What the data for this category looks like
This category of data covers risk identification lists, work permits for hot work, confined space work, lifting operations and similar tasks, online environmental monitoring records, hazardous waste ledgers, emission monitoring data, accident and hidden danger rectification reports, and more. Data sources include industry regulatory standards and codes, enterprise online environmental monitoring systems, portable gas detectors, operator qualification files, equipment isolation lists, and local supervision platforms. Data is submitted in real time upon completion of individual operations, updated periodically per monitoring cycle requirements, and synchronized immediately after rectification of hidden dangers and accidents. Fields include operation type, risk level, monitoring value, qualification validity period, isolation status, and more. There is no unified standardized format, and adaptation to multi-source heterogeneous document structures is required.
What constraints these characteristics impose during deployment and upgrade
Deployments must support multi-format file parsing and adaptation to accommodate multi-source heterogeneous document structures. API connections to local online monitoring systems and local supervision platforms must be compatible. Real-time or high-frequency updated operation and monitoring data requires configuring reasonable concurrent connection parameters during deployment to avoid data synchronization delays. Upgrades must preserve the immutability of historical data to meet strong compliance requirements for operation and monitoring scenarios, while adapting to changes in reporting formats from supervision platforms. Deployments must support custom field mapping to account for differences in fields such as qualification and isolation lists across enterprises. This avoids data synchronization failures caused by hard-coded configurations.
How to set the configurations
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 10–20 connections | Adapts to concurrent requirements of multi-source data synchronization, avoids exhausting database connections |
MONGODB_URI | Exclusive database connection string with authentication information | Stores multi-source heterogeneous unstructured data such as work permits and monitoring records, ensures data isolation and access security |
REDIS_URL | Local cache connection address with password authentication | Caches frequently queried operator qualification and regulatory standard data, reduces database access pressure |
STORAGE_S3_ENDPOINT | Enterprise internal object storage service address | Stores large files such as work permit scans and original monitoring data, adapts to the enterprise’s existing storage architecture |
| File upload size limit | 1000 MB | Adapts to upload requirements for large documents such as hazardous waste ledgers and accident rectification reports |
| API timeout period | 600 seconds | Adapts to long-link requests for supervision platform data reporting, avoids synchronization interruptions |
Three common mistakes
- Symptom:
connection refusederror appears after starting the container, and the service restarts indefinitely. Cause:MONGODB_URIorREDIS_URLis not configured correctly, or the database or cache service fails to start normally, preventing FastGPT from completing initial connections. - Symptom: No matching results appear in knowledge base retrieval after uploading documents such as work permits and monitoring records. Cause:
STORAGE_S3_ENDPOINTis not configured, or the storage path is incorrect, causing parsed documents to not be uploaded to the storage medium correctly, so original file content cannot be read during retrieval. - Symptom: Missing fields or format errors appear after multi-source data synchronization. Cause: Custom field mapping configuration is not enabled, and hard-coded field rules do not match the actual data structure of the enterprise, leading to data synchronization failures.
How to confirm configurations are correct
- Log in to the FastGPT management backend, enter the system configuration page, and verify that the values of parameters such as
DB_MAX_LINK,MONGODB_URI, andREDIS_URLmatch the actual deployed services. - Upload a typical work permit document, wait for parsing to complete, and enter key fields from the document in the knowledge base retrieval module to verify that corresponding content can be retrieved.
- Simulate a supervision platform data reporting request, check whether the system can normally initiate the request and return a valid response, to verify that the API connection configuration is effective.
- View system logs to confirm that there are no error messages such as database connection timeouts or storage medium access failures, to verify that basic operating configurations are normal.
The values provided are common starting points and should be measured against the reader's own samples.