SOMS/.kiro/specs/infrared-camera-data-query-enhancement/tasks.md

# Implementation Plan: Infrared Camera Data Query Enhancement

## Overview

This implementation plan enhances the `FindDatas` method to query infrared camera temperature data from the `OriginalInspectionStoreResult` collection. The implementation adds camera identification logic, fuzzy equipment name matching with 85% similarity, and structured temperature data parsing.

## Tasks

- [x] 1. Implement helper method for infrared camera identification
  - Create `IsInfraredCamera` method that checks if CamName contains "热成像" or ends with 'B'
  - Handle null/empty camera names safely
  - _Requirements: 1.1, 1.2, 1.3_

- [ ]* 1.1 Write property test for infrared camera identification
  - **Property 1: Infrared Camera Identification Completeness**
  - **Validates: Requirements 1.1, 1.2, 1.3**
  - Generate camera names with "热成像" or ending with 'B'
  - Verify IsInfraredCamera returns true for all matching patterns

- [ ]* 1.2 Write unit tests for IsInfraredCamera edge cases
  - Test null camera name
  - Test empty camera name
  - Test camera name with neither pattern
  - _Requirements: 1.1, 1.2, 1.3_

- [x] 2. Enhance CalculateSimilarity method with Levenshtein distance
  - Replace simple character matching with Levenshtein distance algorithm
  - Implement formula: `similarity = 1.0 - (distance / maxLength)`
  - Add null/empty string handling returning 0.0
  - Make comparison case-insensitive
  - _Requirements: 2.4_

- [ ]* 2.1 Write property test for similarity symmetry
  - **Property 2: Similarity Calculation Symmetry**
  - **Validates: Requirements 2.4**
  - Generate random string pairs
  - Verify CalculateSimilarity(A, B) == CalculateSimilarity(B, A)

- [ ]* 2.2 Write property test for similarity bounds
  - **Property 3: Similarity Score Bounds**
  - **Validates: Requirements 2.4**
  - Generate random string pairs
  - Verify 0.0 <= CalculateSimilarity(A, B) <= 1.0

- [ ]* 2.3 Write unit tests for similarity calculation
  - Test identical strings (should return 1.0)
  - Test completely different strings
  - Test strings at 85% similarity threshold
  - Test null and empty string handling
  - _Requirements: 2.4, 5.2_

- [x] 3. Implement equipment name matching method
  - Create `MatchesEquipmentByName` method
  - Compare data.EquipmentInfoName against equipment.Name
  - Compare data.Name against equipment.Name
  - Return true if either similarity >= 0.85
  - _Requirements: 2.2, 2.3, 2.5, 2.6_

- [ ]* 3.1 Write property test for equipment matching threshold
  - **Property 4: Equipment Matching Threshold**
  - **Validates: Requirements 2.5, 2.6**
  - Generate inspection data and equipment with varying name similarities
  - Verify matching returns true when similarity >= 0.85

- [ ]* 3.2 Write unit tests for equipment matching
  - Test exact name match
  - Test 85% similarity match
  - Test 84% similarity (should not match)
  - Test null equipment names
  - Test matching via EquipmentInfoName field
  - Test matching via Name field
  - _Requirements: 2.2, 2.3, 2.5, 2.6_

- [x] 4. Implement temperature data parsing method
  - Create `ParseTemperatureFromInfraredResult` method
  - Split Result field by delimiters: `,`, `，`, `\r\n`, `\n`
  - For each segment, check if contains `:` or `：`
  - Split valid segments into name and value parts
  - Extract numeric temperature using regex: `@"-?\d+(?:\.\d+)?"`
  - Create MeasureTemperatureResultOutput for each valid temperature
  - Add error logging for parsing failures
  - _Requirements: 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 5.1_

- [ ]* 4.1 Write property test for valid temperature parsing
  - **Property 5: Valid Temperature Data Parsing**
  - **Validates: Requirements 3.1, 3.2, 3.3, 3.5, 3.6**
  - Generate Result strings with valid "name:value" format
  - Verify at least one temperature point is extracted with non-null value

- [ ]* 4.2 Write property test for invalid segment skipping
  - **Property 6: Invalid Segment Skipping**
  - **Validates: Requirements 3.4, 3.7**
  - Generate Result strings with mix of valid and invalid segments
  - Verify invalid segments are skipped without errors

- [ ]* 4.3 Write unit tests for temperature parsing
  - Test valid format: "2BL_C相连接点：-12.5℃"
  - Test multiple temperature points separated by commas
  - Test mixed valid and invalid segments
  - Test empty Result string
  - Test Result with no colons
  - Test negative temperatures
  - Test decimal temperatures
  - _Requirements: 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7_

- [ ] 5. Checkpoint - Ensure all helper methods and tests pass
  - Ensure all tests pass, ask the user if questions arise.

- [x] 6. Modify FindDatas method to add infrared camera query logic
  - Add MongoDB filter for infrared cameras using IsInfraredCamera logic
  - Query OriginalInspectionStoreResult collection with time range filters
  - Apply equipment matching using MatchesEquipmentByName when EquipmentInfoId is specified
  - Parse temperature data using ParseTemperatureFromInfraredResult
  - Apply name filter to parsed temperature points
  - Merge results with existing thermal imaging device results
  - _Requirements: 4.1, 4.2, 4.3, 4.4, 4.5_

- [ ]* 6.1 Write property test for filter application order
  - **Property 7: Filter Application Order**
  - **Validates: Requirements 4.1, 4.2, 4.3, 4.4, 4.5**
  - Generate test data with various filter combinations
  - Verify filters are applied in correct order

- [ ]* 6.2 Write integration tests for FindDatas
  - Test full query flow with infrared camera data
  - Test equipment matching integration
  - Test pagination with infrared camera results
  - Test time range filtering
  - Test name filtering on parsed temperature points
  - _Requirements: 4.1, 4.2, 4.3, 4.4, 4.5, 4.6_

- [x] 7. Add comprehensive error handling and logging
  - Add try-catch for MongoDB query failures
  - Add try-catch for temperature parsing failures with graceful degradation
  - Add try-catch for similarity calculation errors
  - Add try-catch for equipment lookup failures
  - Implement logging strategy (Error, Warning, Info levels)
  - _Requirements: 5.1, 5.2, 5.3, 5.4, 5.5_

- [ ]* 7.1 Write property test for parsing error isolation
  - **Property 9: Parsing Error Isolation**
  - **Validates: Requirements 5.1, 5.5**
  - Generate dataset with some invalid Result formats
  - Verify parsing failures don't prevent processing of other records

- [ ]* 7.2 Write unit tests for error handling
  - Test MongoDB query failure handling
  - Test temperature parsing failure handling
  - Test similarity calculation error handling
  - Test equipment lookup failure handling
  - Verify appropriate logging occurs
  - _Requirements: 5.1, 5.2, 5.3, 5.4_

- [ ] 8. Final checkpoint - Ensure all tests pass and integration works
  - Run all unit tests and property tests
  - Run integration tests with real MongoDB data
  - Verify backward compatibility with existing thermal imaging queries
  - Ensure all tests pass, ask the user if questions arise.

## Notes

- Tasks marked with `*` are optional and can be skipped for faster MVP
- Each task references specific requirements for traceability
- Checkpoints ensure incremental validation
- Property tests validate universal correctness properties
- Unit tests validate specific examples and edge cases
- The Levenshtein distance algorithm is critical for accurate 85% similarity matching
- Error handling ensures graceful degradation when parsing fails
- Logging provides visibility into query execution and failures