What the data for this use case looks like
Data related to land pre-review and planning permits comes from the national land spatial planning unified map, project approval and supervision platform, land use status database, and ecological protection redline data. Update frequency is irregular.
Daily documents include land pre-review and site selection opinion, construction land planning permit, construction project planning permit, redline map, and planning conditions. Data fields include project site range coordinates, land use scale indicators, planning control requirements, pre-approval status, and others. There is no unified fixed single-document format. Some data needs to be integrated and spliced from multiple platforms.
What constraints do these characteristics impose on workflow orchestration
Data sources are scattered and updates are irregular. Workflow orchestration must connect multiple external data source nodes to verify data timeliness each run.
Daily document formats are inconsistent. Add field verification nodes to the workflow. The link for checking conflicts between project site selection and planning control, land use scale and redline takes a long time. Set serial manual approval nodes.
Errors affect project approval and construction. Add exception capture links at key workflow nodes to trigger alerts when the process fails.
How to configure
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| HTTP Request Node Output Binding | Enable the variables parameter to bind business data returned by the request | Ensure that land pre-review related data returned by HTTP requests can be passed to the conversation context, resolving issues where output content cannot be displayed |
| Workflow Node Trigger Rule | Bind to nodeId to trigger after the preceding node finishes executing | Ensure the process runs in business order, avoiding incorrect node trigger sequence |
| Loop Body Configuration | Set loop trigger condition to the length of the conflict list returned by the preceding node > 0, process 1000 pieces of data per single loop | Adapt to multi-project conflict checking scenarios, control the volume of data processed per single run, keep resource usage under 1000 MB |
| Application Export and Import Configuration | Select JSON as export format, include all associated data source configurations | Facilitate cross-environment deployment of workflow configurations, ensure complete migration of settings |
| Python Code Node Timeout Setting | Set timeout period to 600 seconds | Adapt to complex calculation scenarios for land pre-review data processing, avoid long periods of unresponsiveness |
Three common mistakes
- Symptom: No return content appears in conversation output after HTTP request node execution. Cause: The output of the HTTP request is not bound to the
variablesparameter, so returned data is not passed to the conversation context. - Symptom: Loop body node does not execute multiple times as expected. Cause: The preceding node bound to
nodeIdis not set correctly, leading to incorrect trigger conditions. - Symptom: Nodes are missing after importing exported application configurations. Cause: The option to include associated data source configurations was not checked, resulting in inability to call data normally after import.
How to confirm the configuration is complete
- Trigger the workflow, check if the
variablesparameter includes business data such as land redline and planning conditions returned by the HTTP request. - Test the loop body node, confirm that the number of loop executions matches the number of conflict projects returned by the preceding node.
- Export the application configuration, import it into a test environment, confirm that all node configurations are not missing.
- Run the Python code node, confirm that the code executes without errors and the returned processing results conform to land pre-review business logic.
The values given 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.