What the data for this category looks like
Data sources for this category include the Chinese Center for Disease Control and Prevention Information System, hospital HIS systems, infectious disease reporting systems, and laboratory testing systems. The update frequency is daily. Commonly processed documents include infectious disease reporting cards, epidemic analysis reports, case correction records, clustered epidemic reports, and epidemiological investigation reports. Core fields cover case unique identifiers, clinical diagnosis information, reporting time limits, correction status, laboratory test results, and more. All data must comply with legal infectious disease reporting management specifications.
Constraints imposed on workflow orchestration by these characteristics
This category of data updates daily and has dispersed sources. Workflows must support daily scheduled triggering, and adapt to field alignment verification across multiple data sources to prevent inconsistencies in case data between different systems. There are many common document types. Corresponding field extraction rules must be preset for different documents to avoid missing core review elements such as case identifiers and reporting time limits. Legal reporting rules have strict requirements. Workflows must include standardized verification nodes to block abnormal data, and support manual review of abnormal cases to ensure compliance with disease control management requirements. Data updates at a high frequency. Workflow execution time must be controlled to avoid delays that cause epidemic alert delays.
How to Set Configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Trigger Time Configuration | Scheduled daily triggering, execute at 2:00 AM daily | Matches the daily data update rhythm, avoids delayed processing of same-day case data |
| Timeout Threshold | 600 seconds | Adapts to the average time required for multi-data-source pulling and multi-document parsing, prevents task mid-execution interruptions |
| Branch Trigger Rule | Execute branches based on verification results | Distinguishes normal cases and abnormal cases, routes them to automatic review and manual review nodes respectively |
| Global Variable Assignment | Read case ID prefix from variables | Unifies case identifier matching rules across all nodes, prevents field inconsistencies |
| Node Identifier Configuration | Set a unique nodeId for each verification node | Facilitates log tracing and quick location of abnormal nodes |
| Parallel Execution Limit | Limit the number of concurrently executing document parsing nodes to the first 3 | Adapts to server resource load, prevents task failures caused by overload |
Three Common Misconfigurations
- Phenomenon: AI response generation nodes and subsequent branch nodes in the workflow trigger simultaneously, causing high resource usage or result conflicts. Cause: No pre-verification rules for branch triggering are configured, and subsequent branches are not restricted to execute only after the preceding node completes.
- Phenomenon: HTTP request nodes time out, return timeout errors or abnormal task status, and the timeout duration cannot be adjusted. Cause: The timeout threshold configuration item is not modified, and the default timeout duration cannot adapt to the time required for multi-data-source pulling or long-document parsing.
- Phenomenon: After uploading multiple PDF documents, information cannot be extracted one by one, resulting in chaotic batch processing or missing fields. Cause: Correct traversal rules are not configured for loop nodes, and independent identifiers are not set for each traversal sub-node via
nodeId.
How to Confirm Correct Configuration
- Manually trigger a test task, check whether the execution order of each node in the task log matches the preset workflow logic.
- Import a standard infectious disease reporting card document, verify whether the fields extracted by the nodes match the document content.
- Modify the timeout threshold, simulate a long-duration task, and confirm that the task does not terminate early.
- Check the global variable values stored in
variables, confirm whether they match the query parameters or API return values.
The values provided here are common starting points, and should be evaluated against samples used in actual deployments.
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.