What Data for This Category Looks Like
Data for this category covers the full cement production process. Sources include online composition analysis equipment, DCS control systems, laboratories, kiln and grinding equipment, raw fuel ledgers, and energy metering systems. Data update frequencies are divided into real-time (such as kiln temperature and composition data), shift-based (such as equipment inspection records), and daily (such as quality control notices and energy reports). Daily documents include raw material batching plans, kiln operation records, laboratory reports, quality control notices, equipment inspection records, and energy reports. Each document includes dedicated fields and units such as raw material ratios, kiln thermal parameters, and clinker strength indicators.
What Constraints These Characteristics Impose on Model Access and Configuration
Multi-source data sources require configuration to be compatible with formats output by different systems including online composition analysis tools, DCS, and laboratories, to prevent models from failing to read valid information due to data format mismatches. Data with different update frequencies need matching context window lengths to ensure that real-time data such as kiln temperatures and shift-based inspection records can be included in model processing in a timely manner. Dedicated document fields require clear mapping of dedicated content such as raw material ratios and clinker strength indicators during configuration, to ensure that generated process documents and exception reports comply with internal enterprise specifications. Errors in the production process directly affect product quality and equipment downtime, so strict control of model output rigor parameters is required.
Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Covers the cumulative content of multiple documents such as the shift raw material batching plan, kiln operation records, and laboratory reports |
maxTokens | 2000–3000 token | Meets the full output length requirements for exception report drafting, including adjustment suggestions and corrective measures |
temperature | 0.3–0.5 | Ensures accuracy of process parameter descriptions, avoids arbitrary adjustments to clinker mineral ratios and thermal systems |
| Workflow/API tool selection | auto | Automatically determines whether to call tools to process real-time DCS data and laboratory results, improving content accuracy |
AIPROXY_API_ENDPOINT | Fill in according to deployment environment | Adapts to the access path for FastGPT v4.17.0 local deployment |
AIPROXY_API_TOKEN | Fill in according to the generated secret key | Ensures identity verification security for model calls, prevents unauthorized access |
Three Common Configuration Errors
- Phenomenon: Model calls return a 400 error with a parameter format exception prompt. Cause: Failed to adapt to the dedicated field format of cement clinker data, passed non-standard raw material ratio values directly into the model request.
- Phenomenon: The model list does not update after modifying the configuration file, and checking the /app/data/config directory in the container shows no corresponding configuration. Cause: Failed to correctly mount the configuration file to the corresponding Docker container path, or the configuration file contains syntax errors.
- Phenomenon: The generated exception report has no valid content, and the stream response is empty. Cause: Incorrect
maxContextparameter configuration leading to context overflow, or incorrect API endpoint configuration preventing normal connection to the model service.
How to Verify Successful Configuration
- Check the FastGPT backend model list, confirm that the target model is loaded and displays a normal status.
- Submit a shift kiln operation record and laboratory report, verify that the generated process document includes core content such as raw material ratio adjustments and kiln thermal system descriptions.
- Trigger the exception report generation process, check that the returned fields match the fields of the enterprise's internal quality control notice.
- Simulate raw fuel fluctuation scenarios, verify that the model's output adjustment suggestions conform to the process logic of cement clinker production.
Note: The values provided are common starting points and should be evaluated against your own operational samples.