What Data for This Category Looks Like
Typical data for this category comes from smelting, electrolysis, laboratory, and scheduling departments of copper, aluminum, lead-zinc smelting enterprises. Daily documents include batching lists, smelting logs, electrolyzer status logs, laboratory reports, anode and cathode quality records, and environmental protection ledgers. Data sources include concentrate and auxiliary material analysis results, DCS system operation data, cell voltage and current monitoring data, laboratory test results, and energy and environmental protection monitoring indicators. Update frequency follows batches of raw materials, per furnace, or per cell. Some real-time monitoring data updates more frequently. Document fields cover raw material composition, equipment operation parameters, test results, and environmental indicators. Units include common industrial values such as current values and concentration units.
What Constraints Do These Characteristics Impose on Deployment and Upgrade
Data sources for this category are scattered and have diverse formats. During deployment, ensure the system supports parsing and storing multi-source files. During upgrade, maintain compatibility with existing historical document formats. High-frequency updated real-time data and batch-imported ledger documents create high database and storage load. Configure reasonable connection parameters and storage quotas in advance. Concurrent access pressure arises when multiple departments submit cross-review requests for documents. Adjust connection pool parameters to avoid request timeouts. Fields vary significantly across different documents. Configure unified metadata mapping rules to ensure cross-document field association and cross-review work properly.
Configuration Settings
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://user:pass@host:27017/fastgpt?authSource=admin | Stores document metadata and cross-review results for this category, specifies authentication source to match enterprise database configurations |
REDIS_URL | redis://:pass@host:6379/1 | Caches frequently queried document fragments and real-time synchronized cell status data, uses database index 1 to isolate business cache |
DB_MAX_LINK | 150–250 connections | Handles concurrent document review requests from multiple departments, meets access load for real-time data, value is determined based on actual testing |
STORAGE_S3_ENDPOINT | http://internal-storage:9000 | Stores batch-imported historical ledgers and newly generated review reports, matches enterprise internal object storage address |
FILE_TOKEN_KEY | 32-character random secure string | Encrypts document access tokens to prevent unauthorized access to sensitive industrial documents |
FE_DOMAIN | https://fastgpt.smelting-plant.com | Configures frontend access domain name to ensure normal redirection and display of review result pages |
Three Common Misconfigurations
- Symptom: After pulling the official latest image, logging into the system returns a 401 unauthorized error. Cause: The
ROOT_KEYconfiguration item was not updated to match the administrator key in the existing database, resulting in authentication failure. - Symptom: When importing bulk laboratory reports or electrolyzer status logs, some fields cannot be parsed correctly. Cause: Unified field mapping rules were not configured via the
FILE_TOKEN_KEYconfiguration, leading to failure to uniformly recognize industrial documents of different formats. - Symptom: After cluster deployment, cross-node document synchronization experiences delays or failures. Cause: Synchronization parameters for cluster nodes were not adjusted based on the cache address configured in
REDIS_URL, leading to inconsistent cache data between nodes.
How to Verify Successful Configuration
- Run the
docker compose pscommand to confirm all service containers are running, with no abnormal exits or restart records. - Upload a tested electrolyzer status log document to check if the system can correctly extract core industrial parameters.
- Submit a cross-document cross-review request to verify if the system can associate relevant fields across different documents.
- Access the configured
FE_DOMAINaddress to confirm the frontend page loads normally, with no resource loading failure prompts.
The values provided are common starting points and should be measured against the reader's own samples.