What the Data for This Category Looks Like
Data for this category comes from product 3D models and GD&T, process routes, coordinate measuring machine (CMM) data, on-site trial feedback, and metrology systems. Daily documents include tooling requirement documents, positioning datum schemes, fixture drawings, gauge operation manuals, acceptance reports, calibration records, and change histories. Data updates are triggered by project progress, product changes, or regular calibration cycles. Document fields include datum tolerances, clamping force parameters, calibration cycles, and change version numbers. Common units are millimeters, newtons, days, and similar units.
What Constraints These Characteristics Impose on Deployment and Upgrades
Data sources cover multiple types of documents from engineering design and metrology and inspection. During deployment, support for multi-format document parsing is required, and interface space for connecting to external metrology systems must be reserved. Update frequency has no fixed cycle. Deployment must support on-demand loading and incremental synchronization to avoid excessive system resource usage from full data pulls. Documents include specialized engineering fields such as GD&T tolerances and CMM data, so dedicated parsing rules must be configured to adapt to industry terminology. During migration deployment, the association between change histories and calibration records must be preserved to ensure complete traceability of inspection data. During upgrades, ongoing project data synchronization must not be interrupted to maintain continuity of inspection records.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://admin:password@localhost:27017/fastgpt?authSource=admin | Adapts to the multi-document association storage requirements of this category, and supports associated queries for change histories and calibration records |
REDIS_URL | redis://:redispass@localhost:6379/0 | Caches frequently accessed tooling document metadata to reduce database query pressure |
STORAGE_S3_ENDPOINT | http://192.168.10.20:9000 | Stores large-volume engineering files such as CMM measurement data and fixture drawings, and adapts to batch upload requirements |
DB_MAX_LINK | 20–30 connections | Matches the single-project data volume of this category, and avoids connection pool overload that affects parallel processing of multiple projects |
FILE_TOKEN_KEY | 32-bit randomly generated string | Ensures interface security for inspection record uploads, preventing unauthorized access |
Three Common Mistakes
- Phenomenon: An architecture incompatible error occurs when starting FastGPT v4.17.0 on an ARM architecture Galaxy Kylin Linux system, and the service cannot start. Cause: The official ARM-compatible image is not used; the default image only supports x86 architecture.
- Phenomenon: After upgrading to FastGPT v4.17.0, an error "Failed to create post presigned url" is prompted when uploading fixture drawings or calibration records. Cause:
STORAGE_S3_ENDPOINTor associated storage access keys are not configured correctly, resulting in a failure to generate the pre-signed URL. - Phenomenon: When switching servers and using non-Docker deployment, existing inspection record data for tooling projects cannot be loaded. Cause: The associated MongoDB and Redis data directories are not migrated, or
MONGODB_URIandREDIS_URLconfigurations are not updated.
How to Confirm Configuration is Correct
- Check the service process or container status to confirm that FastGPT v4.17.0 and dependent MongoDB and Redis services are running normally.
- Upload a small fixture drawing to check whether the file is stored successfully and an accessible preview link is generated, verifying that the pre-signed URL function works properly.
- Import a batch of historical change history data to check whether associated calibration records can be queried normally, verifying that the database connection configuration is correct.
- Start the service on an ARM architecture Galaxy Kylin system to confirm that there are no architecture incompatible errors, verifying that the system adaptation configuration takes effect.
The values provided are common starting points and should be measured against the reader's own samples.