为解决近年来用户行业变化特性加剧导致的难以准确辨识用户档案信息变动的问题,文中提出一种基于数据驱动的负荷特征异常辨识方法。首先,提出一种两阶段行业典型负荷形态构建方法,利用基于层次密度的含噪声应用空间聚类(hierarchical de...为解决近年来用户行业变化特性加剧导致的难以准确辨识用户档案信息变动的问题,文中提出一种基于数据驱动的负荷特征异常辨识方法。首先,提出一种两阶段行业典型负荷形态构建方法,利用基于层次密度的含噪声应用空间聚类(hierarchical density-based spatial clustering of applications with noise,HDBSCAN)提取用户在不同场景下的典型日负荷曲线,并利用改进的K-means算法对提取出的典型日负荷曲线进行聚类分析,构建行业的典型负荷形态;其次,提出一种多维场景负荷特征异常智能研判方法,通过构造用户的负荷特征,使用熵权法评估行业典型场景的相对重要性,并采用单分类支持向量机(one-class support vector machine,OCSVM)算法量化每个场景下的用户负荷特征的异常程度,通过加权计算得到用户的综合嫌疑得分并排序,从而实现对负荷特征异常用户的准确辨识。最后,采用某地区实际用户数据进行算例验证。仿真结果表明,所提方法在行业典型负荷场景构建及负荷特征异常辨识方面表现出良好的可行性与实用价值。展开更多
共享单车是一种便宜、绿色环保的短途出行工具,已经成为缓解城市交通压力的重要方式.对于无桩共享单车,用户无需将自行车归还至停车桩,但这种类型的共享单车在高峰时间可能会过于拥挤.本文提出了一种共享单车停车拥挤区域识别的方法.具...共享单车是一种便宜、绿色环保的短途出行工具,已经成为缓解城市交通压力的重要方式.对于无桩共享单车,用户无需将自行车归还至停车桩,但这种类型的共享单车在高峰时间可能会过于拥挤.本文提出了一种共享单车停车拥挤区域识别的方法.具体来说,以某市某品牌共享单车为例,首先对共享单车数据进行预处理,然后使用GeoHash算法处理经纬度坐标信息并计算判断共享单车开关锁订单属于哪个停车围栏,采用HDBSCAN(hierarchical density-based spatial clustering of application with noise)聚类算法将停车围栏聚类为停车区域,在此基础上提出了基于“留存流量与留存密度的综合指标”的方法识别停车拥挤区域.通过分析,识别出的停车拥挤区域符合实际情况.所提出的停车拥挤区域识别方法能够为“削峰填谷”引导调度提供有效的数据支持,给共享单车企业提供一定的参考.展开更多
A high-precision regional gravity field model is significant in various geodesy applications.In the field of modelling regional gravity fields,the spherical radial basis functions(SRBFs)approach has recently gained wi...A high-precision regional gravity field model is significant in various geodesy applications.In the field of modelling regional gravity fields,the spherical radial basis functions(SRBFs)approach has recently gained widespread attention,while the modelling precision is primarily influenced by the base function network.In this study,we propose a method for constructing a data-adaptive network of SRBFs using a modified Hierarchical Density-Based Spatial Clustering of Applications with Noise(HDBSCAN)algorithm,and the performance of the algorithm is verified by the observed gravity data in the Auvergne area.Furthermore,the turning point method is used to optimize the bandwidth of the basis function spectrum,which satisfies the demand for both high-precision gravity field and quasi-geoid modelling simultaneously.Numerical experimental results indicate that our algorithm has an accuracy of about 1.58 mGal in constructing the gravity field model and about 0.03 m in the regional quasi-geoid model.Compared to the existing methods,the number of SRBFs used for modelling has been reduced by 15.8%,and the time cost to determine the centre positions of SRBFs has been saved by 12.5%.Hence,the modified HDBSCAN algorithm presented here is a suitable design method for constructing the SRBF data adaptive network.展开更多
文摘为解决近年来用户行业变化特性加剧导致的难以准确辨识用户档案信息变动的问题,文中提出一种基于数据驱动的负荷特征异常辨识方法。首先,提出一种两阶段行业典型负荷形态构建方法,利用基于层次密度的含噪声应用空间聚类(hierarchical density-based spatial clustering of applications with noise,HDBSCAN)提取用户在不同场景下的典型日负荷曲线,并利用改进的K-means算法对提取出的典型日负荷曲线进行聚类分析,构建行业的典型负荷形态;其次,提出一种多维场景负荷特征异常智能研判方法,通过构造用户的负荷特征,使用熵权法评估行业典型场景的相对重要性,并采用单分类支持向量机(one-class support vector machine,OCSVM)算法量化每个场景下的用户负荷特征的异常程度,通过加权计算得到用户的综合嫌疑得分并排序,从而实现对负荷特征异常用户的准确辨识。最后,采用某地区实际用户数据进行算例验证。仿真结果表明,所提方法在行业典型负荷场景构建及负荷特征异常辨识方面表现出良好的可行性与实用价值。
文摘共享单车是一种便宜、绿色环保的短途出行工具,已经成为缓解城市交通压力的重要方式.对于无桩共享单车,用户无需将自行车归还至停车桩,但这种类型的共享单车在高峰时间可能会过于拥挤.本文提出了一种共享单车停车拥挤区域识别的方法.具体来说,以某市某品牌共享单车为例,首先对共享单车数据进行预处理,然后使用GeoHash算法处理经纬度坐标信息并计算判断共享单车开关锁订单属于哪个停车围栏,采用HDBSCAN(hierarchical density-based spatial clustering of application with noise)聚类算法将停车围栏聚类为停车区域,在此基础上提出了基于“留存流量与留存密度的综合指标”的方法识别停车拥挤区域.通过分析,识别出的停车拥挤区域符合实际情况.所提出的停车拥挤区域识别方法能够为“削峰填谷”引导调度提供有效的数据支持,给共享单车企业提供一定的参考.
基金funded by The Fundamental Research Funds for Chinese Academy of surveying and mapping(AR2402)Open Fund of Wuhan,Gravitation and Solid Earth Tides,National Observation and Research Station(No.WHYWZ202213)。
文摘A high-precision regional gravity field model is significant in various geodesy applications.In the field of modelling regional gravity fields,the spherical radial basis functions(SRBFs)approach has recently gained widespread attention,while the modelling precision is primarily influenced by the base function network.In this study,we propose a method for constructing a data-adaptive network of SRBFs using a modified Hierarchical Density-Based Spatial Clustering of Applications with Noise(HDBSCAN)algorithm,and the performance of the algorithm is verified by the observed gravity data in the Auvergne area.Furthermore,the turning point method is used to optimize the bandwidth of the basis function spectrum,which satisfies the demand for both high-precision gravity field and quasi-geoid modelling simultaneously.Numerical experimental results indicate that our algorithm has an accuracy of about 1.58 mGal in constructing the gravity field model and about 0.03 m in the regional quasi-geoid model.Compared to the existing methods,the number of SRBFs used for modelling has been reduced by 15.8%,and the time cost to determine the centre positions of SRBFs has been saved by 12.5%.Hence,the modified HDBSCAN algorithm presented here is a suitable design method for constructing the SRBF data adaptive network.
