Review of the field environmental sensing methods based on multi-sensor information fusion technology

Field environmental sensing can acquire real-time environmental information,which will be applied to field operation,through the fusion of multiple sensors.Multi-sensor fusion refers to the fusion of information obtained from multiple sensors using more advanced data processing methods.The main objective of applying this technology in field environment perception is to acquire real-time environmental information,making agricultural mechanical devices operate better in complex farmland environment with stronger sensing ability and operational accuracy.In this paper,the characteristics of sensors are studied to clarify the advantages and existing problems of each type of sensors and point out that multiple sensors can be introduced to compensate for the information loss.Secondly,the mainstream information fusion types at present are outlined.The characteristics,advantages and disadvantages of different fusion methods are analyzed.The important studies and applications related to multi-sensor information fusion technology published at home and abroad are listed.Eventually,the existing problems in the field environment sensing at present are summarized and the prospect for future of sensors precise sensing,multi-dimensional fusion strategies,discrepancies in sensor fusion and agricultural information processing are proposed in hope of providing reference for the deeper development of smart agriculture.

Applications of state estimation in multi-sensor information fusion for the monitoring of open pit mine slope deformation

The traditional open pit mine slope deformation monitoring system can not use the monitoring information coming from many monitoring points at the same time, can only using the monitoring data coming from a key monitoring point,and that is to say it can only handle one-dimensional time series.Given this shortage in the monitoring, the multi-sensor information fusion in the state estimation techniques would be intro- duced to the slope deformation monitoring system,and by the dynamic characteristics of deformation slope,the open pit slope would be regarded as a dynamic goal,the condi- tion monitoring of which would be regarded as a dynamic target tracking.Distributed In- formation fusion technology with feedback was used to process the monitoring data and on this basis Klman filtering algorithms was introduced,and the simulation examples was used to prove its effectivenes.

Sensor Fusion with Square-Root Cubature Information Filtering

This paper derives a square-root information-type filtering algorithm for nonlinear multi-sensor fusion problems using the cubature Kalman filter theory. The resulting filter is called the square-root cubature Information filter (SCIF). The SCIF propagates the square-root information matrices derived from numerically stable matrix operations and is therefore numerically robust. The SCIF is applied to a highly maneuvering target tracking problem in a distributed sensor network with feedback. The SCIF’s performance is finally compared with the regular cubature information filter and the traditional extended information filter. The results, presented herein, indicate that the SCIF is the most reliable of all three filters and yields a more accurate estimate than the extended information filter.

Altitude information fusion method and experiment for UAV

Altitude regulation is a fundamental problem in UAV(unmanned aerial vehicles) control to ensure hovering and autonomous navigation performance.However,data from altitude sensors may be unstable by interference.A digital-filter-based improved adaptive Kalman method is proposed to improve accuracy and reliability of the altitude measurement information.A unique sensor data fusion structure is designed to make different sensors switch automatically in different environment.Simulation and experimental results show that an improved Sage-Husa adaptive extended Kalman filter(SHAEKF) is adopted in altitude data fusion which means that altitude error is limited to 1.5m in high altitude and 1.2m near the ground.This method is proved feasible and effective through hovering flight test and three-dimensional track flight experiment.

Research on Optimal Preload Method of Controllable Rolling Bearing Based on Multisensor Fusion

Angular contact ball bearings have been widely used in machine tool spindles,and the bearing preload plays an important role in the performance of the spindle.In order to solve the problems of the traditional optimal preload prediction method limited by actual conditions and uncertainties,a roller bearing preload test method based on the improved D-S evidence theorymulti-sensor fusion method was proposed.First,a novel controllable preload system is proposed and evaluated.Subsequently,multiple sensors are employed to collect data on the bearing parameters during preload application.Finally,a multisensor fusion algorithm is used to make predictions,and a neural network is used to optimize the fitting of the preload data.The limitations of conventional preload testing methods are identified,and the integration of complementary information frommultiple sensors is used to achieve accurate predictions,offering valuable insights into the optimal preload force.Experimental results demonstrate that the multi-sensor fusion approach outperforms traditional methods in accurately measuring the optimal preload for rolling bearings.

Multi-source information fusion:Progress and future

Multi-Source Information Fusion(MSIF),as a comprehensive interdisciplinary field based on modern information technology,has gained significant research value and extensive application prospects in various domains,attracting high attention and interest from scholars,engineering experts,and practitioners worldwide.Despite achieving fruitful results in both theoretical and applied aspects over the past five decades,there remains a lack of comprehensive and systematic review articles that provide an overview of recent development in MSIF.In light of this,this paper aims to assist researchers and individuals interested in gaining a quick understanding of the relevant theoretical techniques and development trends in MSIF,which conducts a statistical analysis of academic reports and related application achievements in the field of MSIF over the past two decades,and provides a brief overview of the relevant theories,methodologies,and application domains,as well as key issues and challenges currently faced.Finally,an analysis and outlook on the future development directions of MSIF are presented.

The application of modern surveying technology in mining survey

With the unceasing appearance and widespread application of new surveying technology,the present age mining survey has meet huge change.However,lots of prob- lems occurred while using the new techniques since the number of mine is large in China and condition of the mine district is complex,it in some sense influenced the mine exploi- tation and management of China.Summarized the present situation of new technical ap- plication in mining survey,including the advanced instrumentation equipment,the '3S' technology,the information and the network technology and the information fusion tech- nology and so on,and analyzed the problems which exists in the current mining survey,it also provided new ways to present age mining survey from the sustainable development angle.

基于大数据技术的企业信息资源管理系统

企业信息资源管理系统面对大规模和多类型数据涌入时,处理效率低,系统容易崩溃,设计基于大数据技术的企业信息资源管理系统。分析Hadoop平台体系结构,确定资源管理系统整体架构,设计每个子系统对应的功能,部署系统硬件环境;利用大数据融合技术设置软件执行算法,通过模糊调度挖掘资源间关联规则,根据相似性函数,建立同类资源的融合模型,提高业务处理效率,完成系统整体设计。实验表明,该系统的CPU利用率仅在30%左右,处理时间在1.00s以内,大大缩短了处理时间,提高了处理效率。

基于加权D-S证据理论的旋翼故障诊断

旋翼作为直升机的升力面和操作面,其健康状态对直升机的安全至关重要。旋翼故障诊断技术仍是直升机健康与使用监测系统(Health and usage monitoring system, HUMS)领域的薄弱环节,开发旋翼故障诊断技术具有重要价值。基于信息融合技术,首先分析了旋翼故障的诊断机理,建立了旋翼故障模型,通过流固耦合仿真获取了不同故障下桨叶、轮毂和机身的故障特征信息,生成数据集进行网络训练和验证。然后,利用遗传算法反向传播(Genetic algorithm-backpropagation, GA-BP)优化神经网络诊断3种类型的直升机旋翼故障,即后缘调整片误调、变距拉杆误调和桨叶质量不平衡。3个逐级神经网络分别对旋翼故障类型、故障位置和故障程度进行了诊断识别。最后采用加权的Dempster-Shafer(D-S)证据理论对旋翼故障进行诊断和分析。结果证明基于改进D-S证据理论的旋翼故障诊断方法能够成功应用到旋翼故障诊断中,并具有良好的识别效果。

科技情报基础服务知识融合流程自动化框架研究

科技情报的知识融合可以为科技情报分析提供高质量、高效率的基础数据。在知识融合自动化过程中,快速使用和整合现有技术,推进语义层面的知识融合,是科技情报快速感知和问题解决的关键。本文通过梳理科技情报基础数据服务中多层次、多粒度服务对知识融合的需求,设计了基于模块化、分阶段、任务管理式的自动化融合框架。框架可以快速组合基于规则的方法、基于监督学习的方法、大语言模型方法等,对多种来源的科技情报数据进行抽取、转化、合并、知识生成和关系拓展。最后在商业竞争对手跟踪场景中实践了基于该框架的新闻事件提取和关联。结果证明,基于模块化、自动化方式的科技情报融合能够更高效地应用各类方法的优点,为科技情报融合难点问题、流程化问题提供更多元的解决方案。

新时期我国信息与电子领域工程科技发展研究

信息与电子领域作为国家高科技领域之一,已成为各国战略必争之要地。为更好推动我国信息与电子领域发展,本文通过文献分析及访谈调研等研究方法,梳理总结了我国信息与电子领域的发展优势及短板,分析研判了新形势下我国建设世界科技强国对信息与电子领域提出的关键要求,面向我国重大发展战略需求,研究提出了我国信息与电子领域发展战略构想,并从战略谋划、体系发展、双链融合、人才发展四个层面研究提出了推进我国信息与电子领域工程科技发展的对策建议,即实施系统性战略规划,强化信息与电子领域体系化布局发展;夯实基础、超前谋划,提升信息与电子领域底层支撑与前沿引领能力;聚焦网络强国、数字中国建设,加速推进产业链和创新链双链融合;重视信息与电子领域工程科技人才培养和基础研究人才队伍建设,以期助力我国信息与电子领域高质量发展。

“边缘计算+”技术赋能智慧图书馆建设研究

多元技术发展下,单一性的边缘计算将难以满足智慧图书馆数字化转型、智能化服务、资源整合共享、人机交互和服务创新等差异化应用需求,而“边缘计算+”技术为智慧图书馆的建设和发展提供了新的应用方向。论文通过文献分析法探讨技术赋能智慧图书馆建设研究现状,在结合“边缘计算+”技术的基础上,构建“边缘计算+”技术下智慧图书馆的理论与应用模型,并给出应用场景及未来发展趋势。“边缘计算+”技术能够为智慧图书馆带来高效的资源管理、智能的服务提供和数据分析能力,从而构建出更加智能、便捷、协同的图书馆生态体系,为未来智慧图书馆建设提供了坚实的技术支撑和发展方向。

从移植到融合:新兴信息技术嵌入现代大学治理的行动进路

新兴信息技术持续驱动着大学治理模式创新。ChatGTP等新兴技术加速了信息技术嵌入大学治理体系和治理能力现代化的进程。从嵌入性视角看,新兴信息技术参与现代大学治理应从浅表式移植向深层式嵌入和内嵌式嵌入推进,这是一个从“技术赋能”到“主体互动”再到“文化重塑”的演进过程。通过有机融合,新兴信息技术实现了价值转换,大学治理能力现代化获得充分支持,形成新的共同主体及其主体性,这是新兴信息技术嵌入现代大学治理的理想场景。在大学治理实践中,二者往往要经过螺旋式的冲突与耦合。在信息技术嵌入过程中,要注意防范工具主义凌驾、人本价值弱化、大学治理碎片化、新型治理权力鸿沟扩大、信息技术伦理等风险。为此,应坚持嵌入的整体性原则,将提升人的信息技术素养作为嵌入的先决条件,重视大学治理过程中的技术规制,推进信息技术嵌入的制度化建设,坚持大学治理理念的文化传承,构建二者共生机制。

数字出版助力智慧农业建设研究

随着乡村振兴战略的大力实施,我国农业发展驶入快车道。依托于信息技术的飞速发展,农业现代化在数字化、智能化、集成化等方面得到进一步推进。但智慧农业发展还存在技术融合集成度低、人才短缺、信息数据资源共享机制不健全等问题。数字出版以微信公众号、微博、自媒体、手机期刊、短视频、数字出版平台和“三农”出版物等多种形式更加贴近农业从业人员的生活,能够广泛科普现代农业技术知识,实现信息数据共享,突破智慧农业发展核心技术及促进智慧农业的推广应用。

多传感器信息融合技术在电机故障诊断中的应用研究

随着电机技术的不断发展,电机故障诊断技术也面临着新的挑战和机遇。其中,传统的电机故障诊断,基于单参数、单特征的电机故障诊断方法,往往在诊断过程中存在不确定性问题,这难以保证诊断的精确度。因此,主要研究多传感器信息融合技术在电机故障诊断中的应用,旨在通过综合多个传感器的信息,提高诊断的准确性和可靠性。

基于信息融合技术的故障诊断模型和方法研究

当前,工业设备的复杂度和自动化水平在不断提高,设备故障诊断成为确保工业生产的关键环节。信息融合技术以其整合多源信息的优势,在故障诊断领域体现出巨大的发展潜力。对此,提出了一种新型的故障诊断模型,实现了对设备故障的全面分析和准确判断。该模型在故障诊断方面具有较高的实用性,为工业生产故障的维护提供了新的解决方案。

基于大数据技术的广播电视监测工作研究

广播电视监测工作能够有效把控广播电视播出进程,科学维护广播电视播出环境,精准提升电视节目产出质量。基于此,分析广播电视监测监管工作存在的监测对象繁多、监测系统孤立、监测资源缺失等问题,提出基于大数据技术完成对不同监测对象的数据采集、独立监测系统的信息融合、多类监测资源的优化调度等工作,并运用数据挖掘算法和解析模型提升广播电视监测工作的整体效能。

基于视频识别技术的电气火灾自动报警系统

为了提高电气火灾自动报警准确性和实时性,设计基于视频识别技术的电气火灾自动报警系统。首先通过火灾探测器采集电气火灾视频数据,采用D-S证据理论算法对数据进行融合处理,然后采集盒式滤波器对电气火灾视频进行去噪处理,从电气火灾视频序列提取闪烁和色彩特征,神经网络根据电气火灾特征建立电气火灾检测模型,根据检测结果进行电气火灾自动报警,测试结果表明:所提系统减少了电气火灾自动报警响应时间,提高了电气火灾识别准确性,可以满足电气火灾自动报警实际应用要求。

车间环境下多AGV路径智能优化研究

随着我国工业技术的高速发展,多自动运输车(Automated Guided Vehicle,AGV)在自主性和节约成本方面,受到越来越多的重视。基于多AGV在复杂多变的车间环境中的路径规划问题,提出了改进的A~*算法和DWA算法(Dynamic Window Approach)融合的智能优化算法。首先,利用A~*算法规划出从起点到终点的全局路径,然后针对A~*算法多冗余的缺点,提出了一种提取关键点的方法,剔除掉全局路径中的冗余节点和多余拐点,保证了对全局路径的改进。其次,通过对全局路径的分段,设置一系列的局部目标点,利用DWA动态窗口法对新增和移动的障碍物进行动态避障的同时不断到达局部目标点,得到了多AGV从起点到目标点的最优路径,最终实现了对多AGV关于路径长度和安全性方面的改进。

基于数字孪生技术的变电站运行状态信息自动集成方法

由于变电站状态监测信息与设备运行数据离散性高,存在导致信息特征融合不准确的问题,导致集成容错性低。为此,提出基于数字孪生技术的变电站运行状态信息自动集成方法。利用机器学习算法进行数据采集,对采集的状态信息分类过滤,建立物理与虚拟变电站的层次映射关系,求取数据特征融合值域。结合数字孪生嵌入维数,融合运行状态信息,进而求取局部数据库分配系数与状态算子,通过统一语义目标函数实现自动化集成。实验表明,所设计方法的信息集成容错性高,性能优异。