What This Category's Data Looks Like
This category serves transportation bureaus, highway development centers, maintenance units, and road law enforcement agencies. Data mainly comes from highway maintenance management systems, bridge and tunnel monitoring systems, road patrol systems, and traffic flow monitoring data. Data is updated daily. Daily documents include maintenance plans, patrol logs, defect disposal orders, road construction permits, and road damage compensation forms. Data fields cover section numbers, defect types, disposal statuses, permit numbers, compensation amounts, and more. Common units include kilometers, square meters, working days, and similar units. Data structures vary based on document type.
What Constraints These Characteristics Impose on the Database and Operations Link
High-frequency daily updated business data requires the database to have stable concurrent read and write capabilities to avoid data backlog and loss. Storage requirements for multiple types of heterogeneous documents require the database to support flexible field expansion to adapt to the differentiated attributes of different documents. Associated data from road law enforcement and maintenance work orders requires cross-table indexing to ensure the accuracy of ledger aggregation. Data related to road construction and road damage involves responsibility confirmation, so an immutable data mechanism must be configured. Regular full backups must be performed during operations. Access to scattered multi-source data requires unified channel configuration to reduce daily operation and maintenance complexity.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://{username}:{password}@{host}:{port}/{dbName}?authSource=admin | Adapt to unstructured patrol log and defect disposal order storage, support flexible field expansion, and align with FastGPT v4.17.0 document storage logic |
PG_URL | postgresql://{user}:{pass}@{host}:5432/road_maintenance?sslmode=require | Store associated maintenance plan and road construction permit data, support cross-table queries to ensure accurate ledger aggregation |
DB_MAX_LINK | 20–30 | Adapt to daily high-frequency read and write requests, avoid database connection exhaustion, and ensure service stability |
REDIS_URL | redis://:{password}@{host}:6379/0 | Cache frequently queried road section status data, reduce load on the core database, and improve response speed |
Three Common Pitfalls
- Symptom: Database connection results cannot be correctly assigned to an array, returning an empty array. Cause: Asynchronous query callback logic is not handled properly, so the array is not populated before data is returned.
- Symptom: Docker-deployed MongoDB instances cannot connect, with an authentication failure prompt. Cause: MongoDB account and password inside the container do not match the parameters in the
MONGODB_URIconfiguration, or container port mapping is not enabled. - Symptom: After a Function Call retrieves MySQL data, the large language model does not reference the original database snippets. Cause: No context splicing logic for database query results is configured, and query results are not passed to the large language model as part of the Prompt.
How to Confirm the Configuration Is Correct
- Run a database connection test script to check if maintenance plans, patrol logs, and other document data can be read normally, confirming that the connection string configuration is correct.
- Check the database connection pool status, confirm that the current number of connections does not exceed the threshold set by
DB_MAX_LINK, with no connection overflow. - Trigger a ledger aggregation task, check if the generated report includes original snippets from each system, confirming that data association and context splicing logic work properly.
- Check operation and maintenance logs, confirm that daily data update tasks have no timeouts or failure errors, confirming that the scheduled backup configuration is active.
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.