What Data for This Category Entails
This category's data originates from online composition analysis, DCS systems, laboratories, kiln and grinding equipment, raw material and fuel ledgers, and energy metering devices. Daily documents include raw material batching plans, kiln operation records, test reports, quality control notices, equipment inspection records, and energy reports. Data updates occur in real time, per shift, and daily. Document fields cover raw material and fuel component proportions, kiln thermal parameters, clinker mineral composition, cement strength indicators, and more. Corresponding units include percentage, tons per hour, degrees Celsius, and others.
Constraints Imposed on Workflow Orchestration
The multi-source dispersion and varied update cycles of this category's data require workflow configurations to use mixed trigger nodes. These must support real-time pulling of online composition and DCS data, plus scheduled pulling of shift-based inspection records and daily energy reports. Document types include structured batching plans, semi-structured test reports, and unstructured operation records. Adapted format parsing rules must be set for different nodes. Indicator correlations can impact production stability. Workflows must include anomaly check nodes to trigger subsequent processing for issues such as free lime exceeding standards or abnormal grinding output per hour.
Configuration Settings
These configurations are adapted for FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Trigger Node Type | Combination of real-time trigger and scheduled trigger | Matches the differing update cycles of real-time online composition data, shift-based inspection records, and daily energy reports |
| Variable Binding Rule | Bind using document field names to variables | Enables standardized data transfer for category-specific fields such as raw material batching ratios and clinker free lime content |
| HTTP Request Timeout | 600 seconds | Accommodates network latency and transfer needs for pulling batch laboratory data |
| File Type Filter Rule | Only allow upload of .xlsx, .pdf, .jpg | Matches common formats for this category's test reports, inspection records, and image-based inspection materials |
| Node Execution Order | Orchestrate per business data flow | Ensures the business logic sequence: raw material and fuel data first, followed by batching plans, then quality check steps |
nodeId Configuration | Assign a unique identifier to the anomaly alert node | Simplifies workflow debugging and quick location of abnormal nodes |
Three Common Configuration Errors
- Symptom: A "SaaS execution error" pop-up appears when running a code execution node, and the node status shows failed. Cause: The
variablesbinding for category-specific fields such as cement clinker free lime content is not configured, so the code node cannot read the corresponding business data. - Symptom: The HTTP request node does not pass parameters as expected, and the request body is empty. Cause: Ratio parameters from the raw material batching plan are not mapped to workflow
variables, and no corresponding variables are linked in the request configuration. - Symptom: The file upload node does not filter non-target format files, and unrelated documents are included. Cause: No file type filter rules are set, and allowed formats are not restricted to the common ones for this category.
How to Verify Correct Configuration
- Trigger the real-time data node, confirm that pulled DCS data matches on-site instrument display values, and adjust the trigger frequency to match the actual update cycle.
- Run the scheduled pull task, check that the generated raw material batching plan analysis results include preset fields, and confirm the
variablesbinding logic is correct. - Upload test reports and inspection records, verify that the file type filter rule only permits target format files to pass.
- Simulate an abnormal indicator trigger, check that the anomaly alert node starts as expected, and confirm the
nodeIdconfiguration can locate the corresponding execution link.
The values given here are common starting points and should be measured against the reader's own samples.