Monitoring Climate Change and Air Quality Project Initiative: A Case Study of the Collaboration between ECSTAR and TeroSpace in Thailand

In Thailand, the alteration of weather patterns has resulted in an increase in instances of irregular rainfall, contributing to the occurrence of droughts. The decline of water levels in dams, due to the combined effects of climate change and prolonged droughts, has had a significant impact on agricultural productivity. Drought has a profound effect on the terrestrial biosphere and the atmospheric water cycle and can also contribute to air pollution. Researchers have found a strong correlation between air pollution and drought severity. In response to this pressing issue, the Excellence Center of Space Technology and Research (ECSTAR) at King Mongkut’s Institute of Technology Ladkrabang has joined forces with TeroSpace company to launch an initiative aimed at promoting sustainable growth in Chiang Rai, a province in Thailand known for its rich biodiversity. ECSTAR and TeroSpace’s partnership on the sustainable growth initiative in Thailand’s Chiang Rai province focuses on expanding their collaboration to include international organizations such as the Centre National d’Etudes Spatiales (CNES), which will provide access to satellite imagery and climate and weather information to improve decision-making in various areas of development. CNES is a French organization in charge of space-related activities in France. The collaboration between France and Thailand for this project, in the context of the France-Thailand Year of Innovation 2023, will be crucial for the successful initiation and execution of this research project. The project aims to explore the relationship between air pollution and climate change through the deployment of air quality monitoring devices in designated locations, connected to a global data-sharing network. The results of this research will be valuable to policymakers as they consider the interplay between air pollution and climate change and make efforts to address these challenges.

Optimal edge-cloud collaboration based strategies for minimizing valid latency of railway environment monitoring system

Response speed is vital for the railway environment monitoring system,especially for the sudden-onset disasters.The edge-cloud collaboration scheme is proved efficient to reduce the latency.However,the data characteristics and communication demand of the tasks in the railway environment monitoring system are all different and changeable,and the latency contribution of each task to the system is discrepant.Hence,two valid latency minimization strategies based on the edge-cloud collaboration scheme is developed in this paper.First,the processing resources are allocated to the tasks based on the priorities,and the tasks are processed parallly with the allocated resources to minimize the system valid latency.Furthermore,considering the differences in the data volume of the tasks,which will induce the waste of the resources for the tasks finished in advance.Thus,the tasks with similar priorities are graded into the same group,and the serial and parallel processing strategies are performed intra-group and inter-group simultaneously.Compared with the other four strategies in four railway monitoring scenarios,the proposed strategies proved latency efficiency to the high-priority tasks,and the system valid latency is reduced synchronously.The performance of the railway environment monitoring system in security and efficiency will be promoted greatly with the proposed scheme and strategies.

Research on Transmission Line Tower Tilting and Foundation State Monitoring Technology Based onMulti-Sensor Cooperative Detection and Correction

The transmission line tower will be affected by bad weather and artificial subsidence caused by the foundation and other factors in the power transmission.The tower’s tilt and severe deformation will cause the building to collapse.Many small changes caused the tower’s collapse,but the early staff often could not intuitively notice the changes in the tower’s state.In the current tower online monitoring system,terminal equipment often needs to replace batteries frequently due to premature exhaustion of power.According to the need for real-time measurement of power line tower,this research designed a real-time monitoring device monitoring the transmission tower attitude tilting and foundation state based on the inertial sensor,the acceleration of 3 axis inertial sensor and angular velocity raw data to pole average filtering pre-processing,and then through the complementary filtering algorithm for comprehensive calculation of tilt angle,the system meets the demand for inclined online monitoring of power line poles and towers regarding measurement accuracy,with low cost and power consumption.The optimization multi-sensor cooperative detection and correction measured tilt angle result relative accuracy can reach 1.03%,which has specific promotion and application value since the system has the advantages of unattended and efficient calculation.

Towards Cache-Assisted Hierarchical Detection for Real-Time Health Data Monitoring in IoHT

Real-time health data monitoring is pivotal for bolstering road services’safety,intelligence,and efficiency within the Internet of Health Things(IoHT)framework.Yet,delays in data retrieval can markedly hinder the efficacy of big data awareness detection systems.We advocate for a collaborative caching approach involving edge devices and cloud networks to combat this.This strategy is devised to streamline the data retrieval path,subsequently diminishing network strain.Crafting an adept cache processing scheme poses its own set of challenges,especially given the transient nature of monitoring data and the imperative for swift data transmission,intertwined with resource allocation tactics.This paper unveils a novel mobile healthcare solution that harnesses the power of our collaborative caching approach,facilitating nuanced health monitoring via edge devices.The system capitalizes on cloud computing for intricate health data analytics,especially in pinpointing health anomalies.Given the dynamic locational shifts and possible connection disruptions,we have architected a hierarchical detection system,particularly during crises.This system caches data efficiently and incorporates a detection utility to assess data freshness and potential lag in response times.Furthermore,we introduce the Cache-Assisted Real-Time Detection(CARD)model,crafted to optimize utility.Addressing the inherent complexity of the NP-hard CARD model,we have championed a greedy algorithm as a solution.Simulations reveal that our collaborative caching technique markedly elevates the Cache Hit Ratio(CHR)and data freshness,outshining its contemporaneous benchmark algorithms.The empirical results underscore the strength and efficiency of our innovative IoHT-based health monitoring solution.To encapsulate,this paper tackles the nuances of real-time health data monitoring in the IoHT landscape,presenting a joint edge-cloud caching strategy paired with a hierarchical detection system.Our methodology yields enhanced cache efficiency and data freshness.The corroborative numerical data accentuates the feasibility and relevance of our model,casting a beacon for the future trajectory of real-time health data monitoring systems.

Technology of structure damage monitoring based on multi-agent

The health monitoring for large-scale structures need to resolve a large number of difficulties,such as the data transmission and distributing information handling.To solve these problems,the technology of multi-agent is a good candidate to be used in the field of structural health monitoring.A structural health monitoring system architecture based on multi-agent technology is proposed.The measurement system for aircraft airfoil is designed with FBG,strain gage,and corresponding signal processing circuit.The experiment to determine the location of the concentrate loading on the structure is carried on with the system combined with technologies of pattern recognition and multi-agent.The results show that the system can locate the concentrate loading of the aircraft airfoil at the accuracy of 91.2%.

近地小行星天地基协同监测和轨道确定试验

基于中国科学院国家天文台空间碎片试验望远镜,联合“仰望一号”“吉林一号”卫星,设计并开展了近地小行星天地基协同监测试验,利用图像处理和定轨方法,实现了天地基观测图像的目标检测和天文定位,完成了近地小行星轨道的精确确定。经分析,基于现有的天地基设备可实现对近地小行星的协同监测和轨道编目,为研判近地小行星撞击风险提供轨道数据支持。

大学生学习状态监控指标体系及其人机协同监控策略研究

随着人工智能、物联网、大数据等技术的快速发展及其在教育领域的深度应用,教学过程逐渐走向智能化、精准化和个性化。学习状态人机协同监控指教师和智能机器分工协同,共同完成学习状态多模态数据的采集与处理,精准识别学生的个性化学习需求,为教师精准教学与学生个性化学习提供数据支持。通过构建由4个一级指标、11个二级指标和42个三级指标构成的大学生学习状态监控指标体系,并结合该指标体系提出了人机协同监控策略,可以有效解决大学生学习状态监控什么与如何监控的问题。

卫星遥感水利应用进展与展望

监测感知数据是构建数字孪生水利体系的基础,卫星遥感是重要的监测感知手段。随着我国航天事业的蓬勃发展,依托国家科技重大专项、国家重点研发计划等科研项目开展技术攻关,以及国家防汛抗旱指挥系统工程等重大项目的推广应用,水利部构建了“采存算用全自主、遥感处理全链条、主要业务全覆盖、应用服务全贯通”的卫星遥感水利应用技术体系。卫星遥感技术在数字孪生水利数据底板建设、流域防洪、水资源管理与调配、河湖管理、水土保持、水利工程监测监管等业务工作中得到了广泛应用,发挥了显著成效。未来将从持续拓展卫星遥感影像资源、深度融合人工智能技术、强化“天空地”协同监测融合应用等方面,推动卫星遥感水利应用,赋能水利业务。

基于创新平台的环境监测技术专业校企协同育人路径研究

深化校企合作、产教融合是提高人才培养质量,增强职业教育适应性的重要手段。分析了我国高职院校环境监测技术专业校企协同育人现状,聚焦环境监测协同育人存在的困境,提出“平台共筑、人才共育、专业共建、技术共研、成果共享”育人路径,校企共同构筑环境监测人才培养与技术创新平台,打造“人才培养共同体”“专业建设共同体”和“技术研发共同体”,构建校企合作利益分配与共享机制,协同培养环境监测技术技能人才。

广州市大型公立医院医保智能监控体系的构建与提升策略研究

随着医疗保障体系的持续发展和完善,对医保管理提出了更高的标准化和效率要求。选取广州市一所著名的双一流大学附属医院作为案例,探讨了医保智能监控体系的构建和提升策略。首先,从国家、政策和机构层面进行了分析,强调了构建医疗保险智能监控体系的迫切性和必要性。接着,对当前国内外研究现状进行了全面回顾,突出了国内智能审计系统在智能水平、医院级智能监控的复杂性以及规则库标准化方面面临的挑战。之后,详细介绍了广州市公立医院在医保智能监控体系上的实践,包括体系构成、组织工作模式和规则库标准化建设。最后,总结了这些实践对医疗服务行为规范和医保质量管理效率提升的积极影响,并提出了未来研究方向的见解。

Co-Monitor:检测前缀劫持的协作监测机制

在如今的互联网中,网络管理员要想及时地发现前缀劫持事件非常困难.考虑到互联网域间路由系统中存在的自治特性,提出了在多个自治系统之间协作监测前缀的思想,并由此设计了一个实时检测前缀劫持的新方法——Co-Monitor机制.在Co-Monitor中,每个参与者与其他参与者交换自定义的前缀-源自治系统映射信息,同时,利用所学到的前缀-源自治系统映射信息实时地监测本地BGP(border gateway protocol)路由更新.一旦某个参与者发现了不一致就立刻通知相关的参与者,从而可帮助参与者及时、有效地发现前缀劫持.给出了Co-Monitor机制的详细设计,评估了该机制的检测能力,并讨论了几个相关的问题.实验结果表明,只需精心选择60个参与者,就可确保Co-Monitor系统检测前缀劫持的漏检率和误检率都为0%.

平原河网地区典型旱地面源污染排放系数时间变化特征及影响因素研究

【目的】探明平原河网地区典型旱地离田排放系数的变化特征,为农业面源污染监测管理提供参考。【方法】以上海市崇明岛某典型旱地为研究对象,通过构建水文-水质协同监测系统,对旱地面源污染排放系数进行测算,明确排放系数的时间变化特征,分析排放系数的影响因素。【结果】日尺度下,氮、磷的离田排放系数变化范围分别为2.2~9843.9、0.08~667.0 g/(hm^(2)·d);月尺度下,氮、磷离田排放系数变化范围分别为0.02~17.2、0.001~1.04kg/(hm^(2)·m)。在降水产流事件中,氮离田排放系数与降水历时呈极显著正相关,磷离田排放系数与产流事件的总降水量、平均降水强度、最大小时降水强度极显著正相关。施肥后,氮日排放系数迅速增大,磷日排放系数无明显变化。各时间尺度下,氮排放系数与地表径流不存在显著相关性,磷排放系数与地表径流极显著正相关。【结论】氮、磷离田排放系数的日、月际变化幅度较大,氮离田排放系数易受施肥措施影响,磷离田排放系数则更易受降水和地表径流的影响。

煤巷钻锚一体化快速掘进技术与装备及应用

分析煤巷掘进技术与装备现状及存在的问题,介绍研发的钻锚一体化快速掘进新模式、新技术、新装备及井下应用效果。研发出钻锚一体化锚杆支护技术与设备,包括钻锚一体化锚杆、泵注式锚固剂、钻锚一体化钻臂、钻箱、锚杆仓、锚固剂泵注系统及智能控制系统,将传统树脂锚固锚杆安装6道工序简化为1道连续工序,实现了锚杆自动化“一键”打设,单根锚杆作业时间由原来5~6 min减少为3 min。研发出自动化喷涂支护技术与装备,包括高强、速凝、抗变形喷涂护表材料,抗拉强度超过9 MPa,断裂伸长率超过100%,黏结强度高于3 MPa;研发出6自由度喷涂系统,可实现随掘自动化快速喷涂作业。研发出巷道随掘变形动态监测技术与系统,实现随掘大粉尘、多干扰、低照度环境下的巷道表面变形监测,监测精度能够满足围岩稳定性评价要求。提出煤巷掘支锚运分区并行协同快速掘进新模式,研制出钻锚一体化快速掘进成套装备。对试验矿井陕西陕煤曹家滩煤矿回采巷道掘进过程中围岩位移和稳定性进行了监测,在时间上,巷道掘出2~3 h后80%以上的围岩位移已经发生;在空间上,距离掘进工作面相当于2倍巷道宽度的位置,90%以上的围岩位移已经完成。快速掘进引起的巷道围岩位移在很短时间内就基本完成,要求支护必须及时且快速。在曹家滩煤矿10 m超大采高综采工作面特大断面回采巷道完成近6000 m的巷道掘进工程,打设钻锚一体化锚杆60000余根。锚杆施工时间、支护用工人数及作业人员劳动强度大幅降低,掘进速度、效率及自动化水平显著提高。试验巷道顶板位移量小、完整稳定,支护效果良好。煤巷钻锚一体化快速掘进技术与装备井下试验取得成功,具备了特大断面煤巷月掘进进尺1500~2000 m的能力。

面向多播行协同作业的精量播种监测系统研究

针对传统的气吸式播种机在作业过程中常常因导种管堵塞、种箱缺种以及机械故障造成的漏播、重播等现象,设计并开发一套支持12条播行协同作业的精量播种监测系统。该系统包括红外光电监测单元、车速监测单元、控制器单元、中控仪表单元以及故障报警单元的设计。试验结果表明,所设计的监测系统可以实时监控12条播种行的协同作业情况,当出现漏播、重播现象时能够及时、准确地识别播种行行号并进行故障报警,报警成功率达到100%,且平均响应时间在0.5 s以内。且在4~12 km/h的正常作业速度下,系统对不同体积大小的农作物种粒播种数量监测精度平均值超过98.5%。能够实现对多路播行同时作业的实时监测需求,对提高播种质量、降低生产成本具有重要意义。

基于UDI的医疗器械不良事件重点监测智慧管理平台的设计与应用

目的以医疗器械唯一标识(Unique Device Identification,UDI)体系为基础,设计基于UDI编码的医疗器械不良事件重点监测智慧管理平台。方法基于UDI编码和医疗设备全生命周期管理平台,融合移动互联网、工业互联网平台应用、大数据平台等先进架构理念和技术,采用Spring Cloud微服务架构和Kubernetes编排加Docker容器化技术,基于SpringCloud+Mybatis框架、使用Java语言、应用B/S架构,设计协同管理医疗器械不良事件重点监测智慧管理信息平台。平台支持多端多场景融合使用场景,医院、注册人和监测中心三方协同联动,实现不良事件重点监测信息化精准快捷上报。结果基于UDI的医疗器械不良事件重点监测智慧管理平台已收集不良事件及注册人采取的控制措施6例。该平台可实现不良事件高质量数据采集、多方信息精准推送、产品异常快速调查、企业控制措施快速反馈,有效提高医疗器械不良事件数据的准确性和风险发现、报告、处置、反馈的效率,实现医疗器械不良事件的全场景闭环智慧管理。结论该平台实现多家哨点单位由原始手工填报向信息化收集重点监测数据的转变,还可以推广到所有类别的医疗器械不良事件管理场景中,实现医疗器械不良事件的数据化、智能化管理。

基于多源信息融合的矿山边坡滑坡灾害研究现状与展望

为克服单一信息源无法精确表征矿山滑坡灾害演化特征的问题,基于多源信息融合技术,从矿山边坡多源信息获取、矿山边坡多源信息融合、矿山边坡位移预测及滑坡风险评价3个方面概述了矿山边坡滑坡灾害研究进展。总结了典型的“天”“空”“地”边坡监测手段及“天-空-地”一体化协同监测方法;梳理了包含数据级、特征级和决策级融合的边坡多源信息融合流程;整理了位移与应力、位移与水文气象及其他不同类型的监测数据融合形式;阐述了基于多源信息融合的边坡位移预测及滑坡风险评价相关研究现状。基于当前矿山边坡滑坡灾害研究存在的灾害分析的准确性严重依赖监测数据质量、对岩石力学机理知识利用不足等问题,指出了矿山边坡滑坡灾害研究发展趋势:统一多源数据采集接入标准;开发监测数据与岩石力学机理融合的矿山边坡滑坡灾害分析方法;优化“天-空-地”多源信息的时空关联挖掘算法;加强基于多源信息融合的矿山边坡滑坡灾害预警平台建设。

基于多摄像头边云协同视频请求的智能监控预警方法研究

多摄像头边云协同视频已被广泛运用于智能化生活的各个领域,采用边云协同模式已然成为摄像头视频智能监控的数据处理的发展趋势。为了实现对多摄像头边云协同的视频请求任务的高精度检测,此次研究提出在基于多摄像头边云协同请求下的智能监控预警方法。采用边缘计算和云计算的紧密协同处理,在边云协同的数据协同能力下以较低成本完成对数据的高效处理。在多摄像头请求下的边云协同视频的智能监控预警,对视频数据进行处理与分析,提出利用网状关联分析的智能预警模型进行判定。对单摄像头与多摄像头下智能监控预警方法检测的准确率进行分析。得出该方法的检测准确率最高,且每项的检测准确率均在97%左右,证明了其在智能监控预警中的可靠性与有效性。

人机协同的多模态遥感变化检测方法

自然资源领域是技术密集型领域,面向新时代、新任务、新要求,迫切需要应用高新技术构建调查监测技术体系,支撑服务自然资源调查、监测、评价、规划等工作。为了向国土空间开发利用和国土空间治理提供高质量的数据供给,立足人工智能技术发展现状,以卫片执法遥感监测、全国地类变化遥感监测等国家重大战略需求为牵引,针对传统变化检测方法人工劳动繁重和经典变化检测方法工序复杂的固有问题,本文提出了一种网格化人机协同生产方法。通过将遥感影像待检测区域进行北斗网格剖分,构建最小监测单元,规范监测作业模式;创新了一种多模态变化检测技术,以集群处理技术为计算中枢,将多种人工智能技术优势进行融合互补;研究了分布式遥感变化推理引擎,突破机器动态变化感知能力,使机器可以快人一步,动态检测操作视图影像变化,实现人与机器的同步变化感知;探索了人机协同信息化监测作业体系,基于北斗网格位置码建立监测任务调度管理看板,使监测任务进度管理精细可控,实现了作业效率30%和作业质量20%的提升。

倾斜厚煤层动压巷道变形破坏规律及高预紧力锚杆锚索协同支护技术研究

针对庞庞塔煤矿倾斜煤层巷道局部收敛变形过大的问题,以5-1062巷为工程背景,通过理论分析和数值模拟,分析总结了巷道失稳机理和变形破坏的主要原因,探讨了围岩变形对动压的响应机制,揭示了高预紧力锚杆锚索协同支护技术对围岩变形的控制机理,并针对原有支护方式不足提出以高预紧力锚杆锚索协同支护技术为核心的优化方案,结合现场条件进行了工业性试验。研究结果表明:护表面积小、预应力低、锚杆(索)支护不协同是5-1062巷支护效果不佳的主要原因;对于倾斜煤层巷道,垂直动压对两帮的影响大,水平动压对顶板的影响大,与水平动压相比,垂直动压是影响巷道收敛变形的主导因素;高预紧力锚杆锚索协同支护技术能给巷道浅部围岩施加均匀的高预应力,保证围岩处于较为稳定三向应力状态,有效提升围岩的稳定性。现场监测表明,采用支护优化方案后,巷道变形得到有效控制,无锚杆锚索破断现象,局部网兜现象得到抑制,现场支护效果良好,为类似工程地质条件下的煤矿巷道围岩变形控制提供参考。

行车设备施工维修登销记系统规范化研究

铁路营业线施工是保证铁路设备设施始终处于良好状态,保障行车安全、提高运输效率的一项必不可少的工作。调研各铁路局集团公司行车设备施工维修及检查登销记业务需求,分析行车设备施工维修登销记安全风险点。研究提出全路行车设备施工维修登销记系统总体结构,通过全路登销记系统功能和接口的规范化,实现登销记系统与多系统互联互通和数据交换的接口交互;识别施工维修登销记安全风险点,为全路登销记系统的统一管理以及登销记系统与其他系统的协同安全卡控提供参考。