What This Category of Data Looks Like
This category of data covers the mining, raw material, calcination, grinding, quality control, and energy departments of cement enterprises. Daily documents include raw material batching plans, rotary kiln operation records, laboratory composition reports, quality control notices, equipment inspection ledgers, and energy metering reports. Data sources include online composition analysis systems, DCS control systems, laboratory terminals, kiln and mill equipment sensors, raw and fuel ledger systems, and energy metering devices. Update frequencies include real-time, shift-based, and daily summary updates. Fields include material composition ratios, equipment operating parameters, inspection point statuses, energy consumption values, and more, with corresponding measurement standards matched to each data type.
What Constraints These Characteristics Impose on the "Workflow Orchestration" Link
Decentralized data sources and significantly different update frequencies require workflows to support parallel pulling from multiple data sources, to avoid process interruptions caused by single-node blocking. Significant structural differences across multiple document types require configuring nodes to adapt to extraction rules for different fields, as a unified template cannot process all business documents. Different update frequencies for real-time data and batch reports require workflows to flexibly schedule node execution timing based on trigger conditions, to avoid repeated pulling of invalid data. The associated analysis requirement for equipment inspection and energy consumption data requires workflows to connect inspection nodes and data statistics nodes, to implement automatic verification processes after abnormal triggers.
How to Set Configurations
The following configurations are adapted for FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
variables | Configure three types of dynamic variables: material composition thresholds, inspection cycles, and energy consumption warning values | Adapt to parameter adjustment needs for different production scenarios, support reuse of global parameters |
nodeId | Assign independent node IDs to DCS data, inspection data, and laboratory data respectively | Facilitate locating nodes with execution exceptions, enable parallel pulling of multiple data sources |
| Knowledge Base Selection | Bind the dedicated equipment knowledge base for cement clinker and grinding | Match category-specific inspection and maintenance documents, improve Q&A accuracy |
| Trigger Rules | Trigger based on real-time data thresholds, trigger every 6 hours on a scheduled basis, and manual trigger | Adapt to three data update frequencies: real-time, shift-based, and daily |
| Text Extraction Configuration | Configure field extraction rules by document type, such as extracting cylinder temperature from kiln operation records | Accurately extract target business fields, filter irrelevant content |
| Context Length | 800–1200 characters | Cover core information of single-batch production data, avoid node timeouts caused by overly long context |
Three Common Misconfigurations
- Text extraction nodes return empty fields or non-target content. This occurs because dedicated extraction rules are not configured for different document types such as raw material batching plans and kiln operation records, and a unified template is used to process all documents.
- Workflows cannot cyclically execute shift-based inspection data verification. This occurs because the trigger conditions for cyclic nodes are not correctly configured, and the update trigger signal of real-time data is not associated.
- The knowledge base selection in global variables cannot be dynamically switched, causing Q&A calls to use non-category-specific knowledge bases. This occurs because a mapping relationship between dynamic parameters and knowledge base IDs is not established in the
variablesconfiguration.
How to Confirm Proper Configuration
- Trigger the workflow, check the execution logs of each node, and confirm that the data source corresponding to
nodeIdpulls data normally. - View the global variable configuration page, and confirm that the dynamic parameters in
variablesare bound to the corresponding knowledge base IDs and business thresholds. - Submit simulated inspection records or laboratory reports, and check whether the text extraction node returns correct target fields.
- Test the cyclic trigger scenario, and confirm that the workflow can repeatedly execute the data verification process according to the set cycle.
The values provided are common starting points and should be measured against the reader's own samples.