This document applies to the workflow orchestration feature of FastGPT v4.17.0.
What the data for this category looks like
Data for this category comes from metrology management systems, external calibration institution certificates, reference standards, LIMS, and environmental monitoring equipment. Daily documents include gauge ledgers, calibration plans, calibration certificates, interim check records, original test records, reference material ledgers, and deviation disposition forms. Fields include instrument number, calibration date, measured value, measurement uncertainty, traceability number, environmental temperature and humidity, and more. Units include millimeters, newtons, degrees Celsius, and more. Data updates trigger on calibration cycles, completion of single tests, or abnormal events. There is no fixed batch update rhythm.
Constraints on workflow orchestration from this category's characteristics
Data for this category is scattered across multiple sources and uses mixed formats. Workflows must support multi-type data source access nodes. They must adapt to API or file import logic for different systems. Daily documents have many fields with specialized units. Workflows must include field mapping and unit verification steps. This ensures unified summary data formats. Data updates have no fixed cycle. Workflows cannot rely on scheduled triggers. They must support business event or manual triggers. Core data must support quality judgment. Workflows must connect traceability verification, result review, and other nodes. This ensures data compliance.
How to set configurations
| Configuration Item | Recommended Practice | Rationale |
|---|---|---|
| Data source access nodes | Configure metrology management system, LIMS, and external certificate parsing nodes separately by data source type | Data sources for this category are scattered. Different system access formats must be supported |
| Field mapping rules | Establish fixed mappings for core fields including instrument number, calibration date, measured value, measurement uncertainty, and more | Daily documents have many fields with specialized units. Unified fields are required for summarization |
| Trigger condition configuration | Trigger on calibration completion events, test submission events, or manually | Data updates have no fixed cycle. Configuration must match actual business trigger logic |
| Verification node logic | Configure traceability chain verification and measurement uncertainty verification links | Data for this category must support quality judgment. Traceability compliance and data accuracy must be ensured |
| Failure retry configuration | Single retry 3 times, interval 60 seconds | External calibration certificate parsing may fail due to network fluctuations. Reasonable retries reduce exception rates |
| Global variable storage | Use the variables parameter to configure the traceability variable pool | Traceability data for instruments must be retained for subsequent report association. variables supports cross-node data reuse |
Three common configuration mistakes
- Low recognition accuracy for problem classification nodes in workflows. Prompt words do not include category-specific terminology. The cause is that prompt words are not configured with specialized terms including measurement uncertainty and traceability number. Generic models cannot accurately recognize category-specific fields.
- Workflows cannot access external MCP plugins. Nodes have not completed authorization binding. The cause is that access parameters for MCP plugins are not configured in the corresponding nodes, and permission association between plugins and workflows is not completed.
- Code running nodes do not take effect continuously, returning empty logs or execution timeouts. The cause is that the code does not adapt to the unit verification logic for metrology data, or reasonable timeout thresholds are not set, leading to blocking when processing large-volume calibration certificates.
How to confirm configuration is complete
- Manually trigger a workflow run. Check if multi-data source nodes can normally pull corresponding data such as gauge ledgers and calibration certificates.
- View the output of the field mapping node. Confirm that core fields have completed unified formatting and unit matching.
- Trigger an abnormal event, such as manually modifying a calibration date to an expired date. Confirm that the verification node can intercept abnormal data and trigger an alert.
- Simulate a scenario where external interface calls fail. Confirm that failed nodes execute retries according to the configured retry times and interval.
The values provided are common starting points and should be measured against the reader's own samples.