What the data for this category looks like
Data sources include customer color swatches, formula libraries, dye chemical batch records, dye vat operation data, colorimeter test results, setting machine process parameters, and environmental monitoring logs. Data updates are triggered on a per-batch, per-lot, or per-shift basis. Single-batch data includes dyeing formulas, chemical dosing details, dye vat temperature curves, sample confirmation sheets, color difference reports, setting process parameters, and wastewater discharge records. Fields include dye vat operating temperature (℃), chemical dosing quantity (kg), color difference value (ΔE), process duration (hours), and more. All data is generated and archived in real time alongside the processing batch.
What constraints these characteristics impose on workflow orchestration
The independent data per batch and per lot requires the workflow to bind the exclusive dataset of the current work order, to avoid mixing data across batches. The batch-specific nature of wet chemical processing means the workflow must link dynamic documents such as dyeing formulas and dye vat curves, and read the latest colorimeter results and color difference reports in real time. Equipment troubleshooting involves complex process parameter adjustments, so the workflow must support sequential invocation of multiple nodes and result verification. Errors directly lead to full batch scrap or environmental non-compliance, so the workflow must be configured with failure retry and exception alert mechanisms, and adapt to the exclusive parameter formats of different equipment.
How to set configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Variable binding scope | Bind variables associated with the current work order | Ensure only exclusive data for the current processing batch is called, to avoid cross-batch interference |
| Node trigger condition | Trigger based on device data updates bound by nodeId | Match the real-time data update rhythm of equipment such as dye vats and colorimeters |
| Context length limit | 800–1200 characters | Adapt to the single-section content length of process documents such as dyeing formulas and dye vat curves |
| Streaming output switch | Enabled | Support real-time troubleshooting step feedback for equipment maintenance Q&A |
| Retry count | 3 times | Cover temporary parameter reading failures that may occur during equipment troubleshooting |
| Timeout threshold | 600 seconds | Adapt to the processing duration of complex formula correction and process adjustment |
| Concurrency limit | First 10 entries | Match the conventional number of processing batches per shift in dyeing mills |
Three common mistakes
- Phenomenon: When the code execution module calls an external model, streaming output cannot be achieved. Cause: The streaming return parameter in
variablesis not correctly bound, and the data flow link between nodes is not configured. - Phenomenon: After the AI Q&A node receives input from the text splicing node, the returned result lacks variable content. Cause: The output of the text splicing node does not carry the context fields required by
variables, and the parameter mapping configuration between nodes is incorrect. - Phenomenon: When the workflow is called concurrently, the front-end interface enters an unresponsive state. Cause: The workflow is not configured with concurrency limits, and no fallback processing mechanism for node timeouts is set.
How to confirm the configuration is complete
- Trigger a single-batch workflow test, and check whether the output of the text splicing node includes all bound
variablesfields. - Call the FastGPT v4.17.0 API and pass test batch data, and verify whether the expected variable content is included in the returned result.
- Simulate concurrent calls of multiple batches, observe the operation logs of workflow nodes, and confirm that there are no unhandled timeouts or abnormal error reports.
- View the real-time operation interface of the workflow, and confirm that the streaming output fragments of the code execution module are returned in sequence.
The values provided are common starting points and should be measured against the reader's own samples.