What Data for This Category Looks Like
Data for this category covers hot metal and scrap charging, L2 process control, laboratory composition reports, MES production scheduling, energy medium consumption, and equipment operating status. Daily associated documents include heat plans, batching lists, converter smelting records, continuous casting process parameter records, and slab quality judgment sheets. Data updates follow this rhythm: sync after each individual heat is completed, real-time upload of key process parameters, and daily shift summary of energy consumption and equipment status. Core fields include heat identification, smelting duration, composition test values, continuous casting pulling speed, and other key items.
Constraints on Deployment and Upgrade
This category’s multi-source heterogeneous data, differentiated update rhythms, and strong process coupling characteristics impose multiple constraints on deployment and upgrade. Multi-source data requires docking with different types of data sources such as hot metal charging, L2 process control, and laboratories. Compatible database connection parameters must be configured in advance. Real-time uploaded process data requires deployment nodes to have low-latency response capabilities to avoid interfering with continuous production processes. Structured documents generated per heat and real-time time-series data require separate adaptation to different storage strategies to avoid data read-write conflicts. The upgrade process must use rolling updates to avoid production interruptions, and must ensure data compatibility between old and new versions to prevent process disruptions.
Configuration Recommendations
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
DB_MAX_LINK | 20–30 connections | This category needs to connect to multiple data sources such as hot metal, L2, and MES. Sufficient connection count avoids connection exhaustion and ensures data reading efficiency |
MONGODB_URI | mongodb://[username]:[password]@[host address]:[port]/fastgpt?authSource=admin | Unstructured documents such as slab judgment sheets and continuous casting process records for this category are suitable for storage in MongoDB. Correct authentication and target database name must be configured |
REDIS_URL | redis://:[password]@[host address]:[port]/0 | This category needs to cache real-time process data and user sessions. Using an independent database avoids cache conflicts with other services |
STORAGE_S3_ENDPOINT | http://[object storage host]:[port] | Large smelting records and process documents for this category require persistent storage, adapting to standard object storage access formats |
ROOT_KEY | Randomly generated 32-bit string | This category involves production data permission control. A strong key ensures the security of the system's core access links |
Three Common Configuration Mistakes
- Phenomenon: Vector model interface calls return 404 errors. Cause: Incorrect configuration of the vector model access address, or misalignment between the model deployment path and proxy path.
- Phenomenon: Slow system response, single-round question-and-answer takes more than 30 seconds. Cause: Failure to adjust the
DB_MAX_LINKparameter based on the data volume of this category, leading to database connection exhaustion and inability to read historical process data in a timely manner. - Phenomenon: Unable to add non-GPU vector models in the system management interface. Cause: Failure to enable CPU inference mode when deploying the vector model, or failure to configure model loading parameters adapted to the CPU environment.
How to Verify Correct Configuration
Run a database connection test using the FastGPT v4.17.0 system management tool, and verify that connections configured via MONGODB_URI and REDIS_URL operate normally. Upload a simulated converter smelting record document, and check that the system can correctly read and store the document without format or path errors. After starting the vector model service, initiate a quality traceability query, and verify that matching process data is returned without 404 or timeout errors. View the system monitoring panel, confirm that the database connection count stays within the 20–30 connections range, and no connection exhaustion alerts are triggered.
The values provided are common starting points and should be evaluated against the reader's own samples.