What the data for this category looks like
Data for lithium-ion cell manufacturing processes and testing comes from raw material batch ledgers, production MES systems, slurry and coating testing equipment, workshop dew point monitoring devices, formation and grading cabinets, and laboratory safety testing equipment. Data update frequencies cover per production batch, single cell testing, and real-time working condition uploads. Daily documents include batching records, coating and rolling parameter tables, winding/laminating ledgers, liquid injection records, formation and grading data, OCV and safety test reports. Fields include batch identifiers, testing time, process parameter values, and equipment operating metrics. Units include meters per minute, megapascals, hours, and others.
What constraints do these characteristics impose on the "workflow orchestration" link
Multi-source real-time and batched data characteristics require workflows to associate documents and testing data from different links by production batch, to avoid cross-batch confusion. Electrochemical systems have high requirements for environmental parameters and process consistency, so workflows need to embed check nodes for dew point, foreign matter detection, and other checks to intercept abnormal parameters early. The large volume of data from the long-cycle formation and grading link requires workflows to support multi-document concatenated retrieval, while also adapting to low-latency calls for real-time data to avoid process Q&A lagging behind production progress.
Configuration Settings
All configurations below are based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Bind production batch number and cell model as core retrieval variables | Match the business logic of associating production data by batch, ensuring retrieval results accurately correspond to the current production link |
| Global variable configuration | Introduce dew point thresholds and slurry ratio reference parameters | Adapt to the strict check requirements for environmental parameters and process parameters in lithium-ion cell manufacturing |
| Workflow trigger node | Trigger based on real-time testing data updates | Match the real-time update frequency of each batch and single cell, avoiding data lag behind production progress |
| Failure retry count | 2–3 times | Respond to temporary fluctuations in testing equipment causing node call failures, reducing abnormal interruptions |
| Context window size | 800–1200 characters | Adapt to the concatenated length of single process documents and testing data, avoiding context overflow |
nodeId | Bind node IDs of core links such as coating, formation and grading | Accurately associate process specifications and testing data for corresponding links, narrowing the retrieval scope |
Three Common Mistakes
- Workflow API calls return status code 400, with a prompt that
variableswas not passed correctly. The cause is that the production batch number was not bound to the request parameters as a required variable, making it impossible to match process data for the corresponding batch. - Workflow prompt nodes do not load global variables, and retrieval results do not include dew point monitoring parameters. The cause is that the binding range of global variables was not specified in the configuration, and only single-node local variables were configured.
- Single nodes experience timeout errors when handling concurrent requests, returning status code 504. The cause is that multi-node deployment was not enabled, failing to adapt to the high-concurrency real-time data processing requirements of this category.
How to Confirm Proper Configuration
- Submit a simulated request carrying the
variablesparameter with a real production batch number, check if the returned results include coating and rolling parameters and formation and grading data for the corresponding batch. - View workflow node logs, confirm that global variables have been correctly loaded into the prompt template, with no empty field errors.
- Simulate concurrent requests for multiple batches, check if node response delays meet business requirements, and confirm that multi-node deployment configurations are active.
- Trigger an abnormal scenario such as simulating out-of-spec dew point data, check if failure retry and alert nodes are triggered normally.
The values provided are common starting points and should be measured against the reader's own samples.