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2011年科研热词推荐指数非常规突发事件 1 问卷调查 1 重大危险源 1 遗传算法 1 输送能力 1 自动扶梯 1 紧急疏散线路 1 系统最优 1 突发客流 1 研究进展 1 相关性分析 1 疏运模型 1 疏散能力 1 疏散星团和星协:个别:ngc 6791 1 疏散 1 热烧蚀破坏 1 液体金属 1 泊松分布 1 模拟 1 材料 1 最小费用流 1 最大疏运能力 1 时间依赖网络 1 抗激光辐照 1 技术:视向速度 1 惯性行为 1 恒星:距离 1 恒星:丰度 1 应急行为特征 1 应急管理 1 应急疏散行为 1 应急疏散 1 应急救援 1 应急心理反应 1 实证分析 1 大型活动 1 复合壳体结构 1 地铁车站 1 区域路网 1 动态路阻 1 元胞传输模型 1 优化 1 从众行为 1 人群疏散 1 交通组织 1 交通疏散 1 交通环境 1 交通特性 1 交通工程 1 交叉口延误 1 二分回溯法 1 k最短路 1
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2023年消防设施操作员之消防设备基础知识综合练习试卷A卷附答案

2023年消防设施操作员之消防设备基础知识综合练习试卷A卷附答案单选题（共40题）1、《消防法》规定，（）领导全国的消防工作。

A.公安部消防局B.国务院C.地方各级人民政府D.国务院应急管理部门【答案】 B2、油锅起火，不正确的扑救方法是（）。

A.迅速关闭气源B.用灭火毯覆盖C.用水泼灭D.用锅盖闷灭【答案】 C3、（）是火灾中致死的主要燃烧产物之一，其毒性在于对血液中血红蛋白的高亲和力。

A.二氧化碳B.一氧化碳C.水蒸气D.二氧化硫【答案】 B4、《消防法》规定，火灾扑灭后，发生火灾单位和相关人员应当按照（）要求保护现场，接受事故调查，如实提供与火灾有关情况。

A.所在地公安机关B.消防救援机构C.所在地政府机关D.主管部门【答案】 B5、按可燃物类型和燃烧特性不同，下列物质发生火灾属于C类火灾是（）。

A.二甲苯B.铜粉C.丙烷D.中草药材【答案】 C6、应将喷淋消防泵控制柜的启动、停止按钮专用线路直接连接至设置在消防控制室内消防联动控制器的（）。

A.总线控制盘B.总线回路C.手动控制盘D.警报输出端口【答案】 C7、本题那项不符合建筑消防设施巡查要点的内容：A.应急广播扬声外观完好情况;B.防火门、防火卷帘开户是否处开正常状态，外观完好情况，有无遮挡;C.消防电梯的紧急按钮和电梯轿箱内电话的外观;D.发电机是否正在运转。

【答案】 D8、微型消防站灭火器材配置时，错误的是（）A.应设置人员值守、器材存放等用房且必须单独设置。

B.应根据扑救初起火灾需要，配备一定数量的灭火器、水枪、水带等灭火器材，配置外线电话、手持对讲机等通信器材C.应在建筑物内部和避难层设置消防器材存放点，可根据需要在建筑之间分区域设置消防器材存放点D.有条件的微型消防站可根据实际选配消防车辆【答案】 A9、当湿式报警阀进口水压大于0．14MPa、放水流量大于（）L/s时，报警阀应及时启动。

A.1B.1.5C.2D.2.5【答案】 A10、防火的基本原理为限制燃烧必要条件和充分条件的形成。

地铁站疏散Modeling the pedestrian’s movement and simulating evacuation

Modeling the pedestrian’s movement and simulating evacuation dynamics onstairsYunchao Qu,Ziyou Gao ⇑,Yao Xiao,Xingang LiSchool of Trafﬁc and Transportation,Beijing Jiaotong University,Beijing 100044,Chinaa r t i c l e i n f o Article history:Received 23December 2013Received in revised form 15May 2014Accepted 22May 2014Available online 26June 2014Keywords:Pedestrian ﬂowStaircase movement Dynamic characteristics Social force modela b s t r a c tThis paper presents an enhanced social force model to describe the pedestrian’s movement and evacua-tion dynamics on pared with original models that described the pedestrian’s planar motion,our model introduces some mechanisms of the staircase movement,such as the inﬂuence of staircase geometry,the restriction of the step size and the optimal velocity selection.The body shape of each pedestrian is regarded as a set of three circles to precisely quantify the movement.In addition,the rotation dynamics are included into the model to describe the congestion effect.The improved model can obtain individual velocity under different staircase geometries and the ﬂow characteristics of the evacuation dynamics.Some empirical data and a series of observations captured in two subway stations in Beijing are applied to study the characteristics and further validate the model.The results show that our model performs well consistent with the observed data.At last,simulations are implemented to ﬁnd the solutions of estimating the evacuation time and evaluating the capacity of stair.Ó2014Elsevier Ltd.All rights reserved.1.IntroductionStairs are widely used in all kinds of buildings,especially in large scale public places,i.e.,subway stations,shopping malls and ofﬁce buildings.Walking on stairs is very common and important in our daily lives,and scientiﬁc design and effective utilization of stairs are urgently needed for designers and managers (Peacock et al.,2009).In emergency,such as power failure,ﬁre,earthquake or other hazards,the elevators may be out of commission,and the stairs become the primary escape routes.If there are too many peo-ple crowded on stairs,they will pack closer together or even lead to some dangerous situations (Shields and Boyce,2009).Knowing the ﬂow characteristics and predicting the egress time are the key points to grasp the evacuation dynamics and make emergency response plans on stairs (Graat et al.,1999;Oven and Cakici,2009).The characteristics of pedestrian staircase movement are deter-mined by organizational,constructional and behavioral factors:the organizational factors,i.e.,preparation for emergencies;the constructional factors,i.e.,the staircase geometry including riser height,tread depth and step width (Graat et al.,1999;Fujiyama and Tyler,2010);the behavioral factors,i.e.,responses and move-ment characteristics of pedestrians (Yang et al.,2012;Ma et al.,2012).The study of the staircase movement is an interdisciplinaryﬁeld with different focuses,such as biomechanics,physics,physiol-ogy,phycology,computer science,safety science (i.e.,Hankin and Wright,1958;Fruin,1971;Predtechenskii and Milinskii,1978;Templer,1992;Batty,1997;Helbing et al.,2000;Hase and Yamazaki,2002;Nelson et al.,2002;Hoskin,2004;Pauls,2005;Trew,2005;Casburn et al.,2007;Hostikka et al.,2007;Galea et al.,2008;Kretz et al.,2008;Seer,2008;Xu and Song,2009;Fujiyama and Tyler,2010;Galea et al.,2010;Hoskins,2011;Halsey et al.,2012;Yang et al.,2012;Peacock et al.,2012;Burghardt et al.,2013).To make quantitative analyses and detailed descriptions of staircase movement,many researches have carried out a lot of sur-veys,experiments and evacuation drills on stairs,and have collected large amounts of experimental and observational data of staircase movement (please see Table 1for details).In these studies,the ﬂow characteristics of staircase movement are described in individual level and collective level.Pedestrian ﬂow in low densities reﬂects the characteristics in the individual level,and walking speed is inﬂu-enced by physiological feature and body function,such as gender,age,height,weight,heart rate,rate of oxygen consumption and rate of energy expenditure (Irvine et al.,1990;Teh and Aziz,2002;Halsey et al.,2012).It is also inﬂuenced by stairway geometries and movement direction.Pedestrian ﬂow in high densities reﬂects the characteristics in the collective level.The collective behaviors include pedestrians’self-organized behaviors and optimal route choice behaviors (i.e.,Helbing et al.,2000,2005;Moussaid et al.,2011).The researches of pedestrian ﬂow in the collective level focus on three aspects,(1)evaluating evacuation time,(2)reproducing/10.1016/j.ssci.2014.05.0160925-7535/Ó2014Elsevier Ltd.All rights reserved.⇑Corresponding author.Tel.:+861051688193.E-mail address:zygao@ (Z.Gao).fundamental diagram,and(3)describingﬂow characteristics,i.e., inﬂow,outﬂow,capacity.Staircase movement is a complicated three-dimensional move-ment,and modeling the movement is a quite challenging work. Nowadays,researches have integrated behavioral and construc-tional factors,and have established many models to analyze the ﬂow characteristics and simulate evacuation processes in both sin-gle-story and multi-story buildings(Table1).In our work,we mainly focus on the case of single-story staircases.These models are classiﬁed into two categories:macroscopic model and micro-scopic model(Zheng et al.,2009).The macroscopic models regard the crowd as a single entity,and focus onﬁtting the expression of fundamental diagram.Linear,piecewise linear and non-linear functions(i.e.,Fruin,1971;Warren,1984;Tanaboriboon et al., 1986;Weidmann,1993;Lam and Cheung,2000;Proulx,2002; Peacock et al.,2012;Hoskins and Milke,2012)have been applied to describe relationship between velocity and density under differ-ent stair geometries.Compared with macroscopic models,the microscopic models are able to precisely describe the individual behavior,qualitatively explain the evacuation dynamics and reproduce some self-orga-nized phenomena(Helbing et al.,2000).These microscopic models are spatial-discrete models(cellular automation model,i.e., Kirchner et al.,2004;Huang and Guo,2008;Schadschneider and Seyfried,2009)and spatial-continuous models(social force model, i.e.,Helbing et al.,2000).These models have been applied to reveal two-dimensional planar movement,but few of them have described the three-dimensional staircase movement(Song et al., 2006;Pelechano and Malkawi,2008;Xu and Song,2009;Ma et al.,2012).In addition,the spatial-discrete models are some restricted to describe the staircase movement,such as grid size,fatigue factor,route selection,and uneven use of stairs (Pelechano and Malkawi,2008).Although the spatial-continuous models are advantageous to solve most of the aforementioned problems,these models are quite rare.Social force model(Helbing and Molnar,1995)is a well-known spatial-continuous model in theﬁeld of pedestrianﬂow.The model can reproduce several self-organized phenomena,such as lane forming,arching queue,shock waves and clogging effects (Helbing et al.,2005,2007).Moussaid et al.(2011)have proposed a heuristics-based model to replace the social force with a heuris-tics intelligent optimum function.Based on the heuristic social force model,this paper introduced some special rules and estab-lished an enhanced model to describe the mechanisms of pedes-trian movement and evacuation dynamics on stairs.Firstly,the body shape of each pedestrian is regarded as a set of three circles (Thompson and Marchant,1995).Compared with traditional sin-gle-circle shape(i.e.,Helbing et al.,2000),the three-circle shape precisely represents the projection of human body and describes the rotation movement when two pedestrians collide with others. Secondly,pedestrians usually walk more carefully on stairs than on planar,so two‘safety rules’are proposed to describe staircase movement behavior.Theﬁrst rule is that a pedestrian wants to walk upstairs/downstairs with integral steps at a time,and the step-size is restricted by the staircase geometry,such as tread depth,riser height and step width.The second rule is that a pedes-trian tends to walk along the sides,i.e.,holding handrails,propping up against walls.Thirdly,the relaxation time is extended to a var-iable in our model.The relaxation time is deﬁned that a pedestrian tends to correspondingly adapt his/her actual velocity to desired velocity with a certain characteristic time s(i.e.,Helbing et al., 2000;Moussaid et al.,2011).The relaxation time is mostlyTable1The state-of-the-art of staircase movement.Author(s)Year Method Model EvacuationprocessWalking speed NoteDynamics FD Geometry Direction Hankin and Wright1958Data analysis–s d s d–Fruin1971Data analysis–s d s s Planning methodPredtechenskii and Milinskii1978Data analysis Planning model s d s s Planning methodTanaboriboon et al.1986Macro Linear function s d s s Fundamental diagram Weidmann1993Macro Non-linear function s d d d–Frantzich1996Data analysis–s d d d–Graat et al.1999Data analysis–s d s s Capacity estimation Lam and Cheung2000Macro BPR function s d d d Fundamental diagram,capacityestimation Proulx et al.2002Data analysis Non-linear function d d s s SFPENelson and Mowrer2002Data analysis Non-linear function d d s s SFPEHoskin2004Softwaresimulation Coordinate-basedmodeld d d d Simulex32Pauls2005Data analysis–d s s s–Hostikka et al.2007Data analysis–d d s s–Kretz et al.2008Data analysis–s d d d Pedestrian movement on long stairs Seer et al.2008Data analysis–d d s s Flow characteristicsPelechano and Malkawi2008SoftwaresimulationGrid based model d s s s Literature review(STEPS,EXODUS)Galea et al.2008Software Evacuation model d s s s Merging behavior at interactions Xu and Song2009Micro Multi-grid model d s s s Flow characteristics,such as in and outﬂow Fujiyama and Tyler2010Macro Linear function s s d d Individual walking speed Galea et al.2010Data analysis–d d s s Evacuation softwareHoskins2011Macro Linear function d d d d Fundamental diagramYang et al.2012Data analysis–d d d s Evacuation drillLei et al.2012Softwaresimulation–d s s s Software(FDS,EVAC) Hoskins and Milke2012Data analysis–s d s s NISTPeacock2012Data analysis–d d d s NIST,different measurement methods Ma et al.2012Data analysis CA d s s s SimulationBurghardt et al.2013Data analysis–s d s s Fundamental diagramd Represents the factor is included and s represents the factor is not included.190Y.Qu et al./Safety Science70(2014)189–201assumed to be a constant in previous models,but it is not inade-quate to describe the staircase movement.As mentioned before, the individual walking speed on stairs is inﬂuenced by many fac-tors,such as staircase size,movement direction,and physical char-acteristics.For example,pedestrians spend more energy on walking upstairs than downstairs,spend more time on walking steep stairs than gentle stairs.Pedestrians with different age, weight or gender may require different relaxation times when walking on different stairs.Considering the inﬂuence factors,the relaxation time is formulated as a linear function of individual weight,moving height,and the slope of stairs.The linear function is similar to the model(Fujiyama and Tyler,2010),and some parameters are introduced to distinguish the upstairs and down-stairs movement.In this paper,some empirical data in literatures are collected and new observations from subway stations are conducted.In Sec-tion2,the characteristics of pedestrianﬂow on the stairs are dis-cussed.Based on the data and analysis,the constructional and behavioral factors are introduced to the social force model to pre-cisely describe the individual staircase moment in Section3.To validate the model,a series of simulations are implemented,and the simulation results are compared with the observational and empirical data in Section4.Simulations are implemented to ana-lyze theﬂow characteristics and the evacuation process in subway stations in Section5.Finally,the conclusions and the further work are given in Section6.2.Data collection andﬂow characteristics on stairs2.1.Empirical data of pedestrian speedOccupant speed is a very important element of pedestrianﬂow, and pedestrian speed on stairs is mainly affected by the slope of stairway,depth of tread,height of riser,and presence and location of handrails(Gwynne et al.,2009).Graat et al.(1999)had found that speed and capacity on stairs were higher with a normal (30°)slope than a steep(38°)slope.Kretz et al.(2008)had found that some pedestrian accelerate when walking upward a short stairway,and the mean upward walking speed on the short stair-way was found to be roughly twice as large as the one on the long stairway.Fujiyama and Tyler(2010)had proposed a model to pre-dict the walking speed based on the weight,leg power and the gra-dient of the stairs.The evacuation process of a large number of people is another major concern for researchers and designers. During the evacuation,the evacuation process something likes a queuing system that contains the processes of congestion forming, propagation and dissipation(Ma et al.,2012).Besides,the stair width and capacity will affect the route/exit choice behaviors and the evacuation efﬁciency(Lei et al.,2012).Researches have obtained many observational and experimen-tal data of staircase movement.However,this study here is not intended to be an exhaustive review of all researches.The refer-ences which mentioned both staircase geometry and individual speed are taken into consideration.Ten instances of staircase are included in our paper,and more detailed data can be found in the literatures(i.e.,Weidmann,1993;Frantzich,1996;Fujiyama and Tyler,2010;Hoskins,2011;Peacock et al.,2012).From Table2, it is found that walking speeds listed in the studies are different. This may be caused by natural variation of individual capability, staircase geometry,density of crowd and other factors.Besides, different measurement methods of calculating travel distances and areas on stairs may lead to different results(Hoskins and Milke,2012).Fujiyama and Tyler(2010)have made some experi-ments and found that average upstairs and downstairs speeds were0.58m/s and0.67m/s,respectively.Peacock et al.(2012) have mentioned that average downstairs speeds in their study of 0.48±0.16m/s were observed to be quite similar to the range of literature values.Kretz et al.(2008)have pointed that the density also affected the individual speed.Ma et al.(2012)have made a series of evacuation drills to obtain the average downstairs speed of0.547m/s.Even though different researchers have come up with different values for movement on stairs,most give a maximum for density of4.5–5.5pedestrians/m2,a maximum for speed of0.7–1.2m/s,and maximum for capacity of0.8–1.5pedestrians/(m s).2.2.Observations in subway stationsSomeﬁre drill evacuations of ofﬁce buildings have been imple-mented by National Institute of Standards and Technology(NIST), and the collected staircase movement data have included a range of stair geometries and occupant densities(Peacock et al.,2012; Hoskins and Milke,2012).However,in those studies,the local speed and the density were inaccurately estimated according to the collected data.It was because the cameras did not fully record the whole of staircase movement.In the drills,cameras were set every twoﬂoors to record an overhead view of occupant move-ment.The view of each camera only covered the main landing area plus tread depth area for about4–6steps of one story.Between every two cameras,there was a mid-landing,where pedestrian movement was a planar movement,but not a staircase movement. It was impossible to dissociate the planar movement on mid-land-ing from the video,so the calculations of the travel distance and travel time of staircase movement were inaccurate.To precisely investigate the pedestrian movement characteristics on stairs,we improved the method of video recording,and conducted observa-tions of whole staircase movement in two stations of Beijing sub-way Line1.The two stations are Sihui East Station(ascendingﬂow)and Xidan Station(descendingﬂow).The Sihui East Station is a termi-nal station and all the passengers should get off the train and go to the transfer hall;therefore,the pedestrianﬂow on the observed stair is an ascendingﬂow.The Xidan Station is also a transferTable2Individual horizontal speeds in ten instances of staircases with different geometries.ID Riser height(mm)Tread depth(mm)Gradient(°)Horizontal speed(m/s)Source Note#H120021043.60.361("),0.509(;)a Frantzich(1996)Narrow stair #H215030519.00.427("),0.601(;)Lam(2000)MTR #H316327131.00.417("),0.569(;)Lam(2000)KCR #H419027035.10.423(")Kretz(2008)Long stairs #H515029027.30.538("),0.581(;)Fujiyama(2010)Elder people #H615726730.50.590("),0.721(;)Fujiyama(2010)Young people #H718623838.00.488(;)a Peacock(2012)11-Floors #H819125436.90.440(;)a Peacock(2012)18-Floors #H915028028.20.547(;)Ma(2012)SWFC #H1014028026.50.53(;)Yang(2012)Stair No.2#a The speeds were converted to horizontal speeds.Y.Qu et al./Safety Science70(2014)189–201191station and the passengers can get on or off the train by the stair, and there is an escalator on one side of the stair to relieve the coun-tering passengerﬂows.Therefore,the pedestrianﬂow is a descend-ingﬂow.The schematic diagram of the observation stations is shown in Fig.1,and the information of the stairs is shown in Table3.A HD camera was set on the transfer hall to record the trail of each passenger.The observations in the subway stations were made during the afternoon rush hours of weekend(17:30–18:30,Sunday)on May 12,2013.In our observations,383pedestrians(216males,167 females)were collected at the selected staircases in Xidan Station, and221pedestrians(129males,92females)were collected in Sihui East Station.Most of the pedestrians were young and mid-dle-aged people,and their ages mostly ranged from25to55years old.The proportion of children and elderly was very low.In our observations,most of the pedestrians carried light bags and walked in a normal speed on staircases.Because the camera was not right above on the observed region, the passengers were sometimes overlapped in the video.Each pedestrian was recorded by individual characteristics,such as gen-der,age,body size,hair,shirt and pants.Then,the pedestrians were recognized by their features,entering and leaving time.The speed of each pedestrian was calculated by the travel time(leaving time minus entering time)dividing the travel distance on the stairs.The video recordings were processed semi-manually,and the dynamic evacuation characteristics,such as average density,speed andﬂow,were analyzed.Take Xidan station for example,the evac-uation dynamic characteristics and a snapshot were illustrated in Fig.2a–c).It was found that the curve of time-varying density was divided into several segments,and each segment represented a stream of pedestrians entering and leaving the stairs.During the observation,some measures,such asﬂow restriction and guidance, had been adopted to avoid crowdedness,so the density of pedestri-ans on stairs was in a normal(low)level.There were eight local maximum points exceeding1.0pedestrians/m2,and the maximal density was about1.6(1/m2).Velocity showed an opposite trend of density.The velocityﬂuc-tuated between0.4m/s and1.0m/s,and the average velocity was about0.57m/s.The volatility of individual velocities might be caused by different individual capability and desired velocity.In a low density,the pedestrians who walked fast would overtake front pedestrians who walked slowly and blocked them.When the density became larger,the pedestrians began to slow down and follow with others,and then queues might form on stairs.In Fig.2d),the acquainted or familiar people might walk abreast, which is regarded as‘subgroup behavior’(Yang et al.,2012).If they walked slower than the surrounding people,they would form a dynamic bottleneck.Additionally,lane-forming phenomenon was also found.In Fig.3a),the distributions of the speeds during the observa-tions followed normal distributions,which were similar with the reference(Peacock et al.,2012).The average velocities of walking upstairs and downstairs were0.55m/s and0.63m/s,respectively. Affected by gravity,going upstairs was slower than going down-stairs.By gathering the observed data of unidirectionalﬂow,the relationships between the velocity and the density were shown in Fig.3(b).The velocity decreased as the density increased.It should be noted that,in a low density,the velocity of going upstairs was a little higher than downstairs.It was because some of the pedestrians were hurried out of station and ran more than one steps at one time.3.Modeling the pedestrian’s movement on stairs3.1.Body shapesIn the existing models,the projection of a pedestrian’s body shape is usually regarded as a square(i.e.,Kirchner et al.,2004), a rectangular(i.e.,Song et al.,2006;Weng et al.,2007),a circle (i.e.,Helbing et al.,2000),an ellipse(i.e.,Chraibi et al.,2010)or a set of three circles(Thompson and Marchant,1995).In these geo-metrical shapes,the three-circle shape has some geometrical and computational advantages on modeling the staircase movement. First of all,the three-circle shape is a better alternative to describe the pedestrian’s body shape.It is because the occupied space of one pedestrian is restricted by the stairs,and the shoulder width of the pedestrian is larger than lateral width(Xu and Song,2009).In addi-tion,a pedestrian walks with a relative slow speed on stairs,and his/her space requirement keeps almost constant.Secondly,in social force model,the distance of closest approach of two pedes-trians is a key parameter when calculating the self-driven force, the repulsive force and the contact force.The closet distance between two single-circle or three-circle shapes can be easily cal-culated(Thompson and Marchant,1995);however,the calculation of the closest distance of two ellipses is surprisingly difﬁcult (Zheng and Palffy-Muhoray,2007).For the convenience of calcula-tion,the three-circle shape is a better alternative than ellipse shape.Therefore,the three-circle shape is chosen to describe pedestrian’s body shape,and the schematic diagram is shown in Fig.4.3.2.Modiﬁed social force modelThe well-known social force model(Helbing and Molnar,1995; Helbing et al.,2000)is a microscopic force-based model that can reproduce several self-organized phenomena,such as lane form-ing,arching queue,shock waves and clogging effects(Helbing et al.,2005,2007;Moussaid et al.,2011).The model describes pedestrians’movement behavior by introducing the self-driven force~f Di,the contact force with pedestrians~f Cijand walls(obstacles) ~f Ciw.The self-driven force can be calculated by Eq.(1),and the total force~f exerted on pedestrian i can be formulated as Eq.(2).192Y.Qu et al./Safety Science70(2014)189–201~f D i ¼m ~m desiÀ~m isð1Þ~f i ¼~f D i þXj~f C ij þXw~f C iwð2ÞMoussaid et al.(2011)have proposed a heuristics-based modelto replace the social force with a heuristics intelligent optimum function,which can be regarded as a so-called collision prediction process.The model can overcome some difﬁculties in the original versions.Based on the model,we use the three-circle shape,intro-duce some special rules and establish an enhanced model todescribe the staircase movement.In our model,the modiﬁcations are concentrated on the calculations of optimal direction selection,self-driven force and contact force.3.2.1.Selecting optimal direction In Eq.(1),the desired velocity ~m des i can be obtained by the mag-nitude m des i multiplies by the direction ~e des iof desired velocity.The calculation of ~e desi is called ‘optimal direction selection’,which isTable 3Detailed step sizes.ID StationStep number Width (mm)Depth (mm)Height (mm)Gradient (°)Flow direction#O1Xidan Station 16240030014025.0Descending ﬂow #O2Sihui East Station15190033015726.1Ascending ﬂow(b) Change of pedestrian density with time inthe observation of staircase #O1(c) Change of pedestrian speed with time inthe observation of staircase #O1(d) A snapshot of the observations in staircase #O2(a) Change of pedestrian flow with time inthe observation of staircase #O1 Fig.2.Processed data and a snapshot.Y.Qu et al./Safety Science 70(2014)189–201193an important component of the model(Moussaid et al.,2011).In our work,the body shape is extended to three-circle shape,and the calculation becomes a little complex.To make a clear state-ment,some notation and deﬁnitions are given as follow:for pedes-trian i,the large circle’s radius is r i1,the small circle’s radius is r i2, the mass is M i,the maximum velocity is v0i,the location is~l i,the velocity is~m i and the desired destination is~D i.Assume that pedestrian i moves at the velocity v0i along the direction of direction a,and will contact with pedestrian j after D t time.The i0and j0represent the locations of i and j at time t+D t.Then,fðaÞ¼v0iÁD t is the distance to theﬁrst collision with other pedestrian or obstacle in the direction a.If no collision is expected to occur,f(a)is set to a default value d max,which repre-sents the‘maximum horizon distance’of pedestrian i.The calcula-tion of furthest distance without collision f(a)can be improved as follows:l ixm ðtþD tÞ¼l ixmðtÞþv ix D t;l iy mðtþD tÞ¼l iy mðtÞþv iy D tðm;n2f1;2;3gÞl jxn ðtþD tÞ¼l jxnðtÞþv jx D t;l jy nðtþD tÞ¼l jy nðtÞþv jy D tðl ixm ðtþD tÞÀl jxnðtþD tÞÞ2þðl iymðtþD tÞÀl jynðtþD tÞÞ2¼ðr imþr jnÞ2ð3ÞPut theﬁrst two items into the third item,we can get a qua-dratic equation with moving time D t.Given the locations and velocities of pedestrians i and j,the D t can be easily solved.Then, the value fða i m j nÞand dðaÞcan be calculated asfða i m j nÞ¼min f v i Deltat;d max g;fðaÞ¼min f fða i m j nÞg;aÃ¼argmin f dðaÞg dðaÞ¼d2maxþfðaÞÀ2d max fðaÞcosða0ÀaÞð4ÞThe optimal velocity direction~e¼ðcos aÃ;sin aÃÞ;here,aÃ¼argmin f dðaÞg is the optimal direction.Fig.5illustrates the calculation.3.2.2.Self-driven forces and contact forcesThe pedestrian’s staircase movement is a three-dimensional motion,which contains horizontal and vertical motion.Pedestrian should change his/her center of gravity to ascend or descend the stairs,which are shown in Fig.6(a)and(b).The complicated move-ment includes the pedestrian’s physiological activity and energy transformation.In our work,we mainly focus on the pedestrians’horizontal optimal choice and crowding behaviors,so the vertical motion is approximately regarded as a linear motion,which is shown in Fig.6(c).In horizontal motion,pedestrians not only over-take front people with slower speeds but also have to notice the steps and prevent themselves from falling down from stairs.To mathematically depict horizontal movement,some assumptions and rules are introduced to simplify the movement,which is illus-trated in Fig.6(d).Theﬁrst assumption is that a pedestrian wants to move forward within n steps(n is integer).And the pedestrian’s desired destina-tion of next footstep is the center of the forward step.For example, in Fig.6d),pedestrian often moves forward with integer steps,i.e., one step(point A)or two steps(point B).When he/she moves for-ward with non-integer step,i.e.,2.5steps(point C),he/she will move to the edge of the step,and may feel unstable,unsafe or even fall down from the stairs.Therefore,point C is not considered in our model.The pedestrian’s horizontal footstep length is deﬁned as d h¼nDcos b,and is restricted by the tread depths D and riser heights H of the step.Here,b is the included angle between the optimal direction aÃand the x-coordinate.If the pedestrian is obstructed by other pedestrians or obstacles,he/she will slow down and avoid collision,and the footstep length does not exceed the maximal dis-tance fðaÃÞ(Eq.(4))in the optimal direction aÃ.Finally,the footstep length is expressed as:d h¼minnDcos b;fðaÃÞ&'ð5ÞAccording to the model(Moussaid et al.,2011),a pedestrian maintains a distance from theﬁrst obstacle in the chosen walking direction that ensures a time to collision of at least a relaxation time s.In other words,desired velocity of pedestrian i is formu-lated as v des i¼d h s.Additionally,pedestrian’s speed is assumed to not exceed a maximum velocity v max.Then,the horizontal maxi-mum velocity is v max cos h,and the angle h represents the slope of the stair tan h¼HÀÁ.The horizontal desired velocity v des i can be formulated as Eq.(6):v desi¼mind hs;vmaxcos hð6Þ(a) Probability distribution(b) Fundamental diagramFig.3.Speed and fundamental diagram of theobservations.194Y.Qu et al./Safety Science70(2014)189–201。

【计算机应用与软件】_通信状态_期刊发文热词逐年推荐_20140725

推荐指数 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
2011年序号 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
2011年科研热词频率侦听非集中式链接逻辑链接通信状态视景显示虚拟装配网络通信组合web服务稳性计算检查点有限状态自动机有向链接日志记录和回放性能优化小波包变换多线程可逆调试协议半潜船三维仿真 xen虚拟机 opengvs ieee 802.22 推荐指数 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
2008年序号 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
科研热词鲁棒性迷乱变换进程代数贝叶斯算法调度算法认知模型联结主义网络分割粒子滤波器符号主义范式社区波分复用条件状态任务图最短路径并发系统安全性大规模网络同步通信模式分布式系统分层图共享通道保护全局不透明分支偏序简化信号同步人体目标跟踪 soar session bean java j2ee entity bean ejb cdm模型 act
推荐指数 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
2010年序号 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
科研热词防火墙针孔状态包检测灰度值深度包检测机器视觉系统媒体传输协议图像采集系统会话初始协议 vc++ uart pki ocsp ipse ikev2 cvpn camera link

环境安全应急监测信息化平台软件系统平台设计方案

环境安全应急监测信息化平台软件系统平台设计方案1设计原则1.1业务横向、纵向一体化业务横向一体化是指在环保部门之间，系统通过面向服务的设计能同时支持总量控制、监察、执法、项目审批等任务。

纵向一体化是在基地环保局、市局、省厅之间支持业务连续的办理，如区县建设项目审批后，可把业务传递到市局的业务总线上，驱动市局领导审批。

通过横向、纵向一体化的支持，打破原有的条块化的非连续的处理，提供了业务人员的办事效率。

1.2全生命周期管理以业务单元为生命周期为主线，实现对业务单元从产生到销毁的全过程闭环管理。

1.3流程可配置、业务平台化由于传统的信息系统建设是以单个应用为单位开发的，部分软件基础功能存在重复，无法实现共享。

建设的一体化业务平台应支持业务流程的可配置、可维护、可管理，为应用系统开发提供高效开发、配置、部署手段。

1.4面向服务的架构紧紧围绕以面向服务为目的，充分分析用户需求，设计出稳定高效的应用服务足见。

在设计中按照可持续性发展的原则，采用正确的策略、适当的技术和有效的措施确保系统的可伸缩性。

1.5统计设计、个性应用实现对环境保护领域的统一管理和全网共享以及与部级信息的纵向连接，这样一些物理上分散的系统，必须坚持统一性原则。

统一性表现在：➢统一规范➢统一标准➢统一接口➢统一的体系结构区县应用、面向角色的服务要有个性化，个性化表现在：➢流程可定制、可配置➢角色可授权➢页面可配置1.6开放性环保信息化是一个长期的工作，因此项目所建系统能在数据、软件开发和相关借口方面应支持符合国际标准和业界标准的相关接口。

在应用开发中，要使用相应国家标准。

1.7易于维护、便于管理系统涉及在市区多级部门中运行，考虑区县的运行维护技术能力，系统要支持易于管理、便于维护；应支持信息中心管理人员远程配置、调试、升级等工作。

1.8安全可靠环境保护业务中涉及多项机密信息，需要构建全方位的安全防护体系；此外还需要实现完善的运维管理，以确保系统安全稳定运行。

图书馆安全疏散数值分析

ＦＤＳ和安全疏散软件ＦＤＳ＋ＥＶＡＣ对图书馆火灾时的安全疏
线。疏散路线不一定是最短的，常情况下会是接近最通短路线的。人员运动模型的９个核心方程见式（）式１～
（）。９
一
（））１
（）员ｉ受环境力方程见式（）２人所２。
一
的安全通道及图书馆两侧门以保证人员的安全疏散。
（一）＋（。。＋十） ∑ （＋
＋
关键词：全疏散；图书馆火灾；ＦＳＥ安Ｄ＋ＶＡＣ；值模拟数
建辩炎ｊｊ谚麟辞
图书馆安全疏散数值分析
王伟。王静。琪材柯
（汉科技大学资源与环境工程学院，北武汉４０８）武湖３０１
摘要：国内某高校图书馆为例，用火灾模拟软件以使
片一Ａ
图书馆火灾荷载分布密集，由于书架的特殊布置，且使其人员疏散与一般建筑物有很大不同。另外，于我由
一
但（）（１
）（３）
￡４（）
（）员ｉ４人与之间的人体力方程见式（）４。［（一）忌＋嵋］＋最一）（式（）边第二个求和项为Ｈｅｂｇ模型里没有的、４右ｒｉｎ由Ｌｎｓｎ提出的物理衰减力，见文献ｒ］ａｇｔｏ参６。（）员转动控制方程见式（）５人５。

基于事件树和ALOHA软件的CNG加气站储气井泄漏燃烧爆炸事故后果分析

基于事件树和ALOHA软件的CNG加气站储气井泄漏燃烧爆炸事故后果分析姜峰;胥朝波【摘要】通过定性和定量相结合的方法,研究了CNG加气站储气井泄漏所产生的燃烧爆炸事故后果影响.先采用事件树分析法定性分析出CNG加气站储气井天然气发生泄漏时可能发生的事故后果;再运ALDHA软件建立数学模型,并重点针对燃烧爆炸事故危害后果进行数值模拟定量分析,得出直观图表;最后通过图表分析,可得出危害半径.该方法可以对CNG加气站的安全管理、事故应急救援以及新建加气站提供技术支持.【期刊名称】《安全与环境工程》【年(卷),期】2018(025)003【总页数】4页(P141-144)【关键词】CNG加气站;储气井泄漏;燃烧爆炸事故后果;事件树分析;ALOHA软件【作者】姜峰;胥朝波【作者单位】兰州理工大学石油化工学院,甘肃兰州730050;兰州理工大学石油化工学院,甘肃兰州730050【正文语种】中文【中图分类】X928.7;TE88随着人们对天然气资源的有效利用，压缩天然气(Compessed Natural Gas,CNG)加气站开始大量建设并不断投入使用，但由于近年来我国各地不断发生的CNG加气站爆炸事故，其安全问题也逐渐引起人们的重视。

CNG加气站是一个复杂的系统，主要包括天然气预处理系统、调压计量系统、天然气压缩系统、储气系统、售气系统等。

陈杰等[1]通过统计分析 2004 年10月至2007年12 月CNG加气站发生的的百起事故，发现风险最高的是储气系统和售气系统。

储气系统的储气方式主要有储气钢瓶、储气罐、储气井三种，目前国内主要采用储气井的方式储气，因此分析CNG加气站储气井泄漏所产生的燃烧爆炸事故危害后果具有必要性。

目前虽然有各种安全评价方法(HAZOP分析法、事故树分析法、道化学指数评价法、检查表法等)，但都各具优缺点，只能基于已知事故现状做出定性或定量分析，不能对未发生的事故做出相对准确的数值模拟分析与预测，无法满足当前针对事故后果评价提供指导意义的要求；同时，虽然有FLACS、PHAST、SAFETI等安全分析软件，但这些数值模拟软件一般为离线模拟，过程比较复杂且计算耗时，不能在事故发生后实时地得出模拟结果，不利于应急人员及时做出疏散决策。

美国Citilabs公司CUBE软件介绍-Short

使用Citilabs公司产品的用户
其系列产品自1980年代起即受到使用者欢迎，目前在全球有80多个国家的2500 多个城市正在使用我们的Cube软件。
南美: Los Angeles, Houston, Miami, Orlando, Washington. Atlanta, San Francisco, Minneapolis, St. Louis, Tampa, Baltimore, Pittsburgh, Cincinnati, Sacramento, Albuquerque
Cube 软件的优点4: 流程图建模方式，方便模型开发
率先开发出流程图建模方式：专门用于设计和构建交通模型的流程图环境，可以通过下拉菜单访问模块，通过简单的拖拽方式实现数据的输入和输出连接，逐步的构建了模型流程图。
模型流程图嵌套结构
Cube 软件的优点5：灵活，如你所想，随你所愿
配备了自己的综合的脚本语言用于交通建模；不需要复杂难懂的程序语言即可创建定制模型；可以通过简单的菜单选项完成很多脚本的编写；可以移入定制脚本到Cube软件中作为点选式函数使用
▪
▪
Cube Voyager
功能强大的公交程序（Public Transit），可以做到： • • 用户控制公交模型的各个方面真正的小区对间的多路径或者最优单一路径建模按照代表了不同费用函数行为类别进行划分的用户类来细分公交出行需求，从而更好的描述人们的公交出行行为综合全面的费用建模方法，可以模拟各种公交收费政策可以自动/手动生成非公交元素（进入，退出，换乘公交系统的路段，以及Park&Ride/Kiss&Ride 路段），更好的分析公交出行可以计算全路网范围的，指定公交方式的，综合的，平均的出行费用，以及费用组成

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常用疏散软件Company Document number：WTUT-WT88Y-W8BBGB-BWYTT-19998常用的疏散模拟软件疏散模拟软件的发展十分迅速，疏散模型、算法都在不断地更新、完善。

目前，常用的疏散模拟软件有五种：（1）FDS + Evac，由芬兰VTT技术研究中心研发，FDS + Evac的运动计算模型采用Helbing的社会力模型。

软件将人员等价于有自驱动且有几何特性的粒子。

建筑内存在一个符合流体力学规律引导人员“流动”的虚拟流场，就如同在出口设置一台抽风机，吸引人员从建筑中流出来；软件不考虑人员的“再进入行”、“羊群行为”、“回避行为”（2）Building EXODUS，由英国格林威治大学研发，是一个模拟个人、行为和封闭区间的细节的计算机疏散模型。

模型包括了人与人之间、人与建筑之间和人与环境之间的互相作用。

它可以模拟大型建筑物中上千人规模的疏散并可包含火灾烟气影响因素。

在EXODUS中，空间和时间用二维空间网格和仿真时钟表示。

空间网格反映了建筑物的几何形状、出口位置、内部分区、障碍物等。

多层几何形状可以用由楼梯连接的多个网格组成，每一层放在独立的窗口中。

建筑平面图或用CAD产生的DXF文件，也可用交互工具提供，网格由节点和弧线组成，每一个节点代表一个小的空间，每一段弧代表节点之间的距离。

人员沿着弧线从一个节点到另外一个节点。

该软件由5个互相关联的子模型组成，它们是人员、移动、行为、毒性和危险子模型。

模型跟踪每一个人在建筑物中的移动轨迹，以及人们的模拟状态——或者疏散到安全地点，或者被火灾所伤害。

模型基于行为规则和个体属性，每一个人的前进和行为由一系列启发性规则决定。

行为子模型决定了人员对当前环境的响应，并将其决定传递给移动子模型。

行为子模型在两个层次起作用，即全局行为和局部行为，全局行为假设人员采用最近的可用疏散出口或者最熟悉的出口来逃生；局部行为可以模拟以下现象：决定人员对疏散警报的初始响应、冲突的解决、超越以及选择可能的绕行路径等。

这些都取决于人员的个体属性。

毒性子模型决定环境对人员的生理影响，考虑了毒性和物理危险，包括升高的温度、热辐射、C0、C02以及02含量等因素影响，并且估计了人员失去行动能力的时间；它采用‘毒性比例效果剂量’模型(FED)，假设火灾危险的影响由接受到的剂量而不是暴露的浓度决定，并且累计暴露期间的比例。

EXODUS建模可以采用实验数据或者从其他模型得到数值数据,允许CFAST计算数据导入到EXODUS中。

EXODUS模拟完毕后，可以使用数据分析工具来处理数据输出文件。

另外，提供了基于虚拟现实的后处理图形环境，提供疏散的三维动画演示。

（3）Pathfinder，由美国Thunderhead engineering公司研发，Pathfinder包含SFPE和steering两种人员运动模式。

SFPE模式中，通过空间密度确定人员运动速度，人员会寻找最近的出口且相互之间不影响；steering模式根据路径规划、指导机制、碰撞处理相结合控制人员运动，如果人员之间的距离和最近点的路径超过阀值，可以再生新的路径，以适应新的形势（4）Simulex，软件最先是由英国Edinburgh大学设计，后来由苏格兰的Peter Thompson博士继续发展的人员疏散模拟软件，可以用来模拟大量人员在多层建筑物中的疏散过程。

该软件可以模拟大型、复杂几何形状、带有多个楼层和楼梯的建筑物，可以接受CAD生成的定义单个楼层的文件；可以容纳上千人，用户可以看到在疏散过程中，每个人在建筑中的任意一点、任意时刻的移动。

模拟结束后，会生成一个包含疏散过程详细信息的文本文件。

SIMULEX把一个多层建筑定义为一系列二维楼层平面图，它们通过楼梯连接；用三个圆代表每一个人的平面形状，精确地模拟了实际的人员。

SIMULEX的移动特性基于对每一个人穿过建筑物空间时的精确模拟。

模拟了的移动类型包括：正常不受阻碍的行走，由于与其他人接近造成的速度降低、行走超越、身体的旋转和障碍避让。

SIMULEX还模拟了最近路径出口选择机制，而心理影响因素和烟气影响因素将是模型将要进一步发展的一部分。

由于SIMULEX软件的易用性以及它能够较为真实地反映出疏散过程中可能出现的各种情况，已经被越来越多地应用于实际工程中。

（5）STEPS，由英国Mott MacDonald公司研发的一款基于元胞自动机的疏散软件，模型假定人员总是沿着最短路径的单元格行走，人员可以设置许多属性参数，如耐心等级、适应性、个体特征、对环境的熟悉程度、从众程度等；软件可以应用动态决策系统在疏散过程中改变条件，如出口的可用性和人员的出口选择等疏散软件的适用范围不同疏散模拟软件的适用的范围存在区别，上述疏散软件的适用范围如表3所示。

STEPS软件是由英国Mott MacDonaId公司开发的一个三维疏散软件，可以模拟办公区、体育场馆、购物中心和地铁车站等场所，这些场所要求确保在正常情况下的交通，而在紧急情况下可以快速疏散。

在大而拥挤的地方，通过模拟所获得的最优化人流，可以为建筑消防设计提供一个更适宜的环境和更有效的安全疏散设计方案。

目前，STEPS已经被应用于加拿大埃得蒙顿机场、印度德里地铁、美国明尼阿波利斯LRT、英国生命国际中心和伦敦希思罗机场第五出口铁路／地铁。

通过与NFPA基于建筑法规标准的设计作比较，STEPS的有效性已经得到验证。

STEPS软件简介STEPS 的设计是用来预测建筑群内人群在正常及紧急情况下的疏散，这些建筑群是指类似地下车站或者办公大楼的地方。

STEPS具有很大的灵活性，它可以分配具有不同属性的人员，给予他们各自的耐心等级和适应性等心理影响因素；也可以指定年龄、尺寸和性别。

同时，它还考虑了人员对建筑物的熟悉性，它也将影响疏散人员的个体行为。

其中，耐心等级决定了当出口附近的人群拥挤时，人员是继续排队等候，还是动态转向另一个最近的出口。

STEPS也很独特，它具有在疏散过程中改变条件的能力—像日常生活中发生的那样。

烟气可能封闭特定的出口，紧急设施可能开始向人群服务，并且人员在不同的时间从不同的区域开始疏散。

模拟一开始，人群首先依照他们预置的特性进行行动，影响人员行为的因素与现实生活相同—人们向相反的方向移动、阻塞、减速以及排队。

当一个紧急情况产生，每个人的行程将因为从正常模式转到疏散模式而被重新设定，但是仍旧遵循他们的各自特性。

使用者可按照需要将模型平面界定为不同大小的网格系统。

目前STEPS模型中只允许每个人占据一个网格。

当开始计算时，STEPS会使用一种递归算法来寻找每一个网格与出口之间的距离。

STEPS与SIMULEX一样都属于用于人员疏散模拟计算的精细网格模型，都可以用于使用人数众多的多层建筑的疏散模拟分析。

这两个疏散软件各有特色，由于它们在各自擅长的领域的出众特点，它们在工程中的应用也越来越广泛。

STEPS与SIMULEX两种软件特点对比如表5-4-10所示。

选择对象1到达目标所需时间STEPS 认定该时间是人群到目标的距离 D 除以人群行走的速度 W：T Walk = D / WD 从目标潜力表中获得。

W 由用户指定。

2在目标处排队所需时间STEPS 计算该值是在紧急人群之前到达目标的人群数量 N，去除以这个数量的流速率 F。

T Queue = N / F NN是通过比较紧急人群与同一平面上其他人群的 T Walk 得到。

低 T Walk 的人群被认为是在紧急人群之前行走的。

F 由用户指定。

3行走时间的调整T Walk 参数给出了在目标处没有排队时的全部所需时间，因此，直接增加 T Walk 和 T Queue的值意味着延长人群到目标实际所需时间。

当人群实际没有到达队伍的尾端时，需要考虑步行时间的调整。

假定人们将聚集在目标的周围，以期占领单位中具有最低的潜力。

因此，如果我们有一个潜力命令清单，我们就可以找到潜在 D2 的人，只是在前面目前的 1 。

利用这一潜力，我们可以计算时间，将不会走到达到队列的尾端.假定一个人将围绕自己的目标以他最低的潜能占据最小的空间，因此我们需要有一个潜能排序表，这样就能找到具备 D2 这种潜能的人，这个人正好排在队伍的最前面。

利用这个潜能，我们可以不用通过找到队列的最后一个就能计算出准确的时间T Adjustment Walk = D2 / WW 由用户指定。

4实际行走时间我们现在可以计算到达队伍末端实际所需时间：T Real Time to Walk = T Walk –T Adjustment Walk5排队时间的调整T Queue 参数考虑了在紧急人群前到达目标时所有人消耗的时间。

然而，当人群对撞时会耗费时间（这会耗费 T Real Time to Walk），因此，我们有必要对排队时间进行调整。

STEPS 计算在 T Real Time to Walk 期间应该到达目标的人数 N2，然后按下式计算排队时需要调整的时间：T Adjustment Queue = N2 / F6实际排队时间我们现在就能按下式计算到达队伍的尾端实际所需时间：T Real Time to Queue = T Queue –T Adjustment Queue7耐性不同类型的人耐性不同，我们将目标处排队人的耐性按数字 0-1 进行定义，有耐性的人会在目标地点比没有耐性的人做更长时间的等待。

这个系数对排队时间计算的影响如下：C Adjust Queue = 1 –C Patience * –Patience) /C Patience 是 0 与 1 之间的系数，用户在编辑决策对话框中直接定义。

根据不同类型人群对自己排队时间的估算，我们随后就能按下式计算他的实际排队时间：T Estimated Time to Queue = T Real Time to Queue * C Adjust Queue8最终分数目标处的最低分数是：T Total = T Real Time to Walk * C Walking + T Estimated Time to Queue * C QueuingC Walking 是一个系数在 0 与 1 之间，由用户直接在编辑决策过程对话框中定义。

C Queuing 是一个系数在 0 与 1 之间，由用户直接进入编辑决策过程对话框中定义。