What the data for this category looks like
Data sources for this category include China’s Center for Disease Control and Prevention Information System, hospital HIS systems, infectious disease reporting systems, and laboratory testing systems. The update frequency is daily. Daily documents include infectious disease reporting cards, epidemic analysis reports, case correction records, clustered epidemic reports, and outbreak investigation reports. Data fields include case diagnosis information, reporting time limit markers, correction status, epidemic scale parameters, and other relevant items. Formats must comply with legal reporting specifications. No additional custom statistical fields are included.
What constraints these characteristics impose on the workflow orchestration link
Multi-source data access creates unified verification constraints. Standardized verification rules must be configured for the field formats of different systems. The daily data update frequency requires workflows to include scheduled trigger nodes. This ensures full daily data aggregation and reporting is completed on time. Multiple document formats require workflows to branch processing logic by document type. This avoids processing errors caused by cross-type field confusion. Legal notifiable infectious disease reporting rules restrict workflows from skipping mandatory verification steps. Reporting time limits and case information completeness must be enforced for verification. Abnormal interception triggers if these checks fail.
Configuration settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Global variable scope | Session-level isolation | Avoid data contamination across APIs or web requests, match business scenarios with parallel multiple requests |
| Scheduled trigger node cycle | Execute daily at 00:00 | Match the daily data update frequency, ensure daily data is aggregated and reported on time |
variables binding scope | Bind no more than 10 core business fields | Reduce redundant parameter loading, improve workflow execution efficiency |
| Branch judgment node matching rule | Exact field matching | Comply with fixed verification rules for notifiable infectious disease reporting, avoid verification deviations from fuzzy matching |
nodeId configuration | Assign a unique identifier to each verification node | Facilitate locating specific links of workflow execution exceptions |
| Abnormal interception threshold | Calibrate based on actual testing | Adapt to verification strictness levels of different disease control institutions, flexibly adjust interception standards |
Three common configuration errors
- Phenomenon: In workflow branch judgment nodes, using "equals" or "starts with" rules triggers the ELSE branch, while using "contains" or "ends with" rules triggers the IF branch. Cause: The rule logic of FastGPT workflow judgment nodes is not correctly matched, and the corresponding relationship between rules and triggered branches is confused.
- Phenomenon: Cross-API or web request data interference occurs with global variables. Cause: The global variable scope is incorrectly configured as global sharing, and session-level isolation is not enabled.
- Phenomenon: Workflows cannot obtain parameters passed by the system. Corresponding fields are empty. Cause: The mapping relationship between system parameters and workflow nodes is not bound in the
variablesconfiguration, resulting in parameters not being correctly passed in.
How to confirm the configuration is correct
- Check the workflow scheduled trigger configuration. Confirm that the trigger cycle matches the data update frequency.
- Simulate parallel calls of multiple requests to the workflow. Check whether cross-request data contamination occurs with global variables.
- Pass preset test business fields. Verify whether the trigger logic of the branch judgment node meets expectations.
- View the workflow execution log. Confirm that each node identified by
nodeIdexecutes normally without abnormal error reports.
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.