What this category of data looks like
This data originates from slab or billet batches, reheating furnaces, mill automation control systems, online thickness and width measuring equipment, surface inspection devices, and MES systems of hot rolling, cold rolling, section steel, and steel pipe enterprises. It covers documents including rolling plans, heating schedules, rolling specifications, shape and thickness records, surface quality reports, and quality certificates. Data is updated per coil or billet, with real-time update frequency. Fields include batch number, heating temperature, rolling speed, thickness deviation, surface defect level, and other relevant items. Corresponding units include degrees Celsius, meters per second, millimeters, and other common business units.
Constraints Imposed by These Characteristics on Database and Operations Workflows
Real-time high-frequency coil-level data updates require database connection pools to support high-concurrency short connections, to avoid connection exhaustion. Multi-source heterogeneous data sources require databases to support mixed storage of structured and semi-structured data. The core business requirement of full-link traceability for individual batches requires data writes to ensure strong consistency, to avoid batch association errors. Single-batch data volume is stable, but total data volume continues to grow as production capacity expands. Indexing and sharding strategies must be planned in advance, otherwise query delays will occur. The non-negotiable requirement for production ledgers requires database operations to configure automatic backup and fault switching mechanisms, to prevent steel coil downgrades or equipment accidents caused by data loss.
How to Configure Settings
These configurations are adapted for FastGPT v4.17.0.
| Config Item | Suggested Value | Basis for This Setting |
|---|---|---|
MONGODB_URI | mongodb://<user>:<pass>@<host>:27017/steel_rolling_prod?authSource=admin | Stores semi-structured non-standard ledger documents such as surface quality reports and quality certificates |
PG_URL | postgresql://<user>:<pass>@<host>:5432/steel_prod_db?sslmode=require | Stores structured business data with clear fields such as rolling plans, thickness records, and heating schedules |
DB_MAX_LINK | 200–300 connections | Matches real-time high-frequency coil-level data write and query concurrency, to avoid connection exhaustion |
REDIS_URL | redis://:<pass>@<host>:6379/0 | Caches frequently accessed rolling batch associated metadata, to reduce database query latency |
| Automatic backup cycle | Every 6 hours | Matches the tolerance window for production outages, to ensure the safety of core ledger data |
Three Common Misconfigurations
- Phenomenon: Occasional failures during SQL query execution, returning connection timeout or permission errors. Cause: The
DB_MAX_LINKconfiguration was not adjusted for real-time high-frequency coil-level data updates, leading to connection pool exhaustion and some queries failing to obtain database connections. - Phenomenon: MongoDB connection nodes start without errors, but surface quality report data cannot be read. Cause: The
authSourceparameter was not specified in the connection string, leading to database authentication failure and inability to access the corresponding database tables. - Phenomenon: Tool call nodes stall during the database query phase, with no return results. Cause: Redis cache was not configured or connection parameters were incorrect, leading to frequent query misses and slowing down the overall execution chain.
How to Verify Correct Configuration
- Execute preset rolling batch query SQL to verify that structured data can be read normally, and query delay meets business expected thresholds.
- Call the MongoDB connection test interface to verify that semi-structured surface quality report documents can be written and read normally.
- Check database connection pool monitoring metrics to confirm that current active connections do not reach the configured
DB_MAX_LINKlimit. - Trigger an automatic backup task to verify that backup files are generated normally and core ledger data can be restored.
The values given are common starting points and should be measured against the reader's own samples.