A path planning method for robot patrol inspection in chemical industrial parks

Safety patrol inspection in chemical industrial parks is a complex multi-objective task with multiple degrees of freedom.Traditional pointer instruments with advantages like high reliability and strong adaptability to harsh environment,are widely applied in such parks.However,they rely on manual readings which have problems like heavy patrol workload,high labor cost,high false positives/negatives and poor timeliness.To address the above problems,this study proposes a path planning method for robot patrol in chemical industrial parks,where a path optimization model based on improved iterated local search and random variable neighborhood descent(ILS-RVND)algorithm is established by integrating the actual requirements of patrol tasks in chemical industrial parks.Further,the effectiveness of the model and algorithm is verified by taking real park data as an example.The results show that compared with GA and ILS-RVND,the improved algorithm reduces quantification cost by about 24%and saves patrol time by about 36%.Apart from shortening the patrol time of robots,optimizing their patrol path and reducing their maintenance loss,the proposed algorithm also avoids the untimely patrol of robots and enhances the safety factor of equipment.

Maximizing the probability an aerial anti-submarine torpedo detects its target

As a result of the high speed of anti-submarine patrol aircraft as well as their wide range, high efficiency and other characteristics, aerial torpedoes released by anti-submarine patrol aircraft have become the key anti submarine tool. In order to improve operational efficiency, a deep study was made of the target detection probabilities for aerial torpedoes released by anti-submarine patrol aircraft. The operational modes of aerial torpedoes were analyzed and mathematical-simulation models were then established. The detection probabilities of three attacking modes were then calculated. Measures were developed for improving low probabilities of detection when attacking a probable target position. This study provides an important frame of reference for the operation of aerial torpedo released by anti-submarine patrol aircraft.

Do Observations during Patrolling Trips Detect Changes in Wildlife Presence & Diversity in National Parks?

Globally, mountains encompass spectacular landscapes and a great diversity of species. However, Savannah’s mountains in Sudan have been affected by the loss of diversity due to human activities and climate changes. Therefore, this study aims to assess changes in wildlife diversity in Jebel-Eldair Nation Park (JENP) based on only reports from patrolling activities, especially in the absence of regular wildlife monitoring programs. Reports of monthly wildlife observations during patrolling trips were used for the summer season in 2010 and 2018. Findings showed a moderate to high decline in the most important wildlife species (i.e. lion, wild dog, and crest porcupine). Six others are documented as rare species in reserve, including the civet, spotted hyena, striped hyena, jackal, seraval, and Siberian bird. Contrary and despite the diversity reduction, some species witnessed increases in their observation frequency, such as lesser kudu, caracal, monitor, and lunar bird. Moreover, human settlements, agricultural activities, and other anthropogenic factors were found to be the main drivers of biodiversity reduction in JENP. The study recommends adopting this method to detect changes in wildlife communities, especially in a situation of deficiency and lack of funding to conduct regular monitoring programs.

A multi-UAV deployment method for border patrolling based on Stackelberg game

To strengthen border patrol measures, unmanned aerial vehicles(UAVs) are gradually used in many countries to detect illegal entries on borders. However, how to efficiently deploy limited UAVs to patrol on borders of large areas remains challenging. In this paper, we first model the problem of deploying UAVs for border patrol as a Stackelberg game. Two players are considered in this game: The border patrol agency is the leader,who optimizes the patrol path of UAVs to detect the illegal immigrant. The illegal immigrant is the follower, who selects a certain area of the border to pass through at a certain time after observing the leader’s strategy. Second, a compact linear programming problem is proposed to tackle the exponential growth of the number of leader’s strategies. Third, a method is proposed to reduce the size of the strategy space of the follower. Then, we provide some theoretic results to present the effect of parameters of the model on leader’s utilities. Experimental results demonstrate the positive effect of limited starting and ending areas of UAV’s patrolling conditions and multiple patrolling altitudes on the leader ’s utility, and show that the proposed solution outperforms two conventional patrol strategies and has strong robustness.

Determination of Main Design Indicators of Patrol Road in Nature Reserves

In principle,nature reserves are managed and controlled according to the core area and the general control area.At present,there is no relevant design specification for the design system of patrol road in various protected areas.This paper analyzed the factors to be considered in determining the grade,horizontal and vertical design indicators,and cross section indicators of the patrol road in the protected area,and came up with the corresponding design indicators and parameters,so as to provide a certain reference for the subsequent patrol road design.

A cloud Bayesian network approach to situation assessment of scouting underwater targets with fixed-wing patrol aircraft

The battlefield situation changes rapidly because underwater targets'are concealment and the sea environment is uncertain.So,a great number of situation information greatly increase,which need to be dealt with in the course of scouting underwater targets.Situation assessment in sea battlefield with a lot of uncertain information is studied,and a new situation assessment method of scouting underwater targets with fixed-wing patrol aircraft is proposed based on the cloud Bayesian network,which overcomes the deficiency of the single cloud model in reasoning ability and the defect of Bayesian network in knowledge representation.Moreover,in the method,the cloud model knowledge deal with the input data of Bayesian network reasoning,and the advantages in knowledge representation of cloud theory and reasoning of Bayesian network are applied;also,the fuzziness and stochasticity of cloud theory in knowledge expression,the reasoning ability of Bayesian network,are combined.Then,the situation assessment model of scouting underwater targets with fixed-wing patrol aircraft is established.Hence,the directed acyclic graph of Bayesian network structure is constructed and the assessment index is determined.Next,the cloud model is used to deal with Bayesian network,and the discrete Bayesian network is obtained.Moreover,after CPT of each node and the transformation between certainty degree and probability are accomplished;the final situation level is obtained through a probability synthesis formula.Therefore,the target type and the operational intention of the other side are deduced to form the battlefield situation.Finally,simulations are carried out,and the rationality and validity of the proposed method are testified by simulation results.By this method,the battlefield situation can be gained.And this method has a wider application range,especially for large sample data processing,and it has better practicability.

基于混合遗传算法的多无人机巡逻路径优化

假设无人机巡逻的起、终点均为派出所,提出了一种融合传统遗传算法和爬山算法的警用无人机巡逻路径优化模型——混合遗传算法。按照轮盘赌法则,进行种群个体的选择,以增大优秀种群个体被选中的概率,达到较好的优化效果。同时定义了与路径优化相适应的基因交叉和变异规则。仿真结果表明,提出的混合遗传算法在寻优效果上明显优于传统遗传算法。

深化高校巡视巡察成果运用的路径研究

习近平总书记强调,巡视发现问题的目的是解决问题,发现问题不解决,比不巡视的效果还坏[1]。巡视巡察成果运用是深化推动解决问题的关键环节,只有不断强化成果运用的深度、广度和力度,方能实现监督、整改、治理有机贯通。本文在把握巡视巡察成果运用趋势的基础上,基于对北京高校的实证分析,以“四个融入”为核心设计了高校巡视巡察成果运用路径,系统构建了责任落实机制、约谈通报机制、公开考评机制和追责问责机制,为推进高校巡视巡察成果的综合运用、深化运用提供了参考范式。

面向森林火灾的人工巡视与无人机巡视效益评估

为建立森林火灾人工巡视与无人机巡视效益评估框架,推动人工巡视和无人机巡视的综合运用。提出结合人工和无人机巡视影响因素,建立包括4个一级指标、8个二级指标和18个三级指标的巡视效益评估指标体系,通过博弈论组合赋权法确定各指标权重的最优组合权重,运用云模型分别构建出无人机和人工巡视技术效益评估模型。研究结果表明:人工巡视和无人机巡视的效益评估指标体系的一级指标相同,而二级指标不同之处在于物资保障和航行稳定性;将该巡视效益评估模型应用于祁连山森林,验证评估模型的客观性与全面性。研究结果可为人工和无人机森林火灾巡视效益评估提供有效工具。

基于改进YOLOv5s的输电线路螺栓缺销检测方法

针对无人机输电线路巡检图像中螺栓缺销检测精度较低、漏检较多的问题,提出了一种基于改进YOLOv5s的输电线路螺栓缺销检测方法。在Backbone部分嵌入Coordinate Attention注意力模块;在Neck部分原有的“FPN+PAN”结构的基础上,新增一条“自顶向下”的特征信息传递路径,跨越临近的同尺度特征层,与较浅层网络以加权融合的方式进行特征融合;将Head部分设置为解耦检测头,将对螺栓检测的分类任务与定位任务分开进行。改进后的YOLOv5s算法增强了对螺栓特征信息的学习能力。使用本方法在螺栓缺销数据集上实验,精确率提升了2.3%,召回率提升了3.4%,平均精度提升了3.1%,检测速度达到了41.1帧/秒,表明改进后的方法能提升输电线路螺栓缺销的检测能力,在智能巡检中具有一定的应用价值。

数字孪生水利工程建设动态智能监测系统研发与应用

依托大型水利枢纽工程建设,结合项目进度、质量、安全等管理要求,提出数字孪生水利工程动态智能监测系统研发思路。通过研究水下定位和水陆一体化定位,建立水下机器人与无人机智能巡检的统一定位体系;研究海量影像数据存储管理,研究图像识别和机器学习技术自动找出异常点,从而实现施工现场的智慧监控等关键技术。建成集空、天、地、水一体化的监控系统,对各项感知信息进行融合集成和综合利用,对不同时期的数据进行比较分析;建立大数据平台,为大型水利工程设计、工程移民、水土保持、施工安全与质量管理、工程形象展示、水下建筑物安全监测等方面的数字孪生水利工程建设和动态监测提供技术支持,实现大型水利枢纽工程建设过程的智能巡检、智能监测和科学监管。

改进粒子群算法的机器人避障偏差控制方法

为针对巡检机器人避障偏差进行良好控制,提升避障效果,提出改进粒子群算法的机器人避障偏差控制方法设计。先分析巡检机器人正向动力学和逆向动力学,获取双走轮坐标系下机器人的运动情况,建立机器人运动学方程。然后以此为基础,在双走轮坐标系中,通过改进粒子群算法确定巡检机器人全局避障最优路径,采用改进人工势场法完成局部避障路径规划,最后采用前馈补偿控制器为动力学方程和最优路径建立动态补偿,根据控制器输出的训练结果,实现巡检机器人避障偏差自动控制。实验结果表明:所提方法避障规划能力及避障运动控制能力均较强,避障偏差控制效果好,具有一定应用价值。

京沪高铁基础设施无人机智能巡检系统关键技术

近年来我国高速铁路建设里程不断增长,传统巡检技术与方式无法满足日益增长的高铁运行安全保障需求。同时,基于无人机的解决方案已在电力、水利及城市监测等领域投入使用并取得较好效果。开展无人机在高速铁路智能巡检应用方面的关键技术研究,提出高速铁路基础设施无人机综合巡检方案,具体包括:基于无人机搭载多类型传感器的无人机巡检系统构成方案,基于深度学习的声屏障异常检测方法、周边环境异常检测方法、铁路桥梁螺栓缺失及锈蚀检测方法、接触网紧固件缺陷检测方法等;基于上述方案的智能巡检系统在京沪高铁多个区段进行试验验证,结果表明:该系统可以有效检测铁路多专业中的多类缺陷,并与现有巡检方法形成互补,从而提升铁路运行安全保障水平,为无人机铁路巡检技术的大范围应用提供参考。

南宋沿海地区巡检与海患治理

巡检司是宋代为缉拿走私、平息基层变乱、维持地方治安而在各地增设的机构,其主管官的称谓很复杂,简称“巡检”。随着海外贸易的兴盛,南宋时期沿海地区各类走私猖獗,走私者分布广、规模大,海上盗贼势力空前强大,负责缉私与巡查盗贼的巡检数量随之增加。沿海巡检所统领的土兵是当时基层常备巡警力量,根据缉私对象、寇盗活动规模及其治理难易程度的不同,或与附近巡检寨兵联合行动,或配合水军等主力官兵及地方民防力量协同参与弹压行动,在近海海患治理中发挥重要的基础性作用。揭示南宋沿海各地巡检的布局特征,分析其职能变化,探讨巡检在当时海患治理机制中的地位和作用,对于深化和推进地方治理机制的相关研究有重要的意义。

基于迁移联邦学习的输电线路缺陷检测

有效检测输电线路的破损和异物对电路智能巡检至关重要.然而,由于存在着数据孤岛问题,难以收集不同电力公司的数据来训练统一的检测模型.因此,结合迁移联邦学习和目标检测算法提出了一种基于迁移联邦学习的电路缺陷检测方法.具体地,首先选用一个强大的检测模型作为基础检测模型,并冻结模型初始权重.然后通过权重矩阵的低秩分解以及插入适配器层的方式进行对不同客户端的数据进行适应学习,从而大幅降低可训练模型参数的目的.其次,提出一种权重自适应筛选方法,以精确确定模型权重层的低秩分解和适配器层的插入位置,通过简单的适应学习,即可对不同电力公司中的数据分布进行有效适应.最后,在接近真实环境的电力数据集上进行的实验验证表明,在保证客户数据安全性和隐私性的前提下,能够很好地适应不同分布的检测场景.

巡检无人车激光SLAM与路径规划研究综述

激光同步定位与建图(SLAM)及路径规划是决定巡检无人车能否高效完成巡检任务的关键环节。围绕激光SLAM定位算法、环境地图、路径规划3个方面,从其优缺点、适用范围、改进方向出发,介绍了巡检无人车激光SLAM与路径规划研究的概况,系统阐述了国内外研究成果,分析了当前常见的技术难题,并对巡检无人车激光SLAM与路径规划的未来发展做出展望。

无人机技术在公共交通领域的研究

针对无人机执行电力巡线任务时需要进行人工飞行操控,且无人机控制过程繁琐,巡线效率不高的问题,本文无人机应用在公共交通领域,研究无人机巡线航迹规划方法及其系统,实现无人机自动巡线。本文阐述的无人机巡线航迹规划方法及其系统,效果较好,可以对无人机巡线过程中的飞行轨迹进行自动规划,无需人工进行飞行操控,进而可以大幅度提高电力设施巡线任务的工作效率,降低执行该任务的人员成本。巡线航迹规划方法能够对巡视过程中不同类型的电力设施采用不同的巡视策略。实现对目标区域内所有电力设施的全覆盖,准确全面完成所有目标区域的巡视任务,对所有电力设备进行全方位巡查和准确采像,不留监视死角。规划的路径不仅实现了对整个目标巡线区域的全方位巡视,还设计出了最佳的飞行路径,飞行路径的航程较短,这样可以有效缩短巡视时间,提高巡线效率,也具有更好的节能环保效益。

智能变电站巡线机器人设计

本文拟采用一种可控制的机器人代替传统的人工巡线及无人机巡线,机器人在巡线的过程中可以自主地躲避障碍物,并能够将巡线数据反馈给地面操控系统,较好地完成变电站巡线工作任务。机器人是以51单片机作为主控系统,配置摄像头、红外探测仪、传感器等硬件装置,能在各种复杂电网环境下连续稳定工作,具有可靠性高、适应性强、抗干扰能力强、运营维护成本低等优点。本文设计的机器人,可以提升对电力系统运行状态的监控、预警,以及提高智能化、自动化、信息化的水平,是实现智能电网的重要部分。

改进YOLO v7的绝缘子检测与定位

针对电力系统目标检测任务中绝缘子大小不一、背景干扰等影响而导致精度低、漏检率高的问题,提出了基于改进YOLO v7绝缘子检测与定位方法。在YOLO v7骨干网络中加入轻量级注意力机制(convolutional block attention module, CBAM),使网络模型从通道、空间两个方面更加关注绝缘子特征,降低绝缘子检测中的漏检率。在网络模型深层添加集中特征金字塔(concentrated feature pyramid, CFP),使不同尺度的特征图进行信息交换和聚合,进而获得更加全面的绝缘子特征,提高绝缘子检测精度。通过k-means算法对预选框聚类,得到最适合绝缘子预选框大小。实验结果表明,改进以后的YOLO v7网络模型平均检测精度(mean average precision, mAP)达到96.2%,精准率为90.8%,召回率为93.8%。改进的方法在电力系统绝缘子检测中具有较广泛的应用前景。

海上油田生产巡护员思政工作的难点及对策

海上油田生产巡护员工的思想政治工作是保障海上生产安全和员工思想稳定的重要任务。海上生产巡护工作环境特殊,员工长期在海上工作,面临海上环境的复杂和孤独感等困扰,容易产生心理压力、情绪波动和孤独感。同时,员工的家庭背景和个人修养多样,存在年龄、身体素质、技能差异等系列挑战和难点,给员工的思想政治工作带来了难度。文章通过分析海上油田生产巡护员工思想政治工作的难点,提出了相应的对策措施。