What This Category’s Data Looks Like
Inspection and process record data for lithium-ion cell manufacturing comes from MES production systems, slurry and coating testing equipment, dew point monitors, formation and grading cabinets, and laboratory OCV and safety test reports. Data generates in real time per battery batch and individual finished cell.
Daily documents include batching records, coating and rolling parameters, winding or stacking records, liquid injection records, formation and grading data, and OCV test reports. Fields include batch number, cell ID, slurry solid content, coating areal density, compacted density, dew point temperature, internal resistance, capacity, and more. Each document links to a unique raw material batch and cell identifier.
What Constraints Do These Characteristics Impose on Workflow Orchestration
This category’s data has high real-time requirements and generates per batch or individual cell. Workflows must use unique identifiers for triggering to avoid mixing audit processes across different batches.
Multi-source data comes from different devices and systems. Configure field mapping rules between nodes to ensure inspection records in different formats can be uniformly linked.
Electrochemical systems are sensitive to moisture and cleanliness. Add threshold check nodes to the workflow to ensure environmental and process parameters meet requirements.
Data consistency directly affects battery safety and service life. Configure a backtracking and alert mechanism for failed branches to prevent non-compliant records from entering subsequent stages.
How to Configure
These configurations are based on FastGPT v4.17.0. Details are listed below:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Single Search Limit | Top 5 | Lithium-ion cell single-batch inspection records have numerous fields. Excessive search results consume context and reduce audit efficiency |
| Variable Storage Method | Globally persistent variables | Each batch’s cell identifier must be retained across nodes to prevent temporary variables from being reset during node runtime |
| Node Trigger Rule | Trigger by binding batch ID to nodeId | Data updates per batch. Ensure each batch’s audit process stays independent and free from interference from other batches |
| Failure Retry Count | 2 retries, 60-second interval | Temporary network fluctuations may occur when pulling device data. Retries reduce unnecessary process interruptions |
| Node Timeout Period | 300 seconds | Pulling and auditing single-batch inspection data takes time. This setting prevents premature timeout interruptions to the workflow |
| Context Length | 800–1200 token | Single-batch inspection records have moderate content volume. Appropriate context ensures complete audit logic without exceeding limits |
Three Common Misconfigurations
- Phenomenon: Knowledge base search nodes in the workflow return empty results, or errors occur when configuring application variables. Cause: The batch ID in
variablesis not correctly bound to the knowledge base retrieval conditions, or global variable persistence is not enabled, resulting in loss of batch identifiers. - Phenomenon: Multiple variables after an HTTP request cannot be correctly linked to the specified knowledge base. Cause: Independent identifiers are not assigned to each batch’s variables based on
nodeId, leading to mixed matching of variables across multiple batches. - Phenomenon: Global variables are cleared after the workflow runs, making it impossible to retain batch information across nodes. Cause: Persistent storage for
variablesis not configured. By default, temporary variables reset each time a node runs.
How to Confirm Correct Configuration
- Manually trigger the audit process for a single battery batch. Check that variables pass correctly across nodes, and confirm the batch ID in
variablesdisplays normally in subsequent nodes. - Simulate a device data pull failure scenario. Check that the failed branch triggers the retry or alert process, and confirm the failure retry configuration is active.
- Compare fields from multi-source data. Check that field mapping rules correctly link records from different systems by batch number, and confirm the linking logic is correct.
- Run audit processes for multiple batches in parallel. Check that each batch’s variables store independently, with no cross-batch mixing occurring.
Note: The configuration values listed above are common starting points. Adjust them based on your own specific samples and use cases.