Macroscopic simulation method and device for crowd evacuation considering reduction of pedestrian passing rate in crowded areas

Abstract

A macroscopic simulation method and device for crowd evacuation considering the reduction of the pedestrian passing speed in crowded areas, which relate to the field of crowd evacuation simulation in buildings, solve the problems of how to judge the location where crowd congestion occurs and quantitatively calculate the reduction of the pedestrian passing speed at the congested location during the evacuation simulation process. The present invention provides the following solutions: the macroscopic simulation method includes: obtaining the building floor plan of the case to be analyzed, and converting the spatial information of the evacuation scene into an evacuation path geometric network model in the building; inputting the data of the evacuation path geometric network model in the building into the calculation program module of the improved macroscopic simulation method, that is, performing an evacuation process simulation analysis on the case to be analyzed, and calculating the reduction of the pedestrian passing speed at the congested location to obtain the evacuation process simulation result. It is also applicable to the global evacuation situation of large-scale crowd evacuation scenarios.

Description

Technical Field

[0001] The present invention relates to the technical field of crowd evacuation simulation in buildings. Background Art

[0002] The safety of people in crowded places such as various large complexes and high-rise buildings in the city has become the focus of attention of all sectors of society. Studying crowd evacuation under various emergencies, avoiding potential risks in the emergency evacuation process in advance, and improving evacuation efficiency are of great significance for ensuring the lives of people in buildings. With the development of computing technology, evacuation process simulation provides an effective way to study the crowd evacuation process and predict evacuation results. Compared with other methods such as evacuation drills and animal experiments, evacuation simulation can generate hypothetical scenarios to investigate the safety status of a given space under possible emergencies at relatively low cost and risk.

[0003] Currently, in practical engineering applications, engineers and some commercially available software with high versatility still mainly adopt the model-driven method. The model-driven evacuation simulation method can be divided into two types according to the refinement degree of the modeling of the research object: microscopic method and macroscopic method. Since the microscopic method models the behavior of individual evacuees in detail, when using the microscopic method to simulate the evacuation of large-scale buildings, the computational amount and complexity of the model are very large, and it takes a long time to calculate only one working condition, resulting in very low computational efficiency. However, the macroscopic method takes the whole crowd as the research object and focuses on simulating the overall behavior and dynamic changes of the crowd. It ignores the mutual influence between each person, thus reducing the computational complexity and amount, and greatly improving the computational efficiency. Therefore, for large-scale crowd evacuation scenarios, such as large shopping malls, hospitals, airports, stadiums, etc., using the macroscopic method can more efficiently simulate the global evacuation situation. The macroscopic method can help safety management and decision-making personnel evaluate and analyze the efficiency and safety of crowd evacuation as a whole, providing an important basis for formulating scientific and effective emergency plans. Therefore, the macroscopic evacuation simulation method still has a relatively wide range of application requirements.

[0004] In the prior art, the macroscopic evacuation simulation method generally regards the location where congestion occurs as impassable to avoid stampede incidents caused by extreme congestion. However, in most real evacuation processes, when the crowd is in a crowded state, people can usually pass through the location where congestion occurs, but the movement speed of people passing through the crowded area will be significantly slowed down. It can be seen that the above-mentioned processing method cannot accurately reflect the impact of crowd congestion on the evacuation process. Crowd congestion has a relatively serious impact on the overall evacuation efficiency in buildings, and it is necessary to accurately and reasonably consider its impact in the evacuation simulation process. Summary of the Invention

[0005] The present invention solves the problem of how to determine the location where crowd congestion occurs and quantitatively calculate the reduction of the passing rate of people at the congested location during the evacuation simulation process in the prior art.

[0006] To solve the above technical problems, the present invention adopts the following technical solutions:

[0007] A macroscopic crowd evacuation simulation method considering the reduction of the passing rate of people at the congested location, the simulation method includes:

[0008] Step 1, obtain the building floor plan of the case to be analyzed, and convert the spatial information of the evacuation scene into an evacuation path geometric network model in the building;

[0009] Step 2, input the data of the evacuation path geometric network model in the building into the calculation program module of the improved macroscopic simulation method, that is, perform evacuation process simulation analysis on the case to be analyzed, and calculate the reduction of the passing rate of people at the congested location to obtain the evacuation process simulation result.

[0010] Further, a preferred implementation manner is provided. The step of obtaining the spatial information of the evacuation scene and converting it into an evacuation path geometric network model in the building in Step 1 further includes the setting steps of nodes and edges.

[0011] Further, a preferred implementation manner is provided. The setting method of the nodes and edges is: denote the evacuation path geometric network model in the building as G(V, E), set both ends of the building's exits, passages, and stairs as nodes V, and set the passable paths between the nodes as edges, denoted as E, that is, form the evacuation path geometric network model in the building.

[0012] Further, a preferred implementation manner is provided. The improved macroscopic evacuation simulation method in Step 2 further includes the step of equivalently averaging the movement speed of people on the road section during the evacuation process.

[0013] Further, a preferred implementation manner is provided. The equivalent averaging reflects all road sections in the reduction case through a unified equivalent averaging coefficient value.

[0014] Further, a preferred implementation manner is provided. The calculation program module of the improved macroscopic simulation method is used to determine the total evacuation time t of each evacuee i , determine and output the position information of people during the evacuation process, and conduct congestion analysis.

[0015] Solution 2, this solution proposes a macroscopic crowd evacuation simulation device considering the reduction of the passing rate of people at the congested location, the simulation device includes:

[0016] A conversion module, configured to obtain the building floor plan of the case to be analyzed, and convert the spatial information of the evacuation scene into an evacuation path geometric network model in the building;

[0017] A simulation module, configured to input data of the evacuation path geometric network model in a building into a calculation program module of an improved macroscopic simulation method, that is, to perform evacuation process simulation analysis on a case to be analyzed, and calculate the reduction of the personnel passage rate at crowded locations, so as to obtain the evacuation process simulation result.

[0018] Solution Three: A computer device, including a memory and a processor. A computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes the macroscopic crowd evacuation simulation method considering the reduction of the personnel passage rate at crowded locations according to any one of Solution One.

[0019] Solution Four: A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, it implements the macroscopic crowd evacuation simulation method considering the reduction of the personnel passage rate at crowded locations according to any one of Solution One.

[0020] The beneficial effects of the present invention are as follows:

[0021] Based on the determined formula for reducing the personnel passage rate at crowded locations, the present invention proposes a macroscopic crowd evacuation simulation method that can consider the reduction of the personnel passage rate at crowded locations. The simulation method can determine the location where crowding occurs during the macroscopic simulation of crowd evacuation and quantitatively calculate the reduction of the personnel passage rate at the crowded location.

[0022] By comparing the analysis results of the improved macroscopic simulation method proposed by the present invention on a three-story building example with the analysis results of the mainstream microscopic crowd evacuation simulation method in the prior art, it can be seen that the improved macroscopic simulation method proposed by the present invention can more accurately reflect the global evacuation process in a building and the impact of crowding on the evacuation process. At the same time, under the same computing hardware conditions, compared with the microscopic method, the improved macroscopic simulation method can significantly reduce the computing time required for evacuation process simulation, has an obvious computing efficiency advantage, and can improve the computing efficiency. Therefore, it can more efficiently simulate the global evacuation situation of a large-scale personnel evacuation scenario.

[0023] That is, the present invention is also applicable to the global evacuation situation of a large-scale personnel evacuation scenario. Description of the Drawings

[0024] Figure 1 It is a schematic diagram of the horizontal section simplification process described in Embodiment Ten.

[0025] Figure 2 It is a schematic diagram of the actual passable section in the vertical direction described in Embodiment Ten.

[0026] Figure 3Schematic diagram of the 2D representation form of the one-way side corresponding to the vertical section described in Embodiment 10.

[0027] Figure 4 Evacuation road network of the multi-story building case described in Embodiment 10 - Schematic plan of the -01 floor.

[0028] Figure 5 Evacuation road network of the multi-story building case described in Embodiment 10 - Schematic plan of the -02 floor.

[0029] Figure 6 Evacuation road network of the multi-story building case described in Embodiment 10 - Schematic plan of the -03 floor.

[0030] Figure 7 Evacuation road network of the multi-story building case described in Embodiment 10 - 3D schematic diagram.

[0031] Figure 8 Schematic diagram of the AnyLogic microscopic simulation model of the multi-story building case described in Embodiment 10. Specific implementation manners

[0032] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application.

[0033] Embodiment 1. This embodiment proposes a macroscopic simulation method for crowd evacuation considering the reduction of the pedestrian passing rate at crowded places. The simulation method includes:

[0034] Step 1: Obtain the building floor plan of the case to be analyzed, and convert the spatial information of the evacuation scene into an evacuation path geometric network model in the building.

[0035] Step 2: Input the data of the evacuation path geometric network model in the building into the calculation program module of the improved macroscopic simulation method, that is, conduct an evacuation process simulation analysis on the building floor plan of the case to be analyzed, and calculate the reduction of the pedestrian passing rate at the crowded position to obtain the evacuation process simulation result.

[0036] Embodiment 2. This embodiment further limits the macroscopic simulation method for crowd evacuation considering the reduction of the pedestrian passing rate at crowded places described in Embodiment 1. The step of obtaining the spatial information of the evacuation scene and converting it into an evacuation path geometric network model in the building in Step 1 further includes the setting steps of nodes and edges.

[0037] Embodiment 3. This embodiment further limits the crowd evacuation macroscopic simulation method considering the reduction of the passing speed of people in crowded areas described in Embodiment 1. The method for setting nodes and edges is as follows: Denote the geometric network model of the evacuation path in the building as G(V, E). Both ends of the exits, passages, and stairs in the building are set as nodes V, and the passable paths between the nodes are set as edges, denoted as E. That is, a geometric network model of the evacuation path in the building is formed.

[0038] Embodiment 4. This embodiment further limits the crowd evacuation macroscopic simulation method considering the reduction of the passing speed of people in crowded areas described in Embodiment 1. In the improved evacuation macroscopic simulation method in Step 2, it further includes a step of equivalently averaging the movement speed of people on the road sections during the evacuation process.

[0039] Embodiment 5. This embodiment further limits the crowd evacuation macroscopic simulation method considering the reduction of the passing speed of people in crowded areas described in Embodiment 4. The equivalent averaging reduces all road sections in the case by a unified equivalent averaging coefficient value.

[0040] Embodiment 6. This embodiment further limits the crowd evacuation macroscopic simulation method considering the reduction of the passing speed of people in crowded areas described in Embodiment 1. The calculation program module of the improved macroscopic simulation method is used to determine the total evacuation time t of each evacuee i , the determination and output of the position information of people during the evacuation process, and the congestion analysis.

[0041] Embodiment 7. This embodiment proposes a crowd evacuation macroscopic simulation device considering the reduction of the passing speed of people in crowded areas. The simulation device includes:

[0042] A conversion module, which is used to obtain the building floor plan of the case to be analyzed and convert the spatial information of the evacuation scene into a geometric network model of the evacuation path in the building;

[0043] A simulation module, which is used to input the data of the geometric network model of the evacuation path in the building into the calculation program module of the improved macroscopic simulation method. That is, it conducts an evacuation process simulation analysis on the building floor plan of the case to be analyzed, calculates the reduction of the passing speed of people in the congested positions, and obtains the evacuation process simulation result.

[0044] Embodiment 8. This embodiment proposes a computer device, which includes a memory and a processor. A computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes the crowd evacuation macroscopic simulation method described in any one of Embodiments 1 to 7 considering the reduction of the passing speed of people in crowded areas.

[0045] Embodiment 9. This embodiment proposes a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, it implements the steps of the crowd evacuation macroscopic simulation method considering the reduction of the pedestrian passing rate at crowded places described in any one of Embodiments 1 to 7.

[0046] Embodiment 10. This embodiment proposes an example for explaining Embodiments 1 to 9 above, and the specific content is as follows:

[0047] Step 1: Obtain the building floor plan of the case to be analyzed, and convert the spatial information of the evacuation scenario into an evacuation path geometric network model in the building.

[0048] The improved macroscopic simulation method proposed in this embodiment combines the indoor space characteristics of the building and the emergency evacuation movement law, and proposes a modeling method for the evacuation path geometric network model in the building, which mainly includes the setting rules of nodes and edges. In the following content, the evacuation path geometric network model in the building is simply referred to as the evacuation road network. Next, the node setting rules and edge setting rules in the evacuation road network modeling method will be introduced separately.

[0049] 1. Node setting rules

[0050] Denote an evacuation road network in a building as G(V, E). Each room or area with a certain usage function in the building, the exits, passages, and both ends of the stairs in the building are set as nodes (vertex, denoted as V), and the passable paths between the nodes are set as edges (edge, denoted as E), thus forming an evacuation road network. The nodes V in the evacuation road network G(V, E) can be divided into three categories, and their respective setting rules are shown in Table 1.

[0051]

[0052] The settings of the source nodes are also related to the number of people. Currently, it is set that if the number of people in a certain room exceeds 10, multiple source nodes should be set, that is, the room is divided into multiple areas, and a corresponding source node is set in each area to ensure that the number of people at each source node does not exceed 10. Considering that the positions of each person in the room / area are different, and the distances from the room / area exit are different, the geometric center of the room / area is used as the position of the simplified node, that is, the evacuation starting positions of all people in the room / area are simplified and set at the geometric center of the room / area. It can be seen that in the macroscopic simulation method, each source node contains only one group (the number of people in each group ≤ 10), so the number of source nodes is equal to the number of evacuation groups, that is, the source nodes correspond one-to-one with the evacuation groups they contain. Therefore, in this embodiment, the source nodes in the simulation method and the evacuation groups they contain are set with the same number, that is, the number of the source node is the number of the evacuation group it contains.

[0053] The traffic connection parts in the building refer to corridors, foyers, stairs, etc. The positions where intermediate nodes are set are often where the evacuation paths converge or the traffic capacity changes significantly. These positions are the places where congestion may occur during the emergency evacuation process, that is, the positions where intermediate nodes are set include all positions where people may be crowded during the actual evacuation process. In the evacuation road network, the order of setting node numbers is source nodes, intermediate nodes, and exit nodes in sequence, and the starting value of the number is 0.

[0054] 2. Edge setting rules

[0055] All types of traffic connection parts in the building are simplified as edges E to represent the passable sections between nodes. All edges in the evacuation road network are directed edges, with a node connected to each end, that is, there are only two nodes on one edge. The length of the actual passable section corresponding to the starting point (origin) to the end point (terminus) of the edge is used as the weight of this edge.

[0056] The passable sections in the building can be divided into horizontal sections and vertical sections according to the spatial type. Among them, for the details of the simplification process of the edges of the horizontal sections, see Figure 1 as shown. When some emergency events that damage the building structure such as earthquakes and fires occur, some obstacles will be generated, which will affect the passage of people. Therefore, Figure 1 as shown in the actual passable section from node 1 to node 57, for the obstacles such as furniture and building debris existing during the evacuation movement process, the actual passable evacuation section needs to keep a certain distance from these obstacles to ensure the continuous passage of the evacuation process and the safety of people. That is to say, the proposed modeling method of the evacuation road network can reflect the details of the evacuation process of the evacuees avoiding obstacles. Figure 1The green dashed line represents the actual passable sections during the evacuation process, and the black solid line represents the edge E simplified from the actual passable sections. The simplified edge E connects the two end nodes with a straight line, which only represents the passable situation between the nodes and the direction of the section, and cannot reflect the actual shape of the passable sections during the evacuation process.

[0057] In multi-story and high-rise buildings, stairs are currently the only legal means of evacuation. As the most traditional means of evacuation, stairs have always been the first choice for people to evacuate vertically in case of danger in buildings. Therefore, during the establishment of the evacuation road network in multi-story and high-rise buildings, we only use stairs for vertical evacuation, that is, the evacuation road network only contains vertical edges simplified from the corresponding vertical passable evacuation sections of the stairs. The details of the simplification process of the edges corresponding to the vertical sections are as Figure 2 shown. Among them, Figure 2 represents the actual passable section in the vertical direction, Figure 3 represents the form of the one-way edge corresponding to this vertical section in the 2D plane schematic diagram of the evacuation road network. Similar to the horizontal section, the proposed method for modeling the evacuation road network in this embodiment can also consider the impact of obstacles in the vertical section on the personnel evacuation process. In addition, considering that the evacuation process in multi-story buildings aims to evacuate from the building to a safe area outside the building. Therefore, in the evacuation road network, the direction of the vertical sections is all from top to bottom, that is, from the nodes on the high floors to the nodes on the low floors. In the 2D plane schematic diagram of the evacuation road network, the edges corresponding to the vertical sections are represented by yellow single-arrow solid lines, and the specific representation form is shown in Figure 3 shown. Similar to the horizontal edges, the representation form of the vertical edges cannot reflect the actual shape of the passable sections during the evacuation process.

[0058] 3. Schematic diagram of the evacuation road network

[0059] According to the setting rules of the edges, in the schematic diagram of the evacuation road network, the edges corresponding to the horizontal passable evacuation sections are mainly divided into two categories: one-way edges are represented by black single-arrow solid lines, and the arrow direction points to the end point of the one-way edge; two-way edges are represented by blue double-arrow solid lines, and each has an arrow pointing to one of the two end-vertices of the two-way edge. The edges corresponding to the vertical passable sections are one-way edges from top to bottom, represented by yellow single-arrow solid lines. Figures 4 to 7 is a schematic diagram of the evacuation road network established for a multi-story building case following the proposed method for modeling the evacuation road network in this embodiment. Among them Figures 4 to 6 is the plane schematic diagram of the evacuation road network of this multi-story building case, Figure 7 is the 3D schematic diagram of the evacuation road network of this multi-story example. Figures 4 to 6 The setting rules of the nodes and edges in it all follow the relevant requirements of the setting rules of the nodes and the setting rules of the edges.

[0060] Step 2: Input the data of the evacuation path geometric network model in the building into the calculation program module of the improved macroscopic simulation method, that is, conduct evacuation process simulation analysis on the building floor plan of the case to be analyzed, and calculate the reduction of the personnel passage rate at the crowded positions to obtain the evacuation process simulation results.

[0061] The improved evacuation macroscopic simulation method proposed in this embodiment is improved on the basis of the macroscopic simulation method based on the graph network model. The calculation theory of this improved evacuation macroscopic simulation method is based on the relevant calculation theory of graph theory. Next, the improved evacuation macroscopic simulation method will be introduced from two aspects.

[0062] 2.1 Simplification in the improved macroscopic simulation method

[0063] The simplification of the evacuation process commonly adopted in the macroscopic simulation method in the prior art is also adopted in the method we proposed. These simplifications are as follows:

[0064] First, during the passage on a section of the road, all the personnel in each group are in the same position and move simultaneously, that is, the group is regarded as an overall crowd without internal changes, and the moving speed of this overall remains unchanged during the movement process.

[0065] Second, if a person in a certain group is the last one to pass through the first intermediate node, then he / she will be the last one to pass through all subsequent intermediate nodes. Therefore, he / she will be the last one in the group to evacuate to the safe area. In the subsequent evacuation process calculation, at each moment, the position of this person represents the position of the corresponding group, that is, the evacuation process of the group recorded in the output result file is the position label of this person at each moment.

[0066] Third, from the first and second points, it can be obtained that in the crowded state, the time \(t_v\) required for all the personnel in group \(i\) to pass through node \(j\) ij , is the waiting time of the last person in the group queue at the crowded node \(j\). (Hereinafter, the intermediate node where crowding occurs is called the crowded node.)

[0067] Fourth, for the personnel queue waiting to pass through a certain crowded node, the following two rules are used for sorting:

[0068] 1) Sort the groups waiting to pass through this intermediate node according to the arrival time at a certain intermediate node;

[0069] 2) For the groups arriving at the same moment, sort them according to the corresponding source node numbers.

[0070] V. To avoid abnormal streamline conflicts, this embodiment stipulates that during the evacuation of people in an emergency, after each person determines the passable evacuation route, they will move forward along the established route without turning back during the evacuation process.

[0071] In addition, in the improved evacuation macroscopic simulation method proposed in this embodiment, an equivalent average treatment is also performed on the movement speed of people on the road section during the evacuation process. According to the first simplification rule, in the macroscopic simulation method proposed in this embodiment, each evacuation personnel group is regarded as a whole crowd without internal changes. The personnel belonging to the same group move synchronously with the same speed, and the movement speed of the personnel remains unchanged during the movement on the entire road section. However, in the actual evacuation process, the movement speed of each person on the road section will be affected by factors such as the surrounding people and the road section environment, and thus change. That is to say, during the entire evacuation process, the movement speed of people fluctuates and cannot always remain at the peak value. Therefore, in this embodiment, an equivalent average coefficient is independently set respectively to comprehensively reflect the influence of the above factors on the movement speed of people on two types of road sections (horizontal and vertical), that is, the equivalent average treatment of the movement speed on the road section.

[0072] Specifically, in the improved evacuation macroscopic simulation method proposed in this embodiment, the equivalent average treatment of the movement speed on the road section replaces the actual evacuation movement process with speed fluctuations during the process with an evacuation movement process in which the movement speed of people remains stable throughout the whole process. The condition for equivalent replacement is that the time consumed by the two methods is the same for the same distance (the same road section). By further analyzing the passing process of people on the road section, we find that there are many factors affecting the movement speed on the road section, and it is difficult to quantify these factors randomly. In addition, many factors (such as the specific position coordinates of people, the real-time density of the surrounding crowd of a certain person, etc.) cannot be obtained in real time from the calculation process of macroscopic simulation. At the same time, in order to further improve the calculation efficiency of the evacuation process simulation, at present, in the simulation method proposed in this embodiment, no detailed research is carried out on the quantitative calculation of the equivalent average coefficient of the movement speed on the road section. For a certain type of road section (horizontal and vertical), currently, a unified equivalent average coefficient value is selected to reduce all road sections of this type in the case. In the actual application process of the proposed improved macroscopic simulation method, for a specific actual building, the values of the equivalent average coefficients of the movement speed of people on the two types of road sections are determined by the corresponding microscopic model simulation results.

[0073] 2.2 Key calculation functions in the improved macroscopic simulation method

[0074] There are mainly three key calculation functions in the improved macroscopic simulation method: a. Determine the total evacuation time t of each evacuee i; b. Determination and output of the location information of personnel during the evacuation process; c. Crowding analysis. Next, the working principles of these three key calculation functions will be introduced one by one.

[0075] a. Determine the total evacuation time t of each evacuee i

[0076] The global evacuation time t required for all personnel in the building to complete a safe evacuation g is the maximum value of the evacuation time t of each group i in the building, as shown in Equation (1). i as shown in the following formula.

[0077]

[0078] And according to the second simplification rule in 2.1, the evacuation time t of each group i is the same as the evacuation time of the last person in the queue within the group. Therefore, we only need to calculate the evacuation time of the last person in the queue within the group to represent the evacuation time t of the group i . Therefore, the evacuation time t of group i i , can be calculated by Equation (2):

[0079]

[0080] In Equation (11), te ik represents the passing time of group i on edge k, in seconds, and can be calculated by Equation (3); the set E i is the set of edges included in the evacuation path of group i; tv ij represents the passing time of group i at the intermediate node j, in seconds, and can be calculated by Equation (4); the set V i is the set of intermediate nodes included in the evacuation path of group i.

[0081]

[0082] In Equation (3), L k represents the actual passable evacuation section length corresponding to edge k, in meters; v ik represents the evacuation speed of group i on edge k, in m / s.

[0083]

[0084] In Equation (4), C j represents the number of people who can pass side by side at the intermediate node j, in people; Nrpn ij represents the total number of people waiting to pass through node j when group i arrives at the intermediate node j, in people; Cv ijIt represents the actual passing rate of people at the intermediate node j when the group i arrives at the intermediate node j. In Equation (4), it also reflects the congestion occurrence determination condition set in the macroscopic simulation method proposed in this embodiment:

[0085] Nrpn ij >C j (5)

[0086] That is, at the moment when the group i arrives at the intermediate node j, the number of people Nrpn who are ready to pass through the node j ij is greater than the number of people C who can pass side by side at this node j , then congestion occurs. When congestion occurs, some people in the waiting crowd need to wait at this node and cannot pass immediately, that is, the crowded state of the crowd affects the evacuation movement of people, resulting in a decrease in the average speed of the group passing through the crowded node. When Nrpn ij is less than or equal to C j , then there will be no crowd congestion here. At this time, all the people who are currently ready to pass through this node do not need to wait here and can pass directly without generating waiting time, that is, tvij = 0.

[0087] b. Determination and output of personnel position information

[0088] In the calculation result of the macroscopic simulation method proposed in this embodiment, the position of a person at a certain moment is identified by the corresponding position label. Since the numbers of nodes and edges start from 0 as integers, in order to effectively distinguish whether a person is on an edge or at a node, the position label of a person on an edge is taken as the number of the edge where the person is located, and the position label of a person waiting at a node is taken as 100000×the number of the node. We uniformly set the position label of all safe areas to -1. When the macroscopic simulation method calculates a specific evacuation process, starting from the beginning of the evacuation process, the calculation program records the position labels of all evacuated personnel groups at the current moment every 0.1 s and stores them in the personnel position status matrix P_Locs. When all groups reach the safe area (global evacuation is completed), that is, when the labels of all groups are -1, the program completes the calculation of this evacuation process and outputs the complete personnel position status matrix P_Locs to the result file. In the personnel position status matrix P_Locs, the column index corresponds to the number of the group in each source node, and the row index corresponds to the cyclic time step (time interval 0.1 s). Taking Figures 4 to 7 the multi-story building example shown as an example, there are 160 source nodes in the evacuation road network of this building, and the data storage structure of its corresponding P_Locs is shown in the table.

[0089]

[0090] In Table 2: 1) Group ID: the number of the evacuated crowd; 2) td : The moment when the overall evacuation of the building ends; 3) n d : The number of program calculation steps corresponding to the end of the overall evacuation; 4) The column index and row index in Table 2 (i.e., the bold characters in Table 2) are not shown in the result file.

[0091] c. Crowding analysis

[0092] According to the above, crowding has a relatively serious impact on the overall evacuation efficiency in the building. It is necessary to accurately and reasonably consider its impact during the simulation process. Therefore, in order to further make the evacuation simulation results more accurately reflect the actual evacuation situation, the improved macroscopic evacuation simulation method proposed in this study introduces crowding analysis compared with the traditional macroscopic simulation method, and quantitatively considers the impact of crowding on the personnel passing speed during the evacuation process.

[0093] Crowding analysis needs to solve two key problems. One is how to judge whether crowding occurs, and the other is how to quantitatively calculate the reduction of the personnel passing speed in the crowded state. For judging whether crowding occurs, specifically, two pieces of information need to be determined. One is the location where crowding occurs, and the other is the moment when crowding occurs. According to the intermediate node setting rules, in the evacuation road network, the intermediate nodes include all the locations where crowding of the evacuating crowd may occur. According to the relevant description of Equation (5), for the moment when each group of people arrives at a certain intermediate node, it is necessary to judge whether crowding occurs. In summary, the moment (a certain time step) when people arrive at the end of a certain section (a certain intermediate node) is the key time step for crowding analysis in the evacuation simulation calculation process. At this time step, first, it is necessary to judge whether crowding occurs. If crowding occurs, then further calculate the reduction of the personnel passing speed in the crowded state. Next, the specific calculation principles and calculation processes involved in judging whether crowding occurs and quantitatively calculating the reduction of the personnel passing speed in the crowded state will be introduced.

[0094] First, it is the calculation principle for judging whether crowding occurs.

[0095] In the proposed macroscopic simulation method, when the group i arrives at the intermediate node j, the crowding occurrence judgment condition shown in Equation (5) is used to judge whether crowding occurs. According to Equation (5), to judge whether crowding occurs, two parameter values need to be determined, that is, the number of people who can pass side by side at the intermediate node j, C j and the number of people preparing to pass through node j at the current moment, Nrpn ij . The number of people who can pass side by side at the intermediate node j, C j can be calculated by Equation (6).

[0096]

[0097] In the formula, W jis the effective passing width of the intermediate node j, and D is the diameter of the incompressible space occupied by a single person's body. is the floor symbol, so C j takes the largest integer not greater than W j / D. And the number of people Nrpn ij ready to pass through node j at the current moment is defined as: among the evacuation personnel group i, at the moment when the evacuation personnel reach the intermediate node j, in the queuing sequence of the personnel ready to pass through the intermediate node j, the number of people in the queuing sequence l ij from the beginning to group i. Nrpn ij can be calculated by Equation (7).

[0098] Nrpn ij = Nps_wpn ij + Napn ij (7)

[0099] As shown in Equation (7), Nrpn ij is mainly composed of two parts: ① In the queuing sequence l ij , at the current time step n, the number of people Napn ij reaching node j; ② Before the current time step n, the number of people Nps_wpn ij who have been queuing at node j and still have not passed by the current step n. The current time step n here refers to the time step n when the evacuation personnel group i reaches the intermediate node j. In the simulation method, one time step represents one moment, and the time interval between each time step is 0.1 s. Therefore, the macroscopic simulation method we proposed is a discrete method.

[0100] Next, the calculation processes of Napn ij and Nps_wpn ij will be further introduced. First, Napn ij can be calculated by Equation (8).

[0101]

[0102] In Equation (8), the set is the set of groups reaching node j at the current time step n in the queuing sequence l ij , and Neg i represents the number of evacuation personnel in the evacuation group i.

[0103] Secondly, we calculate Nps_wpn mj through the Nrpn ij corresponding to the group m that reached node j last before the current time step n, as shown in Equation (18).

[0104] Nps_wpn ij = Nrpn mj -(tai ij -tai mj )×Cv mj (9)

[0105] In Equation (9), tai ij is the evacuation process time corresponding to when the evacuee group i reaches the intermediate node j; tai mj represents the time when the group m, which is the last group to reach node j before the current time step n, reaches node j; Cv mj represents the actual passing rate of node j at the time when group m reaches node j. According to Simplification Rule Five in 2.1, in an emergency evacuation with a determined evacuation plan, each group can only reach a certain intermediate node once. Therefore, Nrpn ij , Napn ij , Nps_wpn ij The specific values of these three parameters correspond one-to-one with the evacuation plan. That is, for the same evacuation plan, Nrpn ij , Napn ij , Nps_wpn ij are fixed. When the evacuation plan changes, Nrpn ij , Napn ij , Nps_wpn ij will also change accordingly.

[0106] Then, quantitatively calculate the specific calculation principle of the reduction of the personnel passing rate under crowded conditions.

[0107] When group i reaches the intermediate node j, if congestion occurs, it is necessary to further quantitatively calculate the reduction of the personnel passing rate under crowded conditions. Specifically, the reduction coefficient R of the personnel passing rate at the congestion point will be calculated successively Cv , the actual passing rate Cv under crowded conditions, and the waiting time tv of the crowd. The physical meanings and calculation principles of the above three key parameters will be introduced one by one below.

[0108] The reduction coefficient R of the personnel passing rate at the congestion point Cv is a two-dimensional matrix in the simulation method. The physical meaning of any element is the reduction of the personnel passing rate at node j in the current crowded state when group i reaches node j. In the actual calculation process, it is necessary to select according to the space type of the intermediate node j The corresponding calculation formula. When the sizes of the crowd gathering areas in two directions at the position corresponding to the intermediate node are not limited, the crowd gathering form in the crowded state is defined as that the sizes of the crowd gathering areas in two directions are not limited, and only the length-width ratio of the area needs to be within a reasonable range (in this kind of space environment, the crowd gathering form is generally a semi-circle, that is, the length-width ratio is 2). Therefore, in the actual spatial shape of such nodes, The corresponding calculation formula is:

[0109]

[0110] Further combining the parameter symbols and indexes in the simulation method, its specific calculation form in the simulation method is as shown in Equation (10).

[0111]

[0112] In Equation (10), W j is the passable width of node j, and Nrpn ij is calculated by Equation (7). When the spatial size of the crowd gathering area at the position corresponding to the intermediate node is limited in one direction, the crowd gathering form is that the spatial size of the crowd gathering area is limited in one direction, and the upper limit of the size in this direction needs to be set.

[0113] Therefore, in the actual spatial shape of such nodes, The corresponding calculation formula is:

[0114]

[0115] Further combining the node number index of the current simulation method, its specific calculation form in the simulation method is as shown in Equation (11). Consistent with Equation (10), in Equation (11), W j is the passable width of node j, and Nrpn ij is calculated by Equation (7).

[0116] In the simulation method, the dimension of matrix R Cv is N V ×N V , where N V represents the total number of nodes in the evacuation road network. It can be seen from the node setting rules in the first section that the numbers and the total number of the evacuation groups are consistent with their corresponding source nodes. Denote the total number of source nodes in the evacuation road network as N SV , and the total number of the evacuation groups as N G , then in the simulation method, there is

[0117] N SV = N G (12)

[0118] According to the physical meaning, the elements in matrix R Cv that can actually participate in the evacuation process simulation calculation have a range of subscript index values. The effective range of i is all group numbers (i.e., source node numbers), and the effective range of j is all intermediate node numbers. Therefore, the data dimension of the effective data area in matrix R Cv is N SV ×N MV where N MV is the total number of intermediate nodes in the evacuation road network. For the elements in matrix R Cv that are not in the effective area, we set the values of these elements to 1 uniformly. Since these elements do not participate in the evacuation process calculation, their numerical values will not change during the calculation and will always be 1, which will not affect the result of the evacuation process simulation calculation. Before the start of the evacuation process simulation calculation, the initial values of the elements in matrix R Cv are all 1. From it can be seen that indicates that there is no reduction in the personnel passing rate here (at intermediate node j). During the simulation calculation process, the numerical values of Cv in the effective area of matrix R will be calculated according to the actual situation of the evacuation process by Equation (10) or Equation (11).

[0119] After determining , then Cv can be further calculated, where R ij is the personnel evacuation speed reduction coefficient at the location where congestion occurs. In the ideal case without considering the mutual influence between people, the personnel passing rate Cv0 at the location where congestion occurs. Combining the node number indexing in the simulation method, the specific calculation form of Cv cv in the simulation method is shown in Equation (13). ij In Equation (13), Cv

[0120]

[0121] represents the actual personnel passing rate at node j in the current congested state when group i arrives at node j; ij represents the personnel passing rate at node j in the ideal case without congestion, which can be calculated by , where represents the number of people who can pass side by side at this position, V0 is the personnel evacuation speed. D is the diameter of the incompressible space occupied by a single person. W is the passable width at the narrow part of the passage. In the simulation method, the calculation result of Cv is stored in the actual personnel passing rate matrix Cv, and the dimension of matrix Cv is the same as that of matrix R ij Cv ​Consistent. Similarly, for the elements in matrix Cv that can truly participate in the evacuation process simulation calculation, the subscript index values have a range. The valid range of i is all group numbers (i.e., source node numbers), and the valid range of j is all intermediate node numbers. Before the start of the evacuation process simulation calculation, the initial values of each element in matrix Cv are set to the corresponding That is, the initial value of the elements in the j-th column of matrix Cv is During the simulation calculation process, the value of Cv in the valid area of matrix Cv ij will be calculated by Equation (13) according to the actual situation of the evacuation process. For the elements outside the valid area, their values will not change during the calculation process and will always remain the initial values.

[0122] Combined with The definition of the actual pedestrian passing rate Cv at the crowded place calculated in combination with sum and the simplified rule 3 in Section 1.2.1. Among them, N is the total number of people preparing to pass through the crowded place, and t ij is the total time required for all people to pass through the crowded place. According to Cv ij , the time tv required for group i to pass through the intermediate node j where people are crowded is further calculated ij . The calculation formula of tv is shown in Equation (14).

[0123]

[0124] In the formula, the starting time of the waiting time corresponding to tv ij is the moment when group i arrives at intermediate node j, and the ending time is the moment when the last person in group i passes through node j; Nrpn ij is calculated by Equation (7); Cv ij is calculated by Equation (13). The calculation result of tv ij is stored in matrix tv. Matrix tv has the same dimension as matrix Cv and matrix R Cv , which is all N V ×N V . Similarly, for the elements in matrix tv that can truly participate in the evacuation process simulation calculation, the subscript index values have a range. The valid range of i is all group numbers (i.e., source node numbers), and the valid range of j is all intermediate node numbers. Before the start of the evacuation process simulation calculation, the initial value of each element in matrix tv is 0. During the simulation calculation process, the value of tv in the valid area of matrix tv ij will be calculated by Equation (14) according to the actual situation of the evacuation process. In the subsequent calculation process, it will be used to judge whether group i is still waiting at node j at a certain moment. For the elements outside the valid area, their values will not change during the calculation process and will always be 0.

[0125] Based on the main content in the comprehensive congestion analysis, we have sorted out the analysis process that needs to be carried out at the key time steps when congestion analysis is required, which is as follows:

[0126] Step 01: Determine Napn at the current time step according to Equation (8) ij ;

[0127] Step 02: Record the moment tai when the evacuation personnel group i arrives at the intermediate node j ij ;

[0128] In subsequent time steps, Nps_wpn will be called during the calculation ij .

[0129] Step 03: Judgment 1: Determine the number of people preparing to pass Nrpn at the current step according to whether there were people waiting at this node in the previous step ij ;

[0130] If there is waiting, then according to Equation (9), calculate Nps_wpn ij ;

[0131] If there is no waiting, then there is no need to calculate Nps_wpn ij , that is, Nps_wpn ij = 0.

[0132] Step 04: Judgment 2: Judge whether congestion occurs at the current step according to Equation (5). Furthermore, based on the result, determine the position label of the personnel at the current time step

[0133] Nrpn ij ≤ C j , then congestion does not occur, and the current group i can directly pass through the node j without waiting.

[0134] 1) The personnel directly enter the next section, and the position label label_P_Locs is assigned: the ID of the next section;

[0135] 2) The passing time tv of group i at node j ij = 0;

[0136] Nrpn ij > C j , then congestion occurs, and group i cannot pass through at once. Some people need to wait here, and there is an impact of congestion.

[0137] 1) Some personnel in group i wait and stay at this node, and tv needs to be calculated according to Equation (14) ij ;

[0138] 2) The position label label_P_Locs of group i is assigned as 100000 × the number of node j;

[0139] 3) Meanwhile, record the reduction factor at node j at the current time step. It will be called when calculating Nps_wpn in subsequent time steps. ij when calculating.

[0140] So far, the calculation principles of the main functions in the improved macroscopic simulation method proposed in this embodiment have been introduced. The symbols used in the improved macroscopic simulation method are summarized in Table 3.

[0141]

[0142]

[0143]

[0144] Example 2: This example is a certain hospital in City H, which is a three-story building and includes the vertical evacuation process. The implementation process of the improved macroscopic simulation method proposed in this patent on this three-story building example is as follows:

[0145] Step 1: According to the building floor plan of the case to be analyzed, convert the spatial information of the evacuation scene into a corresponding geometric network model of the evacuation path.

[0146] According to the building space layout and usage functions shown in the three-story example building floor plan, combined with the building evacuation road network establishment standard described in this embodiment, a multi-story example evacuation road network as shown in Figures 4 to 7 is established. Among them Figures 4 to 6 is the planar schematic diagram of this multi-story example evacuation road network, Figure 7 is the three-dimensional schematic diagram of this multi-story example evacuation road network. In the mathematical model of the evacuation road network of this multi-story example, there are 161 source nodes, and the corresponding numbers are 0 to 160 in sequence. Among them: there are 49 source nodes on the first floor, numbered 0 to 48; there are 56 source nodes on the second floor, numbered 49 to 104; there are 56 source nodes on the third floor, numbered 105 to 160. The number of evacuees Neg included in the evacuation group i corresponding to a certain source node i i is shown in bold blue numbers next to the source node symbol in Figures 4 to 6 . There are 32 intermediate nodes in total, and the distribution of intermediate nodes within each floor is shown in Table 3. There are 6 exit nodes, all on the first floor, numbered 193 to 198 in sequence. The total number of nodes in the evacuation road network of this multi-story example is 199, the abstract node number of the safe area is 199, and the total number of people participating in the evacuation within the entire building is 555, including 191 people on the first floor, 184 people on the second floor, and 180 people on the third floor. The representation forms of the three types of nodes in the evacuation road network of high-rise and multi-story buildings in the evacuation road network schematic diagram are shown in detail in Table 1.

[0147] The total number of directed edges in the evacuation road network of the multi-layer example is 430. The weight values of each edge are currently obtained through manual measurement and calculation. Subsequently, relevant research has been carried out on automatically generating a mathematical model of the evacuation road network from building floor plans based on a deep learning semantic segmentation model to generate the evacuation road network more efficiently and conveniently. The representation forms of various edges in the schematic plan of the evacuation road network are detailed in the edge setting rules. In the 3D schematic diagram of the evacuation road network, the edges corresponding to vertical sections are shown as red dotted lines, and the starting and ending points of the vertical edges are marked with green solid dots.

[0148]

[0149] After the processing in Step 1, a data file of the evacuation path geometric network model (abbreviation: evacuation road network) of this 3-story building is formed. This file contains 4 data tables:

[0150] 1. Data Table 1: Link Information, which stores the relevant information of each edge in the evacuation road network model, specifically including: 1) the ID of the edge; 2) the starting and ending point information of the edge; 3) the length weight value of the edge; 4) the section type of the edge (horizontal or vertical). In this embodiment, ID represents: number.

[0151] 2. Data Table 2: Number of Source Nodes, which stores the source node information in the evacuation road network model, specifically including: the ID of the source node (corresponding one-to-one with the ID of the evacuation group), and the number of people in the evacuation group corresponding to the source node.

[0152] Therefore, Data Table 2 represents the initial distribution of the evacuees in this building instance.

[0153] 3. Data Table 3: Middle Node capacity, which stores the middle node information in the evacuation road network model, specifically including: 1) the ID of the middle node; 2) the passable width W of the middle node (representing the passing capacity of the middle node); 3) the spatial form of the actual evacuation scenario at the corresponding position of the middle node (represented by the specific value of OPS: OPS = 0.4 represents the spatial form of the evacuation scenario corresponding to the first type of crowd aggregation form; OPS = 0.3 represents the spatial form of the evacuation scenario corresponding to the second type of crowd aggregation form).

[0154] 4. Data Table 4: Exit capacity, which stores the information of the exit nodes in the evacuation road network model, specifically including: 1) the ID of the exit node; 2) the passable width W of the exit node; 3) the spatial form of the actual evacuation scenario corresponding to the position of the exit node (represented by the specific value of OPS: OPS = 0.4 represents the spatial form of the evacuation scenario corresponding to the first type of crowd aggregation form; OPS = 0.3 represents the spatial form of the evacuation scenario corresponding to the second type of crowd aggregation form).

[0155] Step 2: Input the data of the evacuation path geometric network model into the calculation program of the improved macroscopic simulation method, and then conduct the evacuation process simulation analysis and calculation on the case to be analyzed to obtain the evacuation process simulation results.

[0156] Input the evacuation road network data file of the three-story building formed in Step 1 into the calculation program of the improved macroscopic simulation method. Running the program will conduct the simulation calculation of the crowd evacuation process in the three-story building, and finally obtain the evacuation process simulation analysis result file. Its internal data structure is shown in Table 2. The evacuation process simulation analysis result file records the positions of all personnel groups in the three-story building example at each moment during their evacuation process. Subsequently, the key features at the macroscopic level of the global evacuation in the building can be analyzed based on this result file.

[0157] First of all, since the improved crowd evacuation simulation method proposed in this embodiment is established based on the evacuation road network that simplifies the building space information, it belongs to a type of macroscopic simulation method and cannot precisely present the microscopic details of the crowd evacuation process. Therefore, during the process of verifying and comparing with the simulation results of the microscopic method, the main indicators we initially focus on are whether some macroscopic and global indicators are consistent. Specifically, we compare and verify the calculation results of the three-story building example using the improved macroscopic crowd evacuation simulation method with the simulation results of the microscopic evacuation simulation model established using the software AnyLogic, and then focus on the comparison and verification results of the following three macroscopic indicators: 1) the total evacuation time, 2) the last group to complete the evacuation (reach the safe area), 3) the locations where congestion occurs during the evacuation process. The microscopic evacuation simulation model of the three-story example is established in the software AnyLogic as Figure 8 shown. According to the control variable method, during the comparison and verification process, the two models need to maintain the same settings for some key initial conditions of the evacuation simulation. Specifically, as follows:

[0158] 1. The steady-state peak speed of the personnel evacuation movement is set to v = 3.124 m / s for both;

[0159] 2. The personnel size, that is, the diameter of the non-compressible space occupied by the personnel, is set to D = 0.4 m for both;

[0160] 3. During the evacuation process, people need to follow the guidance of the same evacuation plan, and the shortest path plan is selected here.

[0161] 4. The distribution of people in the initial evacuation state of the two models is the same, and the total number of people participating in the evacuation in the building is 191.

[0162] 5. The hardware configurations for the simulation calculations are the same, that is, the two models are run on the same device. The basic configuration of this device is: Intel(R) Core(TM) i7-7700K CPU @ 3.60GHz and 16.0GB of memory.

[0163] Secondly, we will specifically introduce the comparison and verification of three macroscopic indicators. First, it is the comparison of the total evacuation time and the model running time between the two methods. The relevant results are shown in Table 4. From Table 4, we can see that for the multi-story example, the calculated result of the total evacuation time of the improved macroscopic method is 6.7 s less than that of the AnyLogic microscopic model, and the corresponding relative difference percentage is 3.29% < 5%, which is within the acceptable range. At the same time, the calculation time required for the improved macroscopic method to complete the evacuation process simulation is 54.84 s, which is 3.5 times shorter than the minimum calculation time (i.e., the fastest calculation time) required by the AnyLogic microscopic model. Compared with the single-story example, the calculation advantage of the improved macroscopic method in the multi-story example has decreased. The main reason is that the recursive chain part in the recursive function for determining the group position is used in the calculation process of the multi-story example. When the recursive function calls itself in the recursive chain, it occupies a lot of memory, resulting in a decrease in calculation efficiency. Further research needs to be carried out subsequently to find effective methods to optimize the recursive process in this embodiment to further improve the calculation efficiency.

[0164]

[0165] Then, we conduct a comparative verification on the indicator of the last group to complete evacuation. To visually reflect the evacuation process simulation results of the microscopic model, we have stored the animation video of the evacuation process of the multi-story example generated by the AnyLogic microscopic model online (which can be obtained from the following URL: https: / / pan.baidu.com / s / 1BEFpWpEQQ-Y2ii9ZNcoIFQ, verification code: 3333). The file name of this animation video is: Multi-story-overlook-203.5s.wmv. From the operation results of the AnyLogic microscopic evacuation model, we can see that in the multi-story example, under the guidance of the shortest path evacuation plan, Exit 197 is the last to complete evacuation. Among the rooms that escape through Exit 197, the room corresponding to the source node No. 155 is the farthest from the exit. According to the first-come-first-pass rule, we can consider that in the simulation results of the AnyLogic microscopic model, the group of people in the room corresponding to the source node No. 155 is the last to complete evacuation. In the calculation results of the improved macroscopic simulation method for crowd evacuation, it can be more intuitively seen that it is the group No. 155 passing through Exit 197 that is the last to complete evacuation, which is consistent with the simulation results of AnyLogic. Since the calculation results of the macroscopic simulation method are relatively large in length and cannot be detailedly shown in the article, we also store the calculation result file of the macroscopic simulation method online (which can be obtained from the following URL: https: / / pan.baidu.com / s / 1BEFpWpEQQ-Y2ii9ZNcoIFQ, verification code: 3333). The file name of the result file is: aP_Locs-3F1P-N555.xlsx. In the result file, the rows corresponding to the moments when all groups reach the safe zone (i.e., global evacuation is completed) are marked with the entire row data highlighted in green; the columns corresponding to the last group to complete evacuation are marked with the entire column data (i.e., the evacuation process record of this group) highlighted in yellow.

[0166] The last verification indicator is the location where congestion occurs during the evacuation process. Through further observation and analysis of the simulation results of the AnyLogic microscopic evacuation model, we find that in the multi-story example, in the 03 stairwell (for the stairwell numbers, see Figure 6 ), relatively serious congestion occurs at the gathering positions of the stair landings on each floor inside, resulting in some people waiting for a long time. The node numbers corresponding to these positions in the evacuation road network of the multi-story example are 166, 174, 178, and 190 in sequence. The floors where these seriously congested intermediate nodes are located and the corresponding position characteristics are shown in Table 5.

[0167]

[0168] In the calculation results of the improved macroscopic evacuation simulation method, it can also be seen that relatively serious congestion occurred at the above-mentioned nodes. In the result file of the improved macroscopic simulation method, we used orange highlighting for the time periods when congestion occurred at these nodes. Through further statistical analysis, we found from the results of the evacuation macroscopic simulation method that the duration of congestion at each intermediate node in Table 5 exceeded 10 s, and this conclusion can also be observed in the simulation results of AnyLogic. Combining the simulation results of the two models, we found that the congestion occurring at the stairs significantly prolonged the global evacuation time of the multi-story building, which is worthy of attention.

[0169] Based on the verification and comparison results of the three macroscopic indicators in this three-story building example, we can see that although the calculation results of the improved macroscopic crowd evacuation simulation method cannot very accurately approach the results of the microscopic model in terms of evacuation time, they are consistent with the microscopic model in the key macroscopic evacuation indicators. Therefore, we believe that the improved macroscopic method proposed in this study can replace the microscopic model to analyze the macroscopic key characteristics of global evacuation in buildings. In addition, through the comparison of the calculation running times, we can see that the proposed macroscopic method has obvious computational efficiency advantages over the microscopic method used in AnyLogic and can more efficiently simulate the global evacuation situation of large-scale personnel evacuation scenarios.

[0170] In the description of this specification, it is only a preferred embodiment of the present invention and cannot be used to limit the scope of rights of the present invention; in addition, the descriptions with reference to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or N embodiments or examples in a suitable manner.

[0171] In addition, without contradiction, those skilled in the art can combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of the present invention, the meaning of "N" is at least two, such as two, three, etc., unless otherwise specifically defined. Any process or method description in the flowchart or described in other ways herein can be understood as representing a module, segment, or part of code including one or more N executable instructions for implementing a customized logic function or process, and the scope of the preferred embodiments of the present invention includes additional implementations, where the functions can be executed in a substantially simultaneous manner or in the reverse order according to the involved functions, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0172] For the purposes of this specification, the "computer-readable medium" in Embodiment Nine can be any device that can contain, store, communicate, propagate, or transport a program for use in or in connection with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion (electronic device) having one or N wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM).

[0173] Those skilled in the art can understand that the above are only the preferred embodiments of the present invention. The features described in various embodiments and / or claims of the present disclosure can be combined or combined in various ways, even if such combinations or combinations are not explicitly described in the present disclosure. It is not used to limit the present invention. The above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

[0174] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made by those skilled in the art once they learn the basic creative concept. Therefore, the appended claims are intended to be interpreted to include the preferred embodiments as well as all changes and modifications that fall within the scope of the present invention. Obviously, those skilled in the art can make various changes and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A macroscopic simulation method for crowd evacuation considering the reduction of pedestrian passing speed in crowded areas, characterized in that, The simulation method includes the following steps: Step 1: Obtain the architectural floor plan of the case to be analyzed, and convert the spatial information of the evacuation scenario into an evacuation path geometric network model within the building; Step 2: Input the data of the evacuation path geometric network model within the building into the calculation program module of the improved macroscopic simulation method, that is, conduct an evacuation process simulation analysis on the case to be analyzed, and calculate the reduction of the personnel passage rate at the crowded positions to obtain the evacuation process simulation results; The step of converting the obtained spatial information of the evacuation scenario into an evacuation path geometric network model in Step 1 further includes the setting steps of nodes and edges; The setting principle of node V in Step 1 is: Denote the evacuation road network within a building as G(V, E). Each room or area with a certain usage function in the building, the exits of the building, passages, and both ends of the stairs are set as nodes, and the passage paths between the nodes are set as edges, thus forming an evacuation road network; The setting principle of edges is: All types of traffic connection parts within the building are simplified as edges E to represent the passable sections between nodes; all edges in the evacuation road network are directed edges, each connecting a node at both ends, that is, there are only two nodes on one edge; the length of the actual passable section corresponding to the starting point to the ending point of the edge is used as the weight of this edge; The method of inputting the data of the evacuation path geometric network model in the building into the calculation program module of the improved macroscopic simulation method in Step 2 includes determining the total evacuation time of each evacuee t i , the determination and output of the position information of personnel during the evacuation process, and the steps of congestion analysis; Among them, the method for determining the total evacuation time of each evacuee t i is as follows: The global evacuation time required for all the people in the building to complete a safe evacuation t g is the evacuation time of each group i in the building t i and is the maximum value among them (1) (2) (3) In the formula, te ik represents the population i The passing time on the edge k , in seconds; L k represents the edge k The actual true length of the evacuation section corresponding to it, in meters; v ik represents the population i The evacuation speed on the edge k , in m / s; That is, when the group i reaches the intermediate node j, the number of people Nrpn who are ready to pass through node j ij is greater than the number of people C who can pass side by side at this node j , then congestion occurs; when congestion occurs, some people in the crowd waiting to pass need to wait at this node and cannot pass immediately. When Nrpn ij is less than or equal to C j , then there will be no congestion of people here; at this time, all the current people who are ready to pass through this node do not need to wait here and can pass directly without generating waiting time, that is, tv ij = 0; (4) In formula (4), C j represents the number of people who can pass side by side at the intermediate node j , unit: person; Nrpn ij represents when the group i arrives at the intermediate node j , the total number of people waiting to pass through the node j , unit: person; Cv ij represents when the group i arrives at the intermediate node j , the actual passing rate of people at the intermediate node j ; The crowded occurrence determination condition set in the macroscopic simulation method is: (5) The pedestrian passing rate reduction coefficient at the crowded area in Step 2 R Cv is a two-dimensional matrix in the simulation method, and any element in it has the physical meaning that when the crowd i arrives at the node j , the passing rate of pedestrians at the node j in the current crowded state is reduced; when the dimensions of the crowd aggregation area at the corresponding position of the intermediate node are not limited in both directions, the crowd aggregation form in the crowded state is defined as that the dimensions of the crowd aggregation area are not limited in both directions, and only the aspect ratio of the area needs to be kept within a reasonable range. Therefore, under the actual spatial shape of such nodes, the corresponding calculation formula is: (10) When the spatial dimension of the crowd gathering area at the corresponding position of the intermediate node is limited in a certain direction, the crowd gathering form is that the spatial dimension of the crowd gathering area is limited in a certain direction, and the upper limit of the dimension in this direction needs to be set. Under the actual spatial shape of this node, The corresponding calculation formula is: (11)。 2. The macroscopic crowd evacuation simulation method considering the reduction of the passing rate of people in crowded areas according to claim 1, wherein In Step 2, the improved evacuation macroscopic simulation method further includes the step of equivalently averaging the movement speed of personnel on the road sections during the evacuation process.

3. The crowd evacuation macroscopic simulation method considering the reduction of the passing rate of people in crowded areas according to claim 2, characterized in that, The equivalent averaging reflects all road sections in the reduction case through a unified equivalent averaging coefficient value.

4. A macroscopic simulation device for simulating crowd evacuation by reducing based on the passing speed of people in crowded areas, characterized in that, The simulation device includes: A conversion module, used to obtain the architectural floor plan of the case to be analyzed, and convert the spatial information of the evacuation scenario into an evacuation path geometric network model within the building; A simulation module, used to input the data of the evacuation path geometric network model within the building into the calculation program module of the improved macroscopic simulation method, that is, conduct an evacuation process simulation analysis on the case to be analyzed, and calculate the reduction of the personnel passage rate at the crowded positions to obtain the evacuation process simulation results; The setting principle of node V in the conversion module is: Denote the evacuation road network within a building as G(V, E). Each room or area with a certain usage function in the building, the exits of the building, passages, and both ends of the stairs are set as nodes, and the passage paths between the nodes are set as edges, thus forming an evacuation road network; The setting principle of edges is: All types of traffic connection parts within the building are simplified as edges E to represent the passable sections between nodes; all edges in the evacuation road network are directed edges, each connecting a node at both ends, that is, there are only two nodes on one edge; the length of the actual passable section corresponding to the starting point to the ending point of the edge is used as the weight of this edge; The method of inputting the data of the evacuation path geometric network model in the building into the calculation program module of the improved macroscopic simulation method in the simulation module includes determining the total evacuation time of each evacuee t i , the determination and output of the position information of people during the evacuation process, and the steps of congestion analysis; Among them, the method for determining the total evacuation time of each evacuee t i is as follows: The global evacuation time required for all the people in the building to complete a safe evacuation t g is the evacuation time of each group i in the building t i and is the maximum value among them (1) (2) (3) In the formula, te ik represents the population i The passing time on the edge k is in seconds, L k represents the edge k The actual length of the real evacuation section corresponding to it is in meters; v ik represents the population i The evacuation speed on the edge k is in m / s; That is, when the group i reaches the intermediate node j, the number of people Nrpn who are ready to pass through node j ij is greater than the number of people C who can pass side by side at this node j , then congestion occurs; when congestion occurs, some people in the crowd waiting to pass need to wait at this node and cannot pass immediately. When Nrpn ij is less than or equal to C j , then there will be no crowd congestion here; at this time, all current personnel who are ready to pass through this node do not need to wait here and can pass directly without generating waiting time, that is, tv ij = 0; (4) In formula (4), C j represents the number of people who can pass side by side at the intermediate node j , unit: person; Nrpn ij represents when the crowd i arrives at the intermediate node j , the total number of people waiting to pass through the node j , unit: person; Cv ij represents when the crowd i arrives at the intermediate node j , the actual passing rate of people at the intermediate node j ; The crowded occurrence determination condition set in the macroscopic simulation method is: (5) The reduction coefficient of the pedestrian passing rate at crowded places in the simulation module R Cv In the simulation method, it is a two-dimensional matrix, and any element in it The physical meaning is that when the crowd i arrives at the node j at that time, the reduction of the pedestrian passing rate at the node in the current crowded state j When the sizes of the crowd aggregation areas in both directions at the corresponding position of the intermediate node are not limited, the crowd aggregation form in the crowded state is defined as that the sizes of the crowd aggregation areas in both directions are not limited, and only the aspect ratio of the area needs to be kept within a reasonable range. Therefore, under the actual spatial shape of such nodes, The corresponding calculation formula is: (10) When the spatial dimension of the crowd gathering area at the corresponding position of the intermediate node is limited in a certain direction, the crowd gathering form is that the spatial dimension of the crowd gathering area is limited in a certain direction, and the upper limit of the dimension in this direction needs to be set. Under the actual spatial shape of this node, The corresponding calculation formula is: (11)。 5. A computer device, comprising a memory and a processor, characterized in that, The memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes the simulation method according to any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the simulation method according to any one of claims 1 to 3 are implemented.

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