What This Category of Data Looks Like
Data sources for this category include engineering orders, sand, cement and admixture inspection records, aggregate moisture content measurement data, mixing plant control system logs, and project laboratory ledgers. Data update rhythm follows per vehicle production batch, per raw material delivery, and daily summary. Daily documents include mix proportion notices, opening identification forms, production and transport slips, raw material inspection reports, test block test records, and strength verification reports. These documents contain fields such as raw material batch number, concrete batching dosage per cubic meter, transport duration, and test block compressive strength values. Units include cubic meters, kilograms, hours, and others. Data formats include structured logs and unstructured scanned documents.
Constraints on Workflow Orchestration From These Data Characteristics
Per-vehicle high-frequency updates require workflows to support real-time trigger nodes, to avoid batch processing delays. Heterogeneous data from multiple sources requires workflow configuration of cross-node data format conversion steps, to adapt to structured logs from mixing plant control systems and paper scanned documents from laboratories. Daily summary batch tasks need segmented processing thresholds set, to prevent single request overload. Strongly related fields such as mix proportion and test block strength need verification nodes configured in the workflow, to ensure cross-document data consistency. Time window associated data for transport and pouring needs inter-node timing scheduling, to avoid cross-department data disconnection.
How to Set Configurations
Configuration references for FastGPT v4.17.0 are as follows:
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Variable Grouping | Divide into 4 groups: raw materials, production, transport, testing | Match the classification logic of daily documents, facilitate subsequent verification and summary |
| Trigger Node | Trigger based on mixing plant discharge completion signal | Match the update rhythm of per-vehicle production, avoid unnecessary execution |
| Timeout Setting | 600 seconds | Adapt to cross-department data pull delays across laboratories and mixing plants |
| Batch Split Threshold | 20 data entries per group | Adapt to daily summary batch processing requirements, prevent single request overload |
variables Binding | Bind per-vehicle production batch number and raw material batch number | Ensure data association consistency across different nodes |
nodeId Binding | Bind production verification node and summary node | Ensure node call order aligns with business logic |
Three Common Configuration Mistakes
- Symptom: Workflow node outputs empty content when processing image file streams returned by HTTP requests. Cause: No binary data parsing node is configured, and the file stream is directly attempted to be parsed as text format.
- Symptom: Workflow cannot respond to user selection requests to skip retrieval. Cause: No user selection switch variables are configured in
variables, and no conditional branch logic is configured for the corresponding nodes. - Symptom: Daily summary task times out and returns status code 504. Cause: No reasonable timeout threshold is set, and no batch split node is configured, leading to processing too many production batches in a single run.
How to Verify Correct Configuration
- Manually trigger the workflow for a single batch of production data, and check if each node's output matches the field content of the corresponding document
- Simulate abnormal data, and confirm if the verification node triggers an alert
- Configure a test entry for user selection variables, and verify if conditional branch nodes switch execution logic based on selection results
- View workflow run logs, and confirm that the time difference between trigger timing and mixing plant discharge completion signal meets expected standards
The values provided here are common starting points, and should be evaluated against the reader's own samples.