What Data Looks Like for This Category
Data sources for welded structural component manufacturing include design drawings, material quality certificates, welding equipment parameters, filler metal inventory, non-destructive testing systems, and personnel qualification ledgers. Daily documents include welding procedure qualification records, welding procedure specifications, filler metal issue records, welder qualification records, weld inspection reports, and repair work orders. Data is updated per weld, batch, or shift. Each single record includes fields such as weld number, base material grade, filler metal model, welding current and voltage, welder qualification number, non-destructive testing result, and repair count. Field units include grade codes, amperes, volts, and integer counts, among others.
Constraints for Database and Operations & Maintenance
This category has high data update frequency. New records are generated for each weld or batch of materials. The database must support high-concurrency writes and low-latency queries. Multiple types of business documents require flexible storage structures. Fields must include rigid validation items, such as welder qualification validity periods and procedure qualification numbers. Configure field constraints at the database layer. Data association links are long. Create indexes for key fields like weld number and welder ID to improve traceability efficiency. Because failure consequences are severe, configure regular full backups and incremental synchronization mechanisms to ensure data traceability.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://127.0.0.1:27017/weld_structure_qc | Stores unstructured associated data such as welding procedures and non-destructive testing reports |
PG_URL | postgresql://weld_user:secure_pass@192.168.1.100:5432/weld_qc_db | Stores transaction-heavy data such as repair work orders and welder qualification ledgers |
DB_MAX_LINK | 180–220 connections | Adapts to the concurrent demand of high-frequency writes per weld batch, avoids connection overflow |
REDIS_URL | redis://127.0.0.1:6379/1 | Caches frequently accessed weld number indexes and procedure qualification parameters |
| Database backup cycle | Daily full backup + hourly incremental backup | Ensures recoverability of weld traceability data |
This configuration is adapted for FastGPT v4.17.0
Three Common Misconfigurations
- Phenomenon: After entering the FastGPT container, run
npm listto check the mongoose version. No dependency is found in the command output. Review the package.json file, and no mongoose dependency is listed. The container fails to start with a missing dependency error. Cause: The deployment script does not explicitly install the mongoose dependency, or the default package.json is modified, resulting in lost dependencies. - Phenomenon: When querying filler metal issue records associated with a weld, some fields are missing from the returned results. Cause: The database field configuration does not match the business data structure for welded structural components. Required fields such as filler metal model and issue batch are omitted.
- Phenomenon: Write timeout occurs when importing weld data in batches. Cause: The maximum connection number configured for the database connection pool is insufficient, and cannot support concurrent requests for batch writes.
How to Verify Successful Configuration
- Log in to the database configuration page in the FastGPT backend. Verify the connection status of
MONGODB_URI,PG_URL, andREDIS_URL. Confirm the connections are successful. - Manually create a simulated weld record. Query the record to confirm all required fields are stored correctly. Associated data can be retrieved normally.
- Import multiple batches of simulated weld data in bulk. Check that the database write rate matches the business update frequency. No accumulation or error reports are generated.
- View the database backup logs. Confirm that regular backup tasks are executing normally. Data can be recovered normally.
The values provided are common starting points, and should be evaluated against the reader's own samples.