What Data for This Category Looks Like
Data for this category mainly comes from slab or billet batches, heating furnaces, mill automation control systems, online thickness and width measuring equipment, surface inspection devices, and MES systems. Update frequency is real-time per coil or per steel bundle. Daily documents include rolling plans, heating schedules, rolling specifications, shape and thickness records, surface quality reports, and quality certificates. Fields include batch number, heating temperature, rolling reduction, shape deviation value, surface defect level, and others. Units include degrees Celsius, millimeters, square meters, tons, and others.
What Constraints Do These Characteristics Impose on the "Workflow Orchestration" Link
Real-time batch-level data requires workflow triggering rhythm to match the production rhythm of each coil or steel bundle. Do not use fixed-period triggering. Multiple heterogeneous data sources require data alignment nodes in the orchestration. Ensure correct association of heating, rolling, and inspection data for the same batch. Large-volume shape and thickness record data carries context overflow risks. Use split nodes to process segmented data. Documents such as quality certificates must be bound to the inspection results of the corresponding batch. The workflow must support cross-node data association. Errors in this category carry severe consequences. Add pre-check nodes to the orchestration to intercept abnormal parameters in advance.
How to Configure the Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
variables auto-update rule | Bind to batch node completion event | Data for this category updates in real time per coil or bundle. Synchronize and update global variables only after the corresponding batch data collection is complete. |
| Split block length | 800–1200 characters | Single-coil shape and thickness record data has a large volume. Split blocks adapt to context length limits. |
| Single search limit | Top 10 entries | A reasonable number to match batch-level associated data, avoiding redundant information interfering with analysis. |
| Maximum context | 16000 token | Adapts to the total length after merging multiple source data. Adjust based on actual measurements. |
nodeId binding rule | Bind to the node ID of the corresponding mill batch | Ensure workflow nodes correspond one-to-one with actual production equipment batches, avoiding data confusion. |
| Timeout threshold | 300 seconds | Covers the reasonable time required for single-batch data collection and verification, preventing process interruptions. |
Three Common Mistakes
- Phenomenon: Global
variablesvalues do not update with batch data, and interface display fields are empty. Cause: The variable update rule is not bound to the completion event of the corresponding batch node, only fixed-period triggering is configured. - Phenomenon: The workflow prompts that the context length exceeds the limit during execution, and analysis cannot be completed. Cause: Reasonable split block length and maximum context parameters are not set, and large-volume shape record data is not split for processing.
- Phenomenon: Surface inspection images cannot trigger the visual analysis process, and transcoded data is not passed to subsequent nodes. Cause: The image transcoding node is not configured after the surface inspection node, and the triggering timing does not match the production process.
How to Confirm the Configuration Is Correct
- Review the
variablesconfiguration item of the workflow, confirm that the auto-update rule is bound to the completion event of the corresponding batch node. - Test single-batch data input, check whether the context length is adaptable, adjust the split block length and maximum context parameters to meet requirements.
- Verify the image transcoding process after the surface inspection node, confirm that images can be transcoded normally and trigger subsequent visual analysis nodes.
- Confirm that all nodes of FastGPT v4.17.0 are correctly associated, with no missing or incorrectly bound node IDs.
The values provided are common starting points and should be evaluated against the reader's own samples.