What the data for this category looks like
Daily documents for this category include cotton blending or material mixing lists, process setup sheets, equipment operation records, yarn inspection reports, fabric inspection reports, and defect records. Data sources include raw fiber inspection results, ERP systems, real-time data from spinning and weaving machines, data collected by evenness testers and fabric inspectors, and order specifications. Data updates synchronize with each batch of raw materials, each shift’s production, and each piece of fabric produced. Individual documents contain fields such as fiber indicators, process parameters, equipment operating status, and inspection results. Some parameters have clear units, such as twist count and fabric length.
Constraints Imposed During Deployment and Upgrade
This category has high-frequency data updates and scattered data sources. During deployment, adapt to real-time synchronization requirements for multi-source data. Avoid situations where tender quotations do not match actual production capacity due to data lag. The update rhythm of each batch of raw materials, each shift, and each piece of fabric will generate a large amount of incremental data. Reserve sufficient storage and computing resources to support high-frequency write operations. Document fields include industry-specific textile process parameters. Ensure the data parsing module can recognize these professional fields during deployment to prevent parsing errors from affecting the parameter accuracy of tender documents. The large volume of continuous data collection from multiple equipment heads requires the deployment architecture to have horizontal scaling capabilities to handle peak pressure.
Configuration Settings
These configurations are compatible with FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | mongodb://username:password@host:27017/fastgpt?authSource=admin | Adapts to storage requirements for scattered textile production data, supports high-frequency read and write operations |
REDIS_URL | redis://:password@host:6379/0 | Caches frequently accessed process parameters and order data, reduces database load |
STORAGE_S3_ENDPOINT | Fill in the actual address of the local object storage service | Stores large-volume documents such as yarn inspection and fabric inspection, adapts to batch file upload requirements |
DB_MAX_LINK | 50–80 | Matches the peak concurrent write volume of textile production data, prevents database connection exhaustion |
FE_DOMAIN | Fill in the frontend access domain name of the deployment environment | Ensures normal redirection for preview and sharing functions after tender documents are generated |
FILE_TOKEN_KEY | Generate a 32-bit random string | Ensures access security for uploaded process documents, inspection records and other files |
Three Common Configuration Errors
- A
405 method not allowederror occurs when accessing the deployed service. The cause is usually a mismatch between the frontend domain name configuration and the backend service’s cross-origin rules, or incorrect port mapping configuration, with access permissions for the corresponding requests not properly opened. - The configuration entry for the team management function cannot be found. The cause is that the corresponding function switch was not enabled in the deployment configuration, or the used version does not support this function. Confirm compatibility with FastGPT v4.17.0.
- Data sharing exceptions occur during multi-node deployment, with containers and data unable to synchronize normally. The cause is that shared storage and unified database connection parameters were not properly configured. Each node cannot access consistent production data, leading to inconsistent parameters in tender documents.
How to Verify Successful Configuration
- Access the configured frontend domain name, verify that the page loads normally and login can be completed, with no
405 method not allowedor other core errors. - Upload a cotton blending list or process setup sheet document, verify that the system can normally parse fields such as fiber indicators and process parameters in the document, with no parsing failure prompts.
- Initiate a simulated tender document generation request, confirm that the corresponding data source can be called to generate a document containing process parameters, with no missing or incorrect data.
- Check the service logs of each multi-node deployment, confirm that each node can connect to the unified database and storage service, with no connection timeout or rejection errors.
The values given are common starting points and should be measured against the reader's own samples.