College of Mining Engineering, Taiyuan University of Technology
Postdoctoral Workstation, Shanxi Coking Coal Group Co., Ltd
State Key Laboratory of Intelligent Mining Equipment Technology

煤矿生产过程的智能化主要依托于人工智能（AI）技术分析煤矿工业数据，但单一应用场景AI模型无法适用于煤矿复杂的应用场景，且仅使用分布式计算来处理AI模型输入特征值会导致模型应用效率降低。针对上述问题，提出了一种煤矿工业数据AI模型自动推理技术。该技术架构包括数据层、计算驱动层和模型推理层：数据层采集各类监测数据并统一存储，为计算驱动层提供原始数据；计算驱动层将数据层采集的海量原始数据转换成煤矿应用场景AI模型输入特征值，通过煤矿应用场景AI模型输入特征值双计算引擎自动切换机制，根据数据量自动合理地选择使用基于Spark的分布式计算方式或基于Python的单机计算方式，解决了海量数据计算速度慢、数据应用延迟大的问题；模型推理层将特征值输入应用场景AI模型进行推理，引入煤矿应用场景AI模型多触发方式协同推理机制，通过定时触发、人为交互触发、信号反馈触发3种触发方式，解决了在煤矿复杂的应用条件下单一应用场景AI模型利用效果差的问题。测试和应用结果表明，该技术可实现多应用场景AI模型输入特征值的快速计算，以及不同应用场景AI模型的快速、自动、协同推理。

The automation of coal mine production processes has largely relied on artificial intelligence (AI) technology to analyze industrial data. However, AI models developed for single application scenarios prove inadequate for the complex environments in coal mining. Relying solely on distributed computing to process the input features of AI models has led to decreased application efficiency. To address these challenges, an automatic reasoning technology for AI models in coal mine industrial data was developed. The system architecture consisted of three layers: the data layer, the computation-driving layer, and the model reasoning layer. The data layer gathered and stored various types of monitoring data, supplying raw data to the computation-driving layer. The computation-driving layer converted this vast amount of raw data into input features for AI models tailored to coal mining applications. An automatic switching mechanism between two computational engines—based on the input feature values—intelligently selected either Spark-based distributed computing or Python-based local computing, depending on the data volume, thereby resolving the issues of slow processing speeds and high latency in large-scale data applications. In the model reasoning layer, the input features were fed into the AI models for reasoning. A collaborative reasoning mechanism, with multiple triggering methods—scheduled, manual, and feedback-triggered—was introduced to enhance the effectiveness of AI models in complex coal mining scenarios. The results demonstrate that this technology enables rapid calculation of input features for multiple AI models across different application scenarios, as well as fast, automatic, and collaborative reasoning.

国家自然科学基金项目（52274157）；“科技兴蒙”行动重点专项项目（2022EEDSKJXM010）；国家重点研发计划项目（2020YFB1314004）。

张智星，付翔，张小强，等. 煤矿工业数据AI模型自动推理技术[J]. 工矿自动化，2024，50（9）：138-143.

ZHANG Zhixing, FU Xiang, ZHANG Xiaoqiang, et al. Automatic reasoning technology for coal mine industrial data AI models[J]. Journal of Mine Automation，2024，50（9）：138-143.