Reference only; verify against the target FastGPT release and deployment documentation.
What this category's data looks like
This category covers the full on-site law enforcement and monitoring workflow. Daily documents include on-site inspection records, sampling records, monitoring reports, order for correction decisions, and administrative penalty decisions. Data sources include online monitoring platforms, automatic pollution source monitoring data, ecological environment law enforcement systems, and third-party test reports. Data is synchronized daily. Data fields include inspection time, inspection points, operating condition parameters, pollutant concentration, penalty reasons, and more. Some fields use standardized units, such as exhaust gas flow m³/h and pollutant concentration mg/m³. Data formats vary across sources. Unified processing is required before data is stored in the database.
Constraints on database and operations workflows
Multi-source, differently formatted data updated daily requires databases to support batch synchronization and dynamic field adaptation. Different types of law enforcement and monitoring documents require separate database storage. Unstructured law enforcement documents work well with document databases. Structured monitoring ledgers work well with relational databases. Fields with units need validation rules to prevent invalid data from being stored. Dual requirements for multi-terminal concurrent entry and batch synchronization need reasonable database connection pool configuration to avoid connection exhaustion. Data anomalies directly impact law enforcement and rectification decisions. Data consistency verification and failure alert mechanisms must be configured.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://env_agent:secure-pass@mongodb-host:27017/ecology_db?retryWrites=true | Stores unstructured documents such as sampling records and monitoring reports, supports dynamic field adaptation for different law enforcement scenarios |
PG_URL | postgresql://env_admin:secure-pass@pg-host:5432/monitoring?sslmode=require | Stores structured online monitoring data and law enforcement ledgers, supports multi-table association to compare on-site and online data |
DB_MAX_LINK | 25–35 | Meets the connection requirements of daily batch data synchronization and multi-terminal concurrent entry, avoids connection pool exhaustion |
REDIS_URL | redis://:redis-secure-pass@redis-host:6379/1 | Caches frequently queried law enforcement points, pollutant standard values, and other data to reduce database query pressure |
| Batch synchronization batch size | 500–800 records | Adapts to daily batch import of online monitoring data, avoids single import timeout |
Three Common Configuration Mistakes
- Empty results are returned during concurrent workflow calls. Check logs to find database connection count exceeded. The cause is that the
DB_MAX_LINKparameter is not configured reasonably. High concurrency fills all database connections, preventing subsequent requests from being processed. - Fields go missing after online monitoring data synchronization. The cause is that multi-source data is not validated for format consistency. Field names in third-party test reports do not match system preset fields, leading to partial data write failures.
- Law enforcement ledger queries have significant delays. The cause is that Redis is not used to cache frequently queried point and standard value data. Each query directly accesses the relational database, causing timeouts when data volume is large.
How to Verify Correct Configuration
- Run a local database connection test to confirm that the
MONGODB_URIandPG_URLconfigurations can read and write data in their respective databases normally. - Send more than 10 concurrent ledger entry requests. Confirm that the database connection count does not exceed the
DB_MAX_LINKconfigured value. - Check Redis cache hit logs to confirm that frequently queried law enforcement point data has been correctly cached.
- Trigger a daily batch synchronization task. Confirm that 500–800 records can be imported within a reasonable time frame without errors.
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.