What data for this category looks like
Data for this category comes from hot metal and scrap charging records, L2 process control system data, laboratory composition reports, manufacturing execution systems (MES), energy medium monitoring systems, and equipment status ledgers. Daily documents include heat plans, batching lists, converter smelting logs, molten steel composition reports, continuous casting process parameter records, and slab quality judgment sheets. Data update frequencies cover real-time records generated per heat, and shift-level summarized scheduling data. Fields include heat number, steel grade, composition content, target temperature, casting duration, and more. Units include tons, degrees Celsius, percentages, minutes, and others.
What constraints these characteristics impose on the model access and configuration link
Data sources are scattered, and update frequencies vary widely. Real-time heat data requires fast access. Field format differences across multiple systems must be adapted to avoid data misalignment. Document structures are fixed but include many fields. Precise matching of smelting and continuous casting parameters required for bid documents is necessary to prevent irrelevant data from mixing into context and harming content accuracy. High-temperature continuous production process characteristics require that model response times do not become overly long, otherwise the timeliness of bid quotations will be affected. In this scenario, heat and casting sequences are strongly coupled. Content generated by the model must be strictly linked to corresponding heat information to avoid chain issues in subsequent production scheduling. Quality data such as slab judgment sheets must be strictly associated with corresponding heat information. Field association relationships must be retained during model access to prevent data misalignment leading to quotation deviations. Additionally, the bid scenario requires integrating multi-source business data to generate compliant documents. This imposes clear requirements on the model's context carrying capacity and data screening capabilities.
Configuration settings
This page’s configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Must accommodate batching, smelting, and continuous casting historical data for multiple heats, support reference of process parameters for bid quotations, and avoid truncation of critical business context |
maxTokens | 3000–4000 token | Adapt to the standard length requirements of bid documents and quotation sheets, balance generation speed and content completeness |
temperature | 0.2–0.4 | Maintain rigor in quotations and documents, avoid generating arbitrary content that does not comply with iron and steel process specifications |
| Workflow/API tool selection | auto | Automatically call internal tools to retrieve real-time process and raw material data, adapt to dynamic parameter adjustment needs during bidding |
vision | disabled | This scenario primarily uses structured business data and text documents, so multimodal image or video recognition functions are not required |
AIPROXY_API_ENDPOINT | Fill in according to the enterprise intranet deployment address | Meet the internal data security requirements of iron and steel enterprises, avoid leakage of core process data via external interfaces |
Three common misconfigurations
- Phenomenon: SQL query statements generated by the model cannot correctly associate heat data, and query results include irrelevant fields. Cause: Precise retrieval rules for iron and steel industry heat and casting sequence fields were not configured, leading to SQL generation logic that does not meet business needs.
- Phenomenon: Intermediate execution logs from tool calls appear directly in the final bid document. Cause: Output hiding rules for tool calls were not configured correctly, and unexpected intermediate steps were included in the generated results.
- Phenomenon: After enabling multimodal functions, defect markers in slab photos cannot be recognized, or online retrieval fails to obtain intranet raw material price data. Cause: The
visionparameter was not disabled and the model was not adapted to industrial image formats, or online retrieval configuration was not disabled, preventing access to intranet-specific interfaces.
How to verify correct configuration
- Import a single heat’s batching list and composition report, check whether the generated document accurately matches the values and units of the corresponding fields.
- Initiate a simulated bid quotation request, confirm that the tool call logs only display the final result with no intermediate execution steps.
- Test multi-source data access, confirm that process data from irrelevant heats is not mixed into the generated document.
- Verify internal interface access permissions, confirm that core production data is not leaked via external interfaces.
The values provided here are common starting points and should be measured against the reader's own samples.