What the data for this category looks like
Daily documents for this category include cotton blending or batching lists, process setup sheets, equipment operation records, yarn inspection reports, grey fabric inspection reports, and defect logs. Data sources cover raw fiber inspection, ERP systems, spinning and loom collected data, evenness testers, fabric inspectors, and order specification documents. Data update frequency changes with production cycles, covering three dimensions: per batch of raw materials, per shift of production, and per roll of fabric produced. Fields include raw material batch numbers, cotton blending ratios, spinning twist levels, weaving structure parameters, defect types and quantities, and more. Some fields use dedicated units: yarn tenacity is measured in cN/tex, and twist is measured in twists per meter.
Constraints Imposed on Database and Operations Workflows
Multiple heterogeneous data sources require the database to support both structured and unstructured data storage formats. The high-frequency update rhythm requires the database to support high-concurrency writes and low-latency reads. Data association needs across different production nodes require clear field association rules. These rules ensure that order, raw material, equipment, and inspection information can be quickly linked during traceability. The large data volume generated by numerous equipment units requires the database to have horizontal scaling capabilities to avoid storage and query bottlenecks. Additionally, abnormal defect data may prevent subsequent dyeing and finishing processes from repairing defects, so the operations workflow must ensure timely and complete data backups.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PG_URL | postgresql://fastgpt:securepass@db-host:5432/spinning_quality?sslmode=require | Stores structured order, batching list, and inspection report data, supports cross-table associated queries |
DB_MAX_LINK | 20–30 | Adapts to the high-frequency write requirements of multiple devices, prevents database connection exhaustion, calibrated based on actual measurements |
REDIS_URL | redis://:redis-auth@redis-host:6379/1 | Caches commonly used defect classifications and process parameter templates, reduces repeated database read pressure |
MONGODB_URI | mongodb://fastgpt:securepass@mongo-host1:27017,mongo-host2:27017/spinning_logs?replicaSet=rs1 | Stores unstructured equipment operation logs and defect image data, supports sharded scaling |
| Automatic backup trigger rule | After each batch of raw material production is completed + 1 AM daily | Aligns with the production update rhythm of this category, ensures data integrity |
Three Common Configuration Mistakes
- Symptom: A
too many connectionserror occurs when connecting to the database usingPG_URL. Cause: TheDB_MAX_LINKparameter was not adjusted to match the high-frequency write requirements of this category, and the connection count was set too low to support simultaneous writes from multiple devices. - Symptom: A large number of empty fields or format mismatch errors appear when bulk importing grey fabric inspection data. Cause: Dedicated data validation rules were not configured for documents such as batching lists and process sheets for this category, and mandatory validation was not applied to required fields such as raw material batch numbers and defect types.
- Symptom: Request timeouts occur when executing defect traceability queries. Cause: Appropriate joint indexes were not created for associated order, equipment data, and inspection records, resulting in insufficient cross-table query efficiency.
How to Verify a Successful Configuration
- Use the database connection testing tool built into FastGPT v4.17.0 to verify that
PG_URL,MONGODB_URI, andREDIS_URLcan all establish normal connections, with no authentication or network timeout errors. - Import a batch of simulated cotton blending lists and defect logs, check the data write success rate, and confirm that connection blocking issues restricted by
DB_MAX_LINKare not triggered. - Trigger a preset database backup task, confirm that backup files are generated normally, and that the backup cycle aligns with the preset production rhythm rules.
- Execute a cross-table associated query, verify that the association relationship between order, batching list, and inspection data can be returned normally, with no missing fields or format errors.
The values provided are common starting points and should be measured against the reader's own samples.