation structure here—the user sent a system reminder without any actual message content, so there's nothing new to respond to. I should just acknowledge I'm ready for the next request.","signature":"ErgaCkgIDBABGAIqQK1CNIgGkkYeQPgojWl50k9cNfOq6idtyM8ch9PhdsOMJw88IWhPSSRdikTjvnZfFmC/AXUistAP6qNoL9TNn4oSDL6ytIgHCOTuQFDPDhoMgJEIdVpTexpQEqcWIjAV6SnJfPd3Tb5WdlAVeoHLzD2ToF5hJKBCvhyIHVnbrLQbXeYZV62OKOPWwvmCFmEqnRlRIaYXwM6NuUnvKUUJnnZ+20wYY9e8EDsduMpY93zaSmhN31mqVIhtrOW5zA1Zmuzx+ZcvhYA77rPFalj6AZ3uWHTnP9spO+X0U2AOBhEW/JR5mYKf7ITcBfQIU1LuYi0qmzGsoGlivPcXgPr4ErImj3UJiR/X5M7uptTL3BqOaf6bOccbbsGHxiPril+HSBrLkV4+rPfSywxOmE/1W5/k75F1AOXWsuh13Px39ETbjwJQbuy7bNvbUFTkLcz0tuOVw/JSFgiEY9+T/IHMqFJZDdPJCxG9u5D8bMsfzHXG97+dvbXJQ6HoYAeGNfYZPYgC4C21D3g5pT28oSgBnxxmf3CgTk61cAXRsTBKUOGFyPF9SchXf4JQsD0tXRy0WwHzI9yHstvsTPhKWtOYRVvdZfK/FI3HrorvBBk9nsrRhQEoRRtU2DvWOBqnwipiKm6VCB71Sk82+3qotD/pPg2nC8RoRQgw/OGVir5cPqZt/qbo5Y5MgSysTkWxnkf+w0h4BUGSUKQ7EuAdMgXM2XWj85p5rPDT3cF57GQOES/pyDBtFAgPLm04RKe8xg1vNiQ61uW5b1iilZK3FTfNX0yaHt3CPLcuMK01oUM2ulRvrvSNYqLFyLpvqmhr1G+tlCP4VO3e3pmn169YEM8zYWIWq+ADZhltvA2O5JWR7jaWo4JjMDhpKQrL/cPIepKYI4LdV5x+oWrlzyeSQbdIb0jYO3nzrcPXxk26ZADCU05c4VzKyDAF5FxF/79HVmJv98N4+WQ/NWILFD52gwKu28wsEVbqtBVwzRLDDAzpPSHObOzQOb4aN8DAfzSQvofIeahSVnHSw7LZvJYcHFinLBgUazZVod1J61LxqoCyxi5xdlFsIF2F6Na2ZLuWNyeLqlpc0wUORZahHBZGe4B+0zUzSqFPoc2e+UTzZGBFhrX1HVrXHO9dm3MX/cPlPb6bjr0SLK2vYL6AgLTY0t6EdwaUagcPdGVGrDeTg1odxti5FZlO8GGfC9eAT8j+DNztvZy8Nbaz6Ua9uzzkyLB02qVg2oIejcU09DGSTfBcRrcXvkPjLV1hbBvosRcBxXEK3gPiDEouLX3de4ItsJSuRNemUnHpys7C7n4o7aTiN3NwxrSk2S+RHH9nkR+0vb1ldCuWR9B59Nyard80diroAw8gpqwnUHLK+TI18HFE+UtsJYUXzcg7AOkO2B/D2rHSACMKHzevkMeZoFv6UC1RsqgyofSqAtNG6XceMoEKkR2/+xs4HThq197ZpiZTRkjx+qHwOl/KLeXqPZB+fnFRWq2hxaT5iRyX+Xmi+n09Th74feYVhIOW8NEODZttsua+0hQ+FeBMbStcatPHbcX3TCWEcNNPxjpD71/HNXtvhjule2BrR2+H/Aqz++K7r/D04y3cvtHmyb4QIVShZ39b65r2FDDbsR/jcCsxQl4+dGX1j5twgHIC06VIS5bfvzoNbEjLJgn9G1OHkfd5Pg6kZkFSHGr0T/hzYjbaP4dH7hSzNOFiIMq12zOf/wm+ik1Xn49P+LnviJZnuhCqCDk9ZARu6Vdu6XlEzrjSIfhwfHYqyOg0eIx06ZlT7N+6cMT7bJc8Uq9bgQq227rO6h4d69pAh7j1a35yBnZ6yuPSI5Y0DvWoxYBmqhICQrL29JPkSX0LCz2sDwgxivDCWhCMcuR0jutE/J1M37YWZzVm+F54L+Lq9cX2FBoX01r+oYY/qlsWwD9kCffyggHv5Sjijn05SmtCe7h52b3R+PClrQ/sclbJ4h7+f6rhOVFnTQNcKxxX4uP7dGlkegA8Iikxexm7tBkr1GL75MGr2fvXlT1svNwkTKr5aFLrdWBYyPq41clOuBKe1HMiobYx5uEfWcZwKOUule2qvUihQXx6APTWoeqOPHDTnWCDRnChcFDIeZkJ5mabbKrVsJwGxA+j78P1hk2F4sG+itwW6FxId4qd+n9cjGbyp2rtL5d8Tjwv0Gyoeok8S53igePKLKlgDcsY/3khrl4g46Ks/D4d8BjUwWGdUkNwIHP+3sbrjUk/01ZRdBLeu4xUhrmPS43SsRPQgQgSn0JnHMjc1rJ1QDpltCB+Cyk6RlsNYsNImFnpV6fo6G3UtDUGIz6mkb1CfAn2Skpw6DlI2be3QhOiXLZwc6Y2urc2FkspOfqkGdxEOdwA4dhvBbfcEF1jVesLlth7FYoWskrHSbjF1LdGk/0K/WEzGjZXgY+k5/QrhqzvZCAQKbgvyKaoyAYylDK8a1NlBqfUL9U1e3tDe2wS2WDuabNK3aiturjKIUv0AjHDukGCryB/lWTIOd5nBiBoHm1QMv2hk8e7K/LjuKESpnpR9L7Knru0MSgkZirFWgnU9n6RAGZoKqn5TXgsv32MzVHJr5ix2elYBbGWt0ZVx4gjd6JN6dnTwwBQ2OZ4DfVC3ry/L3ns+mTqE8k64bdQpXVd+vo3uJPel9tF1+snrXbFtWtW6XJIubmMfBFBws7HWFR3PPAjHTR/sUQ87MKKoHE4lCGqcIop7ti27vLwmlbtwQM7LbScAP2ZizIx7I7m/ObpSHVsy8larHyVXtTvjK6zfhQ8yNT5oelg26FYHYhH7wm6VIR8qiG7Zx7hdv3E8RjSS3pKkIJTElrbKJ64f2UkjVsI1/wU//PopuT5mkVAyKoD4gw9qHxG0c4ynnxdInVNkKAdddbVP3Kcm1BKyLVpbuFPbIWhiYTrZ8bR4tjLdzSlzsbEp7x4NOarAepPpZ3Dn1GrXmq7Qcfov6KnYGEP/p54ccXzFtyGvqxUStHP7jHoACvygFcqXOMeu+Buwn8UgP/UN0fnMRxXyZXO9fhnxaDk+DqklvOPEjCxMCWzdZt7todskKbw0OoSvKe7b+JFtccgkdpGbJCT55UYbldijAGBf7Xt1Efm+E/zjzPa1UWO1NqHMIsgyFLl/G/8QUbO/47fn/qh6bcqeBQUXD29Ag9C39C3ymk9bgyG/wqfrAsgInNOpY+EsT8bBDoBZkHkmbzhK+a6RdMcKE2jdNVG3boKAcqAhUF2o/lKPBZNNhBnQXyBfAidZJknYO/CRhBLgjmBQDYLpaaSxYjrgriEHdlf5YWVVtm4xq6DQ3lG2DGFlpZizE134Ojrd20kR++0+54T3SveLrczKKwwVvvM5IuZ6Q861Xv/2VMzBYFGraDv/ucGHi7IffNdmywkRJE24Lk3/qVyAOS9XsvaaDu9COsoGaPniN8K8egbkggnkbk0BcFfW3VOvJRu/m2mHUKgpqg1Cf2L5jf8NXh3cYzabIONmlaA0HmJHzw+Ti3fEloAIk3VPqdRAg0TdEHxPfe2GJRF8IiHDm0fHPSkIpQkNVoFwjhn6Bo0iGQkhXs7FTS66rezv/WTHt3NYfRY3+NYAw81eq26Pnmga9GuOew377eG8jmHtQw3UCs41sfvlAfbJzt/5wDbjceq3xdUQMu90I7zi8T1MXDIPzayFHkfm2Jiy7I8o+c/YrhPeJEk+gaIN6OqJ7A7MV9hetWCMQIgEmmN57rGYcyN9LTq0J543HXgdkQOpy7Ge26qlRGVEiclMATJMlO1DKpRvmlTPSJcfeet+U5Tqx1yR5YzjJqAUlW/I1L43Zwr2F+D1MKzIMEvHckQyJZFA7iPiUdjjAaIyiW2XY0SGHIFOqBlidFqso0pk2/e1z43Z+jGUdks7jhxORs1s2JaZGiERYEjslcpPAx6pra2Y7Atl7VgKA5FLpVIQpIZ9TDlcmlUxelqpo7QLDa4ZkDZ5w9xdYuwX4g1p+BBPytKJkzaabFQtL1T4p6T7EFBoo2MvVXmLBN/oIS5H5uGwQioUPtWB2rK4BaN+7m4X+hm8pzdtxvnBnJI6qp639LhcNeSoAbEmXTzF32g47+vxx3JAPP2pa+bsUhtAeUlL74FOa7phVfAhysF549/AaHHI1wuNUCiuOJJSm3QEPm0HXnm6IivAc40TlAKk+oCGKkzLp/H9lB4tu9Iqrp/1F/3G974ZXXqJz+kufc+e2kzWpd4Iz1b2Wx1WrY1S4KS61Ae/C7Y5iLWUaYS5ThLZOyMOQBRAHjaXGkX1fecO8exJUKukjE/qBvPden1tH9uU4KWugVG7jl2UPeuzZLCewAx/7qLoO42+kJ/nubjKZR6IpaXVVEeA8UXv1O8aiQLp0sFZM/uxY4KoM3NxiWxurFaR3XSurNR0ZJAnQjPRCMXTA0qJ5GswS6Jvh+xMZSv9CqEQqVhx8af+OkTRQ2QQsEYOk2zdF5a0hEvQmbP+9R7bi8DDHf7khS8Sm2lFf6Ri2W3NrqpuffXGAE="}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":10,"cache_creation_input_tokens":8060,"cache_read_input_tokens":28877,"cache_creation":{"ephemeral_5m_input_tokens":8060,"ephemeral_1h_input_tokens":0},"output_tokens":1},"context_management":null},"parent_tool_use_id":null,"session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4","uuid":"36ffb90f-2095-4006-8c89-0c56d2b94a83"}
2026-04-17 14:59:26,401 - cc_bot.services.claude - INFO - Parsing stream line: type=assistant
2026-04-17 14:59:26,401 - cc_bot.services.claude - INFO - [execute_stream] 88c39178-6fe2-8ca7-9174-74f8768874e4 text: [Thinking]
The user didn't send a new message. This appears to be just the system reminder being displayed again without any new user content. But wait, looking at the conversation structure again, th
2026-04-17 14:59:30,056 - cc_bot.services.claude - INFO - output: {"type":"assistant","message":{"model":"claude-sonnet-4-6","id":"msg_bdrk_01VNBJ9zCTH596TA3nfXkSBV","type":"message","role":"assistant","content":[{"type":"text","text":"以下是天马山最近5天（2026-04-13 至 2026-04-17）的数据量统计结果：\n\n**汇总：共 959,866 条，总大小 548.58 TB**\n\n| 日期 | 数据类型 | 数量 | 大小 |\n|---|---|---|---|\n| 2026-04-13 | DFDI | 222,590 | 126.57 TB |\n| 2026-04-14 | DFDI | 254,953 | 147.45 TB |\n| 2026-04-15 | DFDI | 233,481 | 133.55 TB |\n| 2026-04-16 | DFDI | 227,785 | 128.08 TB |\n| 2026-04-17 | DFDI | 21,057 | 12.93 TB |\n| **合计** | **DFDI** | **959,866** | **548.58 TB** |\n\n> 5天数据全部为研采（DFDI）类型。4月17日（今天）数据量明显偏少，属于当天数据尚未完全回流的正常现象。"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":10,"cache_creation_input_tokens":8060,"cache_read_input_tokens":28877,"cache_creation":{"ephemeral_5m_input_tokens":8060,"ephemeral_1h_input_tokens":0},"output_tokens":1},"context_management":null},"parent_tool_use_id":null,"session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4","uuid":"e856839b-3eae-4de7-8fce-3008b47dbcdf"}
2026-04-17 14:59:30,056 - cc_bot.services.claude - INFO - Parsing stream line: type=assistant
2026-04-17 14:59:30,056 - cc_bot.services.claude - INFO - [execute_stream] 88c39178-6fe2-8ca7-9174-74f8768874e4 text: 以下是天马山最近5天（2026-04-13 至 2026-04-17）的数据量统计结果：

**汇总：共 959,866 条，总大小 548.58 TB**

| 日期 | 数据类型 | 数量 | 大小 |
|---|---|---|---|
| 2026-04-13 | DFDI | 222,590 | 126.57 TB |
| 2026-04-14 | DFDI | 254,953 | 14
2026-04-17 14:59:30,059 - cc_bot.services.claude - INFO - output: {"type":"result","subtype":"success","is_error":false,"duration_ms":112564,"duration_api_ms":110278,"num_turns":2,"result":"以下是天马山最近5天（2026-04-13 至 2026-04-17）的数据量统计结果：\n\n**汇总：共 959,866 条，总大小 548.58 TB**\n\n| 日期 | 数据类型 | 数量 | 大小 |\n|---|---|---|---|\n| 2026-04-13 | DFDI | 222,590 | 126.57 TB |\n| 2026-04-14 | DFDI | 254,953 | 147.45 TB |\n| 2026-04-15 | DFDI | 233,481 | 133.55 TB |\n| 2026-04-16 | DFDI | 227,785 | 128.08 TB |\n| 2026-04-17 | DFDI | 21,057 | 12.93 TB |\n| **合计** | **DFDI** | **959,866** | **548.58 TB** |\n\n> 5天数据全部为研采（DFDI）类型。4月17日（今天）数据量明显偏少，属于当天数据尚未完全回流的正常现象。","stop_reason":"end_turn","session_id":"88c39178-6fe2-8ca7-9174-74f8768874e4","total_cost_usd":0.5606971500000001,"usage":{"input_tokens":20,"cache_creation_input_tokens":30385,"cache_read_input_tokens":35429,"output_tokens":1464,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":30385},"inference_geo":"","iterations":[],"speed":"standard"},"modelUsage":{"claude-sonnet-4.6*":{"*inputTokens":12641,"outputTokens":4522,"cacheReadInputTokens":296243,"cacheCreationInputTokens":97619,"webSearchRequests":0,"costUSD":0.5606971500000001,"contextWindow":200000,"maxOutputTokens":32000}},"permission_denials":[],"fast_mode_state":"off","uuid":"6faaabc8-f796-4cb7-b77f-7057b0d08f47"}
2026-04-17 14:59:30,059 - cc_bot.services.claude - INFO - Parsing stream line: type=result
2026-04-17 14:59:30,059 - cc_bot.services.claude - INFO - [execute_stream] 88c39178-6fe2-8ca7-9174-74f8768874e4 result: 以下是天马山最近5天（2026-04-13 至 2026-04-17）的数据量统计结果：

**汇总：共 959,866 条，总大小 548.58 TB**

| 日期 | 数据类型 | 数量 | 大小 |
|---|---|---|---|
| 2026-04-13 | DFDI | 222,590 | 126.57 TB |
| 2026-04-14 | DFDI | 254,953 | 14
INFO:     127.0.0.1:57249 - "POST /api/log/hook-langfuse HTTP/1.1" 200 OK
INFO:     127.0.0.1:57248 - "POST /api/log/hook HTTP/1.1" 200 OK

吐槽