What the data for this category looks like
The production data for spinning and weaving typically includes daily documents such as cotton blending or batching lists, process setup sheets, equipment operation records, yarn inspection reports, grey fabric inspection reports, and defect logs. Data sources include raw fiber test results, enterprise ERP systems, real-time collected data from spinning and weaving machines, outputs from devices such as evenness testers and fabric inspectors, and order specification parameters. Data update frequency changes with production milestones. New records are generated each time raw materials are batched, equipment runs per shift, or a roll of fabric finishes processing. Fields include fiber indicators, process parameters, test values, defect types and locations, and some fields use specialized units.
What constraints these characteristics impose on the deployment and upgrade phase
Multiple heterogeneous data sources require that the deployment phase be compatible with device outputs and system interfaces in different formats, to avoid format errors during data synchronization. The high-frequency update rhythm means that the stability of data synchronization must be ensured during the upgrade process, to prevent real-time reporting of production data from being interrupted by version upgrades. The diverse document types and specialized fields require that field mappings be configured in advance during the setup phase, to ensure that the generated ledger reports include correct process and inspection information. Additionally, the business characteristics of large numbers of equipment and the easy continuous spread of defects require that the upgraded system have higher concurrent processing capacity to support the summary and analysis of massive production data.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DB_MAX_LINK | 100–150 connections | Adapts to the concurrent connection requirements of real-time data reporting from multiple spinning and weaving machines |
REDIS_URL | Local Redis instance address, port 6379 | Used to cache frequently read process parameters and defect statistics data, reducing database load |
STORAGE_S3_ENDPOINT | Intranet access address of the enterprise's self-built object storage | Adapts to the storage requirements for batch uploading large production documents such as inspection reports and equipment operation records |
PG_URL | PostgreSQL database address associated with the enterprise ERP system | Used to synchronize core business data such as order specifications and batching lists |
FE_DOMAIN | Enterprise intranet FastGPT frontend access domain name | Used to generate associated links that directly jump to the OA system, ensuring internal access security |
Three common mistakes
- Phenomenon: After upgrading to FastGPT v4.17.0, calls to external model interfaces return connection timeouts. Cause: The intranet access whitelist for
STORAGE_S3_ENDPOINTwas not configured, causing model requests to fail to penetrate the enterprise intranet firewall. - Phenomenon: A large number of empty fields appear after production ledger data is synchronized. Cause: The upper limit of
DB_MAX_LINKconnections was not adjusted. Concurrent writes caused some database connections to be preempted, resulting in failed field writes. - Phenomenon: The generated report cannot associate work order links from the OA system. Cause:
FE_DOMAINwas not correctly set to an intranet-accessible domain name, so the generated links cannot be opened normally within the OA system.
How to confirm that the configuration is correct
- Log in to the database monitoring page in the FastGPT admin backend, and check whether the current active connection count is lower than the
DB_MAX_LINKconfiguration value. - Upload a cotton blending list and a grey fabric inspection record, wait 15 minutes, then check the data synchronization result to confirm that all fields have been correctly written to the database.
- Generate a single-batch production ledger report, and check whether the OA associated links can be normally jumped to in the enterprise intranet environment.
- Call the model interface to test generating a defect analysis report, and confirm that the interface returns results without timeouts or format errors.
- Run the upgrade script
curl --location --request POST 'https://{{host}}/api/admin/initv490', and confirm that the return result indicates success.
The values provided here are common starting points, and should be evaluated against the reader's own samples.