What This Category of Data Entails
This category of data includes documents such as internal control self-assessment reports, audit notices, audit working papers, issue lists, rectification ledgers, and rectification closure certificates. Data sources include enterprise internal control systems, ERP systems, financial sharing systems, audit management systems, and procurement contract project systems. The data update frequency is annual. Core fields include issue number, rectification responsible entity, rectification completion deadline, and supporting material number. The size of individual documents varies widely; some audit working papers contain multi-page voucher scans.
Constraints Imposed on Workflow Orchestration
Data sources are scattered and require cross-system data pulls. Workflows must be configured with multi-node data source call steps to avoid single-node timeouts. Annually updated batch data requires batch processing nodes to prevent overload from single requests. Core fields must be strictly mapped. Workflows must use variables to uniformly pass cross-node core business parameters, ensuring no missing key information such as issue numbers and rectification statuses. Significant differences exist between document formats, so format adaptation nodes must be configured to adapt parsing rules for scanned documents and table-style documents, ensuring accurate data extraction.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Global variable configuration | Bind variables such as rectification ledger ID and audit cycle | Uniformly pass cross-node core business parameters and avoid repeated configuration |
| Node timeout setting | 600–1200 seconds | Adapt to the time required for pulling audit working papers and voucher data across systems |
| Data source call nodes | Split nodes by system type | Correspond to the pull logic for multi-source data and reduce the impact scope of single-node failures |
| Field validation rules | Validate that issue number and rectification status fields are not empty | Ensure the integrity of core business data and comply with internal control audit compliance requirements |
nodeId binding | Assign a unique nodeId to each data source node | Accurately locate failed nodes for subsequent troubleshooting and retries |
| Format adaptation nodes | Set based on actual testing | Adapt to parsing requirements for audit documents and scanned documents of different formats |
| Single pull count | First 200 entries | Adapt to the batch pull requirement for audit working papers and avoid overload from single requests |
Three Common Misconfigurations
- Phenomenon: Rectification ledger fields generated after workflow execution are empty. Cause: The
variablesmapping step is not configured, and core fields pulled across nodes are not correctly bound to the output node. - Phenomenon: A 504 status code is returned after the workflow is triggered. Cause: Cross-system data source call nodes are not split, and single-node pull of multi-system data times out.
- Phenomenon: Duplicate audit issue entries appear in the generated report. Cause: No field deduplication step is added, and rectification ledger data is not deduplicated based on issue numbers, leading to duplicate issues being counted multiple times in the report.
How to Verify the Configuration Is Correct
- Manually trigger workflow execution for a single data entry, and check whether the output rectification ledger fields match the source data.
- View workflow node logs to confirm that each data source call link corresponding to a
nodeIdhas successfully pulled data. - Simulate batch data submission and check whether the workflow executes according to the configured batch rules without timeout errors.
- Verify the transfer effect of global variables, and confirm that the cross-node audit cycle parameter is correctly applied to the report generation link.
The values provided here 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.