What this category of data looks like
Data for this category mainly comes from concentrate and auxiliary material analysis reports, DCS systems, electrolyzer voltage and current monitoring data, laboratory test results, and energy and environmental monitoring devices. Daily documents include batching lists, smelting records, electrolyzer condition records, laboratory reports, anode and cathode quality ledgers, and environmental emission ledgers. Data update frequency changes in real time based on raw material batches, smelting heats, or electrolyzer conditions. A single record includes raw material composition, process parameters, test indicators, batch identifiers, and timestamps. Most fields involve professional measurement items such as metal content, current and voltage values, and material weight.
Constraints imposed on database and operations maintenance
The real-time nature, multi-source dispersion, and professional measurement attributes of this category of data impose multiple constraints on database operations and maintenance. Multi-source high-frequency updates require database connection pools to adapt to high-concurrency write requests, and avoid connection exhaustion that causes partial data capture failures. Format verification for professional measurement fields must align with smelting industry standards, to prevent non-standard data from entering the knowledge base. Data updates change dynamically with heats and tank conditions, so batch-based incremental synchronization must be supported, to avoid excessive resource usage from full synchronization. At the same time, data anomalies directly affect metal balance accounting and product quality, so real-time data verification and abnormal alert mechanisms must be configured.
Configuration settings
The following configuration instructions apply to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | Replica set connection string for dedicated smelting ledger database | Adapts to multi-source dispersed production data storage, and ensures data consistency for high-concurrency writes |
PG_URL | Relational database address for associating historical reports and statistical indicators | Structured storage of summary report metadata and statistical results, enabling fast batch-based retrieval |
DB_MAX_LINK | 200–300 connections | Matches the concurrency requirements of real-time data writes from multiple workstations, and avoids connection pool overflow |
REDIS_URL | Service address configured to cache high-frequency tank condition and laboratory data, with cache expiration time set to 600 seconds | Reduces repeated database query pressure, and speeds up the response speed of ledger summary |
| Incremental synchronization trigger rule | Triggered upon completion of a single furnace or single tank, with synchronization interval no more than 300 seconds | Aligns with the batch update rhythm of smelting production, and avoids resource occupation from invalid synchronization |
Three common configuration mistakes
- Symptom: Only partial production ledger data is captured, and fields are missing or records are incomplete in the knowledge base. Cause: Batch-based incremental synchronization rules are not configured. Full synchronization causes connection timeouts due to high concurrency, and no retry mechanism is triggered.
- Symptom: Request success rate is low in high-concurrency scenarios, and connection timeout errors appear in logs. Cause: The
DB_MAX_LINKconfiguration value is too low, which cannot support the concurrency requirements of real-time data writes from multiple workstations. New requests are rejected after the connection pool is exhausted. - Symptom: Exported knowledge base dataset CSV files only contain the index field, with no content in the content field. Cause: The storage table and index mapping relationship of ledger data are not correctly associated, and the automatic synchronization logic for the content field is not configured.
How to confirm configurations are correctly set
- Run a single heat ledger data synchronization task, and check that no connection timeout errors appear in the database connection logs.
- Export the knowledge base dataset file, and verify that the preset content field and corresponding ledger data are included.
- Simulate concurrent write requests from multiple workstations, and observe whether the database connection pool usage matches the expected configuration.
- Trigger an abnormal data verification rule, and confirm that the alert mechanism can normally send corresponding prompts.
The values provided here are common starting points, and should be evaluated against the reader's own samples.