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CLAUDE.md

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+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Build and Run Commands
+
+```bash
+# Install dependencies
+pip install -r requirements.txt
+
+# Run the Streamlit application
+streamlit run app.py
+
+# Run tests (if tests/ directory exists)
+pytest -q
+```
+
+## Architecture Overview
+
+This is a Streamlit-based traffic safety analysis system with a three-layer architecture:
+
+### Layer Structure
+
+```
+app.py (Main Entry & UI Orchestration)
+    ↓
+ui_sections/ (UI Components - render_* functions)
+    ↓
+services/ (Business Logic)
+    ↓
+config/settings.py (Configuration)
+```
+
+### Data Flow
+
+1. **Input**: Excel files uploaded via Streamlit sidebar (事故数据 + 策略数据)
+2. **Processing**: `services/io.py` handles loading, column aliasing, and cleaning
+3. **Aggregation**: Data aggregated to daily time series with `aggregate_daily_data()`
+4. **Analysis**: Various services process the aggregated data
+5. **Output**: Interactive Plotly charts, CSV exports, AI-generated reports
+
+### Key Services
+
+| Module | Purpose |
+|--------|---------|
+| `services/io.py` | Data loading, column normalization (COLUMN_ALIASES), region inference |
+| `services/forecast.py` | ARIMA grid search, KNN counterfactual, GLM/SVR extrapolation |
+| `services/strategy.py` | Strategy effectiveness evaluation (F1/F2 metrics, safety states) |
+| `services/hotspot.py` | Location extraction, risk scoring, strategy generation |
+| `services/metrics.py` | Model evaluation metrics (RMSE, MAE) |
+
+### UI Sections
+
+Each tab in the app corresponds to a `render_*` function in `ui_sections/`:
+- `render_overview`: KPI dashboard and time series visualization
+- `render_forecast`: Multi-model prediction comparison
+- `render_model_eval`: Model accuracy metrics
+- `render_strategy_eval`: Single strategy evaluation
+- `render_hotspot`: Accident hotspot analysis with risk levels
+
+### Session State Pattern
+
+The app uses `st.session_state['processed_data']` to persist:
+- Loaded DataFrames (`combined_city`, `combined_by_region`, `accident_records`)
+- Filter state (`region_sel`, `date_range`, `strat_filter`)
+- Derived metadata (`all_regions`, `all_strategy_types`, `min_date`, `max_date`)
+
+### AI Integration
+
+Uses DeepSeek API (OpenAI-compatible) for generating analysis reports. Configuration in sidebar:
+- Base URL: `https://api.deepseek.com`
+- Model: `deepseek-chat`
+- Streaming response rendered incrementally
+
+## Coding Conventions
+
+- Python 3.8+ with type hints (`from __future__ import annotations`)
+- Functions/variables: `snake_case`; Classes: `PascalCase`; Constants: `UPPER_SNAKE_CASE`
+- Use `@st.cache_data` for expensive computations
+- Column aliases defined in `COLUMN_ALIASES` dict for flexible Excel input
+- Prefer pandas vectorization over loops
+
+## Data Format Requirements
+
+**Accident Data Excel** must contain (or aliases of):
+- `事故时间` (accident time)
+- `所在街道` (street/region)
+- `事故类型` (accident type: 财损/伤人/亡人)
+
+**Strategy Data Excel** must contain:
+- `发布时间` (publish date)
+- `交通策略类型` (strategy type)
+
+## Configuration (config/settings.py)
+
+Key parameters:
+- `ARIMA_P/D/Q`: Grid search ranges for ARIMA
+- `MIN_PRE_DAYS` / `MAX_PRE_DAYS`: Historical data requirements
+- `ANOMALY_CONTAMINATION`: Isolation Forest contamination rate