What Data for This Category Looks Like
Data sources for this category include medical insurance intelligent monitoring systems, fund supervision rule bases, medical insurance settlement data, drug and consumable traceability codes, and complaint clues. Daily processed documents include audit notices, inspection work papers, suspicion lists, interview records, handling decision letters, and rectification reports. Document fields cover audit targets, suspicion types, supporting material numbers, handling suggestions, and other content. Data is updated on a monthly basis, covering settlement and supervision information for designated medical institutions and insured persons, and provides core support for audit work.
Constraints Imposed on Workflow Orchestration
The monthly data update rhythm requires workflow trigger cycles to match monthly execution, to avoid frequent calls to unupdated data sources. Multi-source data access requires configuration of different types of data source nodes to adapt to content in varying formats such as settlement data and rule bases. The diversity of daily processed documents requires workflows to use variables to store identifiers and field mappings for different documents, to enable unified processing. The suspicion screening link in the audit process takes significant time. Workflows need to set reasonable batch processing thresholds and timeout periods, and add verification links to ensure the accuracy of suspicion data, to avoid node exceptions caused by excessive data volume.
Recommended Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Trigger Cycle | Execute monthly | Match the monthly update frequency of business data, ensuring the latest supervision rules and settlement data are used |
variables | Bind custom environment variables set during deployment to store audit rule IDs | Support calling custom environment variables to adapt to differentiated supervision rules across regions |
nodeId | Assign a unique identifier to batch data processing nodes | Facilitate locating execution nodes in loop bodies to troubleshoot process exceptions |
| Batch Processing Threshold | Calibrated through actual testing (1000-2000 entries per batch) | Adapt to the volume of medical insurance settlement data, avoiding node timeouts or excessive resource usage |
| Failure Retry Count | 3 times | Address query failures caused by temporary fluctuations in data sources, improving process stability |
| Timeout Period | 600 seconds | Match the execution duration of complex audit verification, avoiding forced termination of long-running tasks |
Three Common Misconfigurations
- A custom environment variable referenced in a workflow returns a null value. This occurs because the environment variable was not correctly mounted in the FastGPT deployment configuration, or the variable name case does not match, causing the node to fail to read the configured value.
- The result of the index increment operation in a loop body is null. The symptom is that the index field output by the loop node is empty. This occurs because the starting value and step size of the loop variable were not correctly configured, adapting to the loop logic of the FastGPT v4.17.0 open source version.
- The database connection node runs an SQL query but returns no data. The symptom is that the node returns an empty result set. This occurs because the table names and field names of the medical insurance settlement data do not match the business rule base, or the database permission configuration is insufficient.
How to Confirm Correct Configuration
- Trigger the workflow and check the node logs to confirm that the environment variable value bound to
variablesis correctly loaded. - Run the batch processing node and verify that the number of data entries processed per batch matches the configured threshold.
- Simulate a temporary anomaly in the data source to verify whether the failure retry mechanism is triggered and resumes normal execution after retries.
- Run the loop node and check the output results to confirm that the index variable increases as expected and has no null values.
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.