What this category of data looks like
This category of data originates from manufacturing enterprises’ MES systems, online inspection equipment, laboratory systems, process parameter libraries, customer quality standards, and historical defect databases. Data is continuously collected according to production batches and shifts, covering the entire process from work-in-progress inspection to finished product release. Daily associated documents include control plans, inspection records, SPC reports, finished product inspection reports, nonconforming product review forms, release forms, and quality monthly reports. Fields include inspection item name, detection value, judgment result, responsible person, production batch number, release status, and others. Units change dynamically based on the type of inspection item.
What constraints these characteristics impose on deployment and upgrades
This category of data has scattered sources and flexible update rhythms. During deployment, adapt to multi-source industrial system docking, and reserve configuration parameters for corresponding interfaces. Use the FastGPT v4.17.0 deployment package to adapt to the industrial intranet network environment, avoiding security risks from public network access. Collect data continuously according to production batches and shifts. During deployment, set reasonable collection scheduling rules to avoid high-frequency synchronization occupying system resources. Document types are diverse, and fields have no unified standard. During upgrades, support dynamic field mapping configuration to adapt to the inspection document structures of different enterprises. The data involves quality judgment and release permissions. During deployment, configure role permission isolation storage rules to ensure the security of sensitive quality data.
Configuration settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 100–200 concurrent connections | Adapts to the high-frequency read/write demands of multi-batch quality inspection data, avoids connection pool exhaustion |
MONGODB_URI | Dedicated instance address deployed on the local industrial intranet | Stores unstructured quality inspection documents and historical defect data, ensures data isolation |
PG_URL | PostgreSQL instance address deployed on the industrial intranet | Stores structured quality inspection data and permission configuration information, ensures data consistency |
REDIS_URL | Local cache instance address on port 6379, with cache validity set to 300 seconds | Caches frequently queried batch quality inspection rules and release statuses, reduces database pressure |
STORAGE_S3_ENDPOINT | Intranet endpoint of the enterprise’s internal object storage service | Stores large-volume quality inspection documents such as SPC reports and inspection reports, reduces public network transmission latency |
ROOT_KEY | Random key generated in accordance with enterprise internal security specifications | Configures access credentials for system administrator permissions, ensures the security of core configurations |
FE_DOMAIN | Access domain name deployed on the enterprise intranet | Limits valid domains for front-end access, prevents unauthorized access |
Three common misconfigurations
- Symptom: FastGPT v4.17.0 fails to load the quality inspection ledger page after startup, and the console reports a database connection timeout error. Cause: The
DB_MAX_LINKparameter is misconfigured, and the concurrent connection count is set too low to support simultaneous read/write of multi-batch quality inspection data. - Symptom: Uploaded SPC reports cannot be parsed normally after local deployment, and field mapping is empty. Cause: Dynamic field mapping rules are not configured, and the non-standard field structure of enterprise quality inspection documents is not adapted.
- Symptom: Permission confusion occurs after multiple users log in, and non-quality inspection personnel can view release form data. Cause: The role permission isolation rule corresponding to
ROOT_KEYis not configured, and the access scope of different users is not restricted.
How to verify correct configurations
- Run a database connection test to verify connectivity for the
MONGODB_URIandPG_URLconfigurations, and confirm the connection count adapts to current peak business demands. - Upload a standard finished product inspection report to verify document parsing and field mapping functions work normally, and confirm the
STORAGE_S3_ENDPOINTconfiguration can properly store uploaded files. - Switch test accounts with different roles to verify permission isolation rules take effect, and confirm unauthorized users cannot access quality release related data.
- Trigger a batch quality inspection data synchronization task to verify the
REDIS_URLcache function works normally, and confirm data query latency meets expectations.
The values given are common starting points and should be measured against the reader's own samples.