What the data for this category looks like
Data for this category comes from original fiber inspection reports, ERP systems, spinning and loom operation data, evenness testers, fabric inspection machines, and order specification documents. Daily retained documents include cotton blending or ingredient lists, process setup sheets, equipment operation records, yarn inspection sheets, grey fabric inspection sheets, and defect ledgers. Data updates occur for each raw material batch, each production shift, and each roll of fabric produced. Individual documents include fields such as fiber indicators, spinning twist, weaving structure parameters, defect type, and location. Field units follow the industry standard of the source document.
What constraints these characteristics impose on deployment and upgrades
Since data sources include real-time operation data from local spinning machines and looms, deployments must support direct intranet connections to device interfaces, and avoid external network transit. High-frequency updates across multiple batches and shifts require data synchronization task scheduling intervals to match production rhythms, and avoid overly long intervals. Document types include structured ERP ingredient lists and unstructured inspection records, so deployments need configured multi-format parsing adaptation rules. A large number of equipment units create demand for batch data access, so sufficient storage and computing resources must be reserved. Defect traceability across upstream and downstream production links requires cross-system data link configuration during deployment, to ensure uniform data retrieval across all links.
Configuration Settings
The following configurations are adapted for FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DB_MAX_LINK | 10–20 | Adapts to high-frequency data access concurrency demands from multiple spinning machines and looms, prevents database connection exhaustion |
MONGODB_URI | Intranet-accessible MongoDB instance address with authentication parameters | Stores large volumes of defect ledgers and equipment operation records, requires intranet direct connection to protect data privacy |
REDIS_URL | Intranet-deployed Redis instance address, with key expiration time set to 600 seconds | Caches frequently accessed process parameters and order specification information, reduces cross-system query latency |
STORAGE_S3_ENDPOINT | Access address of the intranet object storage service | Stores unstructured documents such as cotton blending lists and inspection reports, fits intranet storage scenarios |
ROOT_KEY | Randomly generated 32-character string key | Secures access to the backend management interface, only accessible to authorized operation and maintenance personnel |
Three common configuration errors
- Phenomenon: After selecting a model version in the backend, actual calls still consume unexpected model resources. Cause: Model association parameters in the configuration file do not match the name selected in the backend, or the port of the intranet model service is not open.
- Phenomenon: Intranet-deployed associated services restart continuously, with logs showing connection failure messages. Cause: No intranet-accessible data source address is configured, or network policies between containers restrict data interaction ports.
- Phenomenon: An embedding error prompt appears when uploading documents such as inspection records or process sheets. Cause: No parsing rules adapted to unstructured documents of this category are configured, or the capacity threshold for storage parameters is set too low.
How to verify a successful configuration
- Log in to the FastGPT backend, view the data source configuration list, confirm that the interface addresses of intranet spinning machines and looms have been added and show normal status.
- Initiate a simulated defect traceability query, verify that returned results link to the corresponding batch's cotton blending list, equipment operation records and inspection data.
- Check the backend storage monitoring panel, confirm there is no abnormal backlog of data writes, and that it matches the production update rhythm.
- Verify the administrator key access permissions, confirm only authorized personnel can access the backend configuration page.
The values provided are common starting points and should be measured against the reader's own samples.