This document is written based on FastGPT v4.17.0.
What this category of data looks like
Data for this category originates from design history files, device and material batch ledgers, MES systems, clean environment monitoring equipment, sterilization equipment, and inspection and calibration systems. Daily associated documents include production process specifications, batch production records, clean zone environment records, sterilization batch records, inspection reports, software version logs, adverse event and traceability archives. Data updates are performed per product batch, per device, or per clean environment monitoring plan. Core fields include unique device identification (UDI), material batch number, clean zone temperature and humidity parameters, sterilization parameters, software version number, inspection results, and other related items.
What constraints these characteristics impose on workflow orchestration
Data for this category is scattered across multiple systems, with inconsistent formats and field standards. Workflow orchestration must support unified pulling and format conversion of multi-source data. The feature of updating per batch or per plan requires workflows to support triggering execution based on production batches or environmental monitoring cycles. Core fields including mandatory traceability items such as unique device identification (UDI) and sterilization parameters require field integrity check steps to be added to workflow nodes. Adverse event traceability requires full-link audit, so workflows must record each execution node and data source, and must be compatible with special control requirements such as software versions and clean environment conditions.
How to set configurations
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
| Variable Group | Bind production batch ID, UDI, clean zone parameters from variables | Match the core business requirement of batch-based traceability for this category |
| Node Trigger Rule | Trigger based on the production batch node bound to nodeId | Correspond to the update frequency of data updated per batch |
| Field Validation Rule | Validate mandatory fields including UDI, sterilization parameters, and software version number | Ensure compliance with traceability data integrity requirements |
| Exception Branch Handling | Trigger system alerts and record execution logs | Avoid compliance risks caused by omitted defect analysis |
| Timeout Setting | 600 seconds | Adapt to the average time required for pulling data from multi-source systems |
| Context Recall Limit | Top 8 matching records | Cover the volume of associated traceability data for a single product batch |
Three common mistakes
- Phenomenon: Attempting to reuse a form fill node across different branch links causes the node to fail to accept cross-branch execution requests. Reason: No global reuse identifier for the node is configured in workflow orchestration, so branch links cannot share the same node instance.
- Phenomenon: Only the user ID can be obtained in the workflow, not the user's name. Reason: The user information extension configuration in
variablesis not enabled, and only the account ID field is returned by default. - Phenomenon: Timeout errors occur when pulling multi-source data. Reason: The timeout setting does not adapt to the time required for data interaction across systems, and the set value is lower than the actual average pulling duration.
How to confirm the configuration is complete
- Manually trigger the workflow bound to a production batch, check whether the core fields loaded in
variablesmatch the actual data of the current batch. - Simulate a scenario where mandatory fields are missing, check whether the workflow triggers the preset validation alerts.
- Configure cross-branch node reuse, verify that different links can call the same node's execution logic.
- Adjust the timeout parameter, test whether the execution status of multi-source data pulling meets expected requirements.
The values provided are common starting points and should be measured against the reader's own samples.