What Data Falls Under This Category?
This category of data includes documents such as risk identification, work tickets (hot work, confined space, lifting, etc.), environmental monitoring records, hazardous waste ledgers, accident and hidden danger rectification reports. Data sources include regulatory standards, online environmental monitoring equipment, gas detectors, personnel qualification files, equipment isolation lists, and supervision platforms. Data updates are triggered upon completion of individual operations, real-time monitoring, or regular inspections. Individual records contain fields such as operation type, monitoring values, qualification numbers, isolated equipment identifiers, and some monitoring data includes professional units such as ppm, mg/m³.
What Constraints Do These Characteristics Impose on the Database and Operations Maintenance Link?
Multi-source heterogeneous data sources and irregular update cycles require the database to support multiple formats including structured monitoring values, semi-structured work tickets, and unstructured rectification reports. Fields with professional units must retain their original identifiers fully to avoid data distortion. High-frequency writes of real-time monitoring data and batch submissions triggered by operations will cause instantaneous load fluctuations, so connection pool elastic adjustment must be supported. High reliability requirements for compliance-related data require redundant storage and regular verification mechanisms to ensure data is complete and available during accident traceability.
How to Set Configurations
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://user:pass@host:port/dbname?authSource=admin | Adapt to storage requirements for multi-source heterogeneous data, compatible with structured and unstructured data such as work tickets and monitoring records |
PG_URL | postgresql://user:pass@host:port/dbname?sslmode=require | Store structured associated data that requires joint queries, such as personnel qualifications and equipment isolation lists; SSL encryption ensures secure transmission of compliance data |
DB_MAX_LINK | 20–30 connections | Adapt to instantaneous load fluctuations from real-time monitoring and batch operation submissions, avoid exhausting database connections |
REDIS_URL | redis://:pass@host:port/0 | Cache frequently queried qualification information and operation rules to reduce repeated database query pressure |
Three Common Misconfiguration Issues
- Symptom: The database only captures partial work tickets or environmental monitoring records, with some fields missing. Cause: The
DB_MAX_LINKconcurrency connection limit is not configured, and instantaneous high load causes partial write requests to be dropped. - Symptom: The exported knowledge base dataset.csv only contains the index field, with no content field or business data. Cause: Document content and index mapping is not configured correctly, resulting in content fields not being exported synchronously.
- Symptom: In a Docker deployment environment, when uploading job files to start parsing, logs continuously report slow operation xxxxms, with MongoDB response delays. Cause:
DB_MAX_LINKis not adjusted to adapt to batch writes, causing database connection overload and response delays.
How to Verify Successful Configuration
- Run a database connection test to verify connectivity for
MONGODB_URIandPG_URL, with no connection timeout errors. - Submit a test work ticket and monitoring data to confirm complete data writing, with no field loss or tampering.
- Check Redis runtime logs to confirm the cache service configured via
REDIS_URLresponds normally to high-frequency query requests. - Simulate batch operation submissions, observe that database connection count does not exceed the
DB_MAX_LINKconfiguration value.
The values provided are common starting points and should be evaluated against the reader's own samples.