What the data for this category looks like
This category of data typically comes from concentrate and auxiliary material analysis, DCS systems, cell voltage and current monitoring, laboratory testing, energy and environmental monitoring equipment. Daily documents include batching lists, smelting records, electrolytic cell condition records, laboratory reports, anode and cathode quality records, and environmental protection ledgers. Data update cycles cover per batch of raw materials, per furnace or per cell level, with some device parameters updated in real time. Data fields include raw material composition indicators, equipment operating parameters, test results, and environmental monitoring values, covering multi-dimensional information across the entire production process.
What constraints do these characteristics impose on the database and operations maintenance link
The mixed update rhythm from multiple sources, including real-time device parameters and batch-level production records, requires the database to balance high-concurrency write and batch query processing capabilities. The heterogeneous document structure, covering structured test reports and unstructured logs, requires compatible mixed deployment of structured storage and document-oriented storage. Frequent single-parameter updates and batch laboratory data imports require configuring reasonable database connection limits to avoid resource exhaustion. Cross-departmental quality traceability needs to associate multi-dimensional data, requiring the database to support efficient cross-table join queries. The retention requirements for environmental protection-related data require complete supporting data backup and permission control mechanisms.
How to set the configurations
This page content is written based on FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale for This Setting |
|---|---|---|
MONGODB_URI | mongodb://user:pass@host:27017/fastgpt_elecsmelt?authSource=admin | Stores unstructured heterogeneous documents such as smelting logs and cell condition records, adapting to flexible storage of multi-source data |
PG_URL | postgresql://user:pass@host:5432/fastgpt_quality?sslmode=require | Stores structured data such as batching lists, laboratory reports, and quality ledgers, supporting complex cross-table join queries |
DB_MAX_LINK | 200–300 connections | Matches the concurrent write requirements of real-time device parameters and batch laboratory data, avoiding connection exhaustion |
REDIS_URL | redis://:pass@host:6379/0 | Caches real-time data of cell voltage and current for frequent queries, reducing direct database access pressure |
Three common mistakes
- Phenomenon: An internal server error is prompted when installing the database connection plugin. Cause: The access permissions for
MONGODB_URIorPG_URLare not configured correctly, or the network policy restricts outbound connections to database ports. - Phenomenon: The new version fails to start, and a MongoDB connection error appears in the logs. Cause:
DB_MAX_LINKis set too high and exceeds the server resource limit, or the authentication parameters forMONGODB_URIare configured incorrectly. - Phenomenon: The target data cannot be located when exporting the knowledge base. Cause: The storage path is not configured according to the tenant and table structure of production data, and the storage repositories for structured and unstructured data are confused.
How to confirm that the configurations are correct
- Run the database connection test script to verify the connectivity of
MONGODB_URI,PG_URL, andREDIS_URL, and confirm there are no authentication or network errors. - Import a batch of simulated electrolytic cell condition records and laboratory reports, check the data write and cross-table join query response speed, and adjust the value of
DB_MAX_LINKbased on actual business load. - View the system operation logs to confirm there are no database connection exhaustion or timeout errors, and optimize the connection count configuration based on the concurrent volume during peak production hours.
- Export production data for a specified batch, check whether the exported content matches the fields and structure stored in the database, and confirm that the storage path and tenant configuration are correct.
The values given are common starting points and should be measured against the reader's own samples.