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1.
近年来，随着各类传感技术的蓬勃发展，人类实现了对城市各类数据前所未有的高频监测。多源数据的采集、汇聚、共享、管理、融合、分析、应用和服务可以有效降低城市资源能耗，提升城市管理效率，因此成为城市管理者和民众关注的核心问题。基于此，文章以中新天津生态城空间信息公共平台建设和相关应用为例，系统介绍了空间信息公共平台在智慧城市建设中的核心作用，详细阐述了其中涉及的城市数据体系以及数据汇聚、整理和治理融合方法，提出基于城市数据体系的单指标和综合指标城市脉动分析模型，总结展望了智慧城市实践过程中数据共享、分析及应用服务方面所面临的问题和未来发展方向。 相似文献
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为了推进智慧城市建设和管理中公众参与的进程,提升公众参与的层次和深度。基于公众参与理论和众包模式内涵,借鉴成功的众包实践案例,融入社会化媒体优势,本文构建了社会化媒体环境下公众参与智慧城市管理众包的概念模型。该模型包含了政府部门、众包项目运作方、社会化媒体平台和参与公众四大构件,通过社会化媒体公众平台实施,具有较好的实践性和可操作性。论文最后对模型进行了阐释。 相似文献
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《International Journal of Information Management》2016,36(5):748-758
The expansion of big data and the evolution of Internet of Things (IoT) technologies have played an important role in the feasibility of smart city initiatives. Big data offer the potential for cities to obtain valuable insights from a large amount of data collected through various sources, and the IoT allows the integration of sensors, radio-frequency identification, and Bluetooth in the real-world environment using highly networked services. The combination of the IoT and big data is an unexplored research area that has brought new and interesting challenges for achieving the goal of future smart cities. These new challenges focus primarily on problems related to business and technology that enable cities to actualize the vision, principles, and requirements of the applications of smart cities by realizing the main smart environment characteristics. In this paper, we describe the state-of-the-art communication technologies and smart-based applications used within the context of smart cities. The visions of big data analytics to support smart cities are discussed by focusing on how big data can fundamentally change urban populations at different levels. Moreover, a future business model of big data for smart cities is proposed, and the business and technological research challenges are identified. This study can serve as a benchmark for researchers and industries for the future progress and development of smart cities in the context of big data. 相似文献
4.
中国特大城市能耗变化的影响因素分解及其区域差异 总被引：3，自引：0，他引：3
特大城市的能耗已从生产领域转向城市功能和自身发展方面,因此其能耗具有与中小城市不同的特性。本文首先分析2010年61个特大城市能耗的空间分布特点,再利用D氏数指分解法对1996年-2010年间32个特大城市的能耗变化做因素分解分析,在综合考虑城市经济增长、人口规模扩大和空间扩张三方面因素的基础上选取经济规模、单位产值能耗、人均能耗、人口密度、能源空间支持等5项指标,计算其对城市能耗的贡献率,分析共性与区域差异。结论显示:城市所处区域(特别是气候条件、资源禀赋)对其能耗量和能源利用方式有较大影响;经济因素仍是特大城市能耗变化的主要原因,经济因素贡献率较大的城市主要分布在东北、华北地区;人口因素在特大城市能耗变化中也占有重要地位,因此低碳型的生活方式是特大城市节能减排的重要途径;紧凑型空间是城市节能减排的有效途径,应提倡精明增长,建设紧凑型城市可以有效地减少能源消耗。 相似文献
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电气节能,尤其是照明节能是公共建筑节能的重要组成部分。对广西几个主要城市公共建筑电能消耗及电气运行管理水平的现场调研结果进行分析及总结,并提出一些建议。 相似文献
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《Information processing & management》2020,57(1):102122
Big data generated by social media stands for a valuable source of information, which offers an excellent opportunity to mine valuable insights. Particularly, User-generated contents such as reviews, recommendations, and users’ behavior data are useful for supporting several marketing activities of many companies. Knowing what users are saying about the products they bought or the services they used through reviews in social media represents a key factor for making decisions. Sentiment analysis is one of the fundamental tasks in Natural Language Processing. Although deep learning for sentiment analysis has achieved great success and allowed several firms to analyze and extract relevant information from their textual data, but as the volume of data grows, a model that runs in a traditional environment cannot be effective, which implies the importance of efficient distributed deep learning models for social Big Data analytics. Besides, it is known that social media analysis is a complex process, which involves a set of complex tasks. Therefore, it is important to address the challenges and issues of social big data analytics and enhance the performance of deep learning techniques in terms of classification accuracy to obtain better decisions.In this paper, we propose an approach for sentiment analysis, which is devoted to adopting fastText with Recurrent neural network variants to represent textual data efficiently. Then, it employs the new representations to perform the classification task. Its main objective is to enhance the performance of well-known Recurrent Neural Network (RNN) variants in terms of classification accuracy and handle large scale data. In addition, we propose a distributed intelligent system for real-time social big data analytics. It is designed to ingest, store, process, index, and visualize the huge amount of information in real-time. The proposed system adopts distributed machine learning with our proposed method for enhancing decision-making processes. Extensive experiments conducted on two benchmark data sets demonstrate that our proposal for sentiment analysis outperforms well-known distributed recurrent neural network variants (i.e., Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), and Gated Recurrent Unit (GRU)). Specifically, we tested the efficiency of our approach using the three different deep learning models. The results show that our proposed approach is able to enhance the performance of the three models. The current work can provide several benefits for researchers and practitioners who want to collect, handle, analyze and visualize several sources of information in real-time. Also, it can contribute to a better understanding of public opinion and user behaviors using our proposed system with the improved variants of the most powerful distributed deep learning and machine learning algorithms. Furthermore, it is able to increase the classification accuracy of several existing works based on RNN models for sentiment analysis. 相似文献
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保障粮食安全是全球可持续发展的基础及重要议题。粮食可持续生产作为实现粮食安全的基础,同时是应对气候变化、土地退化、生态退化等全球挑战的有效手段。当前,对粮食生产可持续性的监测与评估存在着数据鸿沟,而地球大数据的支撑作用日益凸显。文章总结了地球大数据支撑粮食可持续生产研究的当前实践,包括对地观测技术在粮食生产系统各要素监测中发挥的作用,以及多源数据融合在粮食生产系统综合监测及粮食生产可持续性评估中的应用。在上述实践归纳的基础上,依循实现联合国可持续发展目标(SDGs)的四大杠杆框架,提出了地球大数据支撑粮食可持续生产的2个未来发展方向:多学科模型凝聚地球大数据推动知识发现支撑政府治理;技术创新集成地球大数据搭建产农户智慧生产决策体系。 相似文献
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中国物流业全要素能源效率动态变动及区域差异分析 总被引：2，自引：0，他引：2
物流业是我国能源消耗的重要行业之一,尤其是油料的消耗,位居各行业首位。研究物流业的能源效率,找出其中存在的问题,对于节能减排具有重要的价值。本文基于DEA-Malmquist生产率指数分析方法,构建了物流业全要素能源效率的测度模型。通过分析中国30个省、市、自治区2003-2012年物流业的能源消耗面板数据,对中国物流业的全要素能源效率及技术效率、技术进步、纯技术效率和规模效率指数进行了实证测度,并分析了样本年内中国物流业全要素能源效率的动态变动和区域差异情况。研究发现:2003-2012年中国物流业的全要素能源效率呈现了下降的趋势,下降了2.88%,行业的总体全要素能源效率不高;西部地区物流业的平均全要素能源效率变动最好,达到了0.996,东部次之为0.979,中部最低为0.950;在各地区的对比中,宁夏物流业全要素能源效率的改善最好,青海改善最差,规模效率是导致省域差异的主要原因。文章最后根据中国物流业能源效率的特点,提出了提高该行业能源效率的建议,可以为相关部门的节能减排政策制定提供科学依据。 相似文献
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针对小城镇规划管理中的技术难点和发展瓶颈,借鉴国际前沿应用实践,以相对低成本的投入方式和维护手段,构建多级多节点云计算支撑平台,引入众包公众参与模式,探寻基于云计算和众包模式的小城镇智慧规划支持和管理道路。 相似文献
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超智慧社会旨在提供智慧化的社会管理和公共服务,这就有赖于智能平台的良性运行.在智能平台的建构过程中必须深入理解影响其运作的技术深层逻辑基础.数据的范畴、质量、数量和集合是由人来赋予或界定的,其自然属性影响着智能平台的决策环境,其社会化的属性决定了不能盲目信任智能平台的自动决策;数据的规模也影响到系统环境是牛顿系统还是默顿系统.智能平台的基础协议是由人类理性构建的技术性程序,隐含的假设影响到消费者福利和监管环境.智能平台是公共品,内在的具有共用和互联互通的属性,未来智能平台有待政府端、企业端和社会端的深入合作. 相似文献
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The electrical power sector must undergo a thorough metamorphosis to achieve the ambitious targets in greenhouse gas reduction set forth in the Paris Agreement of 2015. Reducing uncertainty about demand and, in case of renewable electricity generation, supply is important for the determination of spot electricity prices. In this work we propose and evaluate a context-based technique to anticipate the electricity production and consumption in buildings. We focus on a household with photovoltaics and energy storage system. We analyze the efficiency of Markov chains, stride predictors and also their combination into a hybrid predictor in modelling the evolution of electricity production and consumption. All these methods anticipate electric power based on previous values. The main goal is to determine the best method and its optimal configuration which can be integrated into a (possibly hardware-based) intelligent energy management system. The role of such a system is to adjust and synchronize through prediction the electricity consumption and production in order to increase self-consumption, reducing thus the pressure over the power grid. The experiments performed on datasets collected from a real system show that the best evaluated predictor is the Markov chain configured with an electric power history of 100 values, a context of one electric power value and the interval size of 1. 相似文献
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在掌握粤港澳大湾区交通运输领域能源消费状况基础上,构建长期能源替代规划系统(LEAP)-粤港澳大湾区交通模型,通过设置基准情景、能源转型情景和能源深度转型情景,模拟粤港澳大湾区交通运输领域未来的能源消费需求和节能潜力,分别从货运交通、城际客运、市内客运等部门分析其能源转型的方向和路径.研究结果显示:粤港澳大湾区交通运输领域未来的节能压力巨大,基准情景下的能源消费需求将持续增长,能源转型和能源深度转型情景下通过加大政策措施的实施力度,能源消费需求有望于2025-2030年达到峰值.基于分析结果,提出要实现粤港澳大湾区交通运输领域的能源转型,需要大力推进天然气、电力、氢能和生物燃油等清洁能源的应用,大力发展铁路和水路运输以及城市公共交通,并通过强化技术节能和管理水平提升实现交通运输工具的能效提升. 相似文献
14.
随着中国经济的快速发展,物流业需求快速增长,规模不断扩大,也带来了能源消耗的增长.研究中国物流业能源消费水平以及能源需求,有利于物流业节能工作的开展,缓解能源压力.本文选取了影响物流业能源需求的11个主要因素,基于径向基神经网络对2001-2012年间中国物流业能源需求相关数据进行模拟与仿真,在此基础上对2016年和2020年物流业能源需求量进行了预测,并分析了11个影响因素的重要性和测算了物流业的能源效率.研究结果表明:①2001-2012年间中国物流业能源消耗总量在不断增加,随着物流业的进一步发展,到2020年物流业能源消费总量将达到51261.92万t标准煤;②在解决物流业能源需求预测问题时,RBF神经网络比GM(1,1)预测模型,BP神经网络方法有更高的预测精度;③通过RBF神经网络变量重要性分析发现固定资产投资对物流业能源消费量的影响程度最大;④目前物流业能源效率明显低于全国能源效率,为节约能源,提高能源利用效率,物流业需要转变能源利用方式和发展模式. 相似文献
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Acquiring information properly through machine learning requires familiarity with the available algorithms and understanding how they work and how to address the given problem in the best possible way. However, even for machine-learning experts in specific industrial fields, in order to predict and acquire information properly in different industrial fields, it is necessary to attempt several instances of trial and error to succeed with the application of machine learning. For non-experts, it is much more difficult to make accurate predictions through machine learning.In this paper, we propose an autonomic machine learning platform which provides the decision factors to be made during the developing of machine learning applications. In the proposed autonomic machine learning platform, machine learning processes are automated based on the specification of autonomic levels. This autonomic machine learning platform can be used to derive a high-quality learning result by minimizing experts’ interventions and reducing the number of design selections that require expert knowledge and intuition. We also demonstrate that the proposed autonomic machine learning platform is suitable for smart cities which typically require considerable amounts of security sensitive information. 相似文献
16.
Over recent years, organizations have started to capitalize on the significant use of Big Data and emerging technologies to analyze, and gain valuable insights linked to, decision-making processes. The process of Competitive Intelligence (CI) includes monitoring competitors with a view to delivering both actionable and meaningful intelligence to organizations. In this regard, the capacity to leverage and unleash the potential of big data tools and techniques is one of various significant components of successfully steering CI and ultimately infusing such valuable knowledge into CI strategies. In this paper, the authors aim to examine Big Data applications in CI processes within organizations by exploring how organizations deal with Big Data analytics, and this study provides a context for developing Big Data frameworks and process models for CI in organizations. Overall, research findings have indicated a preference for a rather centralized informal process as opposed to a clear formal structure for CI; the use of basic tools for queries, as opposed to reliance on dedicated methods such as advanced machine learning; and the existence of multiple challenges that companies currently face regarding the use of big data analytics in building organizational CI. 相似文献
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自然保护区周边社区薪柴消费影响因素分析 总被引：3，自引：1，他引：2
薪柴作为一种传统能源,依然是自然保护区周边社区的主要生活能源。落后的薪柴采集和消费不仅造成能源浪费,还严重影响着自然保护区的资源生态环境,因此促进自然保护区周边社区的薪柴消费向高效清洁的商品能源消费转变具有重要意义。本研究基于陕西省洋县和宁陕县朱鹮自然保护区周边社区农户薪柴消费微观调查数据,采用多元线性回归模型,对自然保护区周边家庭薪柴消费的影响因素进行了计量分析。结果表明,0.01的显著水平上,家庭人口数、家庭年龄、养殖业收入对农户薪柴消费具有显著正向影响,外出务工收入对农户薪柴消费具有显著反向影响;在0.05的显著水平上,个体经营收入对农户薪柴消费具有显著反向影响。因此,通过提高农民的教育水平,引导农村剩余劳动力进城务工,加强自然保护区转移支付等方式,促进自然保护区周边能源消费从薪柴消费向商品能源消费转变,是解决自然保护区周边社区森林资源破坏的有效途径。 相似文献
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信息服务平台建设可促进相关产业链、供应链和需求链中各主体间的信息共享、资源对接、业务协调，并提高应急物资的产能保障能力。合理运用“大智链云”为代表的数字技术辅助决策、精细治理及资源整合优化，有助于形成互信共勉、协同有序、智能高效的产能保障体系。在剖析产能保障问题的基础上，研究和设计了集成“大智链云”并专注于产能保障的信息服务平台，灾时应急、平时服务是本平台的立足点。最后探讨了平台建设过程中的若干问题。 相似文献
19.
大数据浪潮在全球范围内呈愈演愈烈的趋势。既有的隐私乱象在灵活多变的大数据影响下,会受到更多的挑战,但同时,大数据也为个人隐私的妥善处理与保护带来了多种可能,危机与机遇并存。全文从新的视角出发,运用哲学的思维,采取以定性论述为主,定量建模为辅的方法,重新探讨信息的时效性,并针对现有的隐私问题逐一进行探究,并分别提出相应的对策。隐私问题不仅关乎个人,更关乎国家,良好的隐私意识和智慧保护技术都将保证现有的隐私问题最终得以妥善解决。 相似文献
20.
城市的可持续发展是实现联合国2030年可持续发展目标(SDGs)的关键。地球大数据对于克服城市可持续发展评估中传统统计数据的不足具有重要意义,它能够凭借宏观、动态、多样的优势为城市可持续发展评估提供新的动力。文章在系统梳理国内外城市可持续性评价指标研究与评估实践的基础上,以SDG 11(可持续城市与社区)为核心,兼顾其他SDGs中体现城市可持续发展的维度,充分挖掘地球大数据完备、实时、稳健、客观等优势,选取12个适合运用地球大数据来进行评估的指标。其中,7个直接指标从SDG11具体标中抽取,依次为SDG 11.1、SDG 11.2、SDG 11.3、SDG 11.6、SDG 11.7、SDG 11.a、SDG 11.b等;另外5个关联指标为不包含在SDG 11中,但与城市可持续评估息息相关的代表性评价指标,分别为SDG6.3、SDG 7.2、SDG 8.1、SDG 9c、SDG 15.1等。文章介绍这12个指标在城市可持续评估中可发挥的作用,以及评估的数据、指标与方法。最后,提出基于地球大数据进行可持续指标计算,实现了对多源信息的整合利用,将有助于实现更加定量、实时、精细的城市可持续评价。 相似文献