What the data for this category looks like
Production data for steel rolling and finishing comes from steel slab or billet batches, heating furnaces, mill automation control systems, online thickness and width measuring equipment, surface inspection devices, and MES systems. Daily generated documents include rolling plans, heating schedules, rolling specifications, plate shape and thickness records, surface quality reports, and quality certificates. Data updates occur in real time per coil or per piece. Core fields include coil number, heating temperature, rolling speed, thickness deviation, and surface defect level. Corresponding units are batch identifier, degrees Celsius, meters per second, millimeters, and defect level.
What constraints do these characteristics impose on workflow orchestration
Multi-source heterogeneous real-time data requires the workflow to connect multiple nodes to pull data across systems. This avoids ledger aggregation errors caused by data delays. The precise update rhythm per coil or piece requires the workflow to bind the coil number as the core global variable. This enables independent aggregation and summarization of single-batch production data. Diverse document formats and field structures require the workflow to configure format conversion nodes. This unifies the output formats of documents from different sources. Rich core production fields require the workflow to add a parameter verification link. This filters missing or abnormal data, preventing errors in subsequent report generation.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Variable Setup | Bind coil number and batch ID as global variables | Aligns with the real-time data update logic per coil or piece, to avoid mixing data across batches |
| Node Verification Rules | Check the required field fill rate for the node corresponding to nodeId | Ensures core production data such as rolling temperature and thickness deviation is not missing |
| Data Pull Interval | 10–30 seconds | Adapts to the real-time data update frequency of online inspection equipment |
| Special Character Handling | Automatically escape double quotes and line breaks | Adapts to special formatted content in quality certificates and surface quality reports |
| Benchmark Test Trigger | Calibrate based on actual measurements | Used for model comparison and ablation experiments |
| Timeout Threshold | 600 seconds | Adapts to the time requirements of joint data pulling from multiple data sources |
Three Common Mistakes
- Symptom: Workflow debugging and verification fail, with a prompt to check whether nodes are correctly filled and whether connections are normal. Cause: Core
variablessuch as coil number are not bound to the input parameters of the corresponding nodes, causing the nodes to not trigger with valid data. - Symptom: Format confusion or garbled characters appear in the generated workflow report. Cause: Special character handling rules are not configured, and double quotes and line breaks in quality certificates and surface inspection records are not escaped.
- Symptom: Benchmark test runs have no results or the number of results does not meet expectations. Cause: The dataset range for test triggering is not clearly defined, and the parameter boundaries of the comparison experiment are not limited.
How to Confirm Proper Configuration
- Manually trigger the workflow, input test data for a known coil number, and check whether the ledger fields output by the nodes match the records in the MES system.
- Import a simulated surface quality report containing double quotes and line breaks, and check whether the generated report has no format confusion issues.
- Run the benchmark test, compare the ledger aggregation results under different configurations, and confirm that the coverage range of the test dataset meets business requirements.
- View the workflow operation logs in FastGPT v4.17.0, confirm that all nodes corresponding to
nodeIdhave completed data pulling and verification normally, with no timeout errors.
The values provided are common starting points and should be measured against the reader's own samples.