What Data for This Category Looks Like
The data for this category covers the full process of electrode preparation, cell assembly, formation and grading, and quality inspection. Daily documents include batching records, coating and rolling parameters, winding/stacking ledgers, liquid injection logs, formation and grading curves, OCV and safety test reports. Data sources include raw material batch ledgers, workshop MES systems, slurry and coating testing equipment, dew point monitoring devices, formation and grading cabinets, and laboratory testing systems. Data updates occur per battery batch, per individual cell, and in real time. Fields include technical parameters with clear units, such as slurry solid content, coating areal density, winding tension, and dew point temperature.
Constraints Imposed on Workflow Orchestration by These Characteristics
Data for this category is multi-source, heterogeneous, and has highly varied update rhythms. Workflow orchestration must match the real-time, per-batch data write trigger logic. Fixed-cycle trigger nodes should not be used. Fields across multiple document types have clear technical units. Workflows must include a parameter verification step to ensure consistent units for cross-document, same-dimension parameters. Electrochemical systems have strict requirements for batch consistency. Workflows must support loop nodes that iterate over documents associated with a single batch. They must also adapt to data sources in different formats, such as structured MES ledgers and unstructured test reports. Additionally, parameter anomalies may cause serious safety issues. Workflows must include anomaly alert and failure retry steps.
Configuration Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Trigger Mode | Triggered by data write events | Matches the real-time, per-batch data update rhythm of this category, avoiding delays or omissions from fixed-cycle triggers |
| Loop Node Iteration Limit | First 20 associated batch data entries, calibrated via actual testing | Covers the typical number of associated documents for a single batch, avoiding process timeouts from excessive iteration |
variables parameter configuration | Bind current process context variables | Directly retrieves batch ID and parameter values from the current process, no additional data mapping required |
| Node Timeout Threshold | 600 seconds, calibrated via actual testing | Adapts to the parsing duration of multi-source data for this category, avoiding premature process termination |
nodeId parameter configuration | Bind unique identifiers for corresponding business nodes | Ensures correct call relationships between each node in the process, avoiding node confusion |
| Multi-format Data Parsing Rules | Match corresponding parsing logic by document type, extract field length of 800–1200 characters, calibrated via actual testing | Adapts to the structured format of batching records and unstructured format of test reports, accurately extracting target fields |
Three Common Configuration Errors
- Phenomenon: After setting default values for form input nodes, referencing the
variablesparameter fails to retrieve process context variables. Cause: The output of the form node is not bound to thevariablesscope of the current process, so variables cannot be read by subsequent nodes. - Phenomenon: After querying MES data via a DB node, the result is returned as a JSON array and cannot be directly used for parameter comparison. Cause: No data parsing node is configured to convert structured query results into directly readable fields. Additional parsing via a code node is required.
- Phenomenon: When invoking workflows concurrently 2–3 times per second, the
MCPnode returns empty results. Cause: No concurrent current limiting parameter is configured for theMCPnode. Exceeding service carrying capacity under high concurrency causes requests to be discarded.
How to Confirm Correct Configuration
- Trigger test: Manually write a test data entry that meets the format requirements. Confirm that the workflow starts automatically and completes full process execution.
- Variable verification: Insert a log node into the workflow. Print the content of the
variablesparameter. Confirm that it includes the expected batch ID and parameter values. - Anomaly trigger: Manually construct test data with one parameter anomaly. Confirm that the anomaly alert node triggers and generates corresponding notifications.
- Concurrency test: Simulate concurrent invocations at 2–3 times per second. Confirm that each node returns valid results, with no empty returns or timeouts.
The values provided are common starting points and should be measured against the reader's own samples.