What data looks like for this category
Data for this category covers iron melt and scrap charging data, L2 process control real-time data, laboratory composition test reports, MES production scheduling data, energy medium system operation data, and equipment status monitoring data. Daily ledger documents include heat plans, batching lists, converter smelting records, continuous casting process parameter records, slab quality judgment sheets, and other materials. Data update frequencies cover per heat, real-time, and per shift. Fields include heat number, hot metal weight, carbon-silicon-manganese content, tapping temperature, continuous casting pulling speed, slab thickness, and more. Units include tons, percentage, degrees Celsius, meters per minute, and millimeters.
Constraints imposed on model access and configuration
Multi-source heterogeneous data sources require the model access link to support batch pulling and format integration of cross-system data. This avoids parsing omissions caused by scattered data sources. High-frequency real-time and per-heat update frequencies require configuring a sufficiently large context window to accommodate full-process ledger data for a single heat. Diverse document structures require the model to have structured field recognition capabilities, to adapt to ledgers in different formats such as heat plans and composition reports. Severe consequences of data errors in high-temperature continuous processes require configuring stable call timeouts and retry mechanisms. This prevents production process interruptions caused by abnormal single-heat data.
How to set configurations
This page’s configurations are based on FastGPT v4.17.0. Specific configuration items and recommendations are as follows:
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
AIPROXY_API_ENDPOINT | Fill in the exclusive model proxy address of the enterprise intranet | Adapt to cross-domain request restrictions for cross-system data access, and ensure data transmission security |
AIPROXY_API_TOKEN | Bind the exclusive service key of the production environment | Isolate call permissions for different heat batches, to avoid data confusion |
maxContext | 8000–12000 token | Accommodate full-process ledger documents and real-time monitoring data for a single heat, to prevent context truncation |
maxTokens | 2000–3000 token | Ensure that the generated summary report contains complete process parameters and quality judgment results |
temperature | 0.2–0.4 | Maintain accuracy of ledger data parsing, to avoid generating redundant or off-actual content |
| Workflow/API tool selection | auto | Automatically adapt to parsing requirements for multi-format ledgers, without manual tool switching |
Three common misconfigurations
- Phenomenon: After uploading the complete ledger for a single heat, the summary report returned by the model is missing some field content. Cause: The
maxContextparameter is not configured correctly. The excessively small context window causes full data for the single heat to be truncated. - Phenomenon: After modifying global variables during multi-turn dialogue, subsequent calls do not take effect and data confusion occurs. Cause: Context persistence binding is not enabled in the model access configuration, causing variable state loss across multi-turn sessions.
- Phenomenon: Model call duration exceeds 600 seconds and is forcibly terminated. Cause: The
maxContextconfiguration is too large, causing the time required for the model to parse the context to exceed the threshold.
How to confirm configurations are properly set
- Upload the complete ledger document for a single heat, and check whether the summary content returned by the model includes all preset fields.
- Initiate multi-turn dialogue to modify global variables, and check whether variable values remain consistent in subsequent calls.
- View model call logs, and confirm that request parameters for
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENhave no abnormalities. - Simulate abnormal data input, and check whether the model can correctly recognize field formats and generate reports that meet requirements.
The values provided here are common starting points, and should be evaluated against the reader's own samples.