Construction and Application Exploration of Smart Agriculture Based on Big Data Technology

Big data finds extensive application and many fields.It brings new opportunities for the development of agriculture.Using big data technology to promote the development of smart agriculture can greatly improve the effect of agricultural planting,reduce the input of manpower and material resources,and lay a solid foundation for the realization of agricultural modernization.In this regard,this paper briefly analyzes the construction and application of smart agriculture based on big data technology,hoping to provide some valuable insights for readers.

An approach to estimate tree height using PolInSAR data constructed by the Sentinel-1 dual-pol SAR data and RVoG model

We estimate tree heights using polarimetric interferometric synthetic aperture radar(PolInSAR)data constructed by the dual-polarization(dual-pol)SAR data and random volume over the ground(RVoG)model.Considering the Sentinel-1 SAR dual-pol(SVV,vertically transmitted and vertically received and SVH,vertically transmitted and horizontally received)configuration,one notes that S_(HH),the horizontally transmitted and horizontally received scattering element,is unavailable.The S_(HH)data were constructed using the SVH data,and polarimetric SAR(PolSAR)data were obtained.The proposed approach was first verified in simulation with satisfactory results.It was next applied to construct PolInSAR data by a pair of dual-pol Sentinel-1A data at Duke Forest,North Carolina,USA.According to local observations and forest descriptions,the range of estimated tree heights was overall reasonable.Comparing the heights with the ICESat-2 tree heights at 23 sampling locations,relative errors of 5 points were within±30%.Errors of 8 points ranged from 30%to 40%,but errors of the remaining 10 points were>40%.The results should be encouraged as error reduction is possible.For instance,the construction of PolSAR data should not be limited to using SVH,and a combination of SVH and SVV should be explored.Also,an ensemble of tree heights derived from multiple PolInSAR data can be considered since tree heights do not vary much with time frame in months or one season.

Research on Data Sharing between AutoCAD and MapGIS in Cadastral Database Construction

In the Second Chinese Country Survey, MAPGIS (Map Geographic Information System) is widely used all over the country in the Cadastral Database Construction. There are plenty original data is saved in the AutoCAD (Auto Computer Aided Design) data format. Therefore it is important to find a way which can exchange and share the data between AutoCAD and MAPGIS effectively and efficiently. In order to use MapGIS cadastral management system building cadastral database, it researched the current transformation methods between data of MapGIS and AutoCAD, including analysis defects of ObjectARX technology, southern CASS conversion interface, DXF file conversion. Combining the characteristics of MAPSUV data and southern CASS data, it found a data conversion method through the DWGdirectX control. This method actually read and wrote files provided by the library of MAPSUV and AutoCAD. This way avoids exchange process which can better transform the southern CASS DWG file containing graphics and attributes information to the SUV. The example which showed how DWG file transformed into SUV file. It improves the effectiveness and efficiency of conversion.

Research on Welding Quality Traceability Model of Offshore Platform Block Construction Process

Quality traceability plays an essential role in assembling and welding offshore platform blocks.The improvement of the welding quality traceability system is conducive to improving the durability of the offshore platform and the process level of the offshore industry.Currently,qualitymanagement remains in the era of primary information,and there is a lack of effective tracking and recording of welding quality data.When welding defects are encountered,it is difficult to rapidly and accurately determine the root cause of the problem from various complexities and scattered quality data.In this paper,a composite welding quality traceability model for offshore platform block construction process is proposed,it contains the quality early-warning method based on long short-term memory and quality data backtracking query optimization algorithm.By fulfilling the training of the early-warning model and the implementation of the query optimization algorithm,the quality traceability model has the ability to assist enterprises in realizing the rapid identification and positioning of quality problems.Furthermore,the model and the quality traceability algorithm are checked by cases in actual working conditions.Verification analyses suggest that the proposed early-warningmodel for welding quality and the algorithmfor optimizing backtracking requests are effective and can be applied to the actual construction process.

Conception and Exploration of Using Data as a Service in Tunnel Construction with the NATM

The New Austrian Tunneling Method （NATM） has been widely used in the construction of mountain tun- nels, urban metro lines, underground storage tanks, underground power houses, mining roadways, and so on, The variation patterns of advance geological prediction data, stress-strain data of supporting struc- tures, and deformation data of the surrounding rock are vitally important in assessing the rationality and reliability of construction schemes, and provide essential information to ensure the safety and scheduling of tunnel construction, However, as the quantity of these data increases significantly, the uncertainty and discreteness of the mass data make it extremely difficult to produce a reasonable con- struction scheme; they also reduce the forecast accuracy of accidents and dangerous situations, creating huge challenges in tunnel construction safety, In order to solve this problem, a novel data service system is proposed that uses data-association technology and the NATM, with the support of a big data environ- ment, This system can integrate data resources from distributed monitoring sensors during the construc- tion process, and then identify associations and build relations among data resources under the same construction conditions, These data associations and relations are then stored in a data pool, With the development and supplementation of the data pool, similar relations can then he used under similar con- ditions, in order to provide data references for construction schematic designs and resource allocation, The proposed data service system also provides valuable guidance for the construction of similar projects.

Time-varying Reliability Analysis of Long-span Continuous Rigid Frame bridge under Cantilever Construction Stage based on the Monitored Strain Data

In general,the material properties,loads,resistance of the prestressed concrete continuous rigid frame bridge in different construction stages are time-varying.So,it is essential to monitor the internal force state when the bridge is in construction.Among them,how to assess the safety is one of the challenges.As the continuous monitoring over a long-term period can increase the reliability of the assessment,so,based on a large number of monitored strain data collected from the structural health monitoring system(SHMS)during construction,a calculation method of the punctiform time-varying reliability is proposed in this paper to evaluate the stress state of this type bridge in cantilever construction stage by using the basic reliability theory.At the same time,the optimal stress distribution function in the bridge mid-span base plate is determined when the bridge is closed.This method can provide basis and direction for the internal force control of this type bridge in construction process.So,it can reduce the bridge safety and quality accidents in construction stages.

Input-output Efficiency of Elements of Urban Construction Land in Beijing: Empirical Analysis Based on Panel Data of Jurisdiction

Based on the Data Envelopment Analysis method,and by using CCR and BCC model,Super Efficiency model and Malmquist model guided by input efficiency,the input-output efficiency of elements of urban construction land in different jurisdictions of Beijing from 2005 to 2015 was studied.The results showed that there were obvious differences between input-output efficiency of elements of urban construction land in different jurisdictions of Beijing,among which the efficiency of the core area of capital,Yanqing District,Fangshan District and Huairou District was relatively high,while the efficiency of Daxing District,Fengtai District and Miyun District was relatively low.There was no obvious correlation between efficiency differentiation and location factors,which is mainly caused by whether the land use in each jurisdiction has scale effect,whether the technology is improved,whether the input is redundant and whether the output is insufficient.For the jurisdiction of inefficient land use,we should strengthen the consciousness of intensive land use,improve the technical level,appropriately reduce the redundancy of input elements,and pay attention to the output of social and ecological benefits.

Discussion on the Construction of Spatiotemporal Big Data Platform for Smart City

The so-called smart city is a new form of information technology that is achieved through the integration of the contemporary advanced big data technology,Internet of Things technology,cloud computing technology,and spatial geographic information.At present,the application of this technology is important for the urban construction,planning,and services as well as management.This technology also provides great convenience in these aspects,which has allowed the cities to develop and transform towards the direction of smart cities.Based on this situation,the construction of spatiotemporal big data platforms in smart cities was analyzed in this article,and this analysis may provide a reference for the construction and development of todays smart cities.

Overview of Construction and Application of Big Data Analysis Platform

Big data technology is widely spread around the world,and is constantly developing and applying.In order to enhance the application value of big data analysis platform,it is necessary to constantly improve the data analysis and processing capacity of big data platform,so as to build a complete data analysis platform,realize resource sharing and real-time data collection.As a key point of contemporary information development,big data analysis platform is of great significance to promote social data exchange.Based on this,this paper focuses on two aspects:first,it describes the construction process and content of big data platform;second,it summarizes the relevant application and development of big data platform for reference.

HOW BIG DATA MAKES CONSTRUCTION PROJECT RISK INTACT

Construction project is not a standalone engineering maneuver.It is closely linked to the well-being of local communities in concern.The city renovation in Beijing down center for Olympic 2008 transformed many antique architecture and regional landscape.It gave a world-recognized achievement in China s modem development and manifested a major milestone in China's economic development.In the course of metro construction projects,there are substantial interwoven municipal structures influencing the success of the projects,which including,but the least,all underground cables and ducts,sewage system,the power consumption of construction works,traffic diversion,air pollution,expatriate business activities and social security.There are many US and UK project insurance companies moving into Asia Pacific.They are doing re-insurance business on major construction guarantee,such as machinery damage,project on-time,power consumption,claims from contractors and communities.Environmental information,such as water quality,indoor and outdoor air quality,people inflow and lift waiting time play deterministic roles in construction's fit-touse.Big Data is a contemporary buzzword since 2013,and the key competence is to provide real time response to heuristic syndrome in order to make short-term prediction.This paper attempts to develop a conceptual model in big data for construction

An Improved Frag-Shells Algorithm for Data Cube Construction Based on Irrelevance of Data Dispersion

On-Line Analytical Processing (OLAP) is based on pre-computation of data cubes, which greatly reduces the response time and improves the performance of OLAP. Frag-Shells algorithm is a common method of precomputation.However, it relies too much on the data dispersion that it performs poorly, when confronts large amount of highly disperse data. As the amount of data grows fast nowadays, the efficiency of data cube construction is increasingly becoming a significant bottleneck. In addition, with the popularity of cloud computing and big data, MapReduce framework proposed by Google is playing an increasingly prominent role in parallel processing. It is an intuitive idea that MapReduce framework can be used to enhance the efficiency of parallel data cube construction. In this paper, by improving the Frag-Shells algorithm based on the irrelevance of data dispersion, and taking advantages of the high parallelism of MapReduce framework, we propose an improved Frag-Shells algorithm based on MapReduce framework. The simulation results prove that the proposed algorithm greatly enhances the efficiency of cube construction.

Construction and visualization of 3D vacant place model

The method of building 3D model was discussed at first. Aiming at the feature of mine vacant place,a method to build the 3D vacant place model based on multi TIN (triangular irregular network) was put forward, and the data structure and visualization of vacant place were discussed. Then some crucial technologies of realizing function in 3D-GIS were proposed. In addition,the software about special 3D mapping and assaying was introduced.

Construction of an ontology-based nursing knowledge system

This study proposes the establishment of a knowledge-system ontology in the nursing field. It uses advanced data mining techniques,digital publishing technologies, and new media concepts to comprehensively integrate and deepen nursing knowledge and to aggregate sources of knowledge in specialized technical fields. This study applies all forms of media and transmission channels, such as personal computers and mobile devices, to establish a knowledge-transmission system that provides knowledge services such as knowledge search, update retrieval, evaluation, questions and answers(Q&As), online viewing, information subscription, expert services, push notifications, review forums, and online learning. In doing so, this study creates an authoritative and foundational knowledge service engine for the nursing field, which provides convenient, flexible, and comprehensive knowledge services to members of the nursing industry in a digital format.

A DATA MINING METHOD BASED ON CONSTRUCTIVE NEURAL NETWORKS

In this letter,Constructive Neural Networks (CNN) is used in large-scale data mining. By introducing the principle and characteristics of CNN and pointing out its deficiencies,fuzzy theory is adopted to improve the covering algorithms. The threshold of covering algorithms is redefined. "Extended area" for test samples is built. The inference of the outlier is eliminated. Furthermore,"Sphere Neighborhood (SN)" are constructed. The membership functions of test samples are given and all of the test samples are determined accordingly. The method is used to mine large wireless monitor data (about 3×107 data points),and knowledge is found effectively.

How does the construction of China’s ecological civilization affect the health burden of urban and rural residents?

Accelerating the construction of ecological civilization and promoting the management of health environment problems is one of the core elements of the implementation of the“Beautiful China”and“Healthy China”strategies with Chinese characteristics in the new era.Based on the scientific connotation of ecological civilization,this paper systematically assesses the level of interprovincial ecological civilization construction in China using the combined CRITIC empowerment method and explores the impact of ecological civilization construction on the health burden of urban and rural residents using interprovincial panel data from 2003 to 2018 with the help of panel feasible generalized least squares,simultaneous equation estimation,and the extended regression model.It is found that there is a significant inverted U-shaped relationship between provincial ecological civilization construction and urban and rural residents’economic health burden,as well as a significant U-shaped relationship between ecological civilization construction and urban and rural residents’disease burden.Fiscal decentralization,urbanization,and industrialization are the three important indirect mechanisms through which ecological civilization affects the health burden of urban and rural residents,and fiscal decentralization plays a typical role in crowding out the health effect of ecological civilization.In the eastern and central regions,ecological civilization construction has effectively reduced the health burden of urban and rural residents,but it is more significant for improving the economic health burden of rural residents.While the“catching-up trend”of the progress of ecological civilization construction in the western region is obvious,ecological civilization plays an important role in alleviating the economic health burden of urban residents.After the robustness test,the conclusion still holds.This study provides an important empirical basis for local governments to improve the accuracy of their policies in promoting the“Beautiful China”and“Healthy China”strategies.

Inverse Construction Methods of Heterogeneous NURBS Object Based on Additive Manufacturing

According to the requirement of heterogeneous object modeling in additive manufacturing(AM),the Non-Uniform Rational B-Spline(NURBS)method has been applied to the digital representation of heterogeneous object in this paper.By putting forward the NURBS material data structure and establishing heterogeneous NURBS object model,the accurate mathematical unified representation of analytical and free heterogeneous objects have been realized.With the inverse modeling of heterogeneous NURBS objects,the geometry and material distribution can be better designed to meet the actual needs.Radical Basis Function(RBF)method based on global surface reconstruction and the tensor product surface interpolation method are combined to RBF-NURBS inverse construction method.The geometric and/or material information of regular mesh points is obtained by RBF interpolation of scattered data,and the heterogeneous NURBS surface or object model is obtained by tensor product interpolation.The examples have shown that the heterogeneous objects fitting to scattered data points can be generated effectively by the inverse construction methods in this paper and 3D CAD models for additive manufacturing can be provided.

Digital Twin-Driven Intelligent Construction:Features and Trends

Digital twin(DT)can achieve real-time information fusion and interactive feedback between virtual space and physical space.This technology involves a digital model,real-time information management,comprehensive intelligent perception networks,etc.,and it can drive the rapid conceptual development of intelligent construction(IC)such as smart factories,smart cities,and smart medical care.Nevertheless,the actual use of DT in IC is partially pending,with numerous scientific factors still not clarified.An overall survey on pending issues and unsolved scientific factors is needed for the development of DT-driven IC.To this end,this study aims to provide a comprehensive review of the state of the art and state of the use of DT-driven IC.The use of DT in planning,design,manufacturing,operation,and maintenance management of IC is demonstrated and analyzed,following which the driving functions of DT in IC are detailed from four aspects:information perception and analysis,data mining and modeling,state assessment and prediction,intelligent optimization and decision-making.Furthermore,the future direction of research,using DT in IC,is presented with some comments and suggestions.This work will help researchers gain in-depth and systematic understanding of the use of DT,and help practitioners to better promote its implementation in IC.

机器学习方法在盾构隧道工程中的应用研究现状与展望

随着盾构隧道工程信息化水平的提升,隧道掘进设备作业过程监测技术日益完善,记录的工程数据蕴含了掘进设备内部信息及其与外部地层的相互作用关系。机器学习因其数据分析能力强,无需先验的理论公式和专家知识,相较于传统的建模统计分析方法具有更大的应用空间。通过机器学习方法对收集的信息与数据进行深度挖掘并分析其内在联系,有助于提升盾构隧道工程建设的效率和安全保障水平。简述机器学习方法的基本原理,总结和分析机器学习方法在盾构工程中的应用研究状况,综述基于机器学习的盾构设备状态分析、盾构设备性能预测、围岩参数反演、地表变形预测和隧道病害诊断等5个方面的进展,并分析当前研究的不足。最后,分析盾构隧道工程向智能化方向发展需重点攻克的难题。

基础教育资源配置大数据分析平台的整体架构与运行保障

教育大数据是反映基础教育资源配置现状、需求和问题的数据集合,也是重塑基础教育资源配置结构、构建良好教育生态的有效工具。教育大数据能够为资源精准配置提供客观依据、助力理性决策、拓展实践路径、提供监测反馈,可从数据资源层、处理层、分析层和可视化层,整体架构基础教育资源配置大数据分析平台,实现多学段基础教育数据精准采集、多模态基础教育数据分类储存、多因素基础教育数据指标计量、多维度基础教育数据结果呈现。基础教育资源配置大数据分析平台顺利运行的保障措施主要包括:政策跟进,赋予数据驱动合法地位;强化治理,保障平台教育数据质量;注重成效,发挥数据分析平台价值;多元联动,助力数据分析平台共建。通过这些措施,努力实现数据驱动基础教育资源优化配置,促进决策科学化、供给精准化、评估动态化。