Fuzzy-TOPSIS Evaluation of Power Product-Service System:A Framework Driven by Big Data

Power product-service system(power PSS),which combines industrial electric products with electric energy services,is an effective solution for power enterprises under the background of the rapid development of power systems.In the life cycle of power PSS,evaluation decision of power PSS alternatives is of great significance for subsequent implementation.To address the power PSS alternative evaluation problem,a power PSS evaluation framework is explored driven by the big data of stakeholder comments.Based on the multi-stakeholder comments of power PSS evaluation decision’s influence factors,the index system is constructed through analyzing and summarizing the co-occurrence matrix and semantic network diagram of high-frequency words.To determine the fuzzy index value of power PSS alternative,the stakeholders’vague opinions expressed by trapezoidal fuzzy number are integrated by group decision method.Fuzzy concept is introduced into the classical Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)method and fuzzy-TOPSIS method is put forward by using the fuzzy index value.The improved TOPSIS is adopted to sequence the power PSS alternatives.The case of power PSS evaluation of six alternatives for a power enterprise shows that the explored framework is effective and can provide a feasible solution for power PSS alternative evaluation.

Power Big Data Fusion Prediction

This paper is a research on the characteristics of power big data. According to the characteristics of ＂large volume＂, ＂species diversity＂, ＂sparse value density＂, ＂fast speed＂ of the power big data, a prediction model of multi-source information fusion for large data is established, the fusion prediction of various parameters of the same object is realized. A combined algorithm of Map Reduce and neural network is used in this paper. Using clustering and nonlinear mapping ability of neural network, it can effectively solve the problem of nonlinear objective function approximation, and neural network is applied to the prediction of fusion. In this paper, neural network model using multi layer feed forward network--BP neural network. Simultaneously, to achieve large-scale data sets in parallel computing, the parallelism and real-time property of the algorithm should be considered, further combined with Reduce Map model, to realize the parallel processing of the algorithm, making it more suitable for the study of the fusion of large data. And finally, through simulation, it verifies the feasibility of the proposed model and algorithm.

A New Model of China-US Big Power Relations： A Symbolic Sign in Foreign Affairs

To cope with the current crisis and tensions full-filled China-US relationship, Chinese President Xi Jinping put forward the concept of building a new model of China-US big power relations, which the US agrees. Yet the new model won a heated discussion. In China this new model was evaluated positively and optimistically, while in the US it was perceived as a strategic challenge or even a threat. In the present article, the author proposes that this new model of China-US big power relations is more like a symbolic sign in foreign affairs rather than a strategic challenge or a threat or an effective and workable mechanism at this moment, and meanwhile analyses this view from diachronic and semiotic perspectives. The analyses reveal that the new model functions as a symbolic sign, signifying to the world that conceptually the two big powers have a good and harmonious relationship.

Opportunities and Risk Assessment of BRICs New Development Bank——New Big Powers Coordination Perspective

Under the background of counter economic globalization,the Group of Twenty(G20)has become the primary platform for the east and west powers to seek consultation and dialogue.And the coordination mechanism of the new global powers has come into being slowly.As a multilateral regional bank,the BRICsBank takes the interests of developing countries as the leading factor.Based on international economic cooperation ground,it will broaden the South-South cooperation to create more convenient conditions for foreign direct investment,as well as bilateral and multilateral financial cooperation.From new power coordination perspective,with global economy governance structure transformation as the starting point,this research accurately defines the concept of coordination mechanism,systematically analyzes the evolution of global economic governance structure,intensively interprets the current characteristics of big power coordination mode in global economic governance structure,and objective assesses trends of global political and economic pattern.It is of important policy reference implications for the BRICS member countries to effectively promote the BRICs Bank institutionalization and global governance of new power coordination nature.

Characterizing big data analytics workloads on POWER8 SMT processors

Big data analytics is emerging as one kind of the most important workloads in modern data centers. Hence,it is of great interest to identify the method of achieving the best performance for big data analytics workloads running on state-of-the-art SMT( simultaneous multithreading) processors,which needs comprehensive understanding to workload characteristics. This paper chooses the Spark workloads as the representative big data analytics workloads and performs comprehensive measurements on the POWER8 platform,which supports a wide range of multithreading. The research finds that the thread assignment policy and cache contention have significant impacts on application performance. In order to identify the potential optimization method from the experiment results,this study performs micro-architecture level characterizations by means of hardware performance counters and gives implications accordingly.

Intellectual Property Protection and High-Quality Development of Xinjiang Melons under the Big Food Concept

The development of diversified foods such as melons under the big food concept can reduce the pressure on staple food grain.Xinjiang has the advantages of building a new development pattern of dual circulation of diversified food industry and establishing a national diversified food safety and high-quality assurance center.The construction of an agricultural power needs the strategic support of an intellectual property power.This paper introduced and studied Xinjiang s melon industry and its agricultural intellectual property resources,analyzed the six main problems,including many idle land resources have not been developed into melon fields and the role of melons in diversified food supply systems not well played.Finally,it proposed to vigorously develop diversified food industries such as melons in Xinjiang and establish an eight-point strategy such as national diversified food safety and high-quality assurance center.

大数据背景下的电力营销信息管理平台设计

为了提升电力营销大数据处理和调度效率,设计大数据背景下的电力营销信息管理平台。基于Hadoop和MapReduce设计平台存储和技术框架,利用大数据技术划分电力营销大数据,实现电力营销信息状态安全分析和监控;采用云计算技术分析和处理电力营销数据。测试结果显示,所设计平台的大数据处理吞吐量较高、大数据处理加速比稳定;数据调度计算效率较高、性能稳定;将电力营销数据以对象形式缓存到内存中时,执行时间较短、耗费内存较小。该平台具备较好的数据处理及调度能力。

面向电力调度的事件知识图谱研究现状及发展

事件知识图谱(EKG)是一种可学习事件演化规律的特殊知识图谱,具有推理、预测等功能。针对电力调度业务数据量大、模态多、交互耦合等特点,该文详述了面向电力调度的事件知识图谱的数据集构建、主流方法、技术架构、评价指标、适用场景等,重点分析各场景的可行性,并在应用流程、输入输出、技术架构等方面给出方案,最后对其在电力调度业务长期发展面临的难点和可能的研究方向进行了展望。该文研究为研究电力调度领域特点、事件知识图谱优势和两者结合提供了参考,并为事件知识图谱在电力调度领域中的应用方向提供了指导性思路。

机械加工质量预测研究现状与发展趋势

机械加工质量预测是智能制造的重要组成内容,也是实现质量闭环控制的前提条件,对推动智能制造系统真正落地应用具有极其重要的作用.在对机械加工质量预测的历史进行简要回顾时发现,学者多将研究重点放在机床某一关键部件对加工质量影响的机理研究,却鲜见部件耦合影响的关联性研究.基于上述难题,本文首先剖析影响机械加工质量的7类要素,包括刀具几何参数、切削参数、切削液类型、热误差与热变形、数控机床零部件性能退化、切削颤振以及系统特性;随后,根据各要素数据种类和测量方式的不同,将机械加工质量监测与预测方法划分为4大类,包括机器视觉测量、功率测量、振动测量以及其他测量方法,并对各方法的技术特点、局限性和发展动态进行了阐述;最后,考虑各机械加工质量监测与预测方法的不足,指出材料切削机制研究、数据质量评估方法、面向工业现场数据库构建的标准以及质量预测信息的智能表征与可视化等方面可能是未来的发展趋势.

面向设计研发的核能大数据系统架构及标准化研究

结合核能设计研发基本流程和全生命周期数据特点,开展基于多源异构数据集成的采集、存储、处理、管理、应用等功能需求分析,按照“分层设计”思想和以数据应用为目标的路线,建立核能大数据系统技术架构和数据架构,对架构中的层级、模块、功能进行分析。考虑大数据技术持续应用的特点,基于核能大数据系统架构和技术发展需求,开展标准化研究,提出核能大数据标准体系。该研究为大数据技术在核能设计研发应用提供基础,并为后续开展系统性的核能大数据标准化工作提供技术参考。

AI,大数据与强度机器——信息权力的时代如何重思主体性

大数据是推动生成式AI之诞生和发展的一个重要前提,对大数据的发展演变进行谱系学考察也有助于我们更深刻地理解以ChatGPT为代表的新型AI的本质与前景。从强度性数据到大数据,从第四范式到数据洪水,这或许并非只是一个从量变到质变的过程,而更是数据的本原性强度日渐衰退,人的主体性在数据面前日渐消亡的过程。由此,有必要回归强度这个哲学概念,结合德勒兹和加西亚等哲学家的代表性论述,进而揭示它何以从一种创生之力量逐渐蜕变为经典物理学中的被计量之力,进而又在控制社会之中被彻底驯化为电力。既然肯定性的生命强度举步维艰,那么或许理应转向否定性的死亡强度来探寻出路。加西亚的思辨实在论,德勒兹的无器官身体,以及福柯的致命机器,似乎敞开了三条相关又有别的道路。

高校公权力大数据监督:价值、困境与路径

随着信息技术的发展,利用大数据技术进行监督,成为提升纪检监察工作质效的有效手段。公权力大数据监督根据腐败行为隐蔽化、技术化和网络化的特点,用“实打实”的数据和证据还原行权轨迹,挖掘出隐匿的腐败迹象,及时进行廉政预警。针对高校公权力特点,结合大数据监督体系建设现状和高校监督执纪实际情况,聚焦重点领域和关键环节,设计大数据监督总体方案,构建可行的监督数据模型,为高校公权力大数据监督提供技术支撑。

跨省域智慧能源管理大数据平台架构及关键技术研究与实践

随着广东省能源集团公司下属电站的不断建设,集团公司对各个电站的运营、运维、人员的管理问题愈发凸显。为有效提升各能源场站的综合管理水平,通过搭建基于物联网的智慧能源管理大数据平台对能源场进行集约管理。系统以工业互联网为硬件基础,以大数据平台为软件核心,实现了系统对风电场、光伏电站、煤层气电站等区域的实时远程监控及各项业务管理。实践证明,本系统能减少运行维护成本,提升集团公司的整体核心竞争力。

基于Spark集群的火电机组经济性挖掘

以电厂经济性为挖掘目标,把供电煤耗率作为机组的评价指标,通过最大信息系数法对影响机组煤耗率的运行参数进行特征选择。对基于Spark的并行Mini Batch K-means算法对外部约束条件进行工况划分以及数据离散化,采用并行FP-growth算法挖掘出机组全工况的强关联规则,进而得到机组重要参数的运行优化指导方案。以某电厂机组为例,结果表明,该方法能够提高数据挖掘效率并完成电厂参数最优值的确定,根据挖掘结果可以更好地对机组人员进行指导。

基于净能力及二阶锥规划的分布式光储多场景协同优化策略

针对现有配电网中分布式光储调度模型存在资源协同不足、求解复杂等问题,提出了一种基于净能力及二阶锥规划的分布式光储多场景协同优化调度策略。通过引入储能接入配电网后的功率转移分布因子,提出一种基于系统净能力的储能最优选址计算方法;综合考虑储能的运行特性和分布式光伏的出力不确定性,建立以系统日综合成本和削峰填谷为目标的分布式光储多场景协同优化调度模型;利用二阶锥松弛和Big-M法对潮流约束、储能运行约束进行处理,将原规划模型转化为混合整数二阶锥规划问题。以IEEE 33节点系统和西北某实际系统为算例进行仿真分析,结果表明所提方法能在降低负荷峰谷差和日综合成本、平抑负荷波动的同时,显著提高对分布式光伏的消纳能力,验证了所提方法的有效性和可行性。

基于电力大数据的钢铁企业大气污染物排放核算模型构建及应用

近年来,各级生态环境部门与国家电网公司积极签署战略合作协议,促进电力大数据在生态环境管理信息化平台的应用.本研究通过梳理电力大数据在大气污染防治中的应用,以唐山市钢铁行业为例构建基于电力大数据的大气污染物高时间精度排放核算模型,进一步挖掘电力大数据在大气污染排放控制中的应用潜力.结果表明:①模型核算的2019年唐山市17家钢铁企业的大气污染物排放量与2019年唐山市大气污染物排放清单(简称“城市清单”)结果一致性较好,SO_(2)、NOx和PM_(2.5)排放量分别为1017.90、2047.75、1141.81t,误差介于-0.46%~4.27%之间.②基于工序而言,以PM_(2.5)为例,模型预测结果与城市清单结果的相对误差在-17.34%~10.60%之间.③唐山某钢铁企业2022年SO_(2)、NOx、PM_(2.5)月排放量受钢铁市场价格影响较大,1月和6月分别为最高和最低污染物排放月,而其日排放受行业特征影响较为平稳,小时排放可能受电价波动影响较大.研究显示,基于电力大数据的大气污染物核算模型阐明了电力大数据和污染排放的动态响应关系,一定程度上提升了排放核算的时间精细度,实证了基于电力大数据核算大气污染物排放的研究意义和可行性.

数据驱动与机理耦合的冷轧电机功率智能预测方法

电机功率计算在保证生产安全、制定轧制规程以及发挥设备能力等方面起到了重要作用。为提高计算精度,本文提出一种机理模型与数据驱动方法相结合的电机功率预测模型。首先,机理模型部分采用上界法得到解析解,然后将部分功率解析解(塑性变形功率、张力功率和剪切功率)与相关变量输入长短时记忆(long short-term memory,LSTM)网络中训练,以提取变量数据之间的深度特征和时序相关性。结果表明:本文提出的时序耦合预测模型相对误差不超过±3.9%,且在相邻卷带钢轧制规程变化较大时,预测效果明显优于上界法和人工神经网络(artificial neural networks,ANN);从残差分布直方图中可以看到,上界法由于遗漏了摩擦系数和损失功率与其他参数间的强耦合关系,导致残差集中在±50 kW附近且不符合正态分布,而ANN和本模型均基本遵循正态分布,但本模型拟合精度更高;对比更广泛的模型评估指标,本模型具有更好的综合预测性能。此外,通过模型输出结果分析了电机功率与轧制长度、轧制速度之间的关系,表明本模型除具有高预测精度外,还与已知的参数间物理规律有较强一致性。

基于特征提取的电力客户服务大数据溯源模型

为提高数据溯源算法的运行效率,基于特征提取方法提出电力客户服务大数据溯源模型。定义数据的基因组,查找数据库内的任意两个存在血缘关系的数据,以此建立数据染色体追溯模型;获取电力数据中的四类特征值,建立电力数据多次遗传的转移矩阵,基于特征提取构造电力大数据溯源路径;得出大数据溯源算法,构建电力客户服务大数据溯源模型。实验结果显示,特征提取算法在模型层数以及数据量相同时,溯源所需时间最短,算法运行速度最快。

资本主义一如往昔?——数字知识垄断的启示

本文从作为权力关系的垄断切入,试图回答“资本主义积累模式是否一如往昔”这一问题,研究以当今数字经济、资本主义、科技巨头与知识垄断几大要素的日益勾联为核心,拓展了乌戈·帕加诺(Ugo Pagno)所提出的“知识垄断资本主义”概念,详细探究了大型科技公司垄断权力的新颖性、促生该类垄断的诸多因素以及其藉以控制其他企业的内在机制。文章以占有市场巨量份额的微软以及其他类似产业巨头为例,剖析了其如何与各国政府、大学与科技工作者在政治、学术和技术方面开展合作,实现其知识垄断的目的。文章认为,科技巨头利用工人知识产权与雇主的分离获得市场影响力,借助人工智能、云服务、数据挖掘等技术手段实施数据垄断,通过数字知识垄断操纵企业、控制市场并打破“创新—传播”的循环。因此,今天的资本主义并非以前的资本主义。随着数字技术的迅猛发展,学界亟需开展深入研究,重估资本主义通过知识垄断实现资本积累的手段机制和潜在威胁。

基于LSTM算法的火电厂智能辅助脱硝系统开发与工程应用

随着新能源电站上网电量的快速增长,常规火电机组承担电网调峰任务逐渐增多,锅炉烟气脱硝系统面临更加频繁多变的运行工况。为提高设备运行的可靠性,降低运维人员的监盘工作量,利用大数据分析与人工智能算法赋能电厂传统脱硝设备,为某350 MW超临界锅炉开发了智能辅助监盘系统。经训练优化,脱硝入口参数预测模型、电加热器性能监测模型、热一次风流量异常监测模型预测准确率均达到实用性要求。系统部署应用后,现场未再发生电加热器与热一次风流量不足故障,调峰工况下未再出现操作员过调与欠调问题,有效提升了SCR系统运行可靠性,减轻了人员工作强度,取得了良好的经济效益和环境效益。