What Data for This Category Includes
Data sources for this category include raw material batch ledgers, production MES systems, online testing equipment for slurry and coating, workshop dew point monitoring devices, formation and grading cabinets, and laboratory safety testing equipment. Data update cycles cover per-batch production, single cell output, and real-time operating conditions. Daily documents include batching records, coating and calendering parameter tables, winding/laminating process ledgers, liquid injection records, formation and grading data reports, OCV and safety test reports. Fields include slurry solid content, coating areal density, dew point value, formation curve parameters, and some parameters have dedicated units.
Constraints Imposed by These Characteristics on Workflow Orchestration
Data for this category has strong real-time performance, is multi-source heterogeneous, and has a wide range of update frequencies. Workflow triggers must be bound to real-time operating condition nodes. Fixed periodic scheduling cannot meet the real-time data requirements of this scenario. The risk of field conflicts caused by multi-source data requires that input for variables in workflows must undergo field validation and normalization. Electrochemical systems are sensitive to environmental parameters. The workflow for inspection and maintenance Q&A must first associate core data such as dew point and foreign object detection, and limit context length to ensure response efficiency. Since error consequences involve battery safety, workflows must be configured with failure branches to trigger abnormal alerts and process rollbacks.
Configuration Settings
This configuration guide is based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables field validation | Configured to only allow input of preset lithium-ion cell manufacturing process fields | Avoid field conflicts from multi-source heterogeneous data, and ensure the validity of Q&A context |
| Context length limit | 800–1200 characters | Adapt to the compact characteristics of this category's data, and avoid overly long contexts slowing workflow response |
nodeId binding rules | Bind to real-time data nodes for dew point monitoring and formation and grading cabinets | Ensure that operating condition data called by inspection and maintenance Q&A is the latest real-time value |
| Failure branch trigger threshold | 300 seconds | Respond to workflow anomalies in a timely manner, and avoid safety risks in battery production links |
| Text splicing node configuration | Configure splicing order and interval based on actual measurements | Adapt to the splicing needs of multi-process documents, and ensure complete output content |
Three Common Configuration Mistakes
- Phenomenon: When the code running module calls an external model, streaming output cannot be implemented. Reason: No streaming output trigger rule is configured in the workflow, causing responses to return as full text.
- Phenomenon: The AI Q&A node cannot receive information passed by the text splicing node, while the text splicing node can normally output complete content. Reason: The output format of the text splicing node does not match the input requirements of the AI Q&A node, and no preset field identifier is added.
- Phenomenon: When calling the workflow via API, the variable section does not return content, but splicing works normally during debugging. Reason: Parameters for
variablesin the workflow are not passed according to preset fields in the API request, or there are case or spelling differences in field names.
How to Verify Proper Configuration
- Trigger the workflow, check whether real-time data nodes normally pull corresponding fields such as dew point and coating parameters, and verify that input fields for
variablesmatch the preset whitelist. - Initiate a test via API, check whether the returned content includes preset variable fields, and verify that the output of the text splicing node can be normally received by the AI Q&A node.
- Simulate an abnormal scenario, confirm whether the failure branch triggers the alert process, and verify that the workflow's exception handling logic takes effect.
- Test the streaming output function, confirm that the output of the code running module can be passed to subsequent nodes in a streaming manner.
The values provided here are common starting points, and should be evaluated against in-house operational samples.