What the data for this category looks like
Bridge defect identification data primarily comes from three sources: high-definition aerial images captured during bridge inspections, close-up structural shots from fixed-point cameras, and real-time monitoring data collected by stress and displacement sensors.
Update frequency varies by inspection type:
- Routine monthly inspections upload incremental data weekly
- Special inspections submit full image sets and monitoring records as needed
Each data entry includes four core fields: shooting timestamp, inspection point latitude and longitude coordinates, image file, and combined defect type tag and severity level. Single image files can reach hundreds of megabytes in size. Coordinate fields use either degrees-minutes-seconds or decimal degree units.
Constraints on Deployment and Upgrade
The large image sizes and high-frequency incremental updates of bridge defect inspection data impose clear requirements for deployment storage and database configuration.
Dedicated object storage services are required for large image files to avoid local disk storage and transmission performance bottlenecks. Database connection pool parameters must be adjusted to support high-frequency incremental data writes and point queries, ensuring concurrent stability.
During version upgrades, compatibility with newly added defect tag fields and updated image storage path formats is required to prevent old data from failing to load properly. Cross-node transmission of inspection data requires deployment environments to meet network bandwidth requirements for large file synchronization.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 15–25 connections | Adapt to high-frequency incremental writes and point queries for bridge inspection data, avoid database connection exhaustion |
STORAGE_S3_ENDPOINT | Fill in the access address of the local object storage node | Adapt to storage needs where single image files can reach hundreds of MB, ensure stable large file upload and download |
REDIS_URL | Configure a dedicated Redis instance with no less than 8 GB of allocated memory | Cache inspection point coordinates and recognition results, reduce repeated database query pressure |
PG_URL | Configure a PostgreSQL instance that supports spatial indexing | Adapt to latitude and longitude coordinate queries for inspection points, improve positioning retrieval efficiency |
ROOT_KEY | Generate a random 32-bit string as the authentication key | Ensure identity verification security for API calls, prevent unauthorized access to deployed services |
Three Common Misconfigurations
- A 504 gateway timeout error occurs during upgrades. The root cause is failure to adjust the
DB_MAX_LINKparameter: the database connection pool cannot handle data migration requests during the upgrade phase, leading to service response interruption. - Recognition tasks lag after local deployment, with memory usage remaining consistently high. The root cause is failure to configure
REDIS_URLas a dedicated instance: the caching mechanism does not take effect, leading to repeated database queries that exhaust system resources. - A 413 Request Entity Too Large error is returned when uploading inspection images. The root cause is failure to adapt to the large file upload limit of the storage node associated with
STORAGE_S3_ENDPOINT, and failure to adjust the single-file size threshold of the storage service.
How to Confirm Configurations Are Correct
- Run the specified storage interface call command to upload a 100 MB test image file, confirm that a 200 success status code is returned.
- Check the database connection count monitoring panel, confirm that the current active connection count does not exceed the value set for
DB_MAX_LINK. - Submit a test inspection record containing latitude and longitude coordinates and an image file, confirm that the corresponding record can be retrieved via the point query interface.
- Run the official upgrade script for FastGPT v4.17.0, confirm that the script executes without errors and the service responds to requests normally after restarting.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.