What the data for this category looks like
Coal mining production ledger data comes from underground mining and excavation equipment operation logs, ventilation and gas monitoring sensor data, scheduling and shift ledgers, safety inspection records, and material requisition documents. Real-time ledgers sync every 15 minutes. Daily ledgers complete batch synchronization at early morning each day. Monthly summary ledgers are generated at the start of each month. Single ledger documents include fields such as unique device identifier, operation duration, energy consumption value, inspection time, abnormal status flag, and more. Units include hours, kilowatt-hours, person-times, tons, and others.
What constraints these characteristics impose on model integration and configuration
High-frequency updates of real-time data streams require model integration to adapt to short-cycle context calls. This avoids timeouts caused by context overflow. The multi-field, multi-unit ledger structure requires clear field mapping rules during configuration. This prevents models from confusing different numerical units. The long text volume of monthly summary ledgers requires adjusting the context parameter limit for model calls. This ensures complete reading of historical data. Differences in update rhythms across various ledgers require configuration to support switching call frequencies based on data type. This avoids invalid requests occupying resources.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the long text reading requirements of monthly summary ledgers, avoiding truncation of critical historical data |
maxTokens | 4000 token | Controls the output length of single-round report generation, matching the text volume of ledger summaries |
quoteMaxToken | 2000 token | Limits the total length of retrieved ledger fragments, ensuring the model can focus on valid information |
temperature | 0.2–0.4 | Reduces generation randomness, ensuring accuracy and format consistency of ledger data summaries |
AIPROXY_API_ENDPOINT | Fill in the exclusive address provided by the deployment service provider | Adapts to access restrictions of the coal mining intranet environment, ensuring compliance of the model call link |
toolChoice | auto | Allows the model to automatically choose whether to call retrieval tools, adapting to summary requirements of different types of ledgers |
Three common configuration mistakes
- Phenomenon: Returns code parsing errors when calling the DeepSeek Code model, unable to correctly format ledger data. Cause: Did not adjust the
maxContextparameter to adapt to long text input, leading to parsing failure after monthly ledgers are truncated. - Phenomenon: After configuring a model with internet access, generated reports only use knowledge base content and do not include the latest real-time equipment data. Cause: Did not set the
toolChoiceparameter to allow internet calls, forcing the model to only use static knowledge base data. - Phenomenon: Returns a protocol incompatibility error when attempting to access third-party AI tools via MCP. Cause: Did not adapt to the SSE-format MCP service generated by FastGPT, which does not match the call protocol required by the third-party tool.
How to confirm successful configuration
- Submit a single real-time ledger test, check whether the summary result returned by the model includes complete equipment operation duration and energy consumption values, and verify field matching accuracy.
- Trigger a monthly ledger summary task, check whether the length of the output report meets expectations, and confirm that no text truncation has occurred.
- Compare two generation results after adjusting the
temperatureparameter, confirm that output format and data accuracy do not show meaningless fluctuations. - Check the model call log, confirm that the request link and API key configuration are correct, with no permission-related errors.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.