This page applies to FastGPT v4.17.0.
What Data Looks Like for This Category
Data for this category originates from online composition analysis equipment, DCS control systems, laboratories, kiln and grinding equipment, raw and fuel ledgers, and energy metering devices. Daily generated documents include raw material batching plans, kiln operation logs, laboratory reports, quality control notices, equipment inspection records, and energy reports.
Data update frequencies fall into three categories: real-time (such as kiln temperature and composition data), shift-based (such as inspection records and energy reports), and daily (such as quality control notices). Document fields include material ratios, temperature, pressure, inspection time, energy consumption values, and strength indicators. Corresponding units include percentage, degrees Celsius, megapascals, kilowatt-hours, and others.
Constraints Imposed on Workflow Orchestration
Data sources for this category are scattered, and update rhythms vary widely. Match data injection timing to different trigger nodes to avoid data misalignment.
Core review document types are diverse. Classify field extraction and verification logic for different documents in the workflow.
Time-consuming steps such as raw and fuel fluctuations and rotary kiln thermal system adjustments require the workflow to chain multiple nodes to complete cross-review. This ensures consistency between batching plans, real-time production data, and laboratory results.
Unlike other process industry categories, this category relies on continuous firing and grinding of bulk minerals. Thermal stability and raw material homogenization are core priorities. The workflow must prioritize matching accuracy between batching plans and production data.
Clinker quality fluctuations and kiln system scaling and shutdowns carry severe negative consequences. The workflow must configure failure alerts and rollback mechanisms to ensure reliability of the review process.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Bind in batches by real-time DCS data, shift-based inspection records, and daily laboratory reports | Match the different update rhythms of data for this category, ensure variables take effect only at corresponding trigger nodes |
| Node ID | Name by review link, such as batching verification node ID, kiln temperature verification node ID | Facilitate fault troubleshooting and process adjustments, quickly locate abnormal links |
| Knowledge base file set filtering | Specify three types of files: raw material batching plans, kiln operation records, and laboratory reports | Match the scope of core review documents for this category, avoid irrelevant data interfering with review results |
| LLM temperature setting | 0.6–0.8 | Balance accuracy and flexibility for industrial document review, adapt to strict production scenario requirements |
| Auto-obtain knowledge base ID | Bind the dedicated cement clinker and grinding review knowledge base for this category | Avoid cross-knowledge-base data confusion, ensure uniqueness of review data |
nodeId | Configure a unique identifier for each review sub-node | Facilitate workflow debugging and failed node location, improve maintenance efficiency |
Three Common Misconfigurations
- Phenomenon: Global variables passed to the next node appear empty, with an error prompt indicating missing input parameters. Cause: The corresponding trigger node's real-time data is not bound in the
variablesconfiguration, or the variable scope does not cover subsequent review nodes. - Phenomenon: The AI chat component cannot set the LLM temperature, with no corresponding configuration button. Cause: The custom parameter configuration option is not enabled in the workflow's LLM call node, or permissions for the corresponding LLM service are not associated.
- Phenomenon: The knowledge base search node returns unexpected results, failing to hit the specified files. Cause: The "Search only specified file set" option is not checked in the search configuration, or the file set does not include the target review document types.
How to Verify Successful Configuration
- Trigger the corresponding workflow, view the
variablesinjection log, confirm that global variables for each link have been correctly passed to subsequent review nodes. - Enter the configuration interface of the LLM call node, confirm that the temperature parameter is displayed at the set value and can be adjusted.
- Enter the configuration interface of the knowledge base search node, confirm that the specified file set is checked, and matches the three document types: raw material batching plans, kiln operation records, and laboratory reports.
- Manually trigger the node failure alert rule, verify that a preset notification mechanism is triggered when the node reports an error.
The values provided here are common starting points, and should be evaluated against the reader's own samples.