What the data for this category looks like
Typically, inspection and process record data for cotton spinning, chemical fiber spinning, woven, and knitting enterprises includes cotton blending or ingredient lists, process setup sheets, equipment operation records, yarn inspection, grey fabric inspection, and defect records. Data is collected from raw fiber inspection equipment, ERP systems, spinning and loom data collection modules, evenness testers, fabric inspection machines, and similar tools. Data update frequency follows per raw material batch, per shift, and per fabric roll. Fields include fiber indicators, spinning twist, weaving structure parameters, equipment runtime, yarn tensile strength, fabric surface defect types, inspection pass rate, and more. Units include count, meter, hour, %, and others.
What constraints these characteristics impose on the deployment and upgrade phase
The scattered data sources, high update frequency, and batch-based generation of data for this category require deployments to support multi-device data integration and field mapping with ERP systems. The high-volume batch write requirement for data generated per raw material batch, per shift, and per fabric roll requires sufficient database connection pool and caching configurations. Upgrades must be compatible with historical batch inspection records and process parameters to avoid data interruptions or field incompatibilities. The large number of equipment units requires deployments to account for concurrent processing capacity, to prevent service lag from high-frequency data writes. The batch-based data organization also requires upgrades to retain migration compatibility for existing data, to avoid disrupting inspection records for current production batches.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
MONGODB_URI | Fill in the internal network connection string of the enterprise's self-built MongoDB cluster, set the maximum connection pool size to 150 | Adapt to high-frequency data writes per shift and per fabric roll, match the concurrent request volume reported by spinning and looms simultaneously |
REDIS_URL | Fill in the connection address of the enterprise's internal Redis instance, set the cache expiration time to 3600 seconds | Cache frequently queried cotton blending lists and process parameters, reduce latency from repeated database queries |
STORAGE_S3_ENDPOINT | Fill in the endpoint address of the enterprise's object storage, separate storage directories by raw material batch | Distinguish inspection records and defect data for each raw material batch and fabric roll, facilitate batch-based retrieval and review |
DB_MAX_LINK | Set to 120–180 | Match the concurrent connection demand from the number of equipment units, avoid database connection overflow |
FE_DOMAIN | Fill in the enterprise internal access domain name | Adapt to the front-end access needs of internal enterprise inspection personnel, load inspection reports and audit interfaces |
ROOT_KEY | Generate a 32-bit random string | Used for administrator permission control in private deployments, only authorize operation and maintenance personnel to modify configurations |
Three common mistakes
- A database connection error occurs during local deployment packaging, with the log showing
ECONNREFUSED| The cause is that the internal network connection address forMONGODB_URIwas not configured correctly, and the enterprise firewall did not open the database access port - After successful packaging, FastGPT v4.17.0 is run, and the custom inspection record audit module fails to load on the interface | The cause is that
FE_DOMAINwas not configured correctly, and the front-end static resource path mapping is incorrect - After private deployment, the commercial version service consultation entry cannot be accessed | The cause is that the enterprise-specific permission key was not configured in the deployment settings, and the service consultation link was not correctly associated
How to confirm the configuration is correct
- Log in to the FastGPT backend database monitoring panel, check the
MONGODB_URIconnection status logs, confirm there are no timeout or connection refused errors - Upload a cotton blending list data entry, check the Redis cache panel, confirm the expiration time of the corresponding data matches the 3600-second configuration, verify that the cache is working
- Upload a grey fabric inspection record, check the enterprise object storage directory, confirm that the storage path corresponding to the batch is generated, verify that the
STORAGE_S3_ENDPOINTconfiguration is correct - Access the front-end address corresponding to
FE_DOMAIN, confirm that the inspection record audit interface loads normally, with no resource loading failure prompts - Log in to the backend using the administrator account corresponding to
ROOT_KEY, confirm that permission control is active, and only authorized users can modify deployment configurations
The values provided here are common starting points, and should be evaluated against the user's own samples.