What the data for this category looks like
Data sources for inspection and process record data in electric motor and generator manufacturing include electromagnetic design data, copper wire and insulating material batch information, winding equipment operation data, balancing machine test data, electrical test bench test results, and production work order data from ERP/MES systems. Data is updated once per individual product or batch, and synchronized whenever a formal design change is made. Daily documents include winding process cards, insulation treatment records, dynamic balance reports, voltage withstand insulation test reports, type and factory inspection test reports. Core fields include winding turns, insulation class, vibration amplitude, temperature rise values, test voltage, and other related metrics.
What constraints these characteristics impose on the "workflow orchestration" link
The multi-source heterogeneous data sources for this category require workflows to support integration with multiple types of device interfaces and system data sources, to avoid data silos. The update rhythm of once per batch or individual product requires workflows to support triggering via production work orders, to adapt to both batch and single-piece production scenarios. The need for multi-field validation of core documents requires workflows to have built-in field integrity check rules, to ensure that key information such as winding turns and insulation parameters is not missing. At the same time, abnormal results from high-voltage and temperature rise tests need to quickly trigger branch processing, and the problem of reference resolution in inspection record audits must be addressed to avoid result deviations caused by ambiguous expressions.
How to set up the configuration
This setup is adapted for FastGPT v4.17.0:
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Bind batch number, equipment ID, test number | Each individual product or batch in motor manufacturing has a unique identifier, which needs to be passed across nodes to avoid confusion |
| Question Optimization | Enable, associate the last 3 rounds of conversation records | Resolve the reference resolution problem in inspection record audits, and parse ambiguous expressions |
| Node Trigger Rules | Trigger based on the completion status of test nodes bound to nodeId | Adapt to the sequential execution logic of processes such as winding, insulation, and testing |
| Context Window | Top 2000 tokens | Cover the validation requirements for multiple fields in winding process cards, test reports, and other documents |
| Retry Threshold | 3 times | Address scenarios with temporary abnormalities in equipment data, and reduce misjudgments |
| Timeout Setting | 600 seconds | Adapt to the longer time-consuming scenarios of high-voltage and temperature rise tests |
Three Common Misconfigurations
- Phenomenon: The final workflow output includes results from intermediate AI conversation nodes. Cause: The output hiding rule for intermediate nodes is not configured, causing node results to be included in the global output link.
- Phenomenon: Knowledge base search returns irrelevant results, and the reference resolution problem cannot be solved. Cause: The question optimization configuration is not enabled, and historical conversation records are not associated, leading to incorrect parsing of ambiguous expressions.
- Phenomenon: Workflow startup fails, prompting missing required parameters. Cause: Global
variablesare not configured in advance, causing fields such as batch numbers and equipment IDs passed across nodes to be empty.
How to Confirm Proper Configuration
- Trigger the inspection workflow for a single electric motor, check whether the global variables are automatically bound to the corresponding batch number, equipment ID, and test number.
- Input an ambiguous question, such as "Does the winding turns of this unit meet the requirements", confirm that the system can associate the winding information from historical conversations and return correct validation results.
- Pause the intermediate AI conversation node, then trigger the workflow, confirm that the final output does not include the conversation results from intermediate nodes.
- Simulate a scenario with temporary abnormalities in equipment data, confirm that the workflow triggers the retry mechanism, and triggers the exception handling branch after reaching the retry threshold.
The values provided here are common starting points and should be evaluated against the reader's own samples.