What the data for this category looks like
The production 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 documents include production process specifications, batch production records, clean zone operation records, sterilization batch records, inspection reports, software version records, adverse event and traceability materials. Data updates run per batch or per product. Clean environment monitoring data updates on a scheduled plan. Fields include batch unique identifiers, device and material batch numbers, clean zone temperature and humidity, sterilization parameters, inspection results, and more.
What constraints these characteristics impose on workflow orchestration
Multiple dispersed data sources require workflow orchestration to integrate pull nodes from multiple different systems. It also requires configuring field mapping rules between nodes to unify data formats. The update rhythm of per-batch or scheduled updates requires workflows to support two startup modes: triggered by production batch or scheduled. This avoids repeated pulling of expired data. Diverse document types require workflows to adapt to file parsing for different formats. It also requires adding a field verification step to check the integrity of key data, including device unique identifiers, sterilization parameters, and others. The high-risk business attribute requires workflows to be configured with failure branches and alert mechanisms. This ensures abnormal data is intercepted in a timely manner and prevents unqualified data from flowing into subsequent links.
How to set up the configuration
This document uses FastGPT v4.17.0 for configuration. Specific parameters are as follows:
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
| Global variable configuration | Store core fields such as batch unique identifiers, sterilization parameters, clean zone data, etc., calibrated based on actual measurements | Adapt to unified calls of multi-source data and avoid repeated pulling of duplicate fields |
| HTTP request node configuration | Set single request timeout to 60 seconds, set file transfer format to multipart/form-data | Adapt to batch pulling of medical device production files and txt file transfer requirements, avoid timeout interruptions |
| Variable mapping rules | Map the pulled fields in variables to the corresponding positions in the ledger template | Ensure that the generated ledger includes all required production data |
| Failure branch configuration | Trigger alerts and retries when nodeId is the data pull node | Intercept abnormal data and avoid generation of unqualified ledgers |
| Node retry settings | Set retry count to 2 times, retry interval to 30 seconds | Respond to temporary system fluctuations and reduce the probability of workflow interruptions |
Three common mistakes
- Symptom: The HTTP request node cannot transfer txt format batch record files, returning status code 400. Cause: The file transfer format is not set to multipart/form-data, and backend receiving requirements are not matched.
- Symptom: The generated ledger lacks the clean zone temperature and humidity field. Cause: The return fields of the clean environment monitoring system are not mapped to the
variablesvariable pool, resulting in data not being included in the ledger template. - Symptom: No logs are generated after the workflow is triggered. Cause: The node log collection switch for the workflow is not enabled, making troubleshooting of abnormal links impossible.
How to confirm the configuration is complete
- Manually trigger the workflow for a corresponding production batch, and check whether the generated ledger includes all required document fields.
- View the workflow operation logs to confirm that all data pull nodes have successfully obtained files and data from the corresponding systems.
- Simulate an abnormal scenario, such as closing the simulation data interface of the clean environment monitoring system, and confirm that the failure branch triggers an alert.
- Check whether the batch number, sterilization parameters and other fields in the
variablesvariable pool match the preset production data.
The values provided are common starting points and should be measured against the reader's own samples.