What the Data for This Category Looks Like
The core of the data for this category is the single vehicle VIN. Data sources include ERP/MES systems, VIN and configuration systems, supplier batch ledgers, workshop tightening tools, end-of-line (EOL) offline inspection lines, and quality traceability systems. Daily documents include production orders, VIN lists, assembly work instructions, tightening torque data, offline inspection reports, rework records, and consistency release documents.
Data updates are performed per vehicle. Key tightening and inspection data are synced in real time. Batch and order information is updated per shift. Fields include VIN code, part batch number, torque value (unit: newton meters), and inspection item number, among others.
How These Characteristics Impact Workflow Orchestration
Single-vehicle data centered on VIN requires workflows to bind to a single VIN context to avoid data cross-interference during mixed-line production. Real-time synced tightening and inspection data requires workflow nodes to support low-latency triggering, preventing audit deviations caused by data lag. Multi-source heterogeneous documents such as work instructions and inspection reports require built-in RAG retrieval in workflows to adapt to different document structures.
Batch and order information updated per shift requires workflows to support scheduled pulling of the latest configurations, while handling cross-station rework record association to ensure audits cover full-process defects. Additionally, risks of line stop and recall caused by mismatched or missing parts, vehicles unable to go offline, or safety defects require workflows to have built-in strict field verification and exception branch processing logic.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow trigger mode | Triggered by VIN creation event | Matches the single-vehicle core context to avoid mixed-line data cross-interference |
| RAG retrieval single recall limit | Top 8 entries | Adapts to the retrieval needs of hundreds of configuration items per vehicle, balancing accuracy and latency |
Global variable variables binding | Bind VIN, torque data, part batch number | Pass core production fields across nodes to ensure context consistency |
| Plugin call timeout | 300 seconds | Adapts to time requirements for multi-source data pulling and tool calls |
| Context window size | 8000 token | Adapts to context needs for multi-document retrieval |
Core node nodeId binding | Bind the audit main node ID | Ensure workflow node execution order aligns with production stations |
Three Common Configuration Mistakes
- Workflow prompt signature time does not match actual production time. The symptom is that the audit report signature time lags behind or precedes the current production time. The cause is failure to correctly bind the current time variable provided by the platform, using fixed time or expired data from other nodes.
- Identity authentication options cannot be found in workflow sharing configuration. The symptom is no authentication switch entry on the configuration page. The cause is failure to upgrade to FastGPT v4.17.0; older versions do not include this configuration item.
- Individual plugin export fails. The symptom is the export popup displaying "Resource Unavailable". The cause is incorrect configuration of the plugin's permission scope, or the external service relied on by the plugin has not completed connectivity verification.
How to Verify Correct Configuration
- Trigger the workflow bound to a test VIN, check that the prompt signature time matches the current system time.
- Enter the workflow sharing configuration interface, confirm that an identity authentication configuration entry exists.
- Call the configured plugin, verify that the returned result matches the production data of the corresponding VIN.
- Attempt to export the individual plugin, confirm that the export process has no errors and the file can be obtained normally.
The values provided are common starting points and should be measured against the reader's own samples.