What data for this category looks like
Data for this category originates from slab or billet batches, heating furnaces, mill automation control systems, online thickness and width measuring equipment, surface inspection equipment, and manufacturing execution systems (MES). Data is generated in real time for each coil or individual steel product. Daily documents include rolling plans, heating schedules, rolling specifications, plate shape and thickness records, surface quality reports, and quality certificates. Core fields include batch number, thickness value (unit: mm), surface defect level, heating temperature (unit: ℃), rolling speed, and others. The number of associated documents per batch changes dynamically based on production scale.
What constraints these characteristics impose on workflow orchestration
Real-time data updates for each coil or individual product require workflows to support execution triggered per production batch, to avoid unnecessary runs. Multiple heterogeneous data sources require workflows to integrate outputs from different equipment and systems, with cross-node data mapping rules configured. The wide range of document types and fields requires workflows to have flexible text concatenation and field verification capabilities, to ensure complete and accurate input for the audit phase. The most time-consuming batch scheduling, parameter coordination, and defect determination steps require workflows to automate repetitive tasks, while reserving exception handling branches for emergencies including strip breakage or dimensional deviation.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
variables | Bind batch number, thickness threshold, defect level threshold | Match core rolling data fields, adapt to real-time update rhythm for each coil or individual product |
nodeId | Bind the output of the text concatenation node as the trigger source | Ensure subsequent audit processes start only after complete inspection and process record data is concatenated |
| Single recall limit | Top 8 entries | Match the typical number of associated documents per batch, avoid redundant data interfering with audits |
| Timeout threshold | 300 seconds | Adapt to average time spent pulling multi-source data and calling models |
| Text concatenation variable binding | Calibrate based on actual measurements | Adapt to document field combination logic across different production scenarios |
Three common mistakes
- Phenomenon: Workflows return empty results when calling knowledge base search. Cause: The knowledge base ID was not passed to the search node via
variables, so the retrieval scope is not bound to the exclusive data of the corresponding batch. - Phenomenon: Variables for text concatenation do not return normally during API calls. Cause: The global mapping of
variableswas not configured in the workflow, and it only takes effect during local debugging. - Phenomenon: The AI question answering node cannot receive input from the text concatenation node. Cause: The output format of the text concatenation node was not set to plain text, or the node connection order does not conform to workflow logic.
How to verify correct configuration
- Manually trigger the workflow, pass a test batch number, and check if the input and output of each node match the core fields of rolling data.
- Call the FastGPT v4.17.0 API to initiate a test request, and verify that all configured variable content is included in the returned results.
- Enable the streaming output switch, and check if real-time returned content concatenates inspection and process record data in the correct order.
- Check the binding relationship of
nodeId, and confirm that the connection logic between the trigger node and subsequent nodes conforms to the production process sequence.
The values provided are common starting points and should be measured against the reader's own samples.