What data for this category looks like
The data for this category primarily comes from health supervision information systems, medical institution electronic registration systems, complaint and reporting platforms, and law enforcement inspection records. Daily processed documents include supervision inspection forms, medical institution practice licenses, rectification notices, supervision opinions, and penalty case files. Data is updated on a monthly basis. Document fields include institution qualification numbers, law enforcement officer information, inspection items, rectification requirements, penalty results, and more. There are no unified fixed units. Fields and units adjust dynamically based on the business attributes of individual documents.
What constraints these characteristics impose on the workflow orchestration link
Decentralized data sources and monthly updates require workflows to support cross-system data pulling and regular synchronization, to avoid duplicate data entry. Diverse document types include qualification, law enforcement, and rectification categories. This requires configuring recognition and parsing nodes for multiple file types, to adapt to field extraction logic for different documents. Law enforcement inspection and rectification tracking processes involve multi-stage circulation. This requires supporting permission verification and status synchronization between nodes, to ensure closed-loop processes for supervision opinions and rectification results. Complaint and reporting data must be linked to the inspection records of corresponding institutions. This requires built-in variable linking logic in workflows, to achieve precise data matching.
Reference only; verify against the target FastGPT release and deployment documentation.
How to set configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Variable configuration | Bind variables to institution qualification number, law enforcement date | Achieve precise cross-system data correlation, match the knowledge base and inspection records of corresponding institutions |
| Node trigger rules | Trigger monthly update cycle, link nodeId to data pulling node | Adapt to the monthly data update frequency, avoid invalid execution |
| Automatic image recognition | Enable manual-intervention-free recognition mode | Adapt to automatic parsing requirements for on-site inspection images, reduce operational steps |
| Knowledge base selection | Match knowledge base names by variables, support multiple knowledge base associations | Cover compliance document requirements for different types of medical institutions |
| Concurrency limit | Set to 30-40 concurrency | Adapt to single-node hardware resources, avoid overwhelming the service |
| Timeout threshold | Set to 600 seconds | Adapt to time requirements for cross-system data pulling, avoid process interruption |
Three common mistakes
- Symptom: Workflow cannot obtain URL parameters of the current page, variable fields do not display corresponding parameter values. Cause: Failed to correctly bind
variablesparameters to the URL parameter mapping node, and did not configure parameter extraction rules. - Symptom: Image recognition node does not execute automatically, manual triggering is still required. Cause: Did not enable the automatic running mode of the node, incorrectly configured manual trigger conditions, which does not meet the business requirements for automatic recognition of inspection images.
- Symptom: Service interruption occurs when a single node runs at 50 concurrency. Cause: Did not adjust the concurrency limit based on hardware configuration, the set concurrency value exceeds the single-node carrying capacity, resulting in resource exhaustion.
How to confirm the configuration is complete
- Trigger the workflow, check whether the variable binding node correctly displays the institution qualification number and law enforcement date, confirm that the
variablesparameter mapping logic takes effect. - Upload a supervision inspection on-site image, verify whether the node automatically completes recognition and extracts key information without manual triggering.
- Adjust the concurrency parameter, gradually increase the number of test requests, confirm that the service does not experience interruptions, and set a reasonable limit based on actual carrying capacity.
- Select knowledge bases for multiple institution types, verify whether the workflow can match the corresponding knowledge base by variables and complete retrieval.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.