What This Category of Data Looks Like
Refinery production ledger data primarily comes from three sources: distributed control systems (DCS), equipment operation monitoring systems, and material inbound and outbound warehouse management systems. Real-time process parameters such as reaction tower temperature and pipeline pressure update at a second-level frequency. Material batch ledgers and equipment maintenance records update on a shift, daily, or monthly basis. Each individual ledger document includes process tag, timestamp, parameter value, equipment ID, material batch number, and operator fields. Units include ℃, MPa, m³/h, tons and other refinery-specific measurement standards. Some unstructured inspection records are attached as associated files.
What Constraints These Characteristics Impose on the Database and Operations Maintenance Link
Databases must support high-concurrency writes to handle second-level updates of real-time parameters, avoiding connection blocking and data loss. Multi-source data with varied update frequencies requires tailored storage engines. Structured ledgers suit relational database storage, while unstructured attachments suit document database storage. A large number of specialized fields need targeted indexing, otherwise high-frequency aggregation queries experience delays. Ledger data has high cross-system association requirements, so stable synchronization links between different databases must be maintained. During operations maintenance, indicators such as connection count and write delay must be monitored to prevent single-point failures from disrupting the entire aggregation process.
How to Set Configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Config Item | Suggested Value | Rationale |
|---|---|---|
PG_URL | postgresql://{username}:{password}@{host}:5432/refinery_ledger_db | Structured ledgers for refinery production (batch, material, equipment association data) leverage the join query capabilities of relational databases |
MONGODB_URI | mongodb://{host}:27017/real_time_process | Stores real-time process parameters updated at second-level intervals; document databases flexibly adapt to newly added temporary monitoring fields |
DB_MAX_LINK | 20–30 connections | Covers concurrent real-time data collection and ledger query requests, avoiding service interruptions caused by exhausted connections |
REDIS_URL | redis://{host}:6379/1 | Caches frequently accessed monthly aggregation reports to reduce repeated query pressure on relational databases |
Three Common Misconfigurations
- Phenomenon: An error "no matching driver found" occurs when calling an external SQL Server database. The cause is failure to configure the SQL Server database connection plugin in FastGPT, and failure to add the allowed database IP whitelist.
- Phenomenon: Ledger aggregation tasks return results after more than 15 seconds. The cause is failure to set a reasonable
DB_MAX_LINKconnection count, or failure to create a joint index for the process tag and timestamp fields, resulting in full table scans. - Phenomenon: Some real-time parameter fields are empty in generated reports. The cause is failure to configure the synchronization task update frequency for real-time data, leading to delayed synchronization of some second-level collected data to the ledger database.
How to Confirm Proper Configuration
- Run a test query, enter a known process tag and time range, and confirm that the corresponding number of ledger records are returned.
- View the FastGPT database connection monitoring panel, and confirm that the current active connection count does not exceed the
DB_MAX_LINKsetting value. - Trigger a complete ledger aggregation task, and check that all required fields in the generated report have valid data.
- Simulate multiple concurrent query requests, and confirm that response times meet preset threshold standards. Thresholds must be calibrated based on actual business SLA requirements.
The values provided are common starting points and should be evaluated 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.