What This Category of Data Looks Like
The data for this category comes from slab or billet batches, heating furnaces, mill automation control systems, online thickness and width measuring equipment, surface inspection systems, and MES systems. Daily documents include rolling plans, heating schedules, rolling regulations, shape and thickness records, surface quality reports, and quality certificates. Fields include batch number, heating temperature, rolling speed, shape deviation value, defect type, etc. Units include degrees Celsius, meters per second, millimeters, etc. Data update frequency is per coil or per billet; some key operating parameters are synchronized in real time.
What Constraints Do These Characteristics Impose on Workflow Orchestration
Data for this category is generated and updated in per-coil or per-billet dimensions. Workflow triggers must be bound to exclusive events for rolling batches, using trigger-based execution. Structured process and quality inspection parameters require workflow variables to accurately map to batch-specific fields, to avoid mixing data across batches. Equipment inspection and maintenance must be linked to specific production equipment, requiring workflow nodes to be bound to the corresponding equipment ID identifiers. Sufficient interface call time must be reserved to adapt to delays from multi-system collaboration. Errors can lead to consequences such as steel coil downgrading or equipment damage, so workflow configuration must include a failure retry mechanism to reduce the risk of abnormal interruptions.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
variables | Bind the slab number, heating temperature, and mill model of the current rolling batch | This category of data is generated per coil or per billet, so batch-specific parameters must be linked to avoid data mixing |
nodeId | Bind the corresponding node IDs of heating furnaces, mills, and surface inspection equipment | Equipment inspection must accurately link to actual on-site production equipment to ensure maintenance Q&A targets the correct objects |
| Trigger timing | Trigger after the inspection task for a rolling batch is completed | Matches the per-coil or per-billet data update frequency to avoid unnecessary execution |
| Retry count and interval | 3 retries, 120-second interval | Temporary equipment abnormalities can be recovered via retries, reducing the risk of single-call failure |
| Timeout threshold | 600 seconds | Adapts to interface response times across MES and equipment systems, avoiding interface interruption errors |
| Context scope | Only include process and quality inspection data for the current batch, limit to 800-1200 characters, calibrated via actual testing | Narrow the retrieval scope to improve the accuracy of maintenance Q&A |
Three Common Mistakes
- Phenomenon: Workflow nodes return a Premature close error. Cause: No reasonable timeout threshold configured, causing interface response timeouts and interruptions.
- Phenomenon:
variablesoutput from nodes cannot be correctly written to external systems. Cause: No exclusive fields for the current rolling batch bound, leading to mismatched variable mapping. - Phenomenon: Workflow calls to search tools return no valid results. Cause: No limitation of the search scope to process documents and equipment manuals for this category, leading to retrieval results unrelated to the scenario.
How to Confirm Proper Configuration
The configurations on this page apply to FastGPT v4.17.0.
- Check whether the
variablesconfiguration includes exclusive fields such as the slab number and heating temperature of the current rolling batch. - Verify that
nodeIdcorresponds to actual on-site production equipment nodes such as heating furnaces and mills. - Trigger a test workflow to confirm that it executes after the inspection task for a rolling batch is completed.
- Simulate cross-system interface calls to check that the timeout threshold setting meets system response time requirements.
The values provided are common starting points and should be measured against the reader's own samples.