What data for this category looks like
Data sources for this category include molten iron and scrap steel collection data, L2 process control system data, laboratory test results, manufacturing execution system (MES), energy medium system, and equipment condition monitoring data. Data update cycles cover per heat, real-time, and shift-based frequencies. Daily processed documents include heat plans, batching lists, converter smelting records, composition test reports, continuous casting process records, and slab quality judgment sheets. Data fields include heat number, smelting duration, elemental composition values, slab size parameters, and some physical quantities use temperature, duration, and length units.
What constraints these characteristics impose during deployment and upgrade
Data for this category is multi-source and decentralized, update cycles vary significantly, and document structures are diverse. Business processes involve high-temperature continuous operations, with tight coupling between heats and casting sequences. During deployment, adapt to multi-system data integration to avoid production data collection interruptions from access errors. During upgrade, use gray release to ensure production processes are not disrupted, while maintaining compatibility with new parsing rules for heat and continuous casting-related documents. The characteristics of data updating per heat, real-time, and shift-based require configuring differentiated data synchronization strategies during deployment to balance real-time performance and system load.
The following configurations are formulated based on FastGPT v4.17.0.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DB_MAX_LINK | 200–300 connections | Adapts to concurrent connection requirements for multi-source data synchronization, matches the concurrency volume of data pulling from multiple iron and steel smelting systems |
REDIS_URL | redis://:your_password@redis_host:6379/0 | Stores real-time heat data and session cache, adapts to storage requirements for real-time updated data |
STORAGE_S3_ENDPOINT | Configure per enterprise internal object storage address | Stores composition reports, process records and other documents, adapts to storage requirements for large volumes of production documents |
ROOT_KEY | Generate random string per enterprise security policy | Configures system administrator access keys to ensure access security in production environments |
FE_DOMAIN | https://your-fastgpt-domain.com | Configures frontend access domain, adapts to access paths for internal enterprise production systems |
Three common mistakes
- Database connection failure after startup. Cause:
MONGODB_URIorPG_URLparameters are not configured correctly, and network connectivity to the production environment database has not been verified. - Restarting the service after modifying the root password in
docker-compose.ymlhas no effect. Cause: The corresponding secret key configuration inside the container was not updated at the same time, or the container internal file was modified directly without taking effect via the orchestration file. - Port occupation error during local Windows deployment. Cause: Local services occupying default ports were not closed in advance, or corresponding port parameters were not adjusted in the configuration.
How to confirm the configuration is complete
- Run a data synchronization test to verify that heat data pulled from molten iron, MES and other systems can be correctly entered into the system.
- Access the configured
FE_DOMAINaddress to confirm that the frontend interface loads and logs in normally. - Check the connection count monitor for
DB_MAX_LINKto confirm that concurrent connection counts meet the configured requirements. - Upload a converter smelting record document to confirm that the system can correctly parse and extract key fields.
The values provided are common starting points and should be evaluated against deployment samples tailored to the specific operating environment.