机器学习在运营商门店智能选品中的应用

“新零售”背景下,运营商需要对门店经营进行全面赋能,解决目标客户不明确、商品上下架凭经验、无法准确识别用户需求等问题。文章主要研究在实时海量数据中,如何运用机器学习模型智能、高效地实现运营商门店选品。具体来看,通过采集用户基本信息、订单信息、账单信息、上网行为等数据,生成用户画像及门店画像相关特征,采用相关性分析、因子分析、聚类算法、推荐算法等,生成门店选品策略。研究成果为运营商门店选品提供智能化手段,提高门店经营能力,提高客户满意度,具有广泛的行业应用前景。     

基于MQTT协议的物联网温室实时数据采集和可视化系统

随着物联网技术的兴起,其广泛应用也在深刻地改变着人们的生活。越来越多的技术被应用到智慧农业、智能家居等领域。文章在研究物联网相关的技术发展和整体架构的同时,探讨基于MQTT协议的物联网实时数据采集及数据可视化相关的应用,希望能够通过数据可视化技术,让更多人更方便地使用物联网技术。文章提出的模型使用ArduinoUno作为微处理器,控制各种传感器获取环境中的数据,并使用ESP8266模块将相关数据发送到云平台,在ThingSpeak^TM云平台实现可视化。MQTT协议主要用于将数据传送到应用层。     

软件用户身份识别技术及应用

物联网终端需要电信运营商提供SIM(用户身份识别)卡来实现远程通信,而传统SIM卡在物联网中应用存在通信可靠性低、工业级SIM卡采购成本高、物流及管理成本高等问题;同时提前预配SIM卡易产生漫游和流量结算问题.软SIM以纯软件形态实现SIM的功能,软SIM解决了传统SIM卡在物联网应用中碰到的诸多问题,可实现物联网的规模化发展和M2M(机器到机器)用户的拓展,可为物联网产业链的各个角色(包括电信运营商)带来巨大的收益.     

基于物联网技术的智慧节能服务平台研究

文章构建基于物联网技术的节能服务平台,利用物联网技术实现能耗数据的采集和传送,通过建立碳排放标准评价体系对用户的能耗进行评价,结合行业大数据模型和专家系统仿真提出能耗改进方案,展现预期的节能效果。文章同时在用户和第三方专业能源服务公司、节能设备厂商之间建立联系。通过平台用户可采取多种方式实时监测企业能耗情况,同时也可为各级政府相关部门提供准确、实时的能耗数据。     

面向5G边缘计算的物联网数据终端系统研究

针对现有技术的不足,提出了面向5G边缘计算的物联网数据终端系统研究。通过构建的边缘计算服务器搭建AI前置服务平台,能够负责监控数据图像。设计设备管理云能够实时远程监控物联网数据,通过业务接口向用户发送相关数据。在终端进行数据预处理,减轻服务平台处理压力。设计图像训练服务处理步骤,保证服务器端数据能够实时交互和更新。通过基于神经网络模型的交叉验证,避免监测过程中出现过拟合现象。     

基于物联网技术的智能物流管理系统的设计与应用

基于物联网技术的智能物流管理系统能实现对物流过程的全面监控与数据实时追踪分析,并提供决策的智能化支持。该系统通过物联网技术,能提高物流服务的质量与效率,为物流行业提供高效、智能化的管理解决方案。系统的设计方法和关键技术包括总体设计框架和物联网中的关键技术应用。同时,通过行业应用示例验证了智能物流管理系统的实际应用价值。基于物联网技术的智能物流管理系统具有广阔的应用前景和发展空间,为相关行业提供了参考和借鉴。     

基于Spring Cloud智能船舶服务系统设计与实现

<正>大数据、物联网等现代信息技术的发展，给船舶行业带来很大的影响，船舶的智能化、数据可视化将成为未来船舶行业的发展方向。基于Spring Cloud和阿里巴巴Nacos搭建了智能船舶服务系统，实现船舶联网，船舶数据实时存储，实时数据分析、历史数据统计、状态实时监测等功能。本文从用户需求角度出发，按不同用户确定不同用户场景，根据用户场景确定平台功能，通过数据库设计及架构设计确定平台业务逻辑，实现平台的功能开发。随着数字化、智能化技术不断进步，加之物联网、信息技术、人工智能、5G通讯技术的快速发展，使得整个工业领域在信息化、数字化、智能化等方面有了大幅迈进^[1-3]。     

物联网卡业务运营风险监控系统的研究

在物联网高速发展的同时,也暴露出一系列的安全问题。本文首先总结了物联网卡业务运营过程中出现的风险场景,然后探讨了物联网卡风险行为特征的提取方法,最后提出了物联网卡异常行为的提取规则,搭建物联网业务运营风险监控系统,以告警并协助处理异常网卡,保障物联网的完全运营。     

基于物联网技术的智能电网数据安全问题研究进展

为了保护智能电网设备中的核心数据与用户的个人隐私,分布式计算和同态加密等多项物联网安全技术逐渐受到了关注。近年来,物联网技术的发展推动了电网智能化的快速普及,而智能电网的应用又促进了物联网技术的更新。文中通过介绍智能电网所面临的多种攻击方法,回顾、梳理了智能电网数据安全问题的研究背景和现状。在此基础上,探讨与分析了虚假数据注入攻击及个人隐私保护问题的定义,展望了智能电网数据安全技术未来的研究方向和思路。     

基于极端不平衡学习的泛化低压异常箱表关系识别研究与应用

针对低压配电网箱表关系存在人工核查成本高、异常案例少、难以实现异常规律捕获的问题,采用极端不平衡分类学习方法实现低压异常箱表关系识别的泛化应用推广.通过电压原理识别出部分异常箱表关系样本集,随后构建CNN(卷积神经网络)异常箱表关系识别模型,通过样本三分类赋权值实现类别均衡处理;并在模型推广应用过程中,采用强化学习实现离线模型的在线泛化学习,并以分组模型交互学习和竞争优化的方式筛选出最优泛化识别模型.实验证明,通过人工核查和数据反馈,该方法可实现模型对异常样本数据分布规律的自拟合学习,提高模型对不同应用环境的泛化性,进一步降低人工现场核查工作量,保障低压台区用户拓扑网络关系的准确性.     

物联网的最新发展动态

中央政府目前全局部署物联网产业发展,物联网政策和环境已逐步完善和细化,地方政府也根据自身的地方特点实施物联网应用。物联网技术和标准稳步发展,但物联网技术还有很大的提升空间,未来物联网的技术将在信息感知、信息传输和信息处理技术领域有很大的突破。     

Smart Edge Caching-Aided Partial Opportunistic Interference Alignment in HetNets

The development of the next-generation wireless networks are regarded as the essentials to embrace of Internet of Things (IoT) and edge computing in heterogeneous networks (HetNets). Due to the the spectrum scarcity problem and the large number of connectivity demand of IoT users, intelligent interference management for IoT is worthy of thorough investigation and should be well discussed with consideration on edge computing in heterogeneous networks (HetNets). Two crucial challenges in the context are: 1) placing edge cache based on dynamic request of IoT users, and 2) cache-enabled interference management with time-varying wireless channels. In this paper, we proposed smart edge caching-aided partial opportunistic interference alignment(POIA) with deep reinforcement learning for IoT downlink system in HetNets. Towards this end, the proposed scheme can update the base station (BS) cache dynamically, and then select the optimal cache-enabled POIA user group considering the time-varying user’s requests and time-varying wireless channels. To solve this problem efficiently, the reinforcement learning is exploited that can take advantage of a deep Q-learing to replace the system action. Extensive evaluations demonstrate that the proposed method is effectiveness according to sum rate and energy efficiency of IoT downlink transmission for HetNets.

     

基于信任度评估的社交网络虚假账户检测

在利益驱动下,社交网络中出现大量虚假账户,其发布的虚假消息可对正常用户产生误导。通过对社交网络中大量数据进行分析,发现虚假账户与正常账户在账户特性、行为特性上有较大差异。基于这些差异,结合Rough Set相关理论提出账户信任度的计算模型。所得信任度可用以区分虚假账户,并为正常用户的判断提供依据。实验显示,根据所得信任度对账户排序得到了较好效果,并能够有效区分虚假账户。     

移动用户上网记录安全应用系统

针对移动用户使用手机终端上网造成的个人隐私泄露和大量异常流量等问题,通过搭建移动用户上网记录安全应用系统,采集上网记录查询与分析平台的数据,构建安全模型对数据进行安全分析,发现潜在的安全威胁和隐患,解决用户在使用移动终端上网过程中的安全问题。     

IoT-based systemic lupus erythematosus prediction model using hybrid genetic algorithm integrated with ANN

Internet of things (IoT) is commonly employed to detect different kinds of diseases in the health sector. Systemic lupus erythematosus (SLE) is an autoimmune illness that occurs when the body's immune system attacks its own connective tissues and organs. Because of the complicated interconnections between illness trigger exposure levels across time, humans have trouble predicting SLE symptom severity levels. An effective automated machine learning model that intakes IoT data was created to forecast SLE symptoms to solve this issue. IoT has several advantages in the healthcare industry, including interoperability, information exchange, machine-to-machine networking, and data transmission. An SLE symptom-predicting machine learning model was designed by integrating the hybrid marine predator algorithm and atom search optimization with an artificial neural network. The network is trained by the Gene Expression Omnibus dataset as input, and the patients' data are used as input to predict symptoms. The experimental results demonstrate that the proposed model's accuracy is higher than state-of-the-art prediction models at approximately 99.70%.     

基于网络数据实现对物联网终端网络行为监控的方法

物联网是通过部署具有一定感知、计算、执行和通信等能力的各种设备,获得物理世界的信息或对物理世界的物体进行控制,通过网络实现信息的传输、协同和处理,从而实现人与物通信、物与物通信的网络,每一个物联网传感节点都是一个信息源,数据流源源不断.电信网络运营商可以通过对网络数据的分析,及时发现物联网终端的异常情况,从而保证物联网终端的正常使用.阐述了一种通过分析电信网络中的网络数据、制定物联网终端的网络行为标签、对物联网终端网络行为进行实时监控的方法.并以穿戴行业应用为例,描述了对物联网终端网络行为进行监控的分析过程.     

Internet of things intrusion detection model and algorithm based on cloud computing and multi-feature extraction extreme learning machine

With the rapid development of the Internet of Things (IoT), there are several challenges pertaining to security in IoT applications. Compared with the characteristics of the traditional Internet, the IoT has many problems, such as large assets, complex and diverse structures, and lack of computing resources. Traditional network intrusion detection systems cannot meet the security needs of IoT applications. In view of this situation, this study applies cloud computing and machine learning to the intrusion detection system of IoT to improve detection performance. Usually, traditional intrusion detection algorithms require considerable time for training, and these intrusion detection algorithms are not suitable for cloud computing due to the limited computing power and storage capacity of cloud nodes; therefore, it is necessary to study intrusion detection algorithms with low weights, short training time, and high detection accuracy for deployment and application on cloud nodes. An appropriate classification algorithm is a primary factor for deploying cloud computing intrusion prevention systems and a prerequisite for the system to respond to intrusion and reduce intrusion threats. This paper discusses the problems related to IoT intrusion prevention in cloud computing environments. Based on the analysis of cloud computing security threats, this study extensively explores IoT intrusion detection, cloud node monitoring, and intrusion response in cloud computing environments by using cloud computing, an improved extreme learning machine, and other methods. We use the Multi-Feature Extraction Extreme Learning Machine (MFE-ELM) algorithm for cloud computing, which adds a multi-feature extraction process to cloud servers, and use the deployed MFE-ELM algorithm on cloud nodes to detect and discover network intrusions to cloud nodes. In our simulation experiments, a classical dataset for intrusion detection is selected as a test, and test steps such as data preprocessing, feature engineering, model training, and result analysis are performed. The experimental results show that the proposed algorithm can effectively detect and identify most network data packets with good model performance and achieve efficient intrusion detection for heterogeneous data of the IoT from cloud nodes. Furthermore, it can enable the cloud server to discover nodes with serious security threats in the cloud cluster in real time, so that further security protection measures can be taken to obtain the optimal intrusion response strategy for the cloud cluster.     

Human machine interactive system on smart home of IoT

Along with the development of human-computer interaction technology and improvement of people's living standards, study of Internet of things (IoT) has become the focus for years. Meanwhile, smart home system has drawn more and more attention in IoT. In correlative research, artificial psychology theory and the harmony of human-computer interaction play an important role in smart home of IoT. In this study, software platform of the management system in smart home is designed, and users are Chinese. So the language in the system is Chinese. To analyze the basic interaction between users and computer, calendar, weather forecast, internet browsing, several electrical models are designed. To deal with the humanized interaction between human and computers, some features are implemented such as: analysis of emotional models, creation of the emotional models, text emotion recognition, and other basic functions in human-computer interaction.     

基于机器学习的用户实体行为分析技术在账号异常检测中的应用

伴随企业业务的不断扩增和电子化发展,企业自身数据和负载数据都开始暴增。然而,作为企业核心资产之一的内部数据,却面临着日益严峻的安全威胁。越来越多以周期长、频率低、隐蔽强为典型特征的非明显攻击绕过传统安全检测方法,对大量数据造成损毁。当前,用户实体行为分析(User and Entity Behavior Analytics,UEBA)系统正作为一种新兴的异常用户检测体系在逐步颠覆传统防御手段,开启网络安全保卫从“被动防御”到“主动出击”的新篇章。因此,将主要介绍UEBA在企业异常用户检测中的应用情况。首先,通过用户、实体、行为三要素的关联,整合可以反映用户行为基线的各类数据;其次,定义4类特征提取维度,有效提取几十种最能反映用户异常的基础特征;再次,将3种异常检测算法通过集成学习方法用于异常用户建模;最后,通过异常打分,定位异常风险最大的一批用户。在实践中,对排名前10的异常用户进行排查,证明安恒信息的UEBA落地方式在异常用户检测中极其高效。     

电信运营商物联网发展模式比较分析

近年来，物联网发展如火如荼。对于国内运营商来说，它对目前的转型发展有着十分重要的意义。在此背景上，运营商如何实现在物联网产业“弯道超车”，商业模式至关重要。基于对国内运营商物联网运营现状的梳理，比较国内外物联网行业的发展策略，总结为 3 种不同的发展策略：单一模式、多环节覆盖模式和全产业模式。电信运营商在客户、渠道等方面具有优势，但受限于多方面因素。建议在构建发展模式时，遵循“循序渐进”的原则，充分根据自己的企业定位、结合国家的政策，注重自身在物联网产业链的定位，关注风险。最终实现较好的发展模式，改善电信行业目前“增量不增收”的现状。