This chapter is configured for FastGPT v4.17.0.
What This Category of Data Looks Like
Data sources for this category include online composition analysis, DCS systems, laboratories, kiln and grinding equipment, raw and fuel ledgers, and energy metering devices. Update frequencies fall into three categories: real-time, per-shift, and daily. Daily documents include raw material batching plans, kiln operation records, test reports, quality control notices, equipment inspection records, and energy reports. Fields cover raw and fuel composition parameters, equipment operating duration, clinker quality indicators, energy consumption values, and more. There is no unified fixed format, and data must be split and associated by process link.
Constraints Imposed on the "Workflow Orchestration" Link
Data sources are scattered, and update frequencies vary widely. Node trigger conditions must be configured in the workflow based on real-time, per-shift, and daily update cycles. Document fields are diverse and have no unified format. Field mapping and unified management across multiple data sources must be implemented using variables. Single-shift or single-day data volumes are large, and long-node data structures may exceed AI context limits, so segmented processing is required. Exception reports must be linked to real-time operating conditions and historical reports. The workflow must connect multiple sequential nodes, and the execution order of nodes directly impacts report accuracy.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Define grouped by production link, each group contains no more than 7 field mappings, calibrated via actual measurement | Data sources for cement clinker and grinding cover multiple parameter types; grouping prevents context overflow |
| Maximum input characters per single run | 800–1200 characters | Matches the average length of per-shift kiln operation records, and supports segmented processing for large node data structures |
| Node trigger delay | No more than 60 seconds | Matches the real-time update frequency of online composition analysis and DCS systems, ensuring timeliness of exception reports |
nodeId | Assign a unique identifier to each data source node, orchestrate in the order of raw material preparation → calcination → grinding → quality inspection | Ensures workflow node execution order aligns with production processes, avoiding data association errors |
| Historical data call limit | Top 5 entries | Matches the average number of per-shift inspection records, reducing context load |
| Context window adjustment | No more than 10000 token | Supports analysis requirements for large node data structures, preventing exceedance of AI context limits |
Three Common Misconfigurations
- Global
variablesvalues fail to update automatically. Fields displayed in the interface are empty or do not sync with the latest production data. The cause is failure to bind the execution completion event of the corresponding node to trigger variable updates. - Large node data structures exceed context length, leading to truncated AI response content or errors. The cause is failure to set a reasonable maximum input character count per run, and failure to perform segmented processing on long data.
- Workflow node execution order is incorrect, and exception reports are linked to incorrect production data. The cause is failure to assign a unique
nodeIdto each node, or failure to arrange node order in line with production processes.
How to Verify Successful Configuration
- Trigger a simulated update of raw material batching data, check whether corresponding fields in
variablessync with the latest values. - Import a section of kiln operation records exceeding the preset character count, check whether the workflow automatically performs segmented processing.
- View the
nodeIdidentifiers of workflow nodes, confirm that the execution order aligns with the raw material preparation → calcination → grinding → quality inspection process. - Trigger the workflow via a simulated abnormal operating condition, check whether it can correctly link historical data and real-time parameters from the corresponding process link.
The values provided here are common starting points, and should be evaluated against the reader's own samples.