In order to solve the problems such as low computing efficiency, unsafe transmission, and privacy disclosure in the process of real-time collection of users’ electricity data by smart grid, aggregation and upload communication, this paper proposes a multidimensional aggregation optimization algorithm for power grid privacy data based on homomorphic encryption. Paillier encryption algorithm based on blind factor technology is used to encrypt and sign multidimensional data as a whole, and the encrypted ciphertext and signature are reported to the aggregator. Bilinear pairing scheme is used to verify the signature of the signed data. The homomorphism of Paillier encryption algorithm is used to realize data aggregation. The security of the algorithm is analyzed, and the decryption security of the algorithm is proved in detail and it can resist external and internal attacks, and the batch verification security of the proposed algorithm is verified. Experimental results show that the proposed algorithm has better computational efficiency and communication efficiency than the other four algorithms. Compared with the id-based homomorphic scheme, the data signature time and signature verification time are reduced by about 510 ms and 187 ms on average, respectively, and the time spent in data communication is also reduced by about 449 ms on average.

随着电力的发展电力营销数据持续增长,传统的集中式数据存储模式已经不能满足电力业务数据存储的安全性和高效性需求.针对上述问题,提出了一种基于区块链的多级加密电力营销数据存储架构,该存储架构以区块链技术作为底层技术支撑,结合分布式存储提供稳定性高、安全可靠的电力数据存储方案.同时在区块链的基础上提出多级加密机制,该机制支持电力数据上链及电力数据传输等流程的逐级加密及验证,使得电力数据存储的安全性得到进一步的保证.通过创建分布式存储设施,对提出的存储机制与集中式存储机制进行对比实验,分析实验结果发现提出的存储机制在电力数据存储方面相比于传统的存储机制在系统延迟、响应时间和吞吐量上都更具有优势,表明了该存储机制合理可行,具有良好的应用前景.

客户侧窃电行为不仅造成电能资源大量流失,同时造成线路负荷过载引发火灾等重大安全事故.针对当前客户侧窃电行为的多样性与隐蔽性特征,以约束客户侧窃电行为为目的 ,设计了客户侧窃电态势感知及智能预警关键技术.考虑客户侧窃电行为的多样性与隐蔽性特性,选取额定电压偏离度、电压不平衡率与电流不平衡率等6个客户侧窃电态势感知指标,利用RBF神经网络构建客户侧窃电态势感知模型,将所选取的6个指标与相关数据作为模型输入,通过动态K均值聚类算法优化模型,模型输出结果即为客户侧窃电态势感知结果.基于感知结果,通过声光报警装置与智能设备实现智能预警,实验结果显示,该技术能够有效抑制客户侧窃电行为.

为了解决当前电力缴费终端身份认证和访问控制中存在的口令嗅探、重放攻击、越权操作等问题,提出了一种基于信任和信誉的灵活数据访问控制方案,结合云计算技术将其应用到电力终端设备数据访问控制中.该方案通过使用基于属性的加密和代理重加密、终端设备评估的信任级别和由多个信誉中心生成的用户信誉来共同控制电力终端的数据访问,将用户信任级别和信誉评估的概念集成到加密系统中,以支持各种控制方案和访问策略.通过对所提出方案的安全性和性能分析,证明该方案访问控制的细粒度,数据保密性良好,通信开销灵活可控,计算复杂度低,减少了电力终端设备的负担.

在基于大数据分析理论的指导下,运用客户标签体系及客户画像相关理论,依据电力企业的特征性,进行电力客户全景视图的构建,对电力企业的客户标签维度、客户标签内容及客户分析模型进行论证.同时,对电力企业的客户标签体系管理与运用进行深入剖析,结合客户欠费风险控制难题进行了验证,从实践角度阐述电力企业客户成长性全景视图的构建与运用.

新的经济环境和社会环境下,电力营销服务所面临的服务形势也越来越严峻,主要表现在用户对营业人员提供的服务质量,对营销服务所提供的服务内容、响应速度要求越来越高;而往往由于营销服务人员的知识、技能、素质存在着千差万别;营销服务人员、设备、制度、流程、考核等标准也存在差异;还没有形成营销服务"一口对外",后台全方位支撑前台;营销服务流程全线上、全过程监控的营销服务运营新型模式.

随着用电信息采集系统在重庆市全范围覆盖的应用推广,海量数据的规模效应给数据交互、数据存储、数据处理分析等应用带来了极大的挑战,亟需有效的数据组织管理技术对用电信息采集系统进行优化.基于大数据技术构建用电信息采集系统,主要对前置通信服务层、数据存储层及数据处理分析层进行优化,并以实际应用效果证明此次的系统优化是可行的和高效的.

随着城市建设的快速发展，软件质量监理在管理中的应用为城市的繁荣和健康发展提供了优良的基础条件，加快了电网建设、信息化管理公司的建设步伐，并获取了众多的预期目标数据，大大地提升了应用系统的精益化程度和应用系统的整体稳定性，为探索电力行业营销自动化系统质量管理体系的构建作出了巨大的贡献。

随着社会经济的发展，人们生活水平的提高，电能成为人们生活工作中不可缺少的部分，同时电力行业也成为了社会经济发展中的重要支柱型产业，其作用更是不可估量。在电力营销中，远程用电检查技术尤为重要，对行业发展具有决定性作用。对此，笔者根据多年工作经验，结合自身观点，就电力营销中远程用电检查技术的应用进行简要分析，并提出相应改进策略。

在高速发展的信息化社会中,多元化缴费终端系统与大众生活的关联日益紧密,并逐渐成为现代网络系统发展的必然趋势.本文在详细分析多元化缴费终端现状的基础上,对某供电公司的多元化缴费终端系统进行安全风险分析和研究,提出并阐析了安全域划分、漏洞监控管理、安全基线配置等安全防护措施和解决方案.

In allusion to such difficulties as huge amount of data and low mining efficiency that the data mining of intelligent power utilization has to be faced with, on the basis of Map-Reduce parallel processing model the improved k-means algorithm based analysis on massive power utilization data is performed. Taking family user as the example, a data dimension model of family user's electricity utilization information including residential user's number, floor area of the housing, number of family member, daily electricity consumption, peakand valley-electricity consumption, number of electrical home appliances and so on is established. Utilizing the advantage of k-means algorithm such as simple clustering analysis and rapid convergence and overcoming its defect of easily falling into local optimal solution two factors in the selection of initial clustering center and the selection of cluster number are comprehensively considered; regarding the size of object density as the standard of selecting initial clustering center and taking the distance between the clusters and the degree of dispersion of objects inside the cluster as the important reference for clustering number selection the traditional k-means algorithm is improved; to enhance the data processing efficiency the massive family users' power utilization data is mined under the Map-Reduce parallel processing model. Experiments on the Hadoop cluster are carried out and experimental results show that the proposed algorithm is feasible and its operation is stable and efficient, besides, it possesses good speed-up ratio.