Comprehensive early warning of rock burst utilizing microseismic multi-parameter indices

Rock bursts have become one of the most severe risks in underground coal mining and its early warning is an important component in the safety management. Microseismic(MS) monitoring is considered potentially as a powerful tool for the early warning of rock burst. In this study, an MS multi-parameter index system was established and the critical values of each index were estimated based on the normalized multi-information warning model of coal-rock dynamic failure. This index system includes bursting strain energy(BSE) index, time-space-magnitude independent information(TSMII) indices and timespace-magnitude compound information(TSMCI) indices. On the basis of this multi-parameter index system, a comprehensive analysis was conducted via introducing the R-value scoring method to calculate the weights of each index. To calibrate the multi-parameter index system and the associated comprehensive analysis, the weights of each index were first confirmed using historical MS data occurred in LW402102 of Hujiahe Coal Mine(China) over a period of four months. This calibrated comprehensive analysis of MS multi-parameter index system was then applied to pre-warn the occurrence of a subsequent rock burst incident in LW 402103. The results demonstrate that this multi-parameter index system combined with the comprehensive analysis are capable of quantitatively pre-warning rock burst risk.

Risk Early-Warning Method for Natural Disasters Based on Integration of Entropy and DEA Model

Risk early-warning of natural disasters is a very intricate non-deterministic prediction, and it was difficult to resolve the conflicts and incompatibility of the risk structure. Risk early-warning factors of natural disasters were differentiated into essential attributes and external characters, and its workflow mode was established on risk early-warning structure with integrated Entropy and DEA model, whose steps were put forward. On the basis of standard risk early-warning DEA model of natural disasters, weight coefficient of risk early-warning factors was determined with Information Entropy method, which improved standard risk early-warning DEA model with non-Archimedean infinitesimal, and established risk early-warning preference DEA model based on integrated entropy weight and DEA Model. Finally, model was applied into landslide risk early-warning case in earthquake-damaged emergency process on slope engineering, which exemplified the outcome could reflect more risk information than the method of standard DEA model, and reflected the rationality, feasibility, and impersonality, revealing its better ability on comprehensive safety and structure risk.

Early warning of rock mass instability based on multi-field coupling analysis and microseismic monitoring

In order to overcome the limitation that rock mass instability warnings are caused by a lack of deep consideration of the inherent mechanism of disaster formation, early warning signs of rock mass instability were detected and multi-field coupling was analyzed. A multi-field coupling model of a damaged rock mass was established. The relationship between microseismic activity parameters and rock mass stability was analyzed, and a multi-parameter early warning index system was established and its solution program was compiled. Based on the D-S data fusion theory,an early warning model of rock mass instability combining multi-field coupling analysis and microseismic monitoring was constructed. Taking an underground mine stope as an object, the multi-field coupling model and its solution program were used to analyze mining response characteristics. The seismic field data were used to verify the accuracy of the multi-field coupling analysis. The early warning model was used to predict the instability of stope rock mass,and the early warning result is consistent with a real-world scenario.

Disaster reduction stick equipment: A method for monitoring and early warning of pipeline-landslide hazards

Oil and gas pipelines are of great importance in China,and pipeline security problems pose a serious threat to society and the environment.Pipeline safety has therefore become an integral part of the entire national economy.Landslides are the most harmful type of pipeline accident,and have directed increasing public attention to safety issues.Although some useful results have been obtained in the investigation and prevention of pipeline-landslide hazards,there remains a need for effective monitoring and early warning methods,especially when the complexity of pipeline-landslides is considered.Because oil and gas pipeline-landslides typically occur in the superficial soil layers,monitoring instruments must be easy to install and must cause minimal disturbance to the surrounding soil and pipeline.To address the particular characteristics of pipelinelandslides,we developed a multi-parameter integrated monitoring system called disaster reduction stick equipment.In this paper,we detail this monitoring and early warning system for pipeline-landslide hazards based on an on-site monitoring network and early warning algorithms.The functionality of our system was verified by its successful application to the Chongqing Loujiazhuang pipeline-landslide in China.The results presented here provide guidelines for the monitoring,early warning,and prevention of pipeline geological hazards.

Early warning research on salt-alkalization desertification in western Songnen Plain

Desertification is an environmental issue in the world. The salt-alkalization desertification land area formed by both primary and secondary salt-alkalization has extended in a large scale, which has become a significant eco-environmental problem. Based on the characteristics of eco-environment and the situation of desertification in westem Songnen plain, this paper reports the analyzes of its formation in background and cause. An early waming system on the salt-alkalization desertification is established and the GIS technology is used to abstract the information of desertification evaluation index. Supported by the integrated technology of the GIS and ANN, the orientation and quantitative result of desertification are gained, which is helpful to the eco-environment protection and resource development in westem Songnen Plain.

Prospective study and validation of early warning marker discovery based on integrating multi-omics analysis in severe burn patients with sepsis

Background:Early detection,timely diagnosis and rapid response are essential for case management and precautions of burn-associated sepsis.However,studies on indicators for early warning and intervention have rarely been conducted.This study was performed to better understand the pathophysiological changes and targets for prevention of severe burn injuries.Methods:We conducted a multi-center,prospective multi-omics study,including genomics,microRNAomics,proteomics and single-cell transcriptomics,in 60 patients with severe burn injuries.A mouse model of severe burn injuries was also constructed to verify the early warning ability and therapeutic effects of potential markers.Results:Through genomic analysis,we identified seven important susceptibility genes(DNAH11,LAMA2,ABCA2,ZFAND4,CEP290,MUC20 and ENTPD1)in patients with severe burn injuries complicated with sepsis.Through plasma miRNAomics studies,we identified four miRNAs(hsamiR-16-5p,hsa-miR-185-5p,hsa-miR-451a and hsa-miR-423-5p)that may serve as early warning markers of burn-associated sepsis.A proteomic study indicated the changes in abundance of major proteins at different time points after severe burn injury and revealed the candidate early warning markers S100A8 and SERPINA10.In addition,the proteomic analysis indicated that neutrophils play an important role in the pathogenesis of severe burn injuries,as also supported by findings from single-cell transcriptome sequencing of neutrophils.Through further studies on severely burned mice,we determined that S100A8 is also a potential early therapeutic target for severe burn injuries,beyond being an early warning indicator.Conclusions:Our multi-omics study identified seven susceptibility genes,four miRNAs and two proteins as early warning markers for severe burn-associated sepsis.In severe burn-associated sepsis,the protein S100A8 has both warning and therapeutic effects.

矿井微震与电法耦合监测技术

随着煤炭资源开采区域、深度及强度的增加,矿井工作面采掘扰动极易诱发矿井突水事故,严重威胁我国煤炭资源安全开采。矿井水害事故的发生主要是岩体采动破裂与矿井水渗流共同作用的结果。研究表明,微震信号的纵横波到时、幅值、频率等参量可有效表征岩体破裂位置、强度及其震源机制,地电场信号中的电阻率、激励电流及自然电位等参量可有效表征岩体渗流演化过程。透水通道形成对应微震场特征,地下水渗流对应地电场特征,因此微震电法耦合方法对矿井水害孕育发展过程具有监测预警的功能,可有效对采动破坏过程中形成的突水通道、水源位置及渗流过程等水害三要素进行精准捕捉;减少单一地球物理方法多解性,提高水害监测预警精度,对煤矿防治水意义重大。当前,微震与电法耦合监测技术在煤矿水害实时动态监测中开始得到应用,突破传统微震与电法独立采集的局限,课题组自主研发了国内外首款煤安认证的微震与电法耦合并行监测系统,该系统以微震场与地电场并行采集基站为核心,集被动源微震波场与主被动地电场为一体,通过连接矿井物联网,实现远程数据采集控制、传输及云端存储;其一体化网络服务器主机可连接多个采集基站,并将微震检波器、电法激励电极与电位测量电极进行相互隔离、并行采集,形成微震事件密度与能级分布、电阻率分布、自然电位分布等时空云图来监测预判水害三要素。微震与电法耦合监测技术仍需在微震事件智能识别与定位、地电场的电阻率三维反演以及自然电位渗流反演方面进行专项攻关;结合并行监测的微震电法大数据集,重点在微震电法数据联合反演、智能化监测预警方向开展进一步研究。微震与电法耦合监测技术,作为矿井水害的主动预警监测新方法,可为我国智能化矿井建设发挥重要作用。

基于文本数据的灾害信息风险监测与预警集成方法研究

基于灾害信息的文本数据,该文提出一种风险监测与预警集成方法,以提高灾害信息风险监测效率及监测支持预警的针对性。首先,运用八爪鱼数据采集器采集和处理灾害信息的文本数据;其次,运用BTM主题模型构建灾害信息风险监测模型,得到主题热度趋势并确定具有高风险且亟待预警的话题;然后,提出灾害信息风险预警指标体系和预警等级判定方法,计算所确定话题的风险综合指标预警加权值,并结合预警区间判定预警等级;最后,以2021年“7·20”郑州特大暴雨灾害信息的风险监测和预警为例进行实验分析,验证所提方法的可行性。研究表明,该方法可将灾害信息的文本数据贯穿于风险监测与预警两个过程,有助于为监测结果更有针对性地支持预警工作提供参考。

煤矿灾害预警数据集成管理平台研究与应用

针对深部矿井地质的复杂性与灾害的多样性,开展基于海量数据驱动的深部矿井无人化、智能化监测预警技术与装备研究是提高深部煤矿开采安全保障的有效途径。然而,目前煤矿监测系统间关联性差、数据利用率低、数据深入智能分析不够,导致煤矿防灾措施实施针对性差,可能存在防灾措施不到位或过量的现象。针对煤矿灾害监测预警的需求,提出了煤矿灾害监测预警大数据平台架构,设计了结构数据、非结构数据、时序数据等异构数据的混搭数据存储方案,从数据汇集、数据质量、数据融合、数据指标等方面设计煤矿监测数据治理方法,并提出面向煤矿灾害预警应用的多租户数据集市构建技术,从而支撑煤矿灾害分析应用的多样化数据需求。

大数据画像结合多种教学方法在医学影像技术专业磁共振实习中的应用

磁共振实习一直是医学影像技术专业学生实习的难点,各教学基地对于如何提高磁共振实习质量仍处于探索阶段。文章针对学生实习过程中的困惑、磁共振实习阶段存在问题,提出大数据画像结合多种教学方法,构建医学影像技术专业学生在磁共振实习中的大数据画像模型,详细阐述实施计划及设计流程并对实行此项教学改革的显著效果进行了分析与总结。此项教学改革有效避免了传统实习模式的固化形式,对学生进行因材施教精准培养的同时可提高教师自身专业水平,实现了师生双方的共同成长,为医学影像技术专业学生实习培养提供了新的思路和方法,为培养出更多优秀的毕业生奠定坚实的基础。

项目综合管理策略在重症监护病房耐甲氧西林金黄色葡萄球菌感染防控中的应用

目的 探讨项目综合管理策略在重症监护病房(ICU)落实耐甲氧西林金黄色葡萄球菌(methicillin-resistant Staphylococcus aureus,MRSA)感染防控措施中的应用效果,为降低MRSA医院感染发生率提供循证依据。方法 选取2020-01-2021-12月某三甲医院5个ICU中住院患者送检标本并经微生物培养分离出MRSA的患者为研究对象。2020年为基线调查,2021年实施项目综合管理策略。比较干预前后各项防控措施落实执行情况、手卫生依从性、患者MRSA检出情况和MRSA检出科室分布情况。结果 实施项目综合干预管理策略后,MRSA防控措施的落实情况优于干预前,差异有统计学意义(P<0.05)。各类人员手卫生依从性均有提高,差异有统计学意义(P<0.05)。干预后患者MRSA检出率由35.71%降至27.73%。MRSA检出科室分布情况显示除呼吸内科ICU持平,其他ICU检出率均下降。结论 项目综合管理策略提高了MRSA感染防控措施的执行力和有效性,降低了MRSA检出率,减少了患者感染MRSA的风险,可保障医疗安全。

基于异质集成的井漏预警模型

钻井井漏事故具有突发性和难以控制的特点。因此,迫切需要建立一种有效的井漏预测方法。将随机森林、支持向量机和反向传播神经网络模型相结合的异质积分器Stacking应用于青海省柴达木盆地英西地区。首先对目标区块的数据集进行处理,运用灰色关联对数据进行相关性分析,选择其中10个相关性高的参数,后设置两层堆叠集成,第一层选择随机森林、支持向量机和BP神经网络模型作为基础学习器,第二层选择逻辑回归模型作为元学习器。结果表明,异质集成模型提高了预测精度(0.981的准确率、0.970的精确率、0.963的召回率和0.960的F 1分数),克服了同质分类器的局限性。强调了综合井漏预警预报中考虑多种地质因素的重要性。

2023年6月15日上海青浦M3.1地震预警处理结果分析

2023年6月15日1时39分上海市青浦区发生M3.1地震。上海地震预警台网成功接收中国地震预警网发布的地震预警信息,本地部署的福建预警系统EEW也成功处理并产出了本次地震预警结果。本文对上海地震预警系统产出的7次地震预警处理结果,以及中国地震预警网发布的地震预警信息进行比对分析。此次地震震中位于上海地震预警网内,震后6.8 s发布首次预警信息,全部7次预警处理结果均在震后15 s内产出。与正式地震目录相比,第一次预警处理结果震中位置偏差为5 km,震级偏差为−0.1。随着参与计算的预警台站数量增多,震中位置最终与正式目录一致,震级偏差也保持在0.5。本次地震预警结果表明,上海及周边地区虽然属于地震活动主要以小微震为主的预警一般区,但是由于预警台站密度较大,发生地震后也可以在较短时间内产出比较精确的预警处理结果,这对正在进行的长三角监测预警业务一体化工作具有重要参考意义。

浙江省地质灾害气象风险预警一体化建设的探索与实践

地质灾害预报预警是有效防范地质灾害、减少人员伤亡的重要手段。自2003年开始实施以来,全国有80%的山区省、市、县开展了地质灾害气象预警工作。但这种省、市、县独立建设模式难以满足部省协同、市县联动、全域贯通的业务需求。文章介绍了浙江省地质灾害气象风险预警一体化建设:按照“省级预报到县、市级预报到乡、县级预警到村(点)”的工作原则,在趋势预测、每日预报、短临预报和实时预警与地质灾害防治业务一体化融合方面开展探索,初步建成了以“地灾智治”应用驾驶舱为可视化监管,以“地灾智防”APP为闭环手段的地质灾害全生命周期管控防治业务体系,形成了具有浙江特色的地质灾害预报预警数字化产品体系。

综合生态系统管理的生态阈值理论研究进展

生态阈值是指生态系统结构、属性或功能在一定时间或空间尺度上从一个稳定状态向另一个稳定状态发生转变的突变点,常作为综合生态系统管理的关键性指标。深入了解其概念原理,对生态系统综合管理、生态安全预警具有重要作用。系统阐述了生态阈值的概念、生态系统转换的概念模型、生态阈值类型的划分及生态阈值的研究方法,分析并总结了不同生态阈值研究模型的适宜应用条件和优缺点,并对未来生态阈值研究提出建议:(1)加强生态系统阈值定量化评估方法的探索,旨在减小阈值量化的误差;(2)探究空间尺度转化、多驱动因素交互作用对生态阈值的影响,建立不同时空尺度、不同生态系统类型阈值转换数据库;(3)在未来将生态阈值应用到实际综合生态系统管理中,并从管理者及科研工作者角度提出相应实施建议。

矿井煤自燃灾害监测预警技术及发展趋势

以降低矿井火灾的发生率为目标,梳理了矿井煤自燃灾害现有监测预警技术的发展现状,总结了现有技术的局限性和不足,指出未来发展应注重先进监测技术的应用、预测技术准确性的提升、预警系统和智能化管理的发展等。针对矿井煤自燃灾害隐蔽性、持续性、扩散性和难以控制的特点,提出矿井煤自燃灾害监测预警技术未来发展方向:提升煤自然发火临界值确定方法的综合性,利用新兴设备和先进技术完善煤自然发火临界值的确定方法;针对矿井煤自燃复杂环境,可通过研究声波探测等新型先进技术手段,提高矿井煤自燃监测参数的全面性和精细化;推动矿井煤自燃监测预警技术的创新性和决策的智能化,利用数字孪生技术提供精准的模拟与预测能力、优化防控方案、提供培训和应急管理以及数据分析和决策支持等方面的帮助;聚焦矿井自燃灾害全面感知、精准预测与防控智能一体化建设,加强对矿井自燃灾害数据分析、灾前主动预警、灾后事故治理、火区自动封闭等技术的研究。

2023年我国生猪产业发展情况及未来发展趋势

本研究全面审视了2023年我国生猪产业的发展现状,并结合多维度的经济、政策及市场因素,深入探讨了2024年该行业的发展前景。在2023年,我国生猪产业面临了产能过剩、传统“猪周期”特征弱化、金融产品属性日渐凸显及成本持续上升等多重挑战,使得养殖企业经营难度加大。展望2024—2025年,本研究综合分析了产能调整、宏观经济状况、企业预期盈利及市场动态等多重因素。研究发现,随着产能的逐步优化,生猪市场的供需平衡在2024年得到实质性改善,为猪价上涨奠定基础。同时,经济的稳定增长、消费需求的逐步回升及养殖成本的有望降低,将共同推动生猪产业盈利能力的提升。此外,根据市场走势,生猪价格在2024年第二季度实现企稳回升。为了更好地应对行业变革与市场挑战,本研究提出了创新市场监测预警体系、加强金融与生猪产业深度融合等策略建议,旨在为相关政策制定与决策提供科学依据和有益参考。

礁海空一体化防空体系航空兵配置模型设计

为进一步拓展要地防御纵深,构建多批次拦截防御体系,形成纵深梯次拦截、礁海空一体化的综合防御布势。研究了包括预警机、航空兵、水面舰艇、要地防空导弹/火炮等多种防空武器装备在内的一体化防空火力配置优化问题。针对防空火力单元之间的配置距离、防御纵深、作战扇面角以及敌来袭方向等因素,建立了礁海空一体化防空体系航空配置模型,并通过算例仿真验证了模型的有效性,对礁海空防空体系中航空兵的配置有一定借鉴意义。

基于统一数字底座的煤矿灾害融合管控平台

针对煤矿灾害监测预警与透明地质系统数据多源异构、预警结果可视化手段单一、业务流程不畅、数字化决策支撑不足等问题,提出了基于统一数字底座的煤矿灾害融合管控平台。该平台分为数据采集层、数据存储层、应用支撑层和应用层,以透明地质系统为井巷工程数据来源,利用智能通风系统进行通风网络解算和通风决策分析,集成各灾害预警系统分析结果和控制策略,从空间和业务2条主线按照“隐蔽致灾因素—安全监测—安全管理—灾害预警—仿真规划—协同控制”进行灾害全链路管控。该平台利用Web API和FIP接口对透明地质系统数据与服务进行集成,基于统一的预警信息描述规范对灾害预警数据进行集成,通过“场景—模型—对象—属性”层级结构的数据对象模型对安全监测数据进行集成,解决了数据一致性难题;从巷道拓扑关系生成、巷道三维模型生成和要素空间关系生成等方面进行井巷工程三维参数化建模,实现了基于透明地质系统数据的井巷工程自动更新;采用“模型压缩+浏览器端缓存+渲染调度策略优化”,实现了基于WebGL的地质体三维可视化;通过逐级确认的预警事件协同处置机制,提升了预警结果及处置的可靠性。现场应用结果表明,该平台实现了基于统一数字底座的瓦斯、水害、火灾、矿压、粉尘、智能通风等系统数据同步、功能集成、流程衔接和统一可视化,为开展融合透明地质的灾害预警提供了支撑。

基于改进切线角和变形速率的滑坡预警——以湖南省石门县雷家山滑坡为例

为了研究湖南省滑坡监测预警方法,提高预警成功率,以石门县雷家山滑坡为例展开研究。通过对灾害现场进行详细地质调查,对滑坡特征进行了描述,初步阐述了滑坡发生的成因机理。研究结果表明,独特的地形地貌、结构松散的土体是滑坡形成的内在因素,强降雨和工程切坡活动是导致滑坡的外因。基于GNSS系统获取滑坡的全过程变形-时间曲线,并对变形曲线特征进行分析,发现该滑坡具有明显的初始变形、等速变形、加速变形三阶段演化特征,属于渐变型滑坡。将切线角和变形速率阈值作为滑坡预警的重要指标,建立了预警判据,并对接群防群测信息平台,可以为相关部门展开防灾减灾工作和应急响应提供直接依据。雷家山滑坡实现成功避险,验证了该监测预警方法的实用价值。