Aiming at the problems of poor real-time processing and low security performance caused by the massive information of the Internet of things, a security early warning model of the Internet of things based on deep learning in edge computing environment is proposed. Firstly, the system architecture of the Internet of things is designed by using the edge computing technology, in which the intelligent router is used to obtain the network stub and send it to the nearest edge computing node for anomaly detection. Then, the attention mechanism is used to improve the long short-term memory network (LSTM), and the multidimensional LSTM model is constructed. At the same time, it is used to analyze the combined network data. Finally, according to the set threshold value, judge whether there is abnormal behavior in the network, and give early warning in time to take security defense measures. The experimental analysis of the proposed model based on NS2 simulation platform shows that its early warning success rate and time are 95.2% and 20.6[Formula: see text]ms, respectively, and it can detect and defend various network attacks well, with high security performance.

In today’s era of rapid technological advancement, the emergence of drone technology and intelligent power systems has brought tremendous convenience to society. However, the challenges associated with drone image recognition and intelligent grid device fault detection are becoming increasingly significant. In practical applications, the rapid and accurate identification of drone images and the timely detection of faults in intelligent grid devices are crucial for ensuring aviation safety and the stable operation of power systems. This article aims to integrate Transformer models, transfer learning, and generative adversarial networks to enhance the accuracy and efficiency of drone image recognition and intelligent grid device fault detection.In the methodology section, we first employ the Transformer model, a deep learning model based on self-attention mechanisms that has demonstrated excellent performance in handling image sequences, capturing complex spatial relationships in images. To address limited data issues, we introduce transfer learning, accelerating the learning process in the target domain by training the model on a source domain. To further enhance the model’s robustness and generalization capability, we incorporate generative adversarial networks to generate more representative training samples.In the experimental section, we validate our model using a large dataset of real drone images and intelligent grid device fault data. Our model shows significant improvements in metrics such as specificity, accuracy, recall, and F1-score. Specifically, in the experimental data, we observe a notable advantage of our model over traditional methods in both drone image recognition and intelligent grid device fault detection. Particularly in the detection of intelligent grid device faults, our model successfully captures subtle fault features, achieving an accuracy of over 90%, an improvement of more than 17% compared to traditional methods, and an outstanding F1-score of around 91%.In summary, this article achieves a significant improvement in the fields of drone image recognition and intelligent grid device fault detection by cleverly integrating Transformer models, transfer learning, and generative adversarial networks. Our approach not only holds broad theoretical application prospects but also receives robust support from experimental data, providing strong support for research and applications in related fields.

Source and load prediction is an important basis for virtual power plant(VPP)to make future dispatching plans.A collaborative optimization scheduling method of VPP generation side and user side based on multi-frequency combination short-term source load prediction is proposed.First of all,ensemble empirical mode decomposition(EEMD)is performed on the load data of the time series and reconstructed into two kinds of frequency,which is then predicted by the graph convolution network and long short-term memory(GCN-LSTM)fusion algorithm.The prediction results obtained from the multi-frequency model are aggregated into an uncertain fuzzy set.Considering the demand response,the VPP day-ahead two-layer optimal scheduling model is established.The upper layer takes the user benefit maximization as the goal,comprehensively utilizes the scheduling function of demand response,and optimizes multiple types of controllable loads based on the established time-of-use price.The lower layer aims to minimize the output cost of distributed power supply and take into account the interests of both sides of supply and demand,so as to optimize the internal resources of VPP.The above model is decomposed into main and sub-problems for solving by using the improved column reduction generation algorithm.The economy,robustness,and effectiveness of the proposed model are verified by a case analysis.

Reasonable allocation of resources is an important guarantee for efficient support of power business in edge IoT agents. Facing the above problems of the current power Internet of Things, this paper proposes a resource optimization allocation method based on deep Q-learning. This method first comprehensively considers the communication performance and network security. Involving indicators such as latency and service satisfaction, a complete and reliable mathematical model of the edge Internet of Things proxy network is constructed to achieve efficient and reliable modeling of the power Internet of Things (pIoT), aiming to better fit the practical interaction needs for efficient and secure communication. The Q-learning network model is optimized, and the method combining Reinforcement learning and deep learning is used to solve the model. Used by this network, the optimization and improvement of the deep network model is realized, so that the status, action and other parameters of the network model can be solved in a timely manner, so as to better support the reliable and efficient information interaction of the communication network. The test results prove that the delay of the proposed method can be maintained within 12[Formula: see text]ms in more complex scenarios, and the interaction success rate reaches 0.975, confirming that the proposed method can provide good information interaction guarantee services.

The evaluation of digitally empowered grassroots level to reduce burden and increase efficiency is an important theoretical and practical issue to promote the digital transformation of enterprises. Due to the differences in assessment and evaluation subjects and the tendency of evaluation selection indexes, the evaluation indexes with their respective geographical attributes are adopted, and the digitally empowered grassroots are divided into three levels through the corresponding "digital empowerment grassroots triangular model", based on the concepts of "implementation empowerment, decision-making empowerment, thinking empowerment" and "digital empowerment". Based on the concept of "implementation empowerment, decision-making empowerment, thinking empowerment", the "three-dimensional burden reduction digital empowerment grassroots burden reduction and efficiency evaluation system" has been constructed to cover three secondary indicators, six tertiary indicators and 16 quaternary indicators, including ideological transformation to reduce the burden and increase efficiency, decision-making optimization to reduce the burden and increase efficiency, as well as the implementation of convenient and easy to reduce the burden and increase efficiency. Explore the construction of grid enterprise digital empowerment grassroots load reduction and efficiency evaluation index system, for enterprises to carry out further good digital empowerment grassroots load reduction and efficiency work to provide orientation guide.

随着整县屋顶光伏试点工作的逐步推进,光伏功率消纳的问题日益凸显.为解决光伏发电功率波动大、消纳率低的问题,提出了一种基于台区功率共济的屋顶光伏功率消纳方法.首先,通过边缘物联终端采集配电台区屋顶光伏的发电功率数据,并采用长短期记忆网络对光伏历史发电功率和气象数据进行综合分析,生成多时间尺度的光伏发电预测曲线;其次,通过储能装置、跨台区联络开关建立微电网调度控制模型,计及短期光伏发电功率预测,合理安排用能策略、储能策略和跨配电台区功率共济消纳策略,以提高光伏功率的消纳水平;最后,在某县应用该方法,其光伏功率消纳比例达95.26％,较蒙特卡洛方法提高16.18％,运行结果验证了所提方法的有效性.

针对现有BPLC网络组网协议在回复关联确认消息的数量和时间上存在冗余的问题,对BPLC报文交互过程进行研究,提出一种基于自适应组播的高效组网协议.通过自适应地聚合关联确认消息并采用组播方式发送,在减少控制开销的同时加速发送部分关联确认消息.理论分析显示了该协议的有效性.仿真结果表明,与现有BPLC网络组网协议相比,该协议能够减少5.88％以上的控制开销并降低5.53％以上的入网时延.

综合能源系统是多输入多输出系统,包含多种能源的输入、转换和储存等设备.我国西南地区拥有丰富可再生能源,适合建设多能互补的综合能源系统.文章考虑西南地区的能源结构,针对运行设备效率随环境和出力变化的特点,建立设备的全工况能量转换模型.以系统的年投资运行成本为目标,建立了集电气热冷能于一体的西南地区综合能源系统优化配置模型,并对模型进行分段线性化处理,降低模型的非线性度.最后通过算例分析,得出了西南地区用户侧综合能源系统在全工况用能场景下的最优配置,提高了经济和环保双重效益.

由于异构数据的发布缺乏灵活性与实用性,提出了一种基于聚类分析的个性化异构数据发布方法.首先综合考虑数据的各种属性,通过聚类标签对数据的集群结构进行编码.另外,通过不断迭代更新原始数据能够始终保留数据的聚类结构,进一步在原始数据中加入噪声从而满足-差分隐私的要求.在满足差分隐私原则的前提下,提出了一种同时处理关系数据和集值数据的不确定性算法,不同类型的数据以类似的方式进行匿名化.通过实验验证了该方法能够有效提升异构数据发布的泛化能力,提升安全性与实用性.

为保障移动作业应用安全,基于现有公司移动业务安全防护现状调研,对终端层、网络层以及服务层进行风险分析.根据公司最新移动安全专项防护设计,结合公司移动应用安全防护要求,遵循公司信息安全总体防护策略,充分借鉴等级保护2.0最新要求,从终端安全、网络安全、应用安全和数据安全4个方面设计构建电力移动作业应用安全防护体系,以保障公司移动作业应用安全稳定运行,提升公司移动作业应用的防护能力和防护水平.

In order to deal with the difference between power grid fault judgment experience and field data, a novel model is proposed for power grid fault judgment based on gray experience fusion, and its frameworks, processing flow and main algorithms are presented. The model utilizes the gray information fusion method to integrate the expert experience and field data. Then the cluster matching method is used to extract the expert experience that is highly similar to the field data and to realize the judgment of present faults. In the end, the expertise database is optimized through integration of the final judgment results, disposal schemes and field data. Comparative experiment illustrates that the model has high fault judgment accuracy, comprehensive fault coverage and better time effectiveness.

The data quality is poor and lack of data quality management capacity in Utility industry.Base on the data life cycle,a closed-loop data quality control framework is proposed for SGCC Operation Monitoring Center,which describes a comprehensive definition,profiling,metrics,enhance of data quality,and achieves all-round management of data quality.Meanwhile,this paper focuses on an algorithm of a fuzzy clustering approach for missing value imputation with noisy data immunity.The OKMI(Optimized K-Means Imputation) method aggregates data instances to more accurate clusters for further appropriate estimation via information entropy after resampling pre-clustering and outlier test.The effectiveness of experimental results in SGCC Operational Monitoring Center demonstrate that the OKMI proposed obtains higher precision both on quantitative and on nominal attributive missing value completion than other classic methods under all missingness mechanisms at varying missing rates with abnormal values.

介绍了采用J2EE三层架构模式的电力企业信息门户。该门户系统起到了畅通信息渠道,促进业务集成,整合已建系统、消除信息"孤岛"的作用。以重庆市电力公司企业信息门户为实例分析了实现过程中的不足,提出了改进方案。

该文结合电力系统信息化建设的现状,分析了目前在信息安全评估领域的主要方法,提出了电力系统信息安全风险评估的策略。