What this category of data looks like
Business handling data for coal mining production mainly comes from underground sensor monitoring systems, production dispatch work order platforms, safety supervision ledgers, and equipment operation and maintenance logs. Data update rhythms are divided into real-time (such as underground equipment operating status) and shift/daily updates (such as daily production statistics, maintenance work orders). Single data entries usually include fields like job number, unique device identifier, job type, trigger time, current status, and associated approval node. Field units are mostly tons, units, and hours. The document structure has clear hierarchical levels, and core fields cannot be missing.
What constraints do these data characteristics impose on workflow orchestration
Real-time collected equipment operating status data requires that call delays between workflow nodes stay within a reasonable range. This prevents process lag that slows business response speed. Batch business data updated per shift or daily needs workflow support for parallel batch node processing. This improves overall processing efficiency. The requirement that core fields cannot be missing means a field verification link must be configured at the workflow start node. This filters out invalid business work orders with missing key information. Different job types correspond to differentiated approval and verification processes. This means workflows must support conditional branch configuration to automatically jump to corresponding processing nodes based on job type.
Reference only; verify against the target FastGPT release and deployment documentation.
How to set configurations
| Configuration Item | Recommended Practice | Basis for This Practice |
|---|---|---|
| Global Variables | Bind the job number and device ID fields preset in variables | Match the core fields of coal mining business data to ensure unified calling across all workflow nodes |
| Field Verification Rules | Verify whether the job type and trigger time fields exist | Avoid invalid work orders with missing core fields from entering the workflow |
| Conditional Branch Trigger | Jump to the corresponding approval node according to the job type | Adapt to differentiated handling processes for different job types |
| Batch Processing Node | Process up to 50 batches of work order data at a single time | Match the scale of batch business data updated by shift |
| Delay Threshold | Set to 60–300 seconds | Adapt to the response requirements of real-time data |
nodeId | Specify the unique identifier of the intermediate node | Precisely control the calling order of workflow nodes |
Three Common Misconfigurations
- Phenomenon: After the workflow runs, the output content of the intermediate AI dialogue node is included in the final result. Cause: No node output filtering rules are configured, and the return content of all nodes is integrated into the final output by default.
- Phenomenon: Referential content in historical conversations cannot be correctly identified in the business process, leading to query deviations. Cause: The question optimization configuration is not enabled, and historical conversation data is not associated with the current processing node.
- Phenomenon: The workflow cannot call preset global business fields. Cause: The field mapping relationship of global variables is not correctly bound in
variables.
How to verify correct configuration
- Trigger a test business work order and check whether the core fields pass the verification link.
- Switch between different job types and confirm that the workflow automatically jumps to the corresponding processing node.
- After running the workflow, check the final output content and confirm that only the results of the target node are retained.
- Call the global variable node and confirm that preset business field data can be obtained.
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.