What the data for this category looks like
Data sources include concentrate and auxiliary material composition tests, furnace temperature and current data collected by DCS systems, laboratory-issued material analysis reports, and energy and environmental monitoring data. Updates occur in real time or near real time per batch of raw materials, each furnace run, and each electrolyzer cycle. Daily documents include batching lists, smelting operation records, electrolyzer condition records, laboratory analysis reports, anode and cathode quality records, and environmental protection ledgers. Fields include batch number, furnace/electrolyzer number, material weight, equipment operating parameters, and test result values. Units include tons, amperes, degrees Celsius, cubic meters, and others.
What constraints these characteristics impose on workflow orchestration
Multiple heterogeneous data sources require workflows to pull ledger data from different systems in sequential node order, to avoid data conflicts. Real-time or near-real-time update frequency requires matching node trigger intervals, to ensure the latest furnace and electrolyzer condition and test data is collected. The need to summarize ledgers across multiple batches and electrolyzers requires workflows to support grouping and processing data by batch and furnace/electrolyzer number, while unifying field formats and units across different documents. Complex metal balance accounting and byproduct tracking require chaining multiple data nodes, which imposes high requirements on parameter passing and fault tolerance mechanisms between nodes. FastGPT v4.17.0’s workflow orchestration capability can accommodate this multi-node chained requirement, but configuration must be adjusted to match the business characteristics of this category.
How to configure settings
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
variables | Create independent variable groups for each batch of raw materials and each furnace/electrolyzer, including three categories: batch number, material weight, and test values | Adapt to field mapping and grouping summary requirements for multi-source data, and match the ledger processing logic for multiple batches and electrolyzers in this category |
| Node trigger rules | Trigger based on the delay time of DCS data updates, with a value of 30–60 seconds | Match the real-time or near-real-time equipment data update frequency of this category, to avoid collecting expired data |
nodeId | Configure a unique nodeId for each data pull and accounting node | Ensure accurate positioning of corresponding nodes when the workflow is called, to avoid parameter confusion and invocation errors |
| Global variable visibility scope | Enable full workflow visibility permissions | Meet the requirement for reusing ledger data across nodes, and support report generation across multiple links |
| Code execution node timeout setting | 600 seconds | Accommodate the calculation duration of complex metal balance accounting, to avoid workflow failure due to calculation timeout |
| Single data pull limit | 1000 entries | Control the data processing volume of a single node, to avoid excessive memory usage |
Three common errors
- After calling an external MCP plugin in the workflow, an empty result is returned, and the log shows no matching data. The cause is failure to configure the plugin's data source permissions, and failure to adapt to the dedicated data interface format for non-ferrous metal smelting scenarios.
- A code execution node fails to run, and the log shows a parameter format error. The cause is failure to convert original values collected by the DCS system to numerical types supported by the workflow, and failure to unify units across different ledger documents.
- Knowledge base variable references have no optional values, and global variables cannot be called. The cause is failure to enable the visibility scope of global variables in the workflow configuration, and failure to bind ledger fields to the variable pool.
How to confirm the configuration is correct
- View the workflow's variable configuration interface, confirm that custom variables such as created batch numbers and furnace/electrolyzer numbers exist and can be called by subsequent nodes.
- Trigger a test run of the workflow, check the matching degree between the return results of each data pull node and the fields and units of the original ledger data.
- Run the code accounting node, confirm that the output metal balance results conform to the business logic of this category, with no format errors or abnormal values.
- Call the workflow's public interface, verify that the returned report data includes all required ledger fields, with no missing or redundant content.
The values provided here are common starting points, and should be evaluated against the reader's own samples.