Data Profile for This Category
Data for this category comes primarily from raw fiber inspection reports, ERP systems, real-time collected data from spinning and loom equipment, outputs from evenness testers and fabric inspection machines, and order specification documents. Daily generated documents include cotton blending or batching lists, process setup sheets, equipment operation records, yarn inspection forms, grey fabric inspection forms, and defect registration ledgers. Data updates occur per batch of raw materials, each production shift, and each piece of fabric produced. Fields include fiber indicators, spinning twist, weaving structure parameters, defect types, piece numbers, and more. Units adjust based on the field requirements of the corresponding documents.
Constraints for Workflow Orchestration
Multiple scattered data sources with inconsistent update rhythms require workflows to support cross-source data aggregation and adaptation to multiple trigger modes. Diverse document formats and structured fields require workflows to include built-in general text parsing and field mapping components. Strongly linked business logic between process parameters and inspection results requires retaining complete field mapping rules when passing parameters between nodes, to avoid data misalignment. High-frequency data generated per batch and per piece of fabric requires workflows to support batch and single-instance parallel processing modes.
Configuration Settings
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
variables | Create variable groups by raw material, spinning, weaving, and inspection links. Each group contains no more than 15 fields | Data for this category is stored classified by production link. Grouped variables avoid field conflicts during cross-node parameter passing |
| Trigger Node | Trigger based on three scenarios: single piece of fabric completes inspection, end of each production shift, and raw material batch warehousing | Matches the update rhythm of data per piece of fabric, per shift, per batch, covering audit needs across the entire production process |
| Result Output Node | Only call after the downstream text splicing node completes processing | Inspection results and process parameters must be merged before unified output, to avoid leakage of scattered data in intermediate links |
| Error Handling Node | Set timeout threshold to 600 seconds, trigger alarm push on exception | Equipment collected data delay usually does not exceed 10 minutes, so timeout allows rapid localization of collection or transmission faults |
nodeId | Configure unique identifiers for each data source access node | When multiple data sources access in parallel, identifiers can distinguish data sources, avoiding confusion of collected data from different devices |
Three Common Misconfigurations
- Symptom: Workflow runs with prompt "Cannot convert undefined or null to object". Cause: No null value check is configured for fields in
variables. When a batch of raw material data is missing, parameters passed between nodes contain undefined values. - Symptom: Output of the AI chat node is displayed directly on the page, unable to be processed by subsequent components before output. Cause: The instant output configuration item of the AI chat node was not disabled. By default, AI results are pushed directly to the page dialog box, and output permissions are not transferred.
- Symptom: Unable to pass historical inspection records as variables into workflow nodes. Cause: No mapping field for historical records was added in
variablesconfiguration, and no historical data interface was bound via node parameters. This results in no corresponding callable options in the variable pool.
How to Verify Proper Configuration
The workflow debugging tool included in FastGPT v4.17.0 can be used to manually trigger the inspection workflow for a single piece of fabric. Confirm that parameters passed between each node match the fields in the corresponding documents. A test scenario with a missing field can be simulated. Check whether the error handling node triggers the alarm action as expected. The instant output switch of the AI chat node can be disabled. Run the workflow, and confirm that only the spliced result is displayed in the specified location. The historical inspection record interface can be called. Check whether the workflow correctly reads and binds to the corresponding fields in variables.
The values provided here are common starting points, and should be evaluated against samples from actual deployment scenarios.