What this category of data looks like
Daily documents for this category include cotton blending or ingredient lists, process setup sheets, equipment operation records, yarn inspection sheets, grey fabric inspection sheets, and defect records. Data sources cover raw fiber inspection reports, ERP systems, real-time data from spinning and weaving machines, evenness tester data, fabric inspection machine collected data, and order specification parameters. Data updates occur for each raw material batch, each shift production cycle, and each finished fabric piece. Each document includes exclusive fields such as fiber grade, cotton blending ratio, spinning twist, weaving structure, and defect type. No pre-set statistical values are included.
Constraints on deployment and upgrade from these data characteristics
This page is written for FastGPT v4.17.0. The data characteristics of this category create multiple constraints for deployment and upgrade. Multi-source heterogeneous data sources require configuring access channels compatible with multiple data formats during deployment. This adapts to different input types such as fiber inspection reports, ERP documents, and real-time equipment data. Frequent update cycles require synchronization tasks to support batch triggering. This avoids full data pulls that consume excessive system resources. Documents contain textile-specific fields. Pre-configure field mapping rules during deployment. This ensures the audit process can recognize specific parameters such as cotton blending ratio and spinning twist. Real-time equipment data access requires reserving sufficient network bandwidth and corresponding port permissions for deployment nodes.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 20–30 | Adapts to high-frequency data updates per raw material batch and shift, prevents connection pool exhaustion |
REDIS_URL | Points to a locally deployed Redis instance, configure memory quota to 1000 MB | Caches frequently accessed inspection data and audit rules, adapts to frequent real-time query requirements |
STORAGE_S3_ENDPOINT | Binds to the access address of the enterprise's internal object storage | Stores large-volume documents such as batch defect records and process sheets |
ROOT_KEY | Generate a random key based on the deployment environment | Ensures identity verification security for system background access |
PG_URL | Points to a PostgreSQL instance storing ERP and order data | Connects to the core business database for order specifications and ingredient data |
Three common configuration mistakes
- An error occurs with
ROOT_KEYverification, prompting insufficient permissions. The cause is incorrect configuration of theROOT_KEYvalue during deployment, or failure to synchronously update the corresponding environment variable in docker-compose.yml. - Data synchronization tasks time out. Single synchronization takes more than
600 seconds. The cause is failure to adjust theDB_MAX_LINKvalue based on the batch data scale of each raw material batch, leading to insufficient connection pools to process data in parallel. - Incoming inspection data fields are empty. The cause is failure to pre-configure field mapping rules, so the system cannot recognize the correspondence of textile-specific fields and cannot complete data parsing.
How to confirm correct configuration
- Log in to the system background, verify that the
ROOT_KEYconfigured key can complete identity verification normally, with no login errors. - Trigger a single batch of raw material data synchronization task, check that there are no connection pool exhaustion or timeout errors in the synchronization log.
- Upload a cotton blending sheet document, confirm that the system can correctly recognize fields such as fiber grade and cotton blending ratio, with no null values or parsing errors.
- Check the access status of the object storage, confirm that documents such as defect records and process sheets can be uploaded and read normally.
The values provided are common starting points, and should be evaluated against the reader's own samples.