What This Category of Data Looks Like
Data sources for this category 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. Daily generated documents include batching records, coating and rolling parameters, winding or stacking records, liquid injection records, formation and grading data, and OCV and safety test reports. Data update frequencies fall into three categories: real-time monitoring, per-batch cell, and per-single cell. Fields include material identifiers, process parameter values, test values, and some parameters have clear physical units.
Constraints on Workflow Orchestration
The data for this category is multi-source and dispersed, has widely varying update frequencies, and requires extremely high consistency. These factors create multiple constraints for workflow orchestration. First, connect multiple heterogeneous data source nodes to cover systems for raw material batches, production equipment, laboratory testing, and more. Second, configure separate trigger rules for real-time monitoring data and batch summary data to prevent data synchronization delays or repeated collection. Third, add a field verification step to ensure ledger data fields from different sources match and units are consistent, avoiding downstream errors caused by parameter deviations. Fourth, reserve a failure handling node to address scenarios with missing data or incorrect formatting.
Configuration Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Trigger Mode | Set separately by data update type: real-time nodes trigger every 10 seconds, batch nodes trigger after each batch is completed | Matches the update frequencies of real-time monitoring, per-batch, and per-single cell for this category |
| Data Source Node Configuration | Connect 6 types of system nodes including raw material batches, MES, coating testing, etc. | Covers all data sources for this category |
variables Configuration | Map core fields such as material batch number, coating line speed, dew point temperature | Matches the field structure of business documents |
| Failure Handling Node | Set a timeout threshold of 300 seconds, trigger an alert when an exception occurs | Adapts to the delay tolerance of real-time data collection |
| Field Verification Rules | Verify that parameter units match preset standards | Avoid data errors caused by unit deviations |
| Report Generation Node | Trigger by daily summary or upon completion of a single batch | Matches the output requirements of business reports |
Three Common Configuration Mistakes
- Phenomenon: The AI chat node cannot obtain parameter values output by code running, and the interface prompts that the field is empty. Cause: The output field mapping of the code node is not configured in
variables, causing the variable to not be registered correctly. - Phenomenon: A batch number mismatch error occurs when the workflow runs, with a status code of 400. Cause: No field verification rules are configured, and batch number formats from different data sources are not unified.
- Phenomenon: A timeout error occurs in the real-time data collection node, and the elapsed time exceeds the preset threshold. Cause: The trigger interval is not adjusted according to the collection frequency of real-time data, resulting in excessive node resource occupation.
How to Verify a Correct Configuration
- Manually trigger a single real-time data collection node, and check whether the output log contains the preset parameter fields.
- Import a batch of simulated cell production ledger data, and check whether the fields in the generated report match the business documents after running the workflow.
- Simulate a data source exception scenario, and check whether the preset alert action is triggered by the failure handling node.
- Check the
variablesconfiguration item to confirm that all core business fields have been mapped.
The values provided are common starting points and should be measured against the reader's own samples.