What this category of data looks like
Data sources for this category include hot metal and scrap steel data, L2 process control systems, laboratory test data, MES systems, energy medium systems, and equipment condition monitoring systems. Daily documents include heat plans, batching lists, converter smelting records, composition reports, continuous casting process records, and slab judgment sheets. Data updates run per heat, in real time, or per shift. Fields cover process parameters, composition content, equipment status, and more, with units including temperature, weight, and percentage.
Constraints imposed by these characteristics during model integration and configuration
The real-time, high-frequency update nature requires model call latency to be controlled within a reasonable range, to avoid disrupting production rhythms. Multi-source heterogeneous business data requires precise field mapping rules. This ensures the model can correctly correlate process and quality data from different systems. The scenario where a single heat is linked to multiple documents requires limiting total context length, to avoid model errors. The high-temperature continuous process nature requires model calls to avoid long periods of unresponsiveness. Unresponsive calls can trigger production accidents such as continuous casting interruptions.
How to set the configurations
This document uses FastGPT v4.17.0. The following configuration items are adapted to the iron and steel smelting and continuous casting business scenario:
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total context volume of multiple documents per heat, avoiding exceeding the model's context limit |
temperature | 0.3–0.6 | Balances the rigor of process reasoning and the flexibility of association logic, meeting the professional requirements of quality audits |
maxTokens | 2000–3000 token | Controls the output length of single-round responses, adapting to the rapid response needs of production scenarios |
| Workflow/API tool selection | auto | Automatically selects tools to handle structured data queries, adapting to the extraction and correlation needs of multi-source process data |
AIPROXY_API_ENDPOINT | Enterprise internal privately deployed endpoint | Meets compliance requirements for keeping production data on-site |
AIPROXY_API_TOKEN | Exclusive token generated according to enterprise permissions | Ensures interface call security and permission controllability |
Three common configuration errors
- A "context length exceeded" error appears during model testing. This happens because the
maxContextparameter is not adjusted for the multi-document volume of iron and steel smelting, and the default general-scenario value is used instead. - Garbled text appears in audit results. This happens because special process symbols in smelting records (such as the temperature unit ℃, composition percentage symbols) are not adapted, and correct character encoding rules are not configured.
- Files cannot be analyzed after upload. This happens because the formats of exclusive documents such as continuous casting process records and slab judgment sheets (such as structured CSV, PDF with process tables) are not pre-configured into the system's supported parsing rules.
How to confirm the configuration is complete
- Initiate a model test for single-heat business data. Verify that the returned results include key fields from batching lists and converter records, to confirm the field mapping configuration is correct.
- Initiate multiple consecutive real-time data calls. Check that response latency meets production scenario requirements, and adjust relevant parameters to adapt to latency.
- Upload an example continuous casting process record file. Confirm the system can correctly extract process parameters and defect information, to verify the document parsing configuration takes effect.
- View interface call logs. Confirm there are no authentication or connection errors in the
AIPROXY_API_TOKENandAIPROXY_API_ENDPOINTconfigurations.
The values provided are common starting points and should be measured against the reader's own samples.