Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Core data for this category comes from the national land spatial planning one-map, project approval and supervision platform, land use status database, and ecological protection red line data, with irregular update frequency. Daily business documents include land pre-examination and site selection opinions, construction land planning permits, construction project planning permits, red line drawings, and planning conditions. Each document contains structured fields such as project site selection scope, land scale, control indicators, and approval status. Some documents include vector-format spatial data. There is no unified fixed field order, and corresponding fields must be parsed based on business scenarios.
What constraints do these characteristics impose on workflow orchestration
Multiple data sources with irregular update cycles require workflow orchestration to support on-demand pulling of the latest data sources, to avoid using expired control data. Diverse document structures and non-fixed fields require configuring flexible field mapping rules to adapt to parsing needs for different documents. Mixed storage of spatial and structured data requires adding format validation steps in the workflow to ensure data type matching. Severe consequences of business errors require adding parameter validation logic at key nodes, to block non-compliant projects from proceeding to subsequent processes. For links involving cross-departmental coordination prerequisites, configure cross-system call trigger nodes in the workflow to ensure data synchronization across departments.
How to set configurations
| Configuration Item | Recommended Practice | Rationale |
|---|---|---|
variables | Bind the project ID from the project approval and supervision platform, and the URL field parsed from the red line drawing file | Enable automatic data synchronization between the workflow and the business system, and avoid manual entry errors |
| Global variable configuration | Temporarily store core control indicators of planning conditions, set the validity period to 7200 seconds | Adapt to temporary storage needs for multi-node data transfer, and match the average duration of business processes |
| Node trigger condition | Trigger the validation node after the red line drawing file upload and parsing are completed | Match the pre-validation logic of the land pre-examination process, and ensure data integrity |
| Parameter validation rules | Follow the compliance interval determined by on-site measurement, with the unit of hectare | Prevent non-compliant projects from entering subsequent processes |
nodeId | Bind the unique identifier of the ecological protection red line data validation node | Ensure accurate cross-data-source node calls, and avoid chaotic node execution |
| AI chat component configuration | Associate the parameter mapping items in variables | Support business personnel to adjust the generation parameters of the large model, and adapt to different review scenarios |
Three Common Misconfigurations
- Uploaded project files cannot pass links to workflow parameters. The symptom is that the input fields of workflow nodes display as empty, and there are no transfer records of file URLs in the logs. The cause is that the binding rule between the output of the file upload node and the workflow input items is not configured in
variables, resulting in incorrect parameter mapping. - Global variable modifications cause node errors with empty input. The symptom is that an "Input parameter is empty" error prompt pops up when the node executes, and the debug interface shows that the global variable remains at the initial value. The cause is that the real-time synchronization switch for global variables is not enabled, subsequent nodes call the cached initial value, and non-empty validation is not configured.
- The AI chat component cannot set the temperature parameter. The symptom is that when selecting the variable reference method to specify the large model, there is no parameter configuration entry in the interface, and the generation logic cannot be adjusted. The cause is that the mapping configuration for the temperature parameter is not added in
variables, causing the component to fail to recognize adjustable parameter items.
How to Confirm Proper Configuration
- Upload a test red line drawing file, check whether the workflow automatically triggers the validation node and pulls the control data of the corresponding project
- Modify the control indicator value of the global variable, verify whether subsequent nodes can obtain the updated parameter value
- Call the AI chat component, adjust the generation parameters of the large model through variable reference configuration, and confirm that the configuration takes effect
- Input land scale data that exceeds the compliance range, trigger the parameter validation rule, and verify whether the corresponding error prompt pops up
The values provided here are common starting points, and should be evaluated 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.