This document applies to the workflow orchestration feature of FastGPT v4.17.0.
What Data Looks Like for This Category
Data sources for the coating and painting category include coating supplier TDS/SDS documents, spray booth and oven operating data, data collected by colorimeters and film thickness gauges, MES system work orders and quality inspection records. Update rhythms fall into three categories: documents generated per paint batch, process and environmental protection logs updated per production shift, and continuous collection of equipment data. Daily documents include coating process cards, paint batch sheets, paint mixing records, film thickness and color difference reports, oven curves, defect repair sheets, and VOC logs. Fields cover coating thickness, color difference value, oven temperature, paint viscosity, VOC concentration, and other items, with dedicated units corresponding to different collection nodes.
What Constraints These Characteristics Impose on Workflow Orchestration
Data for this category is scattered and has varied update rhythms. Workflows must support multiple data source connections and mixed trigger rules. Data formats vary widely across sources. Multiple node types must be supported: structured database queries, unstructured document parsing, real-time equipment data collection. The most time-consuming processes—work step matching, color matching control, defect analysis—require workflows to orchestrate parallel nodes to split tasks. This prevents serial blocking. Errors directly impact product appearance and compliance. Failure retry and exception alert nodes must be configured to ensure process stability.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Trigger Mode | Trigger per production batch + real-time equipment data completion | Matches the batch rhythm of coating production and real-time parameter verification needs |
| Parallel Node Limit | 3-5 parallel nodes | Adapts to the business scenario of multi-station parallel processing in coating processes, avoids resource overload |
| Failure Retry Count | 2 retries, 60-second interval | Addresses equipment collection fluctuations or temporary network jitter, reduces process interruption probability |
| Alert Trigger Condition | Trigger when abnormal parameters are detected in variables | Based on preset quality inspection threshold variables, quickly locates quality or compliance risks |
| Single Node Timeout Setting | 120 seconds | Adapts to processing needs of time-consuming links such as oven curve analysis and color difference calculation |
| Variable Cache Duration | 3600 seconds | Caches batch-level process parameters, avoids repeated reading of data sources to improve efficiency |
Three Common Misconfigurations
- Symptom: DB nodes return empty JSON or unstructured data with unmapped fields after querying the database. This data cannot be directly used for subsequent quality inspection judgments. Cause: The specified fields of the query result are not mapped to workflow variables via
variables, resulting in incorrect data transfer. - Symptom: When MCP nodes are called concurrently 2-3 times per second, empty results or no response content are returned. Cause: Node concurrency limits and timeout parameters are not set. System resources are exhausted under high concurrency, leading to request failures.
- Symptom: After a loop node splices multiple batch quality inspection results, Feishu Webhook calls send no messages. Cause: The spliced text length is not verified, or context data corresponding to the
nodeIdis not correctly passed, resulting in abnormal request parameters.
How to Verify Proper Configuration
- Simulate a single production batch to trigger the workflow. Check if each node's output matches the parameters in the coating process card and paint batch sheet.
- Increase concurrent call frequency to 2-3 times per second. Verify that MCP nodes return no empty results or abnormal status codes.
- Construct abnormal parameters that exceed thresholds, such as excessive film thickness or exceeded VOC concentration. Check if alert nodes trigger normally.
- View workflow run logs. Confirm that all fields in
variablesare correctly mapped and passed to subsequent nodes.
The values provided are common starting points and should be measured against the reader's own samples.