What the data for this category looks like
The data for internal office and employee services of urban public transit primarily comes from the scheduling and rostering system, in-vehicle terminal operation and maintenance system, employee attendance management system, and customer service ticket system. Data update cycles cover three tiers: real-time, every 5 minutes, and daily. Vehicle operation logs sync every 5 minutes, customer service tickets are submitted in real time, and driver schedules and employee attendance records update daily. Each individual document includes three types of fields: unique identifier, business subject, and status. For example, rostering documents contain shift ID, driver employee ID, departure time, and route number; operation logs contain vehicle ID, real-time vehicle speed (km/h), fuel consumption (L/100km), and fault code.
What constraints these characteristics impose on deployment and upgrade
Reference only; verify against the target FastGPT release and deployment documentation.
How to set the configurations
| Config Item | Recommended Value | Rationale |
|---|---|---|
DB_MAX_LINK | 150–200 | Public transit business includes high-frequency concurrent requests such as real-time operation logs and tickets. This parameter controls the maximum number of database connections to avoid connection overflow |
PG_URL | Configure as the address of a locally deployed time-series database instance | Public transit operation logs are mostly in time-series format; PostgreSQL supports efficient storage and query of time-series data to meet high-frequency write requirements |
REDIS_URL | Configure as a cluster address with memory usage of no less than 8000 MB | Used to cache frequently accessed rostering information and employee permission data to reduce query pressure on relational databases |
STORAGE_S3_ENDPOINT | Select a low-latency object storage node in the same city | Unstructured data such as in-vehicle logs and inspection photos has large volume; low-latency storage improves data reading and calling speeds |
ROOT_KEY | Generate a dedicated key according to the internal permission system | Used to manage platform-level access permissions, adapting to the permission isolation and hierarchical management needs of multiple internal departments of public transit |
Three common mistakes
- Phenomenon: Frequent hard disk read/write operations occur after Docker deployment, and even storage resources are exhausted. Cause: No appropriate cache strategy is configured for high-frequency real-time log data of public transit, resulting in frequent disk writes to the vector database and relational database.
- Phenomenon: Vector database deployment fails, prompting PG-related configuration errors. Cause: The built-in PostgreSQL configuration in the general vector database compose file is retained by mistake, and the database connection address adapted to public transit business is not replaced.
- Phenomenon: The matching accuracy of knowledge base recall results related to employee services is low. Cause: Unique fields unique to public transit such as routes, stations, and employee IDs are not configured as weighted fields for vector retrieval, resulting in retrieval results that do not match business needs.
How to confirm the configuration is correct
- Log in to the monitoring panel of the FastGPT admin backend, check the database connection status, and confirm that the current number of connections does not exceed the configured
DB_MAX_LINKvalue. - Upload a test piece of public transit rostering data, trigger a vector retrieval operation, and observe whether the response time of the returned results meets business expectations.
- Perform a rolling restart of the service, check the system logs, and confirm that there are no database connection timeout or storage read/write error messages.
- Conduct a department permission test, confirm that the permission rules associated with
ROOT_KEYcan normally isolate the access scope of different departments.
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.