What the data for this category looks like
The core data for this category covers the entire production chain, sourced from raw material batch ledgers, workshop MES systems, slurry and coating testing equipment, dew point monitoring devices, formation and grading cabinets, and laboratory safety test reports. Daily documents include batching records, coating and rolling parameter sheets, winding/lamination process records, liquid injection ledgers, formation and grading curve data, OCV and internal resistance test reports. Data update frequencies are divided by material batch, individual cell, and real-time monitoring items. Full-chain data for a single cell includes dozens of dimensions of parameters and test results.
What constraints do these characteristics impose on the "workflow orchestration" link?
The data for this category is multi-source, scattered, and fine-grained. Workflows must support parallel pulling of data sources from different systems across multiple nodes, and bind unique identifiers for individual cells via variables to avoid cross-batch data confusion. Data real-time requirements are high, so node-triggered timing verification must be configured to ensure subsequent analysis runs only after pre-data collection is complete. Test data includes two categories: images and structured documents. Clear file type identification rules are needed to avoid conflicts in processing logic for different format data. Electrochemical production is sensitive to environmental parameters, so workflows must embed verification nodes for environmental data such as dew point and cleanliness to ensure the validity of traceability data.
How to set the configurations
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
| File Type Identification Rules | Validate by file suffix + MIME type. Limit image types to .jpg/.png/.bmp with MIME type image/*. Classify other files as business documents | Test data for lithium-ion cell manufacturing includes appearance defect images and parameter documents. Precise distinction between the two types of files is required for subsequent processing |
variables Scope | Single cell dimension, bind batch number and test node ID for each cell | Traceability data for each cell must be bound to a unique identifier. The single-cell scope of variables avoids cross-batch data confusion |
| HTTP Request Node Trigger Condition | Trigger when nodeId is the parameter verification node and the return status code is 200 | Ensure that online searches or AI conversations run only after pre-parameter verification passes, to avoid invalid requests consuming resources |
| Node Timeout Period | 60-120 seconds, calibrated based on actual measurements | Pulling formation and grading data and parsing laboratory reports take a relatively long time. A timeout period that is too short causes node interruptions, while one that is too long slows down the overall workflow |
| Multi-Data Source Parallel Pull Configuration | Start 3 parallel nodes simultaneously to pull MES data, formation and grading data, and environmental dew point data respectively | Traceability data for lithium-ion cell manufacturing is scattered across multiple systems. Parallel pulling shortens overall time consumption |
| Multi-Node Shared Storage Configuration | Mount a unified NFS storage path, and enable the multi-node synchronization switch for FastGPT v4.17.0 | In multi-node deployments, variables parameters and file data must be shared across all nodes to avoid workflow execution interruptions |
Three Common Mistakes
- Appearance defect images uploaded in workflows are misidentified as parameter documents, leading to subsequent parsing failures. This occurs when clear file type identification rules are not configured, and only a single suffix is used for judgment, without covering all image formats and business document types.
- HTTP request nodes do not trigger execution and directly enter the AI conversation stage. This happens when the trigger conditions associated with
nodeIdare not set correctly, or the status code returned by the pre-parameter verification node does not meet expectations, causing the trigger logic to fail. - Workflows cannot share data after multi-node deployment, and
variablesparameters are lost when executed on different nodes. This occurs when a unified shared storage is not mounted, and the multi-node data synchronization configuration for FastGPT is not enabled, leading to data isolation between nodes.
How to Confirm the Configuration Is Correct
- Upload an appearance defect image and a batching record document, and check whether the workflow automatically distinguishes between the two types of files and triggers the corresponding processing nodes.
- Insert a test HTTP request node in the workflow, set the trigger condition as the completion of the pre-parameter verification node, submit the test, and check the node logs to confirm that the request was called normally.
- After deploying multi-node instances, upload test data and check whether the
variablesparameters on each node are consistent, to confirm that data sharing is working properly. - Run a workflow that includes formation and grading data pulling, and check whether the node execution duration falls within the preset timeout range.
The values provided are common starting points and should be measured against the reader's own samples.