What this category of data looks like
This category of data includes documents such as tooling requirement documents, positioning benchmark schemes, fixture drawings, gauge operation manuals, acceptance reports, calibration records, and change histories. Data sources include product digital models and GD&T, process routes, coordinate measuring machine data, on-site trial feedback, and metrology system data. Data updates are carried out on a per-project basis, triggered by each product change, and updated at regular calibration cycles. Fields include positioning tolerances, calibration cycles, change version numbers, and other items, with units mostly being measurement-related units such as millimeters, Newtons, hours, and similar.
What constraints do these characteristics impose on the "workflow orchestration" link
Data sources are scattered and linked to multiple production and inspection links, so the workflow must support cross-document associated reading. Update frequencies are not fixed and accompany project changes and calibration actions, so the workflow must support dynamic triggering of nodes. There are various document types, including engineering drawings and structured metrology data, so the workflow must adapt to mixed processing of unstructured images and text and structured tables. Fields include professional metrology items such as positioning tolerances and calibration cycles, so workflow nodes must preset corresponding verification rules. Data from the most time-consuming benchmark conversion and verification links must be included in the verification to avoid errors such as clamping deviations and gauge misjudgments.
How to configure it
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| File upload node allowed formats | .dwg, .dxf, .jpg, .csv | Covers formats commonly used for fixture drawings, coordinate measuring machine data, and on-site feedback documents in this category |
variables global variable configuration | Pre-set tooling ID, calibration cycle, change version number | Matches the core metrology and management fields of this category's data, facilitating cross-node invocation |
| Node triggering rules | Trigger based on project change time and calibration due date | Adapts to the update rhythm of data based on project changes and regular calibrations |
| API call timeout setting | 600 seconds | Meets the parsing and transmission requirements for large-volume engineering drawings and coordinate measuring machine data |
| Knowledge base association configuration | Bind to the global tooling and fixture knowledge base | Supports quick traceability of historical acceptance reports and change history data |
| Image transcoding node | Supports base64 encoding conversion | Adapts to the requirement of importing on-site photos of tooling for visual model analysis |
This configuration is based on FastGPT v4.17.0.
Three common configuration errors
- A workflow run error prompts "unsupported file format". The cause is that the category-specific engineering file upload format is not configured, and only general document formats are allowed, excluding .dwg, .dxf and other tooling drawing formats.
- Calling the API to upload a file returns "missing parameter". The cause is that the global tooling knowledge base association parameter is not configured in
variables, causing the workflow to fail to match the preset knowledge base binding rules. - The visual model analysis result has no matching historical data reference. The cause is that the category-specific knowledge base is not bound, and the workflow does not call associated data such as historical calibration records and acceptance reports as context.
How to confirm the configuration is correct
- Upload category-specific engineering files, confirm that the workflow node can normally recognize and parse file content.
- Trigger the node based on project change time, confirm that the workflow starts automatically according to the preset rules.
- Call the API to pass in the preset global variables, confirm that the parameters in
variablescan be normally invoked by the workflow. - Run the image transcoding node, confirm that a standard base64 encoded string can be generated and passed to subsequent AI nodes.
The values provided are common starting points and should be measured against the reader's own samples.