随着社会经济和煤炭企业的发展，市场对煤炭质量的要求越来越高。煤炭质量提高的关键在于对生产过程中煤质信息的全面管理与把控，目前煤炭企业在煤质管理工作中，缺少一体化管理平台、煤质预测效率低，本文分析煤质管理业务流程，构建了煤质预测及煤层煤质三维可视化模型，设计并实现了煤质全过程管理系统。具体研究内容如下：

（1）煤炭生产是一个典型的多部门协同管控方式，首先梳理煤质管理工作中的业务流程及煤质数据的流转过程，然后详细分析煤炭各生产部门之间的业务关联，建立煤质全过程管理模式，明确企业煤质管理过程所包含的环节及各环节之间的关联关系。

（2）构建基于ADE-Kriging煤质预测模型。针对传统克里金算法(Kriging)在煤质预测时，其变差函数拟合过程中容易出现过拟合问题，引入差分进化算法求解变差函数模型参数；针对差分进化算法存在容易陷入局部最优解和早熟收敛问题，提出一种对其变异和交叉操作中的参数因子进行自适应调整的自适应差分进化算法(ADE)；使用ADE算法求解Kriging变差函数的模型参数；运用煤矿实际煤层煤质数据，进行交叉对比实验，证明基于ADE-Kriging算法实现煤质预测的有效性。

（3）构建煤层结构及煤质预测三维可视化模型。针对煤炭勘探阶段产生的钻孔点稀疏，难以精确反映煤层空间特征的问题，运用ADE-Kriging预测煤层未知点的高程值，增加绘图的数据点，结合TIN-GTP空间数据模型，进行煤层结构三维建模；通过纹理映射和拾取模型将煤质预测结果融入三维模型中，实现煤质预测三维可视化，使煤质管理者更加直观、全面地掌握煤层煤质分布情况。

（4）最后结合SpringBoot+SSM+Vue+uni-app框架，设计并实现一套集基础数据管理、煤质预测、煤质计划、现场管控和煤质信息反馈功能的煤质全过程管理信息系统。研究成果应用于某煤矿实际煤质管理工作中，为煤炭企业建立统一的煤质信息管理平台，保证企业内部煤质信息及时流通，更加科学地调度资源，促进企业取得良好的经济效益。

With the development of social economy and coal enterprises, the market has higher and higher requirements for coal quality. The key to improving coal quality lies in the comprehensive management and control of coal quality information in the production process. At present, coal enterprises lack an integrated management platform and the efficiency of coal quality prediction is low in coal quality management. This thesis analyzes the coal quality management business process, The coal quality prediction and three-dimensional visualization model of coal seam coal quality were constructed, and the whole process management system of coal quality was designed and realized. The specific research contents are as follows:

(1)Coal production was a typical multi-department collaborative management and control method. First, the business process in coal quality management and the circulation process of coal quality data are sorted out, and then the business relationship between various coal production departments is analyzed in detail, the whole process management mode of coal quality was established to establish a comprehensive coal quality management system， The process management mode define the links included in the coal quality management process of the enterprise and the relationship between the links,and clarify the links included in the coal quality management process of the enterprise and the relationship between each link.

(2)A coal quality prediction model based on ADE-Kriging is constructed. In view of the fact that the traditional Kriging algorithm is prone to over-fitting in the process of variogram fitting in coal quality prediction, the differential evolution algorithm is introduced to solve the variogram model parameters; iming at the problem that differential evolution is easy to fall into local optimal solution and premature convergence, an adaptive differential evolution algorithm (ADE) is proposed to adaptively adjust the parameter factors in the mutation and crossover operation; the ADE algorithm is used to solve the model parameters of the Kriging variogram, and Using the actual coal seam coal quality data of coal mines,cross-comparison experiments were carried out to prove the effectiveness of coal quality prediction based on ADE-Kriging algorithm.

(3)A three-dimensional visualization model of coal seam structure and coal quality prediction are constructed. Aiming at the problem that the drilling points generated in the coal exploration stage are sparse, and it is difficult to accurately reflect the spatial characteristics of the coal seam, ADE-Kriging is used to predict the elevation of the unknown area of the coal seam, and the data points of the drawing are added. The coal quality prediction result is integrated into the 3D model through texture mapping and Raycaster to realize the 3D visualization of coal quality prediction, so that the coal quality manager can grasp the distribution of coal quality in coal seams more intuitively and comprehensively.

(4) Finally, combined with the SpringBoot+SSM+Vue+uin-app framework, a set of coal quality whole process management information system integrating basic data management, coal quality prediction, coal quality planning, on-site control and coal quality information feedback functions is designed and implemented. The research results are applied to the actual coal quality management of a coal mine, to establish a unified coal quality information management platform for coal enterprises, to ensure the timely circulation of coal quality information within the enterprise, to schedule resources more scientifically, and to promote the enterprise to achieve good economic benefits.