What the data for this category looks like
Rail transit equipment ledger data primarily comes from three sources: on-board operating sensors, offline maintenance work order systems, and equipment factory archives. Sensor data is uploaded in real time. Maintenance work order data is synchronized and archived daily. Factory archives are imported statically in bulk. Each single ledger document includes core fields such as unique device ID, vehicle model, maintenance execution time, replaced parts list, cumulative operating duration, triggered fault codes, and others. The unit for cumulative operating duration is hours. Fault codes consist of uppercase letters followed by four digits. On average, each document contains 12 core fields.
What constraints these characteristics impose on database and operations maintenance
Real-time on-board sensor data generates high-frequency, small-batch write requests. Database connection pools must support high-concurrency, low-latency connection allocation. Daily bulk-synced maintenance work order data has a large volume. Databases must support bulk insert transactions to avoid import timeouts. Fields include fixed-unit numerical values and fixed-format fault codes. Database-level validation rules must be configured to prevent invalid data writes. The unique device ID as the core primary key requires databases to support unique index constraints to avoid duplicate ledger entries. Static bulk import of factory archives requires databases to support large file reading and bulk storage.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | mongodb://[account]:[password]@[host address]:27017/rail_equipment?authSource=admin | Adapts to unstructured and semi-structured rail transit equipment ledger data, specifies a dedicated database name and authentication source |
PG_URL | postgresql://[account]:[password]@[host address]:5432/rail_maintain?schema=public | Used to store fixed-format maintenance work orders and equipment archives, supports complex queries and transaction operations |
DB_MAX_LINK | 200–300 connections | Balances high-concurrency write requirements for real-time sensor data and database connection resource usage, avoids connection exhaustion |
REDIS_URL | redis://[host address]:6379/0 | Used to cache frequently accessed basic device information, reduces database query pressure |
Three common configuration mistakes
- Symptom: A
Failed to connecterror is returned when connecting to MongoDB in the orchestration workflow, and the background MongoDB status check shows a failure. Cause: The correct authentication source and dedicated database name are not specified inMONGODB_URI, or the account and password configurations are incorrect. - Symptom: A connection timeout error occurs when bulk importing maintenance work order data. Cause: The
DB_MAX_LINKparameter is not adjusted to a value suitable for high-concurrency bulk writes, and the database connection pool is exhausted, causing request timeouts. - Symptom: When a database connection tool is used to connect to an Oracle database, the interface prompts an unsupported database type. Cause: The current version only includes built-in database drivers for MongoDB, PostgreSQL, and SQL Server, and connection drivers for other databases are not integrated.
How to confirm configurations are correctly set
- Access the database management tool, use the configured connection address, account, and password to attempt a connection, and verify connection status.
- Import a simulated rail transit equipment ledger data entry, check whether the entry is successfully written to the database and field formats comply with preset rules.
- View the database monitoring panel in the FastGPT background, confirm that the number of connections falls within the range specified by the
DB_MAX_LINKvalue, and check for no connection exhaustion prompts. - Trigger a ledger summary and report generation workflow, verify that device data in the database can be read normally and a corresponding report can be generated.
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.