With the advantage of exceptional long-range traffic perception capabilities and data fusion computational prowess,the cloud control system(CCS)has exhibited formidable poten-tial in the realm of connected assisted dr...With the advantage of exceptional long-range traffic perception capabilities and data fusion computational prowess,the cloud control system(CCS)has exhibited formidable poten-tial in the realm of connected assisted driving,such as the adap-tive cruise control(ACC).Based on the CCS architecture,this paper proposes a cloud-based predictive ACC(PACC)strategy,which fully considers the road slope information and the preced-ing vehicle status.In the cloud,based on the dynamic program-ming(DP),the long-term economic speed planning is carried out by using the slope information.At the vehicle side,the real-time fusion planning of the economic speed and the preceding vehi-cle state is realized based on the model predictive control(MPC),taking into account the safety and economy of driving.In order to ensure the safety and stability of the vehicle-cloud cooperative control system,an event-triggered cruise mode switching method is proposed based on the state of each sub-system of the vehicle-cloud-network-map.Simulation results indicate that the PACC system can still ensure stable cruising under delays and some complex conditions.Moreover,under normal conditions,compared to the ACC system,the PACC sys-tem can further improve economy while ensuring safety and improve the overall energy efficiency of the vehicle,thus achiev-ing fuel savings of 3%to 8%.展开更多
With the advantage of fast calculation and map resources on cloud control system(CCS), cloud-based predictive cruise control(CPCC) for heavy trucks has great potential to improve energy efficiency, which is significan...With the advantage of fast calculation and map resources on cloud control system(CCS), cloud-based predictive cruise control(CPCC) for heavy trucks has great potential to improve energy efficiency, which is significant to achieve the goal of national carbon neutrality. However, most investigations focus on the on-board predictive cruise control(PCC) system,lack of research on CPCC architecture under CCS. Besides, the current PCC algorithms have the problems of a single control target and high computational complexity, which hinders the improvement of the control effect. In this paper, a layered architecture based on CCS is proposed to effectively address the realtime computing of CPCC system and the deployment of its algorithm on vehicle-cloud. In addition, based on the dynamic programming principle and the proposed road point segmentation method(RPSM), a PCC algorithm is designed to optimize the speed and gear of heavy trucks with slope information. Simulation results show that the CPCC system can adaptively control vehicle driving through the slope prediction, with fuel-saving rate of 6.17% in comparison with the constant cruise control. Also,compared with other similar algorithms, the PCC algorithm can make the engine operate more in the efficient zone by cooperatively optimizing the gear and speed. Moreover, the RPSM algorithm can reconfigure the road in advance, with a 91% roadpoint reduction rate, significantly reducing algorithm complexity.Therefore, this study has essential research significance for the economic driving of heavy trucks and the promotion of the CPCC system.展开更多
采用NASA地球观测系统（EOS）“云与地球辐射能量系统（CERES）”2002年7月至2004年6月CERES SSF Aqua MODIS Edition 1B云资料，选取我国西北地区不同气候环境条件下的4个典型地域，研究了总云量、低层云和高层云云量的空间分布特征以...采用NASA地球观测系统（EOS）“云与地球辐射能量系统（CERES）”2002年7月至2004年6月CERES SSF Aqua MODIS Edition 1B云资料，选取我国西北地区不同气候环境条件下的4个典型地域，研究了总云量、低层云和高层云云量的空间分布特征以及季节和年变化特征。结果表明，低层云量的高值区不仅分布在山脉地区，而且也分布在非山脉地区。但高层云的云量高值区只分布在山脉地区；总体来说，云量大小随地域的不同相差相当大，高层云云量年平均值的最大差异发生在祁连山区和塔克拉玛干沙漠之间，两者相差16．4％。而总云量和低层云量年平均值在季风区和塔克拉玛干沙漠地区相差最大，分别可达27．6％和19．5％。季风区和祁连山区云量最大值一般都出现在夏季，天山和塔克拉玛干沙漠地区云量最大值一般都出现在春季，最小值则均出现在秋冬季。总的来说，3个云量参数值在3～9月较高，最低值出现在10-12月。展开更多
采用NASA地球观测系统（EOS）“云与地球辐射能量系统（CERES）”2002年7月至2004年6月CERES SSF Aqua MODIS Edition 1B云资料，对天山山区和塔克拉玛干沙漠云水资源进行了研究。得到的结果不仅包括云量、云液态水柱含量，还包括云滴...采用NASA地球观测系统（EOS）“云与地球辐射能量系统（CERES）”2002年7月至2004年6月CERES SSF Aqua MODIS Edition 1B云资料，对天山山区和塔克拉玛干沙漠云水资源进行了研究。得到的结果不仅包括云量、云液态水柱含量，还包括云滴尺度，为无人区的人工增水作业和天气气候研究提供了基础数据。与以往的卫星观测云气候全球数据集相比，该资料具有更高的空间分辨率，且其观测仪器和云反演方法得到了进一步改善，因此其结果较以往更可信。研究结果表明，两地区云参量年变化规律不尽相同，在数值上有很大差别。除了动力条件和气候背景以外，这可能与沙尘气溶胶可以影响云的物理特性和生命期有关。由年变化来看，天山山区的月平均总云量为47％～72％，而塔克拉玛干沙漠为12％～50％；天山山区低云的月平均液态水柱含量为56．6～96．0g／cm^2，高云为30．5—59．8g／cm^2。而塔克拉玛干沙漠低云的月平均液态水柱含量为19．4～43．9g／cm^2，高云为9．3～59．0g／cm^2；天山山区的月平均云滴半径低云为12．6～16．0μm，高云为8．6-14．8μm。而塔克拉玛干沙漠地区低云云滴半径8．8～11．3μm，高云为6．1—11．1μm。展开更多
针对仅依赖二维遥感影像提取大豆覆盖度难以剔除杂草等复杂背景干扰的问题,该研究提出一种结合三维密集点云的大豆覆盖度提取方法,利用改进的运动恢复结构(Structure from Motion,SfM)算法与半全局匹配(Semi-Global Matching,SGM)算法...针对仅依赖二维遥感影像提取大豆覆盖度难以剔除杂草等复杂背景干扰的问题,该研究提出一种结合三维密集点云的大豆覆盖度提取方法,利用改进的运动恢复结构(Structure from Motion,SfM)算法与半全局匹配(Semi-Global Matching,SGM)算法从无人机立体影像中生成高精度稠密的大豆叶面真彩色三维点云,通过伽马增强的可见光绿叶指数提取植被信息,采用最佳结构元的局部阈值分割算法消除低矮杂草等噪声干扰,以达到结合可见光谱与三维点云实现复杂背景下大豆覆盖度提取的目的。选取不同时期、不同杂草混杂程度、不同地形起伏背景的大豆种植区无人机可见光影像进行试验。结果表明,该方法适用于复杂背景下的花芽分化期大豆覆盖度提取,伽马增强的绿叶指数可提高植被提取精度,结合三维点云信息的覆盖度提取总体精度达到98%以上,相比支持向量机、结合Lab颜色空间变换与Kmeans分割法、双峰阈值法等常用方法效率提高至少68%,在精度和效率方面明显优于仅利用二维影像的覆盖度提取方法。研究成果对于农田精细化管理和产量估测等具有重要的参考价值。展开更多
针对现代电力系统中设施庞杂、多源异构海量数据难以有效处理、“信息孤岛”长期存在以及整体优化调度管理能力不足等问题,基于云控制系统理论,以智能电厂为研究对象,本文提出了智能电厂云控制系统(Intelligent power plant cloud contr...针对现代电力系统中设施庞杂、多源异构海量数据难以有效处理、“信息孤岛”长期存在以及整体优化调度管理能力不足等问题,基于云控制系统理论,以智能电厂为研究对象,本文提出了智能电厂云控制系统(Intelligent power plant cloud control system,IPPCCS)解决方案.基于智能电厂云控制系统,针对绿色能源发电波动性强、抗扰能力差的问题,利用机器学习算法对采集到的风电、光伏输出功率进行短时预测,获知未来风、光机组功率输出情况.在云端使用经济模型预测控制(Economic model predictive control,EMPC)算法,通过实时滚动优化得到水轮机组的功率预测调度策略,保证绿色能源互补发电的鲁棒性,充分消纳风、光两种能源,减少水轮机组启停和穿越振动区次数,在为用户清洁、稳定供电的同时降低了机组寿命损耗.最后,一个区域云数据中心的供电算例表明了本文方法的有效性.展开更多
基金supported by the National Key R&D Program of China(2021YFB2501000)the Consultancy Research Project on the Strategic Study of the Integration and Innovative Development of Intelligent Connected Vehicles and New Energy Ecology in Zhejiang Province(2023ZL0007)+1 种基金the Hetao Shenzhen-HongKong Science and Technology Innovation Cooperation Zone(HZQB-KCZYZ-2021055)the Open Project of the Key Laboratory of Modern Measurement and Control Technology of the Ministry of Education(KF2022-1123202).
文摘With the advantage of exceptional long-range traffic perception capabilities and data fusion computational prowess,the cloud control system(CCS)has exhibited formidable poten-tial in the realm of connected assisted driving,such as the adap-tive cruise control(ACC).Based on the CCS architecture,this paper proposes a cloud-based predictive ACC(PACC)strategy,which fully considers the road slope information and the preced-ing vehicle status.In the cloud,based on the dynamic program-ming(DP),the long-term economic speed planning is carried out by using the slope information.At the vehicle side,the real-time fusion planning of the economic speed and the preceding vehi-cle state is realized based on the model predictive control(MPC),taking into account the safety and economy of driving.In order to ensure the safety and stability of the vehicle-cloud cooperative control system,an event-triggered cruise mode switching method is proposed based on the state of each sub-system of the vehicle-cloud-network-map.Simulation results indicate that the PACC system can still ensure stable cruising under delays and some complex conditions.Moreover,under normal conditions,compared to the ACC system,the PACC sys-tem can further improve economy while ensuring safety and improve the overall energy efficiency of the vehicle,thus achiev-ing fuel savings of 3%to 8%.
基金supported by the National Key Research and Development Program (2021YFB2501003)the Key Research and Development Program of Guangdong Province (2019B090912001)the China Postdoctoral Science Foundation (2020M680531)。
文摘With the advantage of fast calculation and map resources on cloud control system(CCS), cloud-based predictive cruise control(CPCC) for heavy trucks has great potential to improve energy efficiency, which is significant to achieve the goal of national carbon neutrality. However, most investigations focus on the on-board predictive cruise control(PCC) system,lack of research on CPCC architecture under CCS. Besides, the current PCC algorithms have the problems of a single control target and high computational complexity, which hinders the improvement of the control effect. In this paper, a layered architecture based on CCS is proposed to effectively address the realtime computing of CPCC system and the deployment of its algorithm on vehicle-cloud. In addition, based on the dynamic programming principle and the proposed road point segmentation method(RPSM), a PCC algorithm is designed to optimize the speed and gear of heavy trucks with slope information. Simulation results show that the CPCC system can adaptively control vehicle driving through the slope prediction, with fuel-saving rate of 6.17% in comparison with the constant cruise control. Also,compared with other similar algorithms, the PCC algorithm can make the engine operate more in the efficient zone by cooperatively optimizing the gear and speed. Moreover, the RPSM algorithm can reconfigure the road in advance, with a 91% roadpoint reduction rate, significantly reducing algorithm complexity.Therefore, this study has essential research significance for the economic driving of heavy trucks and the promotion of the CPCC system.
文摘采用NASA地球观测系统（EOS）“云与地球辐射能量系统（CERES）”2002年7月至2004年6月CERES SSF Aqua MODIS Edition 1B云资料，选取我国西北地区不同气候环境条件下的4个典型地域，研究了总云量、低层云和高层云云量的空间分布特征以及季节和年变化特征。结果表明，低层云量的高值区不仅分布在山脉地区，而且也分布在非山脉地区。但高层云的云量高值区只分布在山脉地区；总体来说，云量大小随地域的不同相差相当大，高层云云量年平均值的最大差异发生在祁连山区和塔克拉玛干沙漠之间，两者相差16．4％。而总云量和低层云量年平均值在季风区和塔克拉玛干沙漠地区相差最大，分别可达27．6％和19．5％。季风区和祁连山区云量最大值一般都出现在夏季，天山和塔克拉玛干沙漠地区云量最大值一般都出现在春季，最小值则均出现在秋冬季。总的来说，3个云量参数值在3～9月较高，最低值出现在10-12月。
文摘采用NASA地球观测系统（EOS）“云与地球辐射能量系统（CERES）”2002年7月至2004年6月CERES SSF Aqua MODIS Edition 1B云资料，对天山山区和塔克拉玛干沙漠云水资源进行了研究。得到的结果不仅包括云量、云液态水柱含量，还包括云滴尺度，为无人区的人工增水作业和天气气候研究提供了基础数据。与以往的卫星观测云气候全球数据集相比，该资料具有更高的空间分辨率，且其观测仪器和云反演方法得到了进一步改善，因此其结果较以往更可信。研究结果表明，两地区云参量年变化规律不尽相同，在数值上有很大差别。除了动力条件和气候背景以外，这可能与沙尘气溶胶可以影响云的物理特性和生命期有关。由年变化来看，天山山区的月平均总云量为47％～72％，而塔克拉玛干沙漠为12％～50％；天山山区低云的月平均液态水柱含量为56．6～96．0g／cm^2，高云为30．5—59．8g／cm^2。而塔克拉玛干沙漠低云的月平均液态水柱含量为19．4～43．9g／cm^2，高云为9．3～59．0g／cm^2；天山山区的月平均云滴半径低云为12．6～16．0μm，高云为8．6-14．8μm。而塔克拉玛干沙漠地区低云云滴半径8．8～11．3μm，高云为6．1—11．1μm。
文摘针对仅依赖二维遥感影像提取大豆覆盖度难以剔除杂草等复杂背景干扰的问题,该研究提出一种结合三维密集点云的大豆覆盖度提取方法,利用改进的运动恢复结构(Structure from Motion,SfM)算法与半全局匹配(Semi-Global Matching,SGM)算法从无人机立体影像中生成高精度稠密的大豆叶面真彩色三维点云,通过伽马增强的可见光绿叶指数提取植被信息,采用最佳结构元的局部阈值分割算法消除低矮杂草等噪声干扰,以达到结合可见光谱与三维点云实现复杂背景下大豆覆盖度提取的目的。选取不同时期、不同杂草混杂程度、不同地形起伏背景的大豆种植区无人机可见光影像进行试验。结果表明,该方法适用于复杂背景下的花芽分化期大豆覆盖度提取,伽马增强的绿叶指数可提高植被提取精度,结合三维点云信息的覆盖度提取总体精度达到98%以上,相比支持向量机、结合Lab颜色空间变换与Kmeans分割法、双峰阈值法等常用方法效率提高至少68%,在精度和效率方面明显优于仅利用二维影像的覆盖度提取方法。研究成果对于农田精细化管理和产量估测等具有重要的参考价值。
文摘针对现代电力系统中设施庞杂、多源异构海量数据难以有效处理、“信息孤岛”长期存在以及整体优化调度管理能力不足等问题,基于云控制系统理论,以智能电厂为研究对象,本文提出了智能电厂云控制系统(Intelligent power plant cloud control system,IPPCCS)解决方案.基于智能电厂云控制系统,针对绿色能源发电波动性强、抗扰能力差的问题,利用机器学习算法对采集到的风电、光伏输出功率进行短时预测,获知未来风、光机组功率输出情况.在云端使用经济模型预测控制(Economic model predictive control,EMPC)算法,通过实时滚动优化得到水轮机组的功率预测调度策略,保证绿色能源互补发电的鲁棒性,充分消纳风、光两种能源,减少水轮机组启停和穿越振动区次数,在为用户清洁、稳定供电的同时降低了机组寿命损耗.最后,一个区域云数据中心的供电算例表明了本文方法的有效性.
