Cloud services with big data provide a solution for monitoring and tracking sustainable development goals

To achieve the Sustainable Development Goals(SDGs),high-quality data are needed to inform the formulation of policies and investment decisions,to monitor progress towards the SDGs and to evaluate the impacts of policies.However,the data landscape is changing.With emerging big data and cloud-based services,there are new opportunities for data collection,influencing both official data collection processes and the operation of the programmes they monitor.This paper uses cases and examples to explore the potential of crowdsourcing and public earth observation(EO)data products for monitoring and tracking the SDGs.This paper suggests that cloud-based services that integrate crowdsourcing and public EO data products provide cost-effective solutions for monitoring and tracking the SDGs,particularly for low-income countries.The paper also discusses the challenges of using cloud services and big data for SDG monitoring.Validation and quality control of public EO data is very important;otherwise,the user will be unable to assess the quality of the data or use it with confidence.

Research on the Construction of English Autonomous Learning Monitoring Mode under the Background of Big Data

The era of big data is coming,the combination of big data and traditional teaching can provide more and more accurate services for students'self-learning,and it is a good way to teach students according to their aptitude.In this background,a learning society is coming,which aiming at learning,autonomous learning and lifelong learning.Learning society emphasize the ability of learning autonomy for students unprecedentedly.Learning is no longer limited to the campus.Learning ability will accompany learners'social life and become an active and healthy lifelong activity.Autonomous learning is a learning theory that goes with the requirements of The Times and has a broad development prospect.The study of Autonomous learning not only has a very important guiding significance for the educational and teaching practice in China,but also plays an important role in the life development of every student.The subject of learning is gradually transferred from the classroom,teachers and textbooks to the students themselves.Teachers should not only impart knowledge and answer questions,but also,most importantly,teach students how to exert their autonomy in autonomous learning.After investigating and researching the existing monitoring model of autonomous English learning in colleges and universities,our group found that in practice,there is a lack of corresponding monitoring mechanisms and means,and autonomous learning has gradually become formalized.Therefore,according to the actual situation of autonomous English learning in our country's universities,the monitoring model of autonomous English learning has been reconstructed,and an effective comprehensive evaluation system has been established to effectively improve students'English learning ability.

Design and Analysis of a Water Quality Monitoring Data Service Platform

Water is one of the basic resources for human survival.Water pollution monitoring and protection have been becoming a major problem for many countries all over the world.Most traditional water quality monitoring systems,however,generally focus only on water quality data collection,ignoring data analysis and data mining.In addition,some dirty data and data loss may occur due to power failures or transmission failures,further affecting data analysis and its application.In order to meet these needs,by using Internet of things,cloud computing,and big data technologies,we designed and implemented a water quality monitoring data intelligent service platform in C#and PHP language.The platform includes monitoring point addition,monitoring point map labeling,monitoring data uploading,monitoring data processing,early warning of exceeding the standard of monitoring indicators,and other functions modules.Using this platform,we can realize the automatic collection of water quality monitoring data,data cleaning,data analysis,intelligent early warning and early warning information push,and other functions.For better security and convenience,we deployed the system in the Tencent Cloud and tested it.The testing results showed that the data analysis platform could run well and will provide decision support for water resource protection.

Compliance verification and probabilistic analysis of state-wide power quality monitoring data

This paper introduces the implementation and data analysis associated with a state-wide power quality monitoring and analysis system in China. Corporation specifications on power quality monitors as well as on communication protocols are formulated for data transmission. Big data platform and related technologies are utilized for data storage and computation. Compliance verification analysis and a power quality performance assessment are conducted, and a visualization tool for result presentation is finally presented.

Opportunities and Challenges of College Mental Health Education from the Perspective of Big Data

This paper explores the opportunities and challenges of college mental health education from the perspective of big data.Firstly,through literature review,the importance of mental health education and the current issues are elucidated.Then,from the perspective of big data,the potential opportunities of big data in college mental health education are analyzed,including data-driven personalized education,real-time monitoring and warning systems,and interdisciplinary research and collaboration.At the same time,the challenges faced by college mental health education under the perspective of big data are also pointed out,such as data privacy and security issues,insufficient data analysis and interpretation capabilities,and inadequate technical facilities and talent support.Lastly,the research content of this paper is summarized,and directions and suggestions for future research are proposed.

An Auxiliary Monitoring Method for Well Killing Based on Statistical Data

In the present study,a large set of data related to well killing is considered.Through a complete exploration of the whole process leading to well-killing,various factors affecting such a process are screened and sorted,and a correlation model is built accordingly in order to introduce an auxiliary method for well-killing monitoring based on statistical information.The available data show obvious differences due to the diverse control parameters related to different well-killing methods.Nevertheless,it is shown that a precise three-fold relationship exists between the reservoir parameters,the elapsed time and the effectiveness of the considered well-killing strategy.The proposed monitoring auxiliary method is intended to support risk assessment and optimization in the context of typical well-killing applications.

Efficient Bayesian networks for slope safety evaluation with large quantity monitoring information

New sensing and wireless technologies generate massive data. This paper proposes an efficient Bayesian network to evaluate the slope safety using large-quantity field monitoring information with underlying physical mechanisms. A Bayesian network for a slope involving correlated material properties and dozens of observational points is constructed.

E-Healthcare Supported by Big Data

The era of open information in healthcare has arrived. E-healthcare supported by big data supports the move toward greater trans-parency in healthcare by making decades of stored health data searchable and usable. This paper gives an overview the e-health-care architecture. We discuss the four layers of the architecture-data collection, data transport, data storage, and data analysis-as well as the challenges of data security, data privacy, real-time delivery, and open standard interface. We discuss the necessity of establishing an impeccably secure access mechanism and of enacting strong laws to protect patient privacy.

Which Aspects of Big Data Usage is Creating Information Security Concern?

The present research study proposed some of the big data usage perspectives for testing either they have a role in creating information security concern or not.The researchers first dig out some of the theoretical support for filling the gap regarding big data and information security bridge that was previously noted in literature.The present researches approached big data analytics manager in the Pakistani banking industries for validating the proposed model.The data were analyzed using SPSS Andrews approach due to the nature of the research study.The findings revealed that the proposed perspective including perceived benefits,cloud storage,and online behavior monitoring should be tested in the future studies by proposing their indirect affect in the creation of information security issue.The study brings a new aspect in literature of management regarding big data usage practices.

Supplier Data Association Portrait Model Based on Improved Fuzzy Algorithm

With the advent of the Big Data era,the amount of data on supplier information is increasing geometrically.Buyers want to use this data to find high quality suppliers before purchasing,so as to reduce transaction risks and guarantee transaction quality.Supplier portraits under big data can not only help buyers select high quality suppliers,but also monitor the abnormal behavior of suppliers in real time.In this paper,the supplier data under big data are normalized,correlation analysis is performed,ratings are assigned,and classification is made through fuzzy calculation to give some reference and provide early warning tips for buyers.In addition,this paper is based on the data of active suppliers in the Jiangxi Open Data Innovation Application Competition,and realizes the data mining of two⁃dimensional labels and statistical types,thus forming the supplier portrait model.This paper aims to study supplier data analysis in the big data environment,hoping to provide some suggestions and guidances for the procurement work of related governments,enterprises and individuals.

Continuous land cover change monitoring in the remote sensing big data era

Since the late 20th century,global change issues have attracted lots of attention.As a key component of global changes,land cover and land use information has been increasingly important for improved understanding of global environmental changes and feedbacks between social and environmental systems（Verburg et al.,2015）.

Mapping cultural ecosystem services in mountain forests using mobile phone data

The aim of the work was to determine the spatial distribution of activity in the forest on the area of the Forest Promotional Complex“Sudety Zachodnie”using mobile phone data.The study identified the sites with the highest(hot spot)and lowest(cold spot)use.Habitat,stand,demographic,topographic and spatial factors affecting the distribution of activity were also analyzed.Two approaches were applied in our research:global and local Moran’s coefficients,and a machine learning technique,Boosted Regression Trees.The results show that 11,503,320 visits to forest areas were recorded in the“Sudety Zachodnie”in 2019.The most popular season for activities was winter,and the least popular was spring.Using global and local Moran’s I coefficients,three small hot clusters of activity and one large cold cluster were identified.Locations with high values with similar neighbours(hot-spots)were most often visited forest areas,averaging almost 200,000 visits over 2019.Significantly fewer visits were recorded in cold-spots,the average number of visits to these areas was about 4,500.The value of global Moran’s I was equal to 0.54 and proved significant positive spatial autocorrelation.Results of Boosted Regression Trees modeling of visits in forest,using tree stand habitat and spatial factors accurately explained 76%of randomly selected input data.The variables that had the greatest effect on the distribution of activities were the density of hiking and biking trails and diversity of topography.The methodology presented in this article allows delineation of Cultural Ecosystem Services hot spots in forest areas based on mobile phone data.It also allows the identification of factors that may influence the distribution of visits in forests.Such data are important for managing forest areas and adapting forest management to the needs of society while maintaining ecosystem stability.

Hot metal quality monitoring system based on big data and machine learning

The system of hot metal quality monitoring was established based on big data and machine learning using the real-time production data of a steel enterprise in China.A working method that combines big data technology with process theory was proposed for the characteristics of blast furnace production data.After the data have been comprehensively processed,the independent variables that affect the target parameters are selected by using the method of multivariate feature selection.The use of this method not only ensures the interpretability of the input variables,but also improves the accuracy of the machine learning process and is more easily accepted by enterprises.For timely guidance on production,specific evaluation rules are established for the key quality that affects the quality of hot metal on the basis of completed predictions work and uses computer technology to build a quality monitoring system for hot metal.The online results show that the hot metal quality monitoring system established by relying on big data and machine learning operates stably on site,and has good guiding significance for production.

智能铁路施工监测关键技术研究与发展

从绿色、技术、信息互联3个层面,分析智能铁路施工监测的监测需求。同时,基于文献分析,从研究的数量趋势、主题分布、应用方向3个维度,阐述国内外文献研究概况,识别出北斗卫星导航、5G、人工智能、大数据、物联网、云计算、边缘计算、区块链、AR/VR等关键技术,进一步按照监测设备与通信技术、数据识别与分析方法、监测管理平台三大应用方向,分析关键技术研究现状。然后,基于关键技术特点,展开分析智能铁路施工监测关键技术赋能机制,从基础研究薄弱、融合深度不足、工业化范例缺乏等方面指出关键技术应用障碍,并基于技术发展趋势展望智能铁路施工监测发展。结果表明,智能铁路施工监测关键技术支撑体系在我国已初具雏形,并仍在深化基础研究和促进成果转化中。

基于GIS与大数据分析的矿压监测预警平台

为响应国家关于加快煤矿智能化发展的号召,针对煤矿GIS“一张图”中巷道路径不能实现精确计算,矿压数据不能实现高效甄别、有效提取的问题,以及矿压预警需要实现在线推演、可视化展示的需求,提出建立基于GIS与大数据分析的矿压监测预警平台。采用GIS“一张图”、巷道结点-路径拓扑算法、多源异构监测数据处理技术、矿压大数据分析技术,搭建成能够实现矿井下复杂情况切实反映、矿压监测数据有效提取、采掘风险在线预测、可视化程度高的矿压监测预警平台。通过在李雅庄矿的实际应用,表明该系统运行稳定可靠,可有效提升煤矿的矿压风险监测预警。

基于气象大数据的大跨桥梁温度效应预测方法

为了提高结构健康监测的经济性,扩大其应用范围,提出了一种基于气象站大数据信息的大跨度桥梁结构温度场预测方法,从而实现轻量化和可持续性的桥梁温度场及温度变形计算.以某大跨钢拱桁架桥为研究对象,通过气象数据平台获取气象站大数据信息(包括天气、气温和风速信息),利用Elbadry等改进的凯尔别克辐射模型计算桥梁关键构件各表面受到的辐射强度,基于热交换传递理论计算结构热分析边界条件,结合有限元分析方法进行结构瞬态热分析,从而获得目标桥梁关键构件的时变温度场.温度场的计算结果与桥梁上实测温度在时间历程和结构竖向分布上均吻合良好,平均误差在3%以内.将计算得到的结构温度施加到桥梁结构整体有限元模型上,得到桥梁结构的温度变形,桥梁支座纵向位移的计算值与实测值吻合良好,平均误差在13 mm以内.验证了所提出的基于气象站大数据的桥梁结构温度场预测方法是可行的,利用该方法可进一步得到桥梁结构的温度变形.

ITMA ASIA+CITME2022上新一代信息技术应用浅析

探讨ITMA ASIA+CITME2022上众多新一代信息技术的应用情况。从车间物流数字孪生、原棉精细化管理、细纱机单锭监测、设备远程运维等不同角度,梳理了展会上新一代信息技术应用场景及解决方案。指出:纺机企业研发方向主要面向高效节能、高附加值纺织品的数字化、自动化和智能化生产与质量管控;新一代信息技术使得纺织产业数字化升级更加快速和高效,纺织生产整体智能化水平明显提升;基于工业互联网的纺织产业新生态正在加快形成。认为:新一代信息技术与纺织产业的融合应用,推动了产品研发、制造、服务等环节的协同创新,助力企业实现提质增效与高质量发展的战略目标。

配电网监测数据索引集群的分布缓存方法

针对配电网监测数据急剧增加导致配电网监控系统响应时间过长的问题,本文提出一种热点数据部分链表分布缓存方法。首先,引入缓存技术并分析原有热点数据链表缓存方法的局限性,提出一种新的缓存方法以提高缓存管理效率;然后,设计预热查询实现索引系统的热启动从而提高索引系统的缓存命中率;最后,以配电网监测数据为算例,验证所提方法的效果。测试结果表明,所提出的缓存方法及设计的预热查询能在不同查询条件下大幅减少索引集群的查询时间,有效提高配电网监测数据索引集群的查询效率。

大数据时代皮革行业环境监测技术应用及发展探讨

皮革生产加工过程中不仅涉及多种化学物质的使用,还因为“三废”的排放对环境造成了潜在的风险。大数据时代的到来给皮革行业的环境监测提供了更为有效的手段。本文在分析皮革行业环境监测现状及挑战的基础上,介绍了大数据环境检测技术概念及关键技术应用,并对技术的未来应用进行前景展望,以期为我国皮革行业环保提供借鉴。

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。