基于ThingsCloud平台的冷链监控系统设计与实现

在冷链市场需求不断扩大的情况下,针对现阶段我国冷链物流管理技术面临着无统一数据系统支持、实时性差、监管脱节、无法进行预警、损失率大等问题,文章设计开发了一套冷链监控系统。该系统借助传感技术采集GPS、温湿度信息,采用MQTT通信协议将采集数据上传至ThingsCloud云平台,通过云平台实现多个车载网关与中心监控端的数据交互,对冷藏车资源进行有效的跟踪定位和监测,为冷链物流创造了一种崭新的智慧管理和控制冷藏车辆资源的科学模式。

Differentiating Data Collection for Cloud Environment Monitoring

In a growing number of information processing applications,data takes the form of continuous data streams rather than traditional stored databases.Monitoring systems that seek to provide monitoring services in cloud environment must be prepared to deal gracefully with huge data collections without compromising system performance.In this paper,we show that by using a concept of urgent data,our system can shorten the response time for most 'urgent' queries while guarantee lower bandwidth consumption.We argue that monitoring data can be treated differently.Some data capture critical system events;the arrival of these data will significantly influence the monitoring reaction speed which is called urgent data.High speed urgent data collections can help system to react in real time when facing fatal errors.A cloud environment in production,MagicCube,is used as a test bed.Extensive experiments over both real world and synthetic traces show that when using urgent data,monitoring system can lower the response latency compared with existing monitoring approaches.

Application-Oriented Cloud Monitoring Data Distribution Mechanism

Cloud computing system consists of private clouds and public clouds. It merges its resources on each layer(e.g. Iaa S, Paa S and Saa S), which poses a challenge for resource management. The cloud monitoring system is a solution to managing cloud system data from the heterogeneous resources. This paper discusses the monitoring and collection of the heterogeneous resources, studies the adaptive system, and proposes a real-time extensible distributed framework of monitoring data processing. Based on this framework, a system of monitoring data distribution, publication and subscription is proposed. The simulation results show that the proposed mechanism can adaptively determine the distribution action of monitoring data flow, and effectively reduce the costs for data monitoring and distribution.

Improving Scalability of Cloud Monitoring Through PCA-Based Clustering of Virtual Machines

Cloud computing has recently emerged as a leading paradigm to allow customers to run their applications in virtualized large-scale data centers. Existing solutions for monitoring and management of these infrastructures consider virtual machines （VMs） as independent entities with their own characteristics. However, these approaches suffer from scalability issues due to the increasing number of VMs in modern cloud data centers. We claim that scalability issues can bc addressed by leveraging the similarity among VMs behavior in terms of resource usage patterns. In this paper we propose an automated methodology to cluster VMs starting from the usage of multiple resources, assuming no knowledge of the services executed on them. The innovative contribution of the proposed methodology is the use of the statistical technique known as principal component analysis （PCA） to automatically select the most relevant information to cluster similar VMs. We apply the methodology to two case studies, a virtualized testbed and a real enterprise data center. In both case studies, the automatic data selection based on PCA allows us to achieve high performance, with a percentage of correctly clustered VMs between 80% and 100% even for short time series （1 day） of monitored data. Furthermore, we estimate the potential reduction in the amount of collected data to demonstrate how our proposal may address the scalability issues related to monitoring and management in cloud computing data centers.

Virtual resource monitoring in cloud computing

Cloud computing is a new computing model. The resource monitoring tools are immature compared to traditional distributed computing and grid computing. In order to better monitor the virtual resource in cloud computing, a periodically and event-driven push （PEP） monitoring model is proposed. Taking advantage of the push and event-driven mechanism, the model can provide comparatively adequate information about usage and status of the resources. It can simplify the communication between Master and Work Nodes without missing the important issues happened during the push interval. Besides, we develop ＂mon＂ to make up for the deficiency of Libvirt in monitoring of virtual CPU and memory.

Analyzing Security Threats to Virtual Machines Monitor in Cloud Computing Environment

The data and applications in cloud computing reside in cyberspace, that allowing to users access data through any connection device, when you need to transfer information over the cloud, you will lose control of it. There are multi types of security challenge must be understood and countermeasures. One of the major security challenges is resources of the cloud computing infrastructures are provided as services over the Internet, and entire data in the cloud computing are reside over network resources, that enables the data to be access through VMs. In this work, we describe security techniques for securing a VCCI, VMMs such as Encryption and Key Management (EKM), Access Control Mechanisms (ACMs), Virtual Trusted Platform Module (vTPM), Virtual Firewall (VF), and Trusted Virtual Domains (TVDs). In this paper we focus on security of virtual resources in Virtualized Cloud Computing Infrastructure (VCCI), Virtual Machine Monitor (VMM) by describing types of attacks on VCCI, and vulnerabilities of VMMs and we describe the techniques for securing a VCCI.

HA-SLA: A Hierarchical Autonomic SLA Model for SLA Monitoring in Cloud Computing

Service Level Agreement (SLA) is a fundamental contract between service consumer and service provider which defined the qualities of agreed service. After SLA contraction, it should be monitored during the service invocations to confirm the service level objectives. Most of SLA structure and monitoring frameworks are brought from SOA and grid computing to cloud computing environment while they have different requirements. In this paper, a model is proposed as hierarchical autonomic (HA)-SLA based on cloud computing nature. It is proposed in this model that each SLA has connection with dependent SLAs in different layers of cloud computing, hierarchically, whereby each SLA should be able to monitor its attributes on its own. It is expected that HA-SLA model should able to increase SLA validity and users’ confidence without compromising the respond time. The results produced by this model should be used as one of cloud computing quality of service assurance.

网络安全态势感知标准在金融行业云场景下的应用实践

围绕金融行业云的网络安全运营需求,基于GB/T 42453—2023《信息安全技术网络安全态势感知通用技术要求》,在银联云上设计和构建了具有安全数据湖、流量解析、多源事件实时关联分析、智能AI安全分析等核心功能的网络安全态势感知平台。该平台能够对运营侧与租户侧面临的安全威胁进行持续监测预警和态势展示,有效满足了云上环境的资产发现、未知威胁检测、全量安全日志统一管理、安全事件处置、安全态势可视化等中小金融机构的安全需求,大幅提升其安全运营效果和效率。

基于OneNET云平台的智慧消防远程监控系统的设计

针对传统消防监控系统存在开发成本高、误警率高、实时监控不便的问题,提出一种基于物联网云平台的智慧消防远程监控系统。采用STM32单片机作为中枢控制芯片,经多传感器采集温度、湿度、烟雾、火焰等环境数据,通过窄带物联网(NB-IoT,Narrow Band Internet of Things)上传至OneNET云平台。经数据分析后以可视化方式呈现,对异常数据触发报警实时响应。通过手机APP实现数据实时监测及一键处置。经测试,监控系统报警准确率高于97.2%,数据延迟低于50 ms,表明该系统能够实现消防火警的无线远程监控,并做出快速反应,满足中小微企业和普通家庭用户的消防监控需要。

基于ZigBee和云平台的大棚环境监测系统设计与实现

针对温室大棚环境的实时监测问题,利用ZigBee无线传感网络和GPRS远程传输技术,设计远程监测系统,在云端搭建服务器,采用Spring Boot服务框架,开发Android移动端APP.测试表明,系统能对温室大棚环境进行远程监测和预警,系统运行稳定、部署灵活、便利性好,有利于提高大棚农业管理水平.

智能化视角下抄纸干燥部虚拟可视化监控云平台搭建研究

纸机抄纸过程具有对象复杂性、不确定性及非线性等特点。信息时代下充分利用大数据、云计算技术开发纸页干燥过程监控云平台,对干燥部作业现场各类运行数据进行可视化监测和智能分析,可大幅度提升纸机烘干设备的运行效率和安全保障能力。对此,简单介绍了抄纸过程和智能化控制方法,重点研究了抄纸干燥部虚拟可视化监控云平台的搭建,探究了云平台的总体设计、硬件方案、功能模块和人机交互界面的设计等,并对云平台可视化监控效果进行测试。结果表明云平台运行效果良好,有助于推进抄纸干燥过程的智能化控制。

云调控与变电站设备监测技术融合应用研究

针对变电站设备仿真以及监测系统,结合云技术,展开了云调控与变电站设备监测技术融合应用研究,为智能变电站提供了能量管理和设备管理,实现了对系统有功、无功功率的实时监测,同时也能满足北京电力公司职业技能培训的要求。

水库大坝自动化监测系统应用研究

为解决传统水库大坝人工监测方法效率低、监测成果存在滞后性等弊端,以物联网传感、云服务等技术方法为基础,设计开发水库大坝自动化监测系统。通过对系统框架结构、多方式通信组网进行设计建设,利用多类型监测子系统获取水库大坝安全监测数据,并通过监测云平台进行数据成果可视化展示。为验证监测系统的可靠性,以某大型水库监测项目为研究对象,对监测系统运行期间的稳定性进行分析验证,并将监测系统数据成果与测区气象站、人工复核数据进行对比分析,全方位验证了水库大坝自动化监测系统的运行稳定性及成果可靠性,为水利工程枢纽动态监测提供了便捷可靠的解决方案。

基于NB-IoT技术的天然气管道泄漏监测系统

为了对天然气管道运行情况进行实时监测,在发生泄漏时能够及时做出反应,设计了基于NBIoT技术的天然气管道泄漏监测系统。系统利用压力变送器、DHT11温湿度传感器和GPS/北斗定位系统分别对管道的内部压力、设备周围的温湿度和经纬度位置进行采集,将采集的数据通过STM32控制NBIoT物联网模块上传至OneNET物联网云平台,最终通过云平台对天然气管道进行实时监测。实验表明:该系统能够对天然气管道的数据进行实时上传,能够通过云平台进行天然气管道状态的实时监测。

起重运输机械与工程机械的监测云平台研发及应用

针对起重运输机械或工程机械应用的工业场合,需要监测的部件和设备很多,带来程序管理困难、数据结构复杂、数据量庞大的问题,文中提出了一种基于K8s的设备监测云平台架构方案并开展研究和应用,实现了平台内部监测程序的容器化管理、实时处理大量状态数据、资源可直观监控等功能。最后将电动机监测程序部署的应用案例展示在平台上,体现了平台便捷部署的特性。

楼宇热水供应循环系统综合应用研究

随着城市化进程的加速,楼宇热水供应系统在人们的生活和工作中扮演着越来越重要的角色。对楼宇热水供应循环系统进行了综合研究,为了方便监控与管理搭建了云平台进行实时远程在线监控。

基于TOF相机的振动筛上物料状态动态监测方法

振动筛筛上物料载荷异常不仅影响生产效率还可能导致安全隐患,实时准确地监测筛上物料状态具有重要意义。针对传统人工监测方法劳动强度大、速度慢、有极高安全隐患并且不容易发现设备微小故障等问题。基于TOF相机物料动态监测技术,提出了一种联合KD树与帧差法的点云数据补全方法和基于曲面重构的筛上物料动态监测方法。该点云数据补全方法有效修补了因筛上物料表面水分和物料缝隙导致的TOF相机拍摄过程中的点云数据缺失问题,通过对补全后的筛上物料进行点云曲面重构分析,判断欠载、过载、左偏载和右偏载四种筛上物料状态,实时监测筛上物料状态。研究通过TOF相机直接获取筛上物料状态信息,对筛上物料实时检测,减少了人工干预的需求且具备实时性。为筛上物料状态实时监测问题提供了一个可靠的解决方案。

供热系统二次网单元水力平衡调节的远程监控实现

基于无线传输的方式设计了针对供热系统的二次网在小区单元间水力平衡的远程监控系统。该监控系统由可编程逻辑控制器(PLC)、电动调节阀和传感器等设备完成底层采集监控,通过PLC云网关和4G移动通信系统将二次网数据无线传输至云服务器,采用OPC通信和SQL Server数据库实现云网关服务器和监控系统间的数据传输和存储;在KingSCADA软件和Visual Studio Code软件中分别设计编写本地监控系统和网页远程监控系统,使单元间水力平衡的调节参数可视化。测试结果表明,该监控系统可实现供热系统二次网单元间水力平衡调节的实时远程监控功能,对提高水力平衡调节效率具有现实意义。

气象应用业务融入气象大数据云平台的设计

针对现有气象应用不能直接融入气象大数据平台的现状,该文采用“云+端”融入的方法,对气象应用的数据存储、产品加工、业务监控、应用接口以及应用端进行改造设计,使现有气象应用业务能够快速接入气象大数据云平台。该设计在湖北气象业务进行应用,避免了因气象业务智能化建设而造成的重复建设和资源浪费,有利于推动气象业务智能化发展,为今后气象应用改造提供了参考。

信息化技术在安徽省矿产品价格监测体系中的应用研究

为提升矿产品价格监测的质量和效率,本文立足于安徽省矿产品价格监测工作现状、存在问题以及监测实际需求分析,构建了一套基于大数据和云计算等信息化技术集数据采集、数据管理、综合分析和数据发布为一体的系统化、半自动化、全程可追溯的“安徽省矿产品价格监测信息系统平台”。该平台将数据采集、数据应用分析和成果展示等有效融合,解决了当前矿产品价格监测技术落后、监测数据时效性差和数据分析水平低等问题,补齐了安徽省矿产品价格监测体系信息化建设的短板,实现监测技术信息化、监测数据可视化的目标。