Crowd evacuation macroscopic simulation method and device considering traffic rate reduction of people at crowded place

Abstract

The invention discloses a crowd evacuation macroscopic simulation method and device considering crowded position personnel passing rate reduction, relates to the field of crowd evacuation simulation in a building, and solves the problems of how to judge crowd occurrence positions and quantitatively calculate crowded position personnel passing rate reduction in an evacuation simulation process. According to the scheme, the macroscopic simulation method comprises the steps that a building plan of a case to be analyzed is obtained, and space information of an evacuation scene is converted into an evacuation path geometric network model in a building; and inputting data of the geometric network model of the evacuation path in the building into a calculation program module of the improved macroscopic simulation method, i.e., carrying out evacuation process simulation analysis on a to-be-analyzed case, and calculating the reduction of the passing rate of people at a crowded position to obtain an evacuation process simulation result. And the method is also suitable for the global evacuation situation of a large-volume personnel evacuation scene.

Description

Technical Field

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

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

[0003] At present, in actual engineering applications, engineers and some highly versatile commercial software still mainly use model-driven methods. Model-driven evacuation simulation methods can be divided into two types according to the degree of refinement of the modeling of the research object: microscopic methods and macroscopic methods. Since the microscopic method models the individual behaviors of evacuees in a refined manner, the calculation amount and complexity of the model are very large when the microscopic method simulates the evacuation of large-scale buildings. It takes a long time to calculate only one working condition, and the calculation efficiency is very low. However, the macroscopic method takes the entire population as the research object, focusing on simulating the overall behavior and dynamic changes of the group of people. The mutual influence between each person is ignored, thereby reducing the complexity and amount of calculation and greatly improving the calculation efficiency. Therefore, for large-scale personnel evacuation scenarios, such as large shopping malls, hospitals, airports, stadiums, etc., the use of macroscopic methods can more efficiently simulate the global evacuation situation. The macroscopic method can help security management and decision-makers evaluate and analyze the efficiency and safety of personnel evacuation as a whole, and provide an important basis for formulating scientific and effective emergency plans. Therefore, the macroscopic evacuation simulation method still has a wide range of application needs.

[0004] In the prior art, macroscopic evacuation simulation methods generally regard crowded locations as inaccessible to avoid stampedes caused by extreme crowding. However, in most real evacuation processes, when the crowd is in a crowded state, people are often able to pass through the crowded location, but the movement speed of people passing through the crowded place will be significantly slowed down. It can be seen that the above processing method cannot truly and accurately reflect the impact of crowd crowding on the evacuation process. Crowd crowding has a serious impact on the overall evacuation efficiency in the building. 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 in the prior art of how to determine the location where crowd congestion occurs and quantitatively calculate the reduction in the personnel passage rate at the congested location during an evacuation simulation process.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0007] A macroscopic simulation method for crowd evacuation considering the reduction of the traffic speed of people in crowded places, the simulation method comprising:

[0008] Step 1: Obtain the building 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;

[0009] Step 2: Input the data of the geometric network model of the evacuation path in the building into the calculation program module of the improved macro simulation method, that is, perform a simulation analysis of the evacuation process for the case to be analyzed, and calculate the reduced traffic rate of people in crowded locations to obtain the simulation results of the evacuation process.

[0010] Furthermore, a preferred implementation is provided, wherein the step of converting the spatial information of the evacuation scene obtained in step one into a geometric network model of the evacuation path in the building also includes the step of setting nodes and edges.

[0011] Furthermore, a preferred embodiment is provided, in which the method for setting the nodes and edges is as follows: the geometric network model of the evacuation path in the building is recorded as G(V,E), the exits, passages, and both ends of the stairs of the building are set as nodes V, and the passable paths between the nodes are set as edges, recorded as E, that is, a geometric network model of the evacuation path in the building is constituted.

[0012] Furthermore, a preferred embodiment is provided, in which the improved evacuation macroscopic simulation method in step 2 also includes a step of performing equivalent averaging on the movement speed of personnel on the road section during the evacuation process.

[0013] Furthermore, a preferred embodiment is provided, wherein the equivalent average reflects all road sections in the reduction case through a unified equivalent average coefficient value.

[0014] Furthermore, a preferred embodiment is provided, wherein the calculation program module of the improved macroscopic simulation method is used to determine the total evacuation time t of each evacuee. i , determination and output of personnel location information during evacuation, and congestion analysis.

[0015] Solution 2: This solution proposes a crowd evacuation macroscopic simulation device that takes into account the reduction of the traffic speed of people in crowded places. The simulation device includes:

[0016] A conversion module is used to obtain the building 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;

[0017] The simulation module 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 macro simulation method, that is, to simulate and analyze the evacuation process of the case to be analyzed, and calculate the reduction of the personnel passage rate in the crowded location to obtain the simulation result of the evacuation process.

[0018] Solution three, a computer device, including a memory and a processor, wherein the memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes the crowd evacuation macroscopic simulation method that takes into account the reduction of the traffic speed of people in crowded places as described in any one of Solution one.

[0019] Solution 4: A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the crowd evacuation macroscopic simulation method according to any one of Solution 1 that takes into account the reduction of the traffic speed of people in crowded places.

[0020] The present invention is beneficial in that:

[0021] The present invention proposes a crowd evacuation macro-simulation method that can take into account the reduction of the traffic speed of people in crowded places based on a determined formula for reducing the traffic speed of people in crowded places. The simulation method can determine the location where crowd congestion occurs and quantitatively calculate the reduction of the traffic speed of people at the crowded location during the macro-simulation process of crowd evacuation.

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

[0023] That is, the present invention is also applicable to global evacuation situations in large-scale personnel evacuation scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a schematic diagram of the simplified process of a horizontal section as described in the tenth implementation mode.

[0025] Figure 2 This is a schematic diagram of an actual vertical passable road section described in the tenth implementation mode.

[0026] Figure 3This is a schematic diagram of a 2D representation of a unidirectional edge corresponding to the vertical road segment described in the tenth implementation mode.

[0027] Figure 4 This is a schematic plan view of the evacuation road network on the first floor of a multi-story building as described in Implementation Method 10.

[0028] Figure 5 This is a schematic plan view of the evacuation road network on the 2nd floor of the multi-story building case described in Implementation Method 10.

[0029] Figure 6 This is a schematic plan view of the evacuation road network on the 3rd floor of the multi-story building case described in Implementation Method 10.

[0030] Figure 7 This is a three-dimensional schematic diagram of the evacuation road network for a multi-story building case described in Implementation Method 10.

[0031] Figure 8 Schematic diagram of the AnyLogic micro-simulation model of the multi-story building case described in Implementation Example 10. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solutions and advantages of the implementation methods of the present application clearer, the technical solutions in the implementation methods of the present application will be clearly and completely described below in conjunction with the drawings in the implementation methods of the present application. Obviously, the described implementation methods are only part of the implementation methods of the present application, not all of the implementation methods.

[0033] Embodiment 1: This embodiment proposes a crowd evacuation macroscopic simulation method that takes into account the reduction of the traffic speed of people in crowded places. The simulation method includes:

[0034] Step 1: Obtain the building 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;

[0035] Step 2: Input the data of the geometric network model of the evacuation path in the building into the calculation program module of the improved macro simulation method, that is, perform evacuation process simulation analysis on the building floor plan of the case to be analyzed, and calculate the reduced traffic rate of people in crowded locations to obtain the simulation results of the evacuation process.

[0036] Implementation method 2. This implementation method is a further limitation of the crowd evacuation macro simulation method that takes into account the reduction of the traffic speed of people in crowded places described in implementation method 1. The spatial information of the evacuation scene obtained in step 1 is converted into a geometric network model of the evacuation path in the building, which also includes the steps of setting nodes and edges.

[0037] Implementation method three. This implementation method is a further limitation of the crowd evacuation macroscopic simulation method that takes into account the reduction of the traffic speed of people in crowded places described in implementation method one. The method of setting nodes and edges is as follows: the geometric network model of the evacuation path in the building is recorded as G(V,E), the exits, passages, and both ends of the stairs of the building are set as nodes V, and the passable paths between the nodes are set as edges, recorded as E, that is, a geometric network model of the evacuation path in the building is constructed.

[0038] Implementation method 4. This implementation method is a further limitation of the crowd evacuation macro-simulation method that takes into account the reduction of the traffic speed of people in crowded places as described in implementation method 1. The improved evacuation macro-simulation method in step 2 also includes a step of equivalent averaging the movement speed of people on the road section during the evacuation process.

[0039] Implementation method five: This implementation method is a further limitation of the crowd evacuation macro-simulation method that takes into account the reduction of the traffic speed of people in crowded places described in implementation method four. The equivalent average reduces all road sections in the case through a unified equivalent average coefficient value.

[0040] Embodiment 6: This embodiment further defines the crowd evacuation macro simulation method considering the reduction of the traffic speed of people in crowded places described in embodiment 1. The calculation program module of the improved macro simulation method is used to determine the total evacuation time t of each evacuee i , determination and output of personnel location information during evacuation, and congestion analysis.

[0041] Embodiment 7: This embodiment proposes a crowd evacuation macroscopic simulation device that takes into account the reduction of the traffic speed of people in crowded places, and the simulation device includes:

[0042] A conversion module is used to obtain the building 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] The simulation module 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 macro simulation method, that is, to simulate and analyze the evacuation process of the building floor plan of the case to be analyzed, and calculate the reduced traffic rate of people in crowded locations to obtain the simulation results of the evacuation process.

[0044] Embodiment 8. This embodiment proposes a computer device, including a memory and a processor, wherein the memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes the crowd evacuation macroscopic simulation method that takes into account the reduction of the passage rate of people in crowded places as described in any one of embodiments 1 to 7.

[0045] Embodiment 9. This embodiment proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the crowd evacuation macro simulation method considering the reduction of the traffic speed of people in crowded places described in any one of embodiments 1 to 7.

[0046] Embodiment 10: This embodiment provides an example, which is used to explain the above embodiments 1 to 9. The specific contents are as follows:

[0047] Step 1: 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;

[0048] The improved macro simulation method proposed in this embodiment combines the indoor space characteristics of the building with the law of emergency evacuation movement, and proposes a modeling method for the geometric network model of the evacuation path in the building, which mainly includes the setting rules of nodes and edges. In the following content, the geometric network model of the evacuation path in the building is 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 respectively.

[0049] 1. Node setting rules

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

[0051]

[0052] The setting of the source node is also related to the number of people. At present, if the number of people in a 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 for each area to ensure that the number of people in each source node does not exceed 10. Considering that the position of each person in the room / area is different, and the distance from the exit of the room / area is near or far, the geometric center of the room / area is used as the position of the simplified node, that is, the evacuation departure position of all people in the room / area is 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 (each group has ≤10 people), then the number of source nodes is equal to the number of evacuation groups, that is, the source node corresponds to the evacuation group contained in it one by one. Therefore, this embodiment sets the source node in the simulation method and the evacuation group contained in it to the same number, that is, the number of the source node is the number of the evacuation group contained in it.

[0053] The traffic connection part in the building refers to the corridor, hallway, staircase and other parts. The location where the intermediate node is set is often where the evacuation paths converge or the traffic capacity changes significantly. These locations are where congestion may occur during emergency evacuation. That is, the location where the intermediate node is set includes all the locations where congestion may occur during the actual evacuation process. In the evacuation road network, the node numbering order is source node, intermediate node, exit node, and the starting value of the numbering is 0.

[0054] 2. Edge Setting Rules

[0055] All types of traffic connections in the building are simplified as edges E to represent the passable sections between nodes. Each edge in the evacuation road network is a directed edge, with one node connected at the beginning and end, that is, there are only two nodes on an edge. The length of the actual passable section from the origin of the edge to the terminus of the edge is used as the weight of the edge.

[0056] According to the space type, the accessible sections in the building can be divided into horizontal sections and vertical sections. For details on the simplification process of the edges of horizontal sections, see Figure 1 As shown. When earthquakes, fires and other emergencies that damage building structures occur, some obstacles will be created to affect the passage of people. Therefore, Figure 1 As shown in the actual passable road section from node 1 to node 57 in the figure, for obstacles such as furniture and building debris during the evacuation process, the actual passable evacuation road section needs to maintain a certain distance from these obstacles to ensure the continuity of the evacuation process and the safety of personnel. In other words, the proposed evacuation road network modeling method can reflect the details of the evacuation process of evacuees avoiding obstacles. Figure 1The green dotted line in the middle represents the actual passable road section during the evacuation process, and the black solid line represents the edge E simplified from the actual passable road section. The simplified edge E connects the first and last nodes with a straight line. It only indicates the passability between the nodes and the direction of the road section, and cannot reflect the actual shape of the passable road section during the evacuation process.

[0057] In multi-story buildings, stairs are currently the only legal way to evacuate. As the most traditional way to evacuate, stairs have always been the first choice for people to evacuate vertically in dangerous situations in buildings. Therefore, in the process of establishing the evacuation network of multi-story buildings, we only use stairs for vertical evacuation, that is, the evacuation network only contains vertical edges simplified from the vertically accessible evacuation sections corresponding to the stairs. The details of the simplification process of the edges corresponding to the vertical sections are as follows: Figure 2 As shown. Among them, Figure 2 It indicates the actual vertical passable road section. Figure 3 It represents the expression form of the one-way edge corresponding to the vertical section in the two-dimensional plane diagram of the evacuation road network. Similar to the horizontal sections, the evacuation road network modeling method proposed in this embodiment can also consider the impact of obstacles in the vertical sections on the evacuation process of personnel. In addition, considering that the evacuation process in a multi-story building is to leave the building and reach a safe area outside the building as the evacuation goal. Therefore, in the evacuation road network, the direction of the vertical sections is from top to bottom, that is, from the nodes on the high floors to the nodes on the low floors. In the two-dimensional plane diagram of the evacuation road network, the edges corresponding to the vertical sections are represented by yellow one-way solid lines. For its specific representation, see Figure 3 As shown in Figure 2, the representation of vertical edges cannot reflect the actual shape of the passage section during the evacuation process.

[0058] 3. Schematic diagram of evacuation road network

[0059] According to the edge setting rules, in the evacuation road network diagram, the edges corresponding to the horizontally accessible 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 the two end-vertices of the two-way edge. The edges corresponding to the vertically accessible sections are one-way edges from top to bottom, represented by yellow single arrow solid lines. Figures 4 to 7 This is a schematic diagram of an evacuation road network for a multi-story building case established by following the evacuation road network modeling method proposed in this implementation method. Figures 4 to 6 This is a plan view of the evacuation network for this multi-story building case. Figure 7 This is a three-dimensional schematic diagram of the multi-layer evacuation road network. Figures 4 to 6 The setting rules of nodes and edges follow the relevant requirements of node setting rules and edge setting rules.

[0060] Step 2: Input the data of the geometric network model of the evacuation path in the building into the calculation program module of the improved macro simulation method, that is, perform evacuation process simulation analysis on the building floor plan of the case to be analyzed, and calculate the reduced traffic rate of people in crowded locations to obtain the simulation results of the evacuation process.

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

[0062] 2.1. Improving Simplified Processing in Macro Simulation Methods

[0063] The simplified processing of the evacuation process commonly used in the macro simulation method in the prior art is also adopted in our proposed method. These simplified processing are as follows:

[0064] 1. During the passage of the road section, all people in each group are in the same position and move at the same time, that is, the group is regarded as a whole group of people with no internal changes, and the movement speed of this whole group does not change during the movement.

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

[0066] 3. From 1 and 2, we can conclude that in a crowded state, the time tv required for all people in group i to pass through node j is ij , is the waiting time of the last person in the group queue at the crowded node j. (The intermediate node where crowd congestion occurs is referred to as the crowded node below.)

[0067] 4. For the queue of people waiting to pass through a congested node, sort them according to the following two rules:

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

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

[0070] 5. In order to avoid abnormal flow conflicts, the present implementation mode stipulates that during the evacuation of a crowd in an emergency, each person will move forward along the established route after determining a passable evacuation path, and will not return in the middle of the evacuation process.

[0071] In addition, the improved evacuation macro-simulation method proposed in this embodiment also performs equivalent average processing on the movement speed of personnel on the road section during the evacuation process. The first simplified rule shows that in the macro-simulation method proposed in this embodiment, each evacuation personnel group is regarded as a whole group of people with no internal changes, and each person belonging to the same group moves synchronously with the same speed, and the movement speed of the personnel does not change during the movement of 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 personnel and the road section environment, and then change. In other words, during the entire evacuation process, the movement speed of the personnel fluctuates and cannot always remain at the peak value. Therefore, this embodiment comprehensively reflects the influence of the above factors on the movement speed of personnel on two types of road sections (horizontal and vertical) by independently setting an equivalent average coefficient, that is, equivalent average processing of the movement speed on the road section.

[0072] Specifically, the equivalent average processing of the movement speed on the road section in the improved evacuation macro simulation method proposed in this embodiment is to replace the actual evacuation movement process with speed fluctuations in the process with the evacuation movement process in which the movement speed of the personnel is stable and unchanged throughout the whole process. The condition for equivalent replacement is that the time consumed by the two methods is consistent at the same distance (the same road section). Through further analysis of the passage process of personnel on the road section, we found that there are many factors affecting the movement speed on the road section, and the randomness of these factors is difficult to quantify. In addition, many factors (such as the specific location coordinates of personnel, the real-time density of the crowd around a certain person, etc.) cannot be obtained in real time from the calculation process of macro simulation. At the same time, in order to further improve the calculation efficiency of the evacuation process simulation, at this stage, in the simulation method proposed in this embodiment, the quantitative calculation of the equivalent average coefficient of the movement speed on the road section is not studied in detail. For a certain type of road section (horizontal and vertical), a unified equivalent average coefficient value is currently selected to reduce all sections of this type in the case. In the practical application of the proposed improved macroscopic simulation method, for a specific actual building, the equivalent average coefficient of the speed of people moving on the two types of road sections is determined by the corresponding microscopic model simulation results.

[0073] 2.2. Improving key computational functions in macroscopic simulation methods

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

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

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

[0077]

[0078] According to the second simplified rule in 2.1, the evacuation time t of each group is i It is consistent with the evacuation time of the last person in the queue within the group, so we only need to calculate the evacuation time of the last person in the queue within the group to express the evacuation time t of the group. i Therefore, the evacuation time t of group i is i , can be calculated by formula (2):

[0079]

[0080] In formula (11), te ik represents the time that group i spends on edge k, in units of s, which can be calculated by formula (3); set E i is the set of edges contained in the evacuation path of group i; tv ij represents the transit time of group i at the intermediate node j, in units of s, which can be calculated by formula (4); set V i is the set of intermediate nodes contained in the evacuation path of group i.

[0081]

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

[0083]

[0084] In formula (4), C j Nrpn represents the number of people who can pass side by side at the intermediate node j, unit: person; ij Indicates the total number of people waiting to pass through node j when group i reaches the intermediate node j, unit: person; Cv ijIt represents the actual traffic rate of people at the intermediate node j when group i arrives at the intermediate node j. Formula (4) also reflects the congestion occurrence judgment condition set in the macro simulation method proposed in this embodiment:

[0085] Nrpn ij >C j (5)

[0086] That is, when group i reaches the intermediate node j, the number of people ready to pass through node j is Nrpn ij The number of people that can pass through the node side by side is greater than C j , then congestion occurs. When congestion occurs, some people in the crowd waiting to pass through will have to wait at this node and cannot pass immediately. That is, the crowd congestion state has an impact on the evacuation of personnel, causing the average speed of the group passing through the congested node to decrease. ij Less than or equal to C j , then there will be no crowd congestion here. At this time, all the people who are currently preparing 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 location information

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

[0089]

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

[0091] c. Crowding analysis

[0092] According to the above content, crowd crowding has a serious impact on the overall evacuation efficiency in the building, and it is necessary to accurately and reasonably consider its impact in 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 to the traditional macroscopic simulation method, and quantitatively considers the impact of crowd crowding on the traffic rate of people during the evacuation process.

[0093] Congestion analysis needs to solve two key problems: one is how to judge whether congestion occurs, and the other is how to quantitatively calculate the reduction in the speed of people passing through the congested state. To judge whether congestion occurs, specifically, two pieces of information are mainly determined, one is the location where the congestion occurs, and the other is the time when the congestion occurs. It can be seen from the intermediate node setting rules that in the evacuation road network, the intermediate nodes contain all the locations where the evacuated people may be crowded. From the relevant explanation of formula (5), it can be seen that for the moment when each group of people arrives at a certain intermediate node, it is necessary to judge the occurrence of congestion. In summary, the moment (a certain time step) when the personnel arrives at the end of a certain section (a certain intermediate node) is the key time step for congestion analysis in the evacuation simulation calculation process. At this time step, it is necessary to first determine whether congestion occurs. If congestion occurs, the reduction in the speed of people passing through the congested state is further calculated. Next, the specific calculation principles and calculation processes involved in judging whether congestion occurs and quantitatively calculating the reduction in the speed of people passing through the congested state will be introduced.

[0094] First, the calculation principle for determining whether congestion occurs.

[0095] In the proposed macro simulation method, when group i reaches the intermediate node j, the crowding occurrence judgment condition shown in formula (5) is used to determine whether crowding occurs. It can be seen from formula (5) that to judge the occurrence of crowding, two parameter values ​​need to be determined, namely, the number of people who can pass through the intermediate node j side by side C j and the number of people Nrpn who are ready to pass through node j at the current moment ij The number of people who can pass through the intermediate node j is C j It can be calculated by formula (6).

[0096]

[0097] Where W jis the effective passage width of the middle node j, D is the diameter of the incompressible space occupied by a single person’s body, is the floor sign, so C j The value is not greater than W j / D is the maximum integer. The number of people who are ready to pass through node j at the current moment is Nrpn ij The definition is: when the evacuated personnel group i arrives at the intermediate node j, the queue sequence of personnel preparing to pass through the intermediate node j is taken from the beginning to the queue sequence l of group i ij Number of people. ij It can be calculated by formula (7).

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

[0099] As shown in formula (7), Nrpn ij It mainly consists of two parts: ① Queuing sequence l ij In the current time step n, the number of people reaching node j is Napn ij ② The number of people who have queued at node j before the current time step n but have not passed by the current step n is Nps_wpn ij The current time step n refers to the time step n when the evacuated personnel group i arrives at the intermediate node j. In the simulation method, one time step represents a moment, and the time interval between each time step is 0.1s, so the macro simulation method we proposed is a discrete method.

[0100] Next, we will further introduce Napn ij With Nps_wpn ij First, Napn ij It can be calculated by formula (8):

[0101]

[0102] In formula (8), the set is the queue sequence l ij In the current time step n, the set of groups that reach node j is Neg i represents the number of evacuees in evacuation group i.

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

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

[0105] In formula (9), tai ij is the evacuation process time corresponding to when the evacuee group i arrives at the intermediate node j; mj represents the time when the last group m to reach node j before the current time step n arrives at node j; Cv mj represents the actual traffic rate of node j when group m arrives at node j. According to the simplified rule 5 in 2.1., in an emergency evacuation determined by an evacuation plan, each group can only reach a certain intermediate node once, so Nrpn ij , Napn ij , Nps_wpn ij The specific values ​​of these three parameters correspond to the evacuation plan one by one, that is, for the same evacuation plan, Nrpn ij , Napn ij , Nps_wpn ij Fixed, when the evacuation plan changes, Nrpn ij , Napn ij , Nps_wpn ij Will also change accordingly.

[0106] Then the specific calculation principle of reducing the speed of people passing through under congested conditions is quantitatively calculated.

[0107] When group i reaches the middle node j, if congestion does occur, it is necessary to further quantitatively calculate the reduction of the personnel passage rate under the congested state. Specifically, the reduction coefficient R of the personnel passage rate at the congested place will be calculated in turn. Cv , the actual traffic speed Cv in a crowded state, and the waiting time tv of the crowd. The following will introduce the physical meaning and calculation principle of the above three key parameters one by one.

[0108] The speed reduction factor R of people passing through crowded places Cv In the simulation method, it is a two-dimensional matrix, in which any element The physical meaning of is that when group i arrives at node j, the traffic rate of people at node j in the current crowded state is reduced. In the actual calculation process, it is necessary to select according to the spatial type of the intermediate node j. The corresponding calculation formula. When the size of the crowd gathering area at the corresponding position of the intermediate node is not restricted in both directions, the crowd gathering shape in the crowded state is defined as the size of the crowd gathering area in both directions is not restricted, and it is only necessary to keep the length and width ratio of the area within a reasonable range (in this spatial environment, the crowd gathering shape is generally a semicircle, that is, the length and width ratio is 2). Therefore, in the actual spatial shape of this type of node, The corresponding calculation formula is:

[0109]

[0110] Further combined with the parameter symbols and indexes in the simulation method, the specific calculation form in the simulation method is shown in formula (10).

[0111]

[0112] In formula (10), W j is the traversable width of node j, Nrpn ij It is calculated by formula (7). When the spatial size 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 size of the crowd gathering area in a certain direction is limited, and the upper limit of the size in this direction needs to be set.

[0113] Therefore, in the actual spatial shape of this type of node, The corresponding calculation formula is:

[0114]

[0115] Further combined with the node number index of the current simulation method, its specific calculation form in the simulation method is shown in formula (11). Consistent with formula (10), in formula (11), W j is the traversable width of node j, Nrpn ij It is calculated by formula (7).

[0116] In the simulation method, the matrix R Cv The dimension is N V ×N V , where N V Represents the total number of nodes in the evacuation network. According to the node setting rules in the first section, the number and total number of evacuation groups are consistent with their corresponding source nodes. The total number of source nodes in the evacuation network is N SV , the total number of evacuated groups is N G , then in the simulation method,

[0117] N SV =N G (12)

[0118] according to The physical meaning of the matrix R Cv The elements that can really participate in the evacuation process simulation calculation have a range of subscript index values. The valid range of i is all group numbers (that is, source node numbers), and the valid range of j is all intermediate node numbers. Therefore, the matrix R Cv The data dimension of the valid data area is N SV ×N MV , where N MV is the total number of intermediate nodes in the evacuation network. Cv For the elements that are not in the valid area, we set the values ​​of these elements to 1. Because these elements do not participate in the evacuation process calculation, their values ​​will not change during the calculation process and will always be 1, and will not affect the results of the evacuation process simulation calculation. Before the evacuation process simulation calculation begins, the matrix R Cv The initial value of each element in is 1. It can be seen that Indicates that there is no reduction in the traffic rate of people here (at the middle node j). In the simulation calculation process, the matrix R Cv In the effective area The value of can be calculated by formula (10) or formula (11) according to the actual situation of the evacuation process.

[0119] After confirming Afterwards, you can Further calculation of Cv ij , where R cv is the reduction coefficient of the evacuation speed of personnel at the congestion location. In the ideal case where the mutual influence between personnel is not considered, the personnel passage rate Cv0 at the congestion location is Cv0. Combined with the node number index in the simulation method, Cv ij The specific calculation form in the simulation method is shown in formula (13).

[0120]

[0121] In formula (13), Cv ij It represents the actual traffic rate of people at node j in the current congested state when group i arrives at node j; In the ideal case where no congestion occurs, the traffic rate of people at node j can be expressed as Calculate and determine, where represents the number of people who can pass through this position side by side, V0 is the evacuation speed of people. D is the diameter of the incompressible space occupied by a single person. W is the passable width at the narrowest part of the passage. In the simulation method, Cv ij The calculation results are stored in the actual personnel traffic rate matrix Cv, and the dimension of matrix Cv is the same as that of matrix R CvSimilarly, the subscript index values ​​of the elements in the matrix Cv that can actually participate in the evacuation process simulation calculation are within 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 evacuation process simulation calculation begins, the initial values ​​of each element in the matrix Cv are set to the corresponding That is, the initial values ​​of the elements in the jth column of the matrix Cv are During the simulation calculation process, Cv in the effective area of ​​the matrix Cv ij The value of will be calculated by formula (13) according to the actual situation of the evacuation process. The values ​​of the elements that are not in the effective area will not change during the calculation process and will always remain at the initial value.

[0122] Combination The definition of the actual passenger flow rate Cv in the congested area is the same as that of the simplified rule 3 in Section 1.2.1., where N is the total number of people preparing to pass through the congested area, t sum The total time required for all people to pass through the congestion, according to Cv ij Further calculate the time tv required for group i to pass through the intermediate node j where crowding occurs ij ,tv ij The calculation formula of is shown in formula (14).

[0123]

[0124] Type tv ij The corresponding waiting time starts when group i reaches the middle node j, and ends when the last person in group i passes through node j; Nrpn ij Calculated by formula (7); Cv ij Calculated by formula (13). ij The calculation results are stored in the matrix tv, which is related to the matrix Cv and the matrix R Cv The dimensions are consistent, both are N V ×N V Similarly, the subscript index values ​​of the elements in the matrix tv that can actually participate in the evacuation process simulation calculation are within 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 evacuation process simulation calculation begins, the initial values ​​of all elements in the matrix tv are 0. During the simulation calculation, the tv in the valid area of ​​the matrix tv ij The value of will be calculated by formula (14) according to the actual situation of the evacuation process. In the subsequent calculation process, it will be used to determine whether group i is still waiting at node j at a certain moment. The value of the element that is not in the valid area will not change during the calculation process and is always 0.

[0125] Based on the main contents of congestion analysis, we have sorted out the analysis process that needs to be carried out at the key time step where congestion analysis is required, as follows:

[0126] Step 01: Determine Napn at the current time step by formula (8) ij ;

[0127] Step 02: Record the time when the evacuated group i arrives at the intermediate node j ij ;

[0128] In subsequent time steps, Nps_wpn is calculated ij It will be called during the process.

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

[0130] If there is waiting, it is necessary to calculate Nps_wpn according to formula (9) ij ;

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

[0132] Step 04: Judgment 2: Determine whether congestion occurs at the current time step according to formula (5). Then, based on the result, determine the location label of the person at the current time step.

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

[0134] 1) The person directly enters the next road section, and the location label label_P_Locs is assigned the value of the next road section ID;

[0135] 2) Group i at node j passes time tv ij =0;

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

[0137] 1) If there are people in group i waiting at the node, tv needs to be calculated according to formula (14) ij ;

[0138] 2) The location label label_P_Locs of group i is assigned a value of 100000 × node j number;

[0139] 3) At the same time, record the reduction coefficient at the node j at the current time step Calculate Nps_wpn at subsequent time steps ij , it will be called.

[0140] At this point, the calculation principle of the main functions in the improved macro simulation method proposed in this embodiment has been introduced. The symbols used in the improved macro simulation method are now summarized in Table 3.

[0141]

[0142]

[0143]

[0144] Example 2: This example is a hospital in H City, which is a 3-story building and includes a vertical evacuation process. The implementation process of the improved macro simulation method proposed in this patent on this 3-story building example is as follows:

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

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

[0147] The total number of directed edges in the evacuation road network of the multi-layer example is 430. The weight value of each edge is currently obtained by manual measurement and calculation. In the future, relevant research has been carried out to automatically generate the mathematical model of the evacuation road network from the building floor plan based on the deep learning semantic segmentation model, so as to generate the evacuation road network more efficiently and conveniently. The representation of various types of edges in the evacuation road network plan diagram is detailed in the edge setting rules. In the three-dimensional schematic diagram of the evacuation road network, the edges corresponding to the vertical sections are displayed in the form of red dashed lines, and the starting and ending points of the vertical edges are marked with green solid dots.

[0148]

[0149] After processing in step 1, a data file of the evacuation path geometric network model (abbreviated as evacuation path network) of the three-story building is formed, which contains 4 data tables:

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

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

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

[0153] 3. Data Table 3: Middle Node capacity, 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 (indicating the passable capacity of the middle node); 3) the spatial form of the actual evacuation scene at the corresponding position of the middle node (expressed in the specific value of OPS: OPS = 0.4 represents the spatial form of the evacuation scene corresponding to the first type of crowd gathering form; OPS = 0.3 represents the spatial form of the evacuation scene corresponding to the second type of crowd gathering form).

[0154] 4. Data Table 4: Exit capacity, stores the exit node information 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 scene at the corresponding position of the exit node (expressed in the specific value of OPS: OPS = 0.4 represents the spatial form of the evacuation scene corresponding to the first type of crowd gathering form; OPS = 0.3 represents the spatial form of the evacuation scene corresponding to the second type of crowd gathering form).

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

[0156] The evacuation road network data file of the three-story building formed in step 1 is input into the calculation program of the improved macro simulation method. The program is run to simulate the evacuation process of the crowd in the three-story building, and finally the evacuation process simulation analysis result file is obtained. 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 in the evacuation process. The key features of the global evacuation in the building at the macro level can be analyzed based on this result file.

[0157] First of all, because the improved crowd evacuation simulation method proposed in this embodiment is established on the basis of simplifying the evacuation road network of building space information, it is a kind of macro simulation method and cannot accurately present the micro details of the crowd evacuation process. Therefore, in the process of verifying and comparing the simulation results with the micro method, we initially focus on whether some macro global indicators are consistent. Specifically, we compare and verify the calculation results of the three-story building example on the improved crowd evacuation macro simulation method with the simulation results of the micro evacuation simulation model established by the software AnyLogic, and then focus on: 1) the total time of the evacuation process, 2) the last group to complete the evacuation (reach the safe area), and 3) the location where congestion occurs during the evacuation process. The three-story example establishes a micro evacuation simulation model in the software AnyLogic. Figure 8 According to the control variable method, during the comparative verification process, the two models need to maintain the same settings on some key initial conditions of the evacuation simulation, specifically as follows:

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

[0159] 2. Personnel size, that is, the diameter of the incompressible space occupied by personnel is set to D = 0.4m;

[0160] 3. During the evacuation process, personnel must follow the same evacuation plan. Here, the shortest path plan is used;

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

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

[0163] Secondly, we will introduce the comparison and verification of three macro indicators in detail. First, the total time of the evacuation process and the model running time are compared between the two methods, and the relevant results are shown in Table 4. From Table 4, we can see that for the multi-layer example, the total time calculation result of the evacuation process of the improved macro method is 6.7s less than the result of the AnyLogic micro 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 macro method to complete the evacuation process simulation is 54.84s, which is 3.5 times shorter than the minimum calculation time (i.e., the fastest calculation time) required by the AnyLogic micro model. Compared with the single-layer example, the computational advantage of the improved macro method in the multi-layer example has decreased. The main reason is that the recursive chain part of the recursive function of the group position determination function is used in the calculation process of the multi-layer example. In the process of calling itself in the recursive chain, the recursive function takes up a lot of memory, resulting in a decrease in computational efficiency. Further research is needed to find effective methods to optimize the recursive process in this implementation to further improve the computational efficiency.

[0164]

[0165] Then, we compared and verified the indicator of the last group to complete evacuation. In order to intuitively reflect the simulation results of the evacuation process of the micro-model, we have stored the animation video of the multi-story example evacuation process generated by the AnyLogic micro-model online (available from the following URL: https: / / pan.baidu.com / s / 1BEFpWpEQQ-Y2ii9ZNcoIFQ, verification code: 3333). The file name of the animation video is: Multi-story-overlook-203.5s.wmv. From the running results of the AnyLogic micro-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 the evacuation. Among the rooms that escape from the building through Exit 197, the room corresponding to the source node 155 is the farthest from the exit. According to the first-come, first-pass rule, we can assume that in the simulation results of the AnyLogic micro-model, the group of people in the room corresponding to the source node 155 is the last to complete the evacuation. In the calculation results of the improved crowd evacuation macro simulation method, it can be seen intuitively that the group No. 155 passing through Exit 197 was the last to complete the evacuation, which is consistent with the simulation results of AnyLogic. Since the calculation results of the macro simulation method are too long to be presented in detail in this article, we also store the calculation result file of the macro simulation method online (available at 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 row corresponding to the moment when all groups reach the safe zone (i.e., global evacuation is completed) is highlighted in green; the column corresponding to the last group to complete the evacuation is highlighted in yellow (i.e., the evacuation process record of the group).

[0166] The last verification indicator is the location where crowding occurs during the evacuation process. Through further observation and analysis of the simulation results of the AnyLogic micro-evacuation model, we found that in the multi-story case, the 03 staircase (staircase numbering is detailed in Figure 6 ) The gathering places of the people on the staircase platforms on each floor of the evacuation network have experienced serious congestion, causing some people to wait for a long time. The corresponding node numbers of these locations in the evacuation network of the multi-layer example are 166, 174, 178, and 190. The floors where these severely congested intermediate nodes are located and the corresponding location characteristics are shown in Table 5.

[0167]

[0168] In the calculation results of the improved evacuation macro simulation method, it can also be seen that the above nodes have experienced serious congestion. In the result file of the improved macro simulation method, we use orange highlight marks for the time periods when these nodes are congested. Through further statistical analysis, we found from the results of the evacuation macro simulation method that the duration of congestion at each intermediate node in Table 5 exceeded 10s. 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 at the stairs caused a significant extension of the global evacuation time of multi-story buildings, which needs to be paid attention to.

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

[0170] The descriptions in this specification are only preferred embodiments of the present invention, and the scope of rights of the present invention cannot be limited thereto; in addition, the descriptions with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. mean that the specific features, structures, materials or characteristics described in conjunction 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 may be combined in any one or N embodiments or examples in a suitable manner.

[0171] In addition, in the absence of mutual contradiction, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples. In addition, the terms "first" and "second" are used only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "N" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. Any process or method description in the flowchart or otherwise described herein may be understood to represent a module, fragment or portion of a code including one or more N executable instructions for implementing the steps of a custom logic function or process, and the scope of the preferred embodiment of the present invention includes other implementations, in which the functions may be performed in a substantially simultaneous manner or in a reverse order according to the functions involved, which should be understood by those skilled in the art of the present invention.

[0172] For the purposes of this specification, the "computer-readable medium" in Embodiment 9 may be any device that can contain, store, communicate, propagate or transmit a program for use with an instruction execution system, device or apparatus or in conjunction with such instruction execution system, device or apparatus. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection having one or N wirings (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM).

[0173] Those skilled in the art will appreciate that the above are only preferred embodiments of the present invention, and the features described in the various embodiments and / or claims of the present disclosure may be combined or combined in a variety of ways, even if such combinations or combinations are not explicitly described in the present disclosure. The above are not intended to limit the present invention, and the above are only preferred embodiments of the present invention, and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art may still modify the technical solutions described in the aforementioned embodiments, or perform equivalent substitutions on some of the technical features therein, and any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

[0174] Although preferred embodiments of the present invention have been described, additional changes and modifications may be made to these embodiments by those skilled in the art once the basic inventive concepts are known. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention. Obviously, those skilled in the art may make various changes and modifications 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 equivalents, 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 the traffic speed of people in crowded places, characterized by: The simulation method comprises: Step 1: Obtain the building 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; Step 2: Input the data of the geometric network model of the evacuation path in the building into the calculation program module of the improved macro simulation method, that is, perform a simulation analysis of the evacuation process for the case to be analyzed, and calculate the reduced traffic rate of people in crowded locations to obtain the simulation results of the evacuation process.

2. The crowd evacuation macroscopic simulation method considering the reduction of the traffic speed of people in crowded places according to claim 1 is characterized in that: The conversion of the spatial information of the evacuation scene obtained in step 1 into a geometric network model of the evacuation path in the building also includes the steps of setting nodes and edges.

3. The crowd evacuation macroscopic simulation method considering the reduction of the traffic speed of people in crowded places according to claim 1 is characterized in that: The method for setting the nodes and edges is as follows: the geometric network model of the evacuation path in the building is recorded as G(V,E), the exits, passages, and both ends of the stairs of the building are set as nodes V, and the passable paths between the nodes are set as edges, recorded as E, that is, a geometric network model of the evacuation path in the building is constituted.

4. The crowd evacuation macroscopic simulation method considering the reduction of the traffic speed of people in crowded places according to claim 1 is characterized in that: The improved evacuation macro simulation method in step 2 also includes a step of equivalently averaging the movement speed of personnel on the road section during the evacuation process.

5. The crowd evacuation macroscopic simulation method considering the reduction of the traffic speed of people in crowded places according to claim 4 is characterized in that: The equivalent average reflects all road sections in the reduction case through a unified equivalent average coefficient value.

6. The crowd evacuation macroscopic simulation method considering the reduction of the traffic speed of people in crowded places according to claim 1 is characterized in that: The calculation program module of the improved macroscopic simulation method is used to determine the total evacuation time t of each evacuee i , determination and output of personnel location information during evacuation, and congestion analysis.

7. A macroscopic simulation device for simulating crowd evacuation based on the reduction of the traffic rate of people in crowded places, characterized in that: The simulation device comprises: A conversion module is used to obtain the building 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; The simulation module 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 macro simulation method, that is, to simulate and analyze the evacuation process of the case to be analyzed, and calculate the reduction of the personnel passage rate in the crowded location to obtain the simulation result of the evacuation process.

8. A computer device comprising a memory and a processor, characterized in that A computer program is stored in the memory. 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 6.

9. 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 6 are implemented.

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