What data for this category looks like
Data sources include agricultural and rural department planting entity financing monitoring databases, agricultural financial institution credit reporting systems, and financing information independently submitted by planting business entities. The update cadence updates new financing records from the previous workday daily. Full summary documents are generated weekly. Documents use structured table format. Each single record includes these fields: entity name, planting category, credit limit, actual loan amount, financing purpose, loan date, financing term. Field units: credit limit and loan amount use ten thousand yuan as the unit. Financing term uses natural days as the unit.
Constraints imposed on workflow orchestration
Multiple heterogeneous data sources require workflow nodes for unified format conversion. These nodes adapt to CSV and JSON data exported by different systems. The daily T+1 update cadence requires setting the workflow to trigger at a fixed daily time. Add a step to verify the integrity of that day's data. This avoids missing financing records from the previous workday. Structured documents may have missing fields. Configure built-in required field validation rules in the workflow. Generate alerts for records with missing core fields. Planting categories cover multiple segments including grain crops and cash crops. Configure the workflow to support branch logic based on planting category. This adapts to financing statistics rules for different categories.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
cronExpression | 0 30 1 * * ? | Adapts to the daily T+1 update cadence of crop farming financing daily reports, avoids occupying daytime business resources |
requiredFields | ["Subject Name", "Cultivation Variety", "Credit Limit", "Loan Amount"] | Covers core statistical dimensions of crop farming financing daily reports, ensures valid data integrity |
httpRequestBodyTemplate | {"target_date": "{{prev_workday}}", "data_sources": ["agri_monitor", "bank_credit"]} | Adapts to parameter specifications for multi-source data interfaces, specifies fetching full financing data from the previous workday |
branchCondition | planting_category | Splits workflow branches by planting category, adapts to financing statistics rules for different categories |
flowRetryCount | 2 | Addresses temporary fluctuations in multi-source data interfaces, prevents workflow interruptions from single failed requests |
codeNodeRuntime | PYTHON_3_9 | Adapts to Python data processing script versions written by users, ensures normal code execution |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Symptom: HTTP request node returns 400 error code. Logs show "invalid request body". Cause: Parameter names and values in the Python script request body are not configured correctly. File links are mistakenly used as parameter names instead of corresponding parameter values. This causes the interface to fail to parse request content.
- Symptom: Knowledge base search node returns no matching results. Interface prompt shows "No matching knowledge base found". Cause: The knowledge base ID is not passed as a workflow variable to the configuration item of the knowledge base search node. The node uses a default null value parameter.
- Symptom: Output from the code execution node is not returned in streaming mode. Only full results are returned after execution completes. Cause: Streaming output callback logic is not configured in the Python script. This prevents the workflow from receiving segmented intermediate results.
How to confirm correct configuration
- Manually trigger the workflow once. Check the output of the data conversion node. Verify that core fields match the original imported data.
- Review scheduled trigger log records. Confirm whether the workflow starts automatically at the preset time. Check that no timeout or error prompts appear.
- Insert a single test data record. Verify that the branch filtering rule correctly splits workflow branches by planting category.
- Call the test function of the code execution node. Pass simulated financing daily report data. Confirm that the script runs normally and returns expected processing results.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.