What the data for this category looks like
Data sources for this category include enterprise metrology management systems, external calibration institution certificates, LIMS systems, and environmental monitoring equipment. Daily generated documents include gauge ledgers, calibration plans, calibration certificates, periodic verification records, original test records, reference material ledgers, and deviation handling forms. Data updates are triggered based on calibration cycles, completion of single tests, or abnormal conditions. Fields include unique instrument numbers, calibration dates, measurement uncertainty, ambient temperature and humidity, instrument status, and more. Units include ℃, mm, mg, day, and more.
Constraints on workflow orchestration
Multi-source data access requires workflows to support cross-system data pulling and format unification, to avoid data format conflicts. Non-fixed update frequencies require trigger nodes to support three trigger methods: scheduled, manual, and event-based, to adapt to calibration plans and abnormal scenarios. Multi-field traceability requirements require workflows to retain full-process field links, to facilitate subsequent traceability of the impact scope of inaccurate instruments. Severe consequences of errors require workflows to add pre-check nodes, to filter invalid data before entering the audit link.
Configuration Settings
These configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Knowledge base search scope | Limited to the collection of calibration certificates and test record files bound to the current project | Data for this category is concentrated in metrology-specific documents, to avoid irrelevant results interfering with audits |
| Batch execution node loop configuration | Triggered by the single calibration process of one instrument, with a 10-second interval per loop | Adapts to the execution rhythm of calibration plans, to avoid concurrent overload |
| Large model variable reference parameters | Enable custom parameters, set temperature to 0.7–0.9, calibrated based on actual measurements | Balances rigor and flexibility in calibration result audits |
| Node timeout threshold | 600 seconds | Adapts to the time required for calibration certificate parsing and traceability chain sorting |
| Global variable configuration | Bind the variables parameter as the full-process fields of the workflow | Retains full-link data required for instrument traceability |
| Node ID allocation | Assign unique nodeId in the order of workflow nodes | Facilitates log troubleshooting and process traceability |
Three common mistakes
- Batch execution nodes function correctly during online debugging, but terminate after completing part of the loops during API calls, with no clear error message. The root cause is that the API request fails to correctly pass the loop context in the
variablesparameter, preventing the node from identifying subsequent iterative tasks. - The knowledge base search returns a large number of non-metrology-related documents, and cannot accurately match calibration records. The root cause is that the search scope is not configured to limit results to metrology-specific file collections, resulting in the search covering the entire knowledge base.
- After referencing a large model prompt with variables, the temperature parameter setting entry disappears, and the generation parameters cannot be adjusted. The root cause is that after switching to the variable reference mode, the configuration entry for custom parameters is not retained, and the platform's default general parameters are used.
How to confirm correct configuration
- View the configuration panel of the knowledge base node, confirm that the limited search scope option is checked, and the corresponding metrology-specific file collection is bound.
- Trigger the batch execution node, check the execution log, confirm that the number of loops matches the number of calibration plans to be reviewed.
- Edit the variable reference configuration of the large model node, confirm that the temperature parameter range can be adjusted manually.
- Verify the
nodeIdconfiguration, confirm that each node has a unique ID and can be displayed normally in logs.
The values provided are common starting points and should be measured against the reader's own samples.