What the data for this category looks like
Typical entities in this category are manufacturers of battery packs, energy storage cabinets, and BMS systems. Daily managed documents include cell grouping rules, BOM lists, welding and torque records, BMS flashing logs, insulation and voltage withstand reports, EOL test data, and system configuration lists. Data sources include cell capacity grading data, MES systems, welding equipment, torque tools, BMS software version records, and EOL test benches. A unique record is generated for each pack or cabinet, and corresponding configuration data is updated each time the BMS software version changes.
What constraints these characteristics impose on workflow orchestration
Data sources for this category are scattered and linked to individual product batches. Workflow orchestration must bind a unique batch identifier to ensure accurate data traceability. Collecting multi-source heterogeneous data requires configuring multiple parallel nodes in the workflow to pull records from different systems, preventing data omissions. High-frequency updates per pack or cabinet require the workflow to support triggering execution by batch, adapting to fast-paced production rhythms. Complex document structures require the workflow to pass different types of fields via variables, ensuring information such as grouping rules and test data can be called across nodes. Severe consequences of errors require the workflow to configure failure retry and exception capture mechanisms, preventing defective products from flowing to downstream links.
How to set up the configuration
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
| Trigger Node | Trigger by batch ID in variables | Match the unique identifier of each pack or cabinet, ensure data is strictly bound to the batch |
| Multi-source Data Source Nodes | Bind multi-source data including MES, welding equipment, EOL test benches, etc. | Cover full-link quality records, avoid missing traceability data |
| Retry Mechanism | Retry 2 times after failure, 60-second interval | Respond to temporary device disconnections or interface jitter, improve execution success rate |
| Variable Transfer Rule | Transfer test reports associated with nodeId | Bind complete EOL test and configuration data for a single pack |
| Knowledge Base Retrieval Parameters | Pass batch ID and cell parameters in variables | Accurately match grouping rules and test standards for the corresponding batch |
| Single Retrieval Limit | Top 3 entries | Limit the number of retrieval results, avoid node overload |
Three Common Mistakes
- Phenomenon: Tool call module returns empty results or fails to match relevant documents. Cause: The batch ID in
variablesis not passed as a retrieval parameter to the knowledge base node, leading to overly broad retrieval scope or matching errors. - Phenomenon: Code execution module throws ModuleNotFoundError. Cause: Dependency libraries are not installed in the global Python environment of the FastGPT v4.17.0 container.
- Phenomenon: No retry action after workflow execution fails. Cause: Failure retry trigger conditions and interval duration are not configured, relying solely on single-execution logic.
How to Confirm Proper Configuration
- Manually trigger the workflow bound to single-batch cell data, check if the batch ID passed in
variablesis correctly linked to all subsequent data nodes. - View workflow execution logs, confirm that all data source nodes have successfully pulled records of the corresponding type, with no missing fields.
- Simulate an abnormal scenario of tool call timeout, verify whether the retry mechanism starts as configured.
- Check the dependency library configuration of the code execution module, confirm it matches the Python version of the FastGPT v4.17.0 container environment.
The values provided here are common starting points, and should be evaluated against the relevant samples for deployment.