What data falls under this category
This category of data includes risk identification records, work permits for hot work, confined space work, lifting work and similar tasks, monitoring records, hazardous waste ledgers, emission monitoring data, accident and hidden danger rectification reports, and more. Data sources include regulatory standards, online environmental monitoring equipment, gas detectors, personnel qualification files, equipment isolation lists, and supervision platforms. Data updates are triggered by individual work, updated in real time per monitoring requirements, or updated via regular inspections. Document structures include fields such as basic work information, inspection elements, and disposal results. Some data is associated with unique identifiers such as personnel qualification numbers and equipment codes. There is no unified fixed unit; matching measurement requirements according to data type.
What constraints do these characteristics impose on database and operations workflows
This category of data has dispersed sources, covering device-side data, supervision platform data, and manually entered documents. The database must support multi-source heterogeneous data access. Data update rhythms vary significantly: some are high-frequency writes from real-time monitoring, while others are batch submissions triggered by individual work. This places constraints on the database’s concurrent processing capabilities. Strong associations exist between data points: for example, work permits are linked to personnel qualifications and equipment isolation lists. Cross-table association indexes must be established to ensure query efficiency. Some data relates to safety and environmental compliance requirements. Redundant backup mechanisms must be configured to prevent data loss. Data volume grows continuously with production work frequency. Reasonable storage expansion space must be reserved.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://ehs_user:ehs_pass@db-host:27017/ehs_ledger?authSource=admin | Stores unstructured data such as monitoring records and work permit scans; separates business databases from general system databases |
PG_URL | postgresql://meta_user:meta_pass@pg-host:5432/ehs_metadata?sslmode=require | Stores structured associated data such as personnel qualifications, equipment isolation lists, and compliance ledgers; ensures transaction consistency |
DB_MAX_LINK | 200–300 | Adapts to write concurrency from high-frequency real-time monitoring data and connection reuse for cross-table queries; prevents connection exhaustion |
REDIS_URL | redis://redis-pass@redis-host:6379/1 | Caches frequently accessed compliance standards and work template data; reduces access pressure on the primary database |
| Data write timeout threshold | 30 seconds | Adapts to batch ledger data writes triggered by individual work; prevents write failures caused by network latency |
Three common misconfigurations
- Symptom: After Docker deployment, the database runs continuous high-frequency read/write operations, and hard disk usage rises rapidly. Cause: No reasonable storage sharding and connection pool limits are configured for real-time monitoring data related to safety, environmental protection and special operations management, leading to unrestricted occupation of database resources.
- Symptom: Field mismatch errors are returned when creating a dataset collection via a specified API interface. Cause: The unique data structures of this category such as work permits and hazardous waste ledgers are not adapted, and the request parameters do not include corresponding business-specific fields.
- Symptom: Old business records cannot be deleted according to rules in the MongoDB database. Cause: Storage partitions separated by business type are not established. Old ledger data is mixed with other business data, making it difficult to accurately locate and clean up.
How to confirm the configuration is properly applied
- Run the database connection verification script included with FastGPT v4.17.0 to verify the connection validity of the
MONGODB_URIandPG_URLconfigurations, and confirm normal read and write operations to the corresponding business databases. - Submit single and batch simulated work data, check the response status of database writes and associated queries, and confirm that the connection pool configuration adapts to current business concurrency requirements.
- View the cache monitoring panel, confirm that frequently accessed personnel qualification and compliance standard data has been correctly cached, and verify that the
REDIS_URLconfiguration is effective. - Trigger a preset data archiving task, verify that old ledger data can be migrated to archive storage according to business rules, to prevent exhaustion of primary storage resources.
The values provided are common starting points and should be measured against the reader's own samples.