What the data for this category looks like
This category’s data originates from design drawings, material quality certificates, welding equipment parameters, filler metal storage inventories, non-destructive testing systems, and personnel qualification ledgers. Daily documents include welding procedure qualifications, welding procedure specifications, filler metal issue records, welder qualification records, weld inspection reports, and repair work orders. Data updates follow the rhythm of individual welds, individual batches of materials, or individual work shifts. Fields include weld location, filler metal grade, welder identification number, non-destructive testing results, repair count, and more. There is no unified fixed format, and the structure adjusts based on the enterprise’s production workflows.
What constraints these characteristics impose during deployment and upgrade
Data sources for this category are scattered across multiple independent systems. During deployment, adaptation rules for cross-system data docking must be configured. The high-frequency, fine-grained update rhythm requires setting reasonable incremental sync trigger intervals during deployment to avoid excessive resource usage. The diverse document types and non-uniform field structures require pre-configuring custom field mapping and multi-format file parsing parameters to ensure accurate extraction and association of data during the audit process. Additionally, rigidly constrained welder qualification and welding procedure qualification data require setting trigger conditions for dedicated verification logic during deployment. This document provides configuration instructions based on FastGPT v4.17.0.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 15–25 | Adapts to the high-frequency concurrent read demand for weld data of this category, and prevents exhaustion of the database connection pool |
MONGODB_URI | Local Docker-deployed MongoDB instance address | Stores unstructured business data such as non-destructive testing reports and repair work orders |
REDIS_URL | Local Redis service address bound to port 6379 | Caches frequently queried data such as welder qualifications and welding procedure qualifications to reduce repeated read overhead |
STORAGE_S3_ENDPOINT | Enterprise internal object storage service address | Stores large-volume files such as filler metal issue records and design drawings |
| Incremental sync trigger rules | Set based on actual measurement | Matches the update rhythm of individual welds or individual batches of materials, balancing real-time performance and resource usage |
Three common mistakes
- Phenomenon: An audit interface call returns a database connection error, and session logs show the connection count has exceeded the limit. Cause: The
DB_MAX_LINKparameter was not set correctly, and the database connection pool is exhausted when processing multiple weld audit requests concurrently. - Phenomenon: The front-end interface fails to respond when loading audit data, and Docker container resource monitoring shows continuous increase in memory usage. Cause: The sync interval was not set to match the update frequency of this category, and frequent full business data pulls lead to resource overload.
- Phenomenon: Valid fields cannot be extracted after a filler metal issue record is uploaded. Cause: The parsing rules for the corresponding document were not configured, and the professional field naming format of this category’s documents was not adapted.
How to confirm the configuration is correct
- Execute a single weld audit process, and check whether the returned result includes the key fields of the corresponding business documents.
- Simulate concurrent submission of 3 to 5 audit requests, and check whether the database connection status matches the preset configuration range.
- Upload a welding procedure specification document, and confirm that the parsed text content is complete and the fields match business requirements.
- View the cache service logs corresponding to
REDIS_URL, and confirm that frequently queried data is properly cached.
The values provided are common starting points and should be measured against the reader's own samples.