What this category of data looks like
This category of data covers the full administrative penalty process for law enforcement agencies including market supervision, ecological environment, transportation, emergency management, and urban management. Core documents include on-site inspection records, interrogation records, evidence lists, prior notices of administrative penalties, hearing notices, penalty decisions, and service receipts. Data sources include administrative law enforcement case handling systems, law enforcement recorder footage, the national enterprise credit information publicity system, and departmental supervision databases. Data updates occur daily. The data includes structured fields such as illegal subject name, illegal facts, penalty basis, penalty decision, and service date. It also links to unstructured images and transcript files. Data from different sources must be uniformly mapped to standard law enforcement case file fields.
Constraints on database and operations workflows
Daily high-frequency updated data sources require database connection pools to support stable concurrent writes and queries, to avoid connection exhaustion that disrupts case handling efficiency. Multi-source heterogeneous data includes structured case system data, unstructured image files, and data from external publicity systems. Each type requires adaptation to different storage engines. Structured case file data requires strong transaction consistency guarantees. Unstructured evidence must support vector retrieval for quick case matching. Penalty case files must form a complete evidence chain. The database must support cross-table associated queries to ensure one-to-one correspondence between illegal facts, penalty decisions, and evidence materials. Missing or inconsistent data in any link will lead to failed case file review. The sensitivity of law enforcement data requires the database to have strict access permission controls, only allowing case handling personnel and operations personnel to access corresponding data.
How to configure settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PG_URL | postgresql://fastgpt_law-enforcement:secure_pass@192.168.1.100:5432/penalty_case | Stores structured administrative penalty case file data. PostgreSQL supports strong transactions and cross-table associations, adapting to evidence chain associated queries for case files |
MONGODB_URI | mongodb://fastgpt_evidence:secure_pass@192.168.1.101:27017/evidence_archives | Stores unstructured files including on-site inspection records and law enforcement recorder footage. MongoDB is suitable for large file and unstructured data storage |
DB_MAX_LINK | 8–12 | Adapts to the concurrent request volume of daily law enforcement agency case handling, avoiding query failures caused by database connection pool exhaustion |
REDIS_URL | redis://:secure_pass@192.168.1.102:6379/1 | Caches daily synchronized data from the national enterprise credit information publicity system, reducing delay from external interface queries |
Three common configuration errors
- Unable to execute SQL commands after entering the dockerized pgvector image: Symptoms include "authentication failed" when connecting to the database, or empty results after executing SQL. Causes include incorrect port mapping and username/password configuration for the database container, and failure to grant the corresponding user query permissions on law enforcement case file tables.
- Unable to extract SQL results when configuring a MySQL database connection: Symptoms include "query execution failed" when calling the tool, with empty extraction results. Causes include the connection string not specifying the correct target database, or the database user not being granted read permissions on the corresponding tables.
- Confusing concurrent thread count and response parameter configuration: Symptoms include timeout errors when generating ledgers in batches, or truncated report content. Causes include incorrectly setting the maximum response tokens as the concurrent thread count, instead of using the correct connection pool parameters to control concurrent request volume.
How to confirm the configuration is successful
- Run a database connection test, enter the connection string corresponding to
PG_URL, and verify that the first 10 entries in the administrative penalty decision table can be queried, to confirm normal structured storage. - Run the daily data synchronization script, check if data can be pulled from administrative law enforcement case handling systems and the national enterprise credit information publicity system and written to corresponding storage, to confirm normal multi-source data synchronization.
- Check database connection pool monitoring, confirm that the current connection count does not exceed the
DB_MAX_LINKconfiguration value, to verify that the connection pool configuration is effective. - Test the batch ledger generation function, initiate multiple concurrent requests, verify that no connection exhaustion or timeout errors occur, to confirm normal concurrent control.
The values provided here 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.