What the data for this category looks like
Core data for lithium-ion cell manufacturing 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 testing equipment. Daily archived production documents include batching records, coating and calendering parameter sheets, winding/stacking process logs, liquid injection ledgers, formation and grading data, OCV and safety test reports. Data updates cover fine-grained levels per batch of material or individual cell. Some testing parameters, such as workshop dew point and coating areal density, are uploaded in real time. Fields include professional production parameters like slurry solid content, coating areal density, winding tension, dew point temperature, formation and grading curve parameters, and cell internal resistance.
What constraints these characteristics impose on workflow orchestration
Multi-source heterogeneous real-time and batch data requires workflows to support parallel access to different data sources. Workflows must also adapt to fine-grained data verification for single batches and individual cells. Many professional fields have high precision requirements. Variable definitions in workflows must strictly match field names and units from production documents. This prevents verification failures caused by parameter mismatches. Frequently updated data triggers frequent workflow runs. Node trigger rules must be configured to avoid repeated execution. Electrochemical systems are sensitive to environmental parameters. Workflows must include anomaly verification links. These links interrupt processing for situations like abnormal dew point or areal density deviation.
Recommended Configuration Settings
| Configuration Item | Recommended Practice | Basis for This Practice |
|---|---|---|
| Global variable configuration | Define variables as batching batch, coating areal density, dew point temperature according to production document definitions | Matches core production and testing fields for lithium-ion cell manufacturing |
| Node ID binding | Bind nodeId to core nodes such as coating testing, formation and grading | Accurately associate data sources with corresponding workflow steps |
| Context retention rounds | Retain 1 round for preceding AI nodes, 3 rounds for subsequent verification nodes | Adapt to information needs of different links, avoid redundant data occupying resources |
| Variable update rules | Support null value override updates | Handle abnormal scenarios where detection data is missing |
| External parameter passing | Enable URL parameter binding to global variables | Receive query parameters such as externally passed batch numbers |
| Node timeout setting | 600 seconds | Adapt to data acquisition for long-duration links such as formation and grading |
Three Common Configuration Errors
Three common configuration errors are as follows:
- Attempting to set the output of a previous AI model to a null value via variable updates has no effect. The target field always retains the old value. The cause is that the variable override switch is not enabled, or the update rule does not match the scope of
variables. - The workflow fails to read passed values after external URL parameter transfer. The global variable field shows as empty. The cause is that the mapping relationship between URL parameters and
variablesis not correctly configured. - Context data from preceding nodes is not reset when the workflow is accessed multiple times with the same ID. Conversation results include historical redundant information. The cause is that context cache is not cleared when triggering a node.
How to Verify a Correct Configuration
- Production parameters for a test batch are submitted. Core fields such as batching batch, coating areal density, and dew point temperature are confirmed to be correctly loaded into global variables.
- The coating testing node is triggered to run. The data source bound to
nodeIdsynchronizes the latest testing data normally. - A URL parameter containing a batch number is passed.
variablescorrectly receives the passed value. - Testing is conducted using the FastGPT v4.17.0 platform. The same workflow is triggered multiple times. The context rounds are confirmed to be reset according to the configuration.
The values provided here are common starting points and should be evaluated against relevant in-house samples.