What the data for this category looks like
Data sources for internal office work in coal mining production include underground IoT sensors, ground dispatching management systems, employee attendance terminals, and safety inspection ledgers. Data update rhythms fall into three categories:
- Underground equipment operating parameters are updated in real time
- Shift inspection records are uploaded in batches after the shift ends
- Employee attendance and energy consumption data are summarized at midnight daily
Single data entries include the following fields: unique device identifier, deployment location, collection time, parameter value, and associated work order ID. Field units are as follows: gas concentration (%), operating duration (hours), inspection times (times), energy consumption (kilowatt-hours).
What constraints these characteristics impose on the "workflow orchestration" link
Real-time sensor data from underground areas requires that workflow trigger delay stays within a reasonable range. Failure to do so prevents timely response to safety warnings. Field formats of multi-source heterogeneous data vary. For example, UTC timestamps from the dispatching system do not match the local time format of inspection ledgers. This requires a pre-workflow data standardization node. Batch-generated inspection records and employee attendance data from each shift need to be associated and matched. This requires workflows to support batch data processing nodes. After high-priority safety parameters are triggered, non-critical nodes must be skipped to enter the exception handling branch directly. This prevents process blocking that disrupts production progress.
How to set the configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
variables | Preset gas concentration threshold of 1.0%, employee shift information | Covers common business requirements for safety parameters and attendance data in coal mining production |
| Trigger node type | Trigger based on device exception thresholds | Matches the update rhythm of real-time data from underground sensors, avoids wasting resources from fixed polling |
| Batch processing node limit | 30-50 items per batch | Adapts to the single batch size of shift inspection records, avoids node timeouts |
| Node timeout duration | 240-300 seconds | Covers network delay for underground data transmission and time spent on security verification |
| Variable reference location | Target field in workflow body | Ensures parameters are passed to the corresponding processing link, complies with FastGPT variable calling rules |
Three common errors
- A workflow throws an error directly in the tool call phase after starting, returning a parameter missing prompt. The cause is that associated device ID parameters are not configured in
variables, causing the target data to fail to be read during tool calls. - Global variables do not update as expected, and subsequent nodes cannot read the latest values. The cause is that the variable assignment node is not bound to the correct
nodeId, causing the update operation to not trigger. - A parsing error occurs when attempting to write global variables directly into the workflow body. The cause is that variable references are not wrapped according to the workflow body field rules, causing parameters to fail to be correctly identified.
How to confirm the configuration is complete
- Manually trigger a simulated abnormal parameter data, check whether the workflow enters the exception handling branch according to the preset settings.
- View the
variablesconfiguration panel, confirm that the values of all preset parameters match business requirements. - Submit batch test data, verify whether the number of items processed by the node matches the preset limit.
- Check the logs of the tool call node, confirm that the passed parameter fields match expectations.
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.