平行矿山: 从数字孪生到矿山智能

针对新时代下我国矿区智能化发展诉求与矿山无人化进程中遇到的复现难、协同难的技术问题, 本文融合智慧矿山理念、ACP (Artificial societies + computational experiments + parallel execution)平行智能理论和新一代智能技术, 设计并实现了智慧矿山操作系统 (Intelligent mine operation system, IMOS), 为平行矿山智能管理与控制一体化提出了解决方案. 本文首先分析露天煤矿产业发展趋势; 国内外露天矿山智能化发展情况; 面向露天矿山无人化与智能化需求, 深度融合数字四胞胎理论, 设计了虚实融合的IMOS架构; 详细阐述了IMOS子系统架构与功能, 包括: 单车作业系统、多车协同系统、车路协同系统、无人驾驶智能系统、调度管理系统、平行系统、监管系统、远程接管系统和通信系统; 并探讨了IMOS关键技术, 即平行矿山仿真建模技术、无人驾驶技术、矿区通信技术和协同作业技术. 该操作系统是国内首套露天矿山无人化与智能化的一体化解决方案, 并能够迁移到不同矿区不同作业场景, 推动矿区智能化无人化发展, 减少人工干预从而降低安全风险, 大幅度降低人工成本, 提高生产作业效率, 并可结合社会发展要素为实现绿色可持续发展矿区提供支撑.     

Correlated Differential Privacy of Multiparty Data Release in Machine Learning

Differential privacy(DP)is widely employed for the private data release in the single-party scenario.Data utility could be degraded with noise generated by ubiquitous data correlation,and it is often addressed by sensitivity reduction with correlation analysis.However,increasing multiparty data release applications present new challenges for existing methods.In this paper,we propose a novel correlated differential privacy of the multiparty data release(MP-CRDP).It effectively reduces the merged dataset's dimensionality and correlated sensitivity in two steps to optimize the utility.We also propose a multiparty correlation analysis technique.Based on the prior knowledge of multiparty data,a more reasonable and rigorous standard is designed to measure the correlated degree,reducing correlated sensitivity,and thus improve the data utility.Moreover,by adding noise to the weights of machine learning algorithms and query noise to the release data,MP-CRDP provides the release technology for both low-noise private data and private machine learning algorithms.Comprehensive experiments demonstrate the effectiveness and practicability of the proposed method on the utilized Adult and Breast Cancer datasets.     

延庆赛区工程建设与生态保护

北京2022年冬奥会和冬残奥会延庆赛区秉承“绿色办奥”理念,根据赛区自然条件特点,在工程建设中全面开展生态保护工作.本文根据延庆赛区建设的工作经验,阐述了对延庆赛区工程建设与生态保护的思考.     

基于Sentinel-2遥感影像的烟草种植信息精准提取

为探索快速、实时、精准和更具成本效益的烟草面积和产量监测方法,选取云南省德宏傣族景颇族自治州芒市为研究区域,采用Sentinel-2多光谱数据为数据源,对2020年3月份云南省德宏州芒市烟草、林地、水体等地物光谱特征和植被指数、红边指数进行了分析,应用面向对象的方法精准提取烤烟种植区域信息,提取的烟草面积为3 873.34hm²。精度评价结果表明,提取的总体精度达到94.38%,Kappa系数为0.93,可满足烟叶生产管理的实际需求。认为以Sentinel-2多光谱影像为数据源结合面向对象的方法可以有效实现烟草种植信息的精准提取,进而为县域范围内烟草生产管理提供数据支撑。     

聚氨酯硬质泡沫用发泡剂的发展现状与HCFC-141b替代面临的挑战

介绍了我国聚氨酯硬质泡沫塑料用发泡剂的发展现状,主要包括烷烃类发泡剂、水发泡剂和氢氟烷烃类发泡剂,分析了我国发泡剂发展过程中面临的主要挑战。     

采煤沉陷区建筑地基稳定性分析方法对比研究

近年采煤沉陷区土地被广泛用作建设用地,建筑地基的稳定性直接决定沉陷区新建建筑物的可行性,而稳定性分析方法的选取是建筑地基稳定性分析的重要一环。为筛选出适用于采煤沉陷区建筑地基稳定性分析的最优方法,以淮北中湖采煤沉陷区为研究对象,针对沉陷区新建建筑物的地基稳定性分析问题,探讨了影响地基稳定性的主要因素,总结了地基稳定的评价标准,分别使用概率积分法、力学解析法和经验公式法对研究区地基进行了稳定性分析,并综合分析过程中的工作量、准确性、适用条件和模型构建因素对各方法进行了对比分析,指出了采煤沉陷区建筑地基稳定性分析的最适方法。研究表明：经验公式法在实际的地基稳定性分析中工作量小、准确性高、适用性强,模型可调性高,更适用于采煤沉陷区建筑地基稳定性分析的实际工程应用,力学解析法其次,概率积分法再次;概率积分法、力学解析法和经验公式法对研究区的地基稳定性评价结果与工程实际基本一致,研究区地基处于稳定状态;概率积分法实现了研究区未来十年的地表残余变形预计,今后研究区仍处于缓慢移动的状态,最大残余下沉量441 mm,最大残余倾斜1.6 mm/m,最大残余曲率0.02 mm/m²,...     

Correlated Differential Privacy of Multiparty Data Release in Machine Learning

Differential privacy(DP)is widely employed for the private data release in the single-party scenario.Data utility could be degraded with noise generated by ubiquitous data correlation,and it is often addressed by sensitivity reduction with correlation analysis.However,increasing multiparty data release applications present new challenges for existing methods.In this paper,we propose a novel correlated differential privacy of the multiparty data release(MP-CRDP).It effectively reduces the merged dataset's dimensionality and correlated sensitivity in two steps to optimize the utility.We also propose a multiparty correlation analysis technique.Based on the prior knowledge of multiparty data,a more reasonable and rigorous standard is designed to measure the correlated degree,reducing correlated sensitivity,and thus improve the data utility.Moreover,by adding noise to the weights of machine learning algorithms and query noise to the release data,MP-CRDP provides the release technology for both low-noise private data and private machine learning algorithms.Comprehensive experiments demonstrate the effectiveness and practicability of the proposed method on the utilized Adult and Breast Cancer datasets.