What the Data for This Category Looks Like
The core data for this category covers documents related to the full lifecycle of special equipment, including use registration certificates, periodic inspection reports, maintenance records, hidden danger rectification notices, accident reports, and operator certificates. Data sources include the special equipment safety supervision system, inspection and testing institution systems, user unit ledgers, and the operator certificate database. Data is updated once per month. Each data entry includes fields such as equipment ID, equipment type, inspection validity period, maintenance unit, and operator certificate number. Units include sets, times, days, and others. Some data is structured forms, while some consists of scanned documents or unstructured text.
Constraints on Database and Operations Workflows
This category’s data sources are scattered, requiring integration with multiple heterogeneous systems. Deployments must use databases that support cross-source data synchronization and field mapping. Monthly updated batch synchronization tasks generate high-frequency concurrent access, so teams must limit resource usage on production systems. Errors in ledger data for high-risk equipment can lead to serious consequences, so deployments must configure databases with read-write separation and backup mechanisms. Teams must create targeted indexes for frequently queried fields such as equipment inspection cycles and operator qualifications to optimize query efficiency.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | Read-only replica address pointing to the backup database of the special equipment supervision system | Used to store unstructured report scanned documents and ledger snapshots, avoiding business impact from direct access to the production database |
PG_URL | Read-only address of the primary database linked to the inspection and testing system | Used to connect structured data such as periodic inspection reports and maintenance records, ensuring query performance |
DB_MAX_LINK | 15–25 | Adapt to the concurrent requirements of monthly updated batch synchronization, avoiding blocking caused by exceeding the database connection limit |
REDIS_URL | Local cache instance with a 600-second expiration time | Caches frequently queried equipment ledger and operator qualification data to reduce database access pressure |
Three Common Misconfigurations
- Database connection steps in workflow executions return the
connect ETIMEDOUTerror, while local database access functions normally. The cause is that the network security policy of the FastGPT deployment environment does not open the target database port, or cross-network segment access permissions are not configured. - Database connection execution time for the same workflow using FastGPT v4.17.0 is significantly higher than older versions. The cause is that no database read-only replica is configured, and directly connecting to the production business database results in resources being occupied by high-frequency business operations.
- The number of result items returned by batch ledger summary tasks does not match expectations. The cause is that reasonable database query pagination parameters are not set, or targeted indexes are not established for frequently queried fields such as equipment ID and inspection cycle.
How to Verify Successful Configuration
- Execute a test workflow, call the database connection plugin, and verify that the returned ledger data fields match the original data from the business system.
- Check database connection logs, confirm that connection latency stays within a reasonable range, and there are no frequent timeout errors.
- Simulate a monthly updated batch synchronization task, verify that the concurrent connection count does not trigger the
DB_MAX_LINKlimit. - Check the expiration time configuration of the Redis cache instance, confirm that cache hits for frequently queried ledger data are normal.
The values given 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.