A high-speed rail station crowd dispersing route optimization method, system, device and medium

Abstract

The application discloses a high-speed railway station crowd dispersing route optimization method, system and device and a medium, wherein the method comprises the following steps: acquiring layout information of a high-speed railway station, representing a high-speed railway station scene as a complete graph according to the layout information; screening edges in the complete graph according to a preset screening condition; according to the screened complete graph, representing a high-speed railway station crowd dispersing facility layout design problem as an optimization problem of solving a multi-dimensional binary coding vector; acquiring historical passenger flow data of the high-speed railway station, constructing a simulation model of the high-speed railway station scene according to the historical passenger flow data and the optimization problem, optimizing the simulation model through a discrete competitive particle swarm algorithm, and obtaining a final crowd dispersing route. The application abstractly simplifies the crowd flow guiding facility design into an optimization problem for solving, and adopts the competitive particle swarm algorithm for optimization, so that the high-speed railway station crowd dispersing facility layout optimization problem can be better solved, and the application can be widely applied to the fields of system simulation and intelligent calculation.

Description

Technical Field

[0001] This invention relates to the fields of system simulation and intelligent computing, and in particular to a method, system, device and medium for optimizing crowd control routes at high-speed railway stations. Background Technology

[0002] Due to the convenience of high-speed rail, more and more people are choosing to travel by high-speed rail to other cities. However, each city generally has only a limited number of high-speed rail stations, and peak travel periods see a concentration of travel demand, making short-term, high-density crowd flow a significant characteristic of high-speed rail stations. In such relatively enclosed and densely populated public places, panic and stampedes are highly likely to occur. Such emergencies would seriously threaten public safety and have significant negative social impacts. Scientifically and effectively designing crowd control measures in high-speed rail stations and other public places can effectively reduce the uncertainty and risk of potential accidents in these locations. Summary of the Invention

[0003] In order to at least partially solve one of the technical problems existing in the prior art, the purpose of this invention is to provide a method, system, device and medium for optimizing crowd dispersal routes at high-speed railway stations.

[0004] The technical solution adopted in this invention is:

[0005] A method for optimizing crowd control routes at high-speed railway stations includes the following steps:

[0006] Obtain the layout information of the high-speed rail station, and represent the high-speed rail station scene as a complete graph based on the layout information; where obstacles in the high-speed rail station scene are used as nodes of the complete graph, and the edge between any two nodes is used to represent a fence that prohibits pedestrian passage.

[0007] The edges in the complete graph are filtered according to preset filtering conditions to delete edges that do not meet the conditions.

[0008] Based on the filtered complete graph, the design problem of the layout of crowd control facilities in high-speed railway stations is expressed as an optimization problem of solving multidimensional binary encoded vectors based on simple crowd simulation evaluation.

[0009] Historical passenger flow data of high-speed railway stations is obtained. Based on the historical passenger flow data and the optimization problem, a simulation model of the high-speed railway station scenario is constructed. The simulation model is then optimized using the discrete competitive particle swarm optimization algorithm to obtain the final crowd control route.

[0010] Furthermore, the obstacles in the high-speed rail station scenario include gates, support columns, and elevators;

[0011] The process of representing the high-speed rail station scene as a complete graph based on the layout information includes:

[0012] Based on the obstacle objects in the high-speed rail station scene, obtain the node set V = {v1, v2, ..., v...} N};

[0013] By connecting any two nodes in the node set V, a complete graph G = (V, E) is constructed; where E represents the edge set of the complete graph G, and the element e in the edge set E is... ij Let v represent an edge in a complete graph G, representing the edge at node v. i and node v j A fence between them;

[0014] For element e ij have:

[0015] e ij ∈{(v i ,v j )|i≠j,(v i ,v j )∈V 2}

[0016] Furthermore, the step of filtering edges in the complete graph according to preset filtering conditions includes:

[0017] Set the upper limit of the side length L max ; In a complete graph G, the length exceeds the upper limit of the side length L max After deleting the edges, we obtain the optimized edge set E:

[0018] E = {(i,j)|i,j∈V,Distance(v)} i ,v j ) <L max}

[0019] In the formula, Distance(v i ,v j ) represents node v i and node v j The distance between them.

[0020] Furthermore, based on the filtered complete graph, the design problem of the layout of crowd control facilities at high-speed railway stations is simplified into an optimization problem of solving multidimensional binary encoded vectors, including:

[0021] Let |E| = n, and each particle in the population have an n-dimensional position:

[0022] x i (t)={x i,1 (t),x i,2 (t),…,x i,n (t)}

[0023] x i(t) represents a candidate solution for the placement of obstacles on each of the n edges, x i Only {0, 1} can be used, representing a discrete binary code indicating whether the i-th edge is passable.

[0024] Furthermore, the optimization of the simulation model using the discrete competitive particle swarm optimization algorithm to obtain the final crowd control route includes:

[0025] Randomly initialize the positions of the population particles. The population P(t) contains m randomly initialized particles, where t is the generation index.

[0026] Each particle is initialized with an n-dimensional velocity vector:

[0027] v i (t)={v i,1 (t),v i,2 (t),…,v i,n (t)}

[0028] With the particle's position x i Input the simulation model, which provides the simulated crowding degree of this candidate solution, as the fitness value f(x) for each particle. i );

[0029] Based on the fitness value f(x) i Obtain the final crowd control route.

[0030] Furthermore, for a given value of E in graph G, the simulated congestion degree C of graph G is calculated in the following way:

[0031]

[0032] Where ST is the maximum simulation time and NP is the number of passengers in the subway station; and Let be the angle between the forward directions of passengers ni and nj at time ti in the simulation, their respective radii, and the Eulerian distance between them; when When the value is large, it indicates that there are relatively few passengers in the station and the passage is smooth; when When the value is small or negative, the congestion level inside the station is relatively high; λ∈(0,1) is a hyperparameter that controls the proportion of congestion optimization between homogeneous and heterogeneous passengers. When λ=1, the congestion level C only considers the static congestion level within the scenario (when passengers are close to each other, the high-speed rail station is considered to be relatively crowded); when λ=0, the congestion level C considers the congestion level between passengers with different destinations (directions of travel) (heterogeneous passengers), while the congestion level between passengers with the same direction of travel (homogeneous passengers) is ignored.

[0033] Furthermore, the step based on the fitness value f(x) iObtain the final crowd control routes, including:

[0034] A1. Randomly divide the population particles into m / 2 groups, and perform a fitness test f(x) between the two particles in each group. i The two particles are compared and labeled as winner and loser based on the comparison result; where m represents the number of particles in the population.

[0035] A2. In the k-th comparison of generation t, the positions and velocities of the winner and loser are denoted as x. w,k (t),x l,k (t) and

[0036] Update the speed of the losers in sequence:

[0037]

[0038] Update the position vector of the losing particle:

[0039]

[0040]

[0041] Where r1(k,t), r2(k,t), r3(k,t)∈[0,1] n Let be three random vectors generated after the k-th comparison and learning in the t-th generation. It is the average position of related particles, representing the current consensus of a particle. It is control The parameters that affect the position; d is the dimension index of the position.

[0042] A3. Re-evaluate all losing particles and update the global optimal solution x. gbest If the evolution has reached the maximum number of evaluations, proceed to step A4; otherwise, return to step A2 to enter the next round of evolution.

[0043] A4, Decoding x gbest The optimal layout of obstacles is obtained, which serves as the final crowd evacuation route.

[0044] Another technical solution adopted in this invention is:

[0045] A high-speed railway station crowd management route optimization system includes:

[0046] The layout and graphing module is used to obtain the layout information of the high-speed rail station and represent the high-speed rail station scene as a complete graph based on the layout information. Among them, the obstacle objects in the high-speed rail station scene are used as nodes of the complete graph, and the edge between any two nodes is used to represent a fence that prohibits pedestrian traffic.

[0047] The edge set filtering module is used to filter edges in the complete graph according to preset filtering conditions, so as to delete edges that do not meet the conditions.

[0048] The problem representation module is used to represent the design problem of the layout of crowd control facilities at high-speed railway stations as an optimization problem of solving multidimensional binary encoded vectors, based on the filtered complete graph.

[0049] The model optimization module is used to acquire historical passenger flow data of high-speed railway stations, construct a simulation model of the high-speed railway station scenario based on the historical passenger flow data and the optimization problem, and optimize the simulation model through discrete competitive particle swarm optimization algorithm to obtain the final crowd control route.

[0050] Another technical solution adopted in this invention is:

[0051] A crowd control route optimization device for high-speed railway stations includes:

[0052] At least one processor;

[0053] At least one memory for storing at least one program;

[0054] When the at least one program is executed by the at least one processor, the at least one processor implements the method described above.

[0055] Another technical solution adopted in this invention is:

[0056] A computer-readable storage medium storing a processor-executable program, which, when executed by a processor, performs the method described above.

[0057] The beneficial effects of this invention are: this invention simplifies the design of corresponding crowd flow guidance facilities into an optimization problem for solution, and uses a competitive particle swarm optimization algorithm for optimization, which can better solve the optimization problem of crowd flow guidance facility layout in high-speed railway stations. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following description is provided with accompanying drawings of the relevant technical solutions in the embodiments of the present invention or the prior art. It should be understood that the accompanying drawings described below are only for the purpose of clearly illustrating some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 This is a flowchart illustrating the steps of a method for optimizing crowd control routes at high-speed railway stations, as described in an embodiment of the present invention.

[0060] Figure 2This is a flowchart illustrating the optimization of crowd control routes at high-speed railway stations based on the discrete competitive particle swarm optimization algorithm in an embodiment of the present invention.

[0061] Figure 3 This is a coding example diagram of a scene facility placement scheme in an embodiment of the present invention. Detailed Implementation

[0062] The embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. The step numbers in the following embodiments are set only for ease of explanation, and there is no limitation on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0063] In the description of this invention, it should be understood that the orientation descriptions, such as up, down, front, back, left, right, etc., are based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0064] In the description of this invention, "several" means one or more, "more than" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. The use of "first" and "second" in the description is merely for distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0065] In the description of this invention, unless otherwise explicitly defined, terms such as "set up," "install," and "connect" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this invention in conjunction with the specific content of the technical solution.

[0066] The layout design of crowd guidance facilities is a graph structure optimization problem. Traditional approaches in the construction field for such problems rely on domain expertise and employ techniques like incremental or numerical optimization. However, since guidance facilities, as edges of a graph, involve few optimization-related attributes, the solution can be simplified to finding an n-dimensional binary encoded vector (representing whether a facility should be placed). Simple crowd simulations can evaluate the merits of this solution, allowing evolutionary algorithms to optimize the problem. Particle swarm optimization (PSO), proposed by Kennedy and Eberhart in 1995, is an evolutionary algorithm for solving optimization problems. It mimics the social behavior of animals, such as flocks of birds. Due to its simplicity, PSO has gained rapid popularity over the past few decades. It offers fast convergence and relatively stable solution quality, finding wide application in fields like power systems, bioinformatics, and logistics planning. In recent years, researchers have further developed improved PSO algorithms for solving large-scale optimization problems, with competitive particle swarm optimization being one of the most representative.

[0067] like Figure 1 As shown in the figure, this embodiment provides a method for optimizing crowd control routes at high-speed railway stations, including the following steps:

[0068] S1. Obtain the layout information of the high-speed rail station and represent the high-speed rail station scene as a complete graph based on the layout information; where obstacles in the high-speed rail station scene are used as nodes of the complete graph, and the edge between any two nodes is used to represent a fence that prohibits pedestrian traffic.

[0069] Collect entity elements from the target scene (a high-speed rail station), such as gates, support columns, elevators, and other obstacles, and treat them as scene nodes. Abstract these nodes into a graph node set V = {v1, v2, ..., v...}. N}. Connect any two nodes from the node set V to construct a complete graph G = (V, E); where E represents the edge set of the complete graph G, and the element e in the edge set E is... ij Let v represent an edge in a complete graph G, representing the edge at node v. i and node v j A fence between them; for element e ij have:

[0070] e ij ∈{(v i ,v j )|i≠j,(v i ,v j )∈V 2}

[0071] S2. Filter the edges in the complete graph according to the preset filtering conditions to delete the edges that do not meet the conditions.

[0072] Construct a graph G = (V, E) with V as the node, where:

[0073] E = {(i,j)|i,j∈V,Distance(v)} i ,v j ) <L max}

[0074] In graph G, an edge represents a barrier (diversion) facility that can be placed between two nodes. max Distance(v) represents the maximum length that a facility can span between scene nodes. i ,v j ) represents v i and v j The distance between them. Therefore, E is the set of edges for all deployable facilities in the scene. For example... Figure 3 As shown, one value of E represents a way of placing guidance facilities in the scene.

[0075] S3. Based on the filtered complete graph, the design problem of the layout of crowd control facilities at high-speed railway stations is expressed as an optimization problem of solving multidimensional binary encoded vectors.

[0076] Let |E| = n, and each particle in the population have an n-dimensional position:

[0077] x i (t)={x i,1 (t),x i,2 (t),…,x i,n (t)}

[0078] x i (t) represents a candidate solution for the placement of obstacles on each of the n edges, x i Only {0, 1} can be used, representing a discrete binary code indicating whether the i-th edge is passable.

[0079] S4. Obtain historical passenger flow data of the high-speed rail station, construct a simulation model of the high-speed rail station scenario based on the historical passenger flow data and the optimization problem, optimize the simulation model through discrete competitive particle swarm optimization algorithm, and obtain the final crowd diversion route.

[0080] Passenger flow data is collected at the high-speed railway station, including historical passenger flow data for all entrances and exits, elevators, passageways, platforms, etc. This historical passenger flow data is then combined with a virtual crowd simulation model to establish a realistic high-speed railway station scene simulation model. Optimization search is then performed. Specifically, step S4 includes steps S41-S43:

[0081] S41. Set algorithm parameters (population size, maximum number of evaluations, social consensus parameter). Randomly initialize the positions of particles in the population. The population P(t) contains m randomly initialized particles, where t is the generation index.

[0082] Each particle is initialized with an n-dimensional velocity vector:

[0083] v i (t)={v i,1 (t),v i,2 (t),…,v i,n (t)}

[0084] S42, with the particle's position x i Input the simulation model, which provides the simulated crowding degree of this candidate solution, as the fitness value f(x) for each particle. i ).

[0085] For a given value of E in graph G, the simulated congestion degree C of graph G can be calculated:

[0086]

[0087] Where ST is the maximum simulation time and NP is the number of passengers in the subway station. and Let be the angle between the forward directions of passengers ni and nj at time ti in the simulation, their respective radii, and the Eulerian distance between them. When the value is large, it indicates that there are relatively few passengers in the station and the passage is smooth; when When the value is small or negative, the congestion level within the station is relatively high. λ∈(0,1) is a hyperparameter that controls the weighting of congestion optimization between homogeneous and heterogeneous passengers. When λ=1, the congestion level C only considers the static congestion level within the scenario (when passengers are relatively close together, the high-speed rail station is considered relatively crowded); when λ=0, the congestion level C considers the congestion level between passengers with different destinations (directions of travel) (heterogeneous passengers), while the congestion level between passengers with the same direction of travel (homogeneous passengers) is ignored.

[0088] S43, based on the fitness value f(x) i Obtain the final crowd control route.

[0089] Based on the evaluation of this simulation model, the corresponding solution space can be effectively searched using the discrete competitive particle swarm optimization algorithm. For details, see... Figure 2 Step S43 includes steps S431-S434:

[0090] S431. Randomly divide the population particles into m / 2 groups, and perform a fitness test f(x) between the two particles in each group. iThe two particles are compared, and based on the comparison results, they are labeled as the winner and the loser, respectively.

[0091] S432. In the k-th comparison of generation t, the positions and velocities of the winner and loser are denoted as x. w,k (t),x l,k (t) and

[0092] Update the speed of the losers in sequence:

[0093]

[0094] Update the position vector of the losing particle:

[0095]

[0096]

[0097] Where r1(k,t), r2(k,t), r3(k,t)∈[0,1] n Let be three random vectors generated after the k-th comparison and learning in the t-th generation. It is the average position of related particles, representing the current consensus of a particle. It is control The parameters that affect the position; d is the dimension index of the position.

[0098] S433. Re-evaluate all losing particles and update the global optimal solution x. gbest If the evolution has reached the maximum number of evaluations, proceed to step S434; otherwise, return to step S432 to enter the next round of evolution.

[0099] S434, Decoding x gbest The optimal layout of obstacles is obtained, which serves as the final crowd evacuation route.

[0100] Particle swarm optimization (PSO) is widely used due to its simple concept, ease of execution, and fast convergence speed. However, traditional PSO algorithms are prone to getting trapped in local optima when dealing with large-scale problems. The crowd control facility layout optimization problem addressed in this embodiment has a solution space that is not only multi-dimensional but also discrete. To address these characteristics, this embodiment uses a competitive PSO algorithm, which is better suited for large-scale optimization problems, and performs discrete mapping on the particle solutions, thereby improving the algorithm's performance in optimizing the crowd control facility layout for high-speed rail stations.

[0101] This embodiment also provides a high-speed railway station crowd control route optimization system, including:

[0102] The layout and graphing module is used to obtain the layout information of the high-speed rail station and represent the high-speed rail station scene as a complete graph based on the layout information. Among them, the obstacle objects in the high-speed rail station scene are used as nodes of the complete graph, and the edge between any two nodes is used to represent a fence that prohibits pedestrian traffic.

[0103] The edge set filtering module is used to filter edges in the complete graph according to preset filtering conditions, so as to delete edges that do not meet the conditions.

[0104] The problem representation module is used to represent the design problem of the layout of crowd control facilities at high-speed railway stations as an optimization problem of solving multidimensional binary encoded vectors, based on the filtered complete graph.

[0105] The model optimization module is used to acquire historical passenger flow data of high-speed railway stations, construct a simulation model of the high-speed railway station scenario based on the historical passenger flow data and the optimization problem, and optimize the simulation model through discrete competitive particle swarm optimization algorithm to obtain the final crowd control route.

[0106] This embodiment of the high-speed railway station crowd management route optimization system can execute the high-speed railway station crowd management route optimization method provided in the method embodiment of the present invention, and can execute any combination of implementation steps of the method embodiment, and has the corresponding functions and beneficial effects of the method.

[0107] This embodiment also provides a crowd control route optimization device for high-speed railway stations, including:

[0108] At least one processor;

[0109] At least one memory for storing at least one program;

[0110] When the at least one program is executed by the at least one processor, the at least one processor implements Figure 1 The method shown.

[0111] This embodiment of the high-speed railway station crowd management route optimization device can execute the high-speed railway station crowd management route optimization method provided in the method embodiment of the present invention, and can execute any combination of the implementation steps of the method embodiment, and has the corresponding functions and beneficial effects of the method.

[0112] This application also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform... Figure 1 The method shown.

[0113] This embodiment also provides a storage medium storing instructions or programs that can execute the crowd control route optimization method for high-speed railway stations provided in the method embodiment of the present invention. When the instructions or programs are run, any combination of implementation steps of the method embodiment can be executed, and the method has the corresponding functions and beneficial effects.

[0114] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this invention are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is altered and sub-operations described as part of a larger operation are executed independently.

[0115] Furthermore, although the invention has been described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the described functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the invention. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of conventional skill of an engineer. Therefore, those skilled in the art can implement the invention as set forth in the claims using ordinary techniques without excessive experimentation. It is also understood that the specific concepts disclosed are merely illustrative and not intended to limit the scope of the invention, which is determined by the full scope of the appended claims and their equivalents.

[0116] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0117] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0118] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0119] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0120] In the foregoing description of this specification, references to terms such as "one embodiment," "another embodiment," or "some embodiments" indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of the present invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0121] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0122] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A method for optimizing crowd control routes at high-speed railway stations, characterized in that, Includes the following steps: Obtain the layout information of the high-speed rail station, and represent the high-speed rail station scene as a complete graph based on the layout information; where obstacles in the high-speed rail station scene are used as nodes of the complete graph, and the edge between any two nodes is used to represent a fence that prohibits pedestrian passage. The edges in the complete graph are filtered according to preset filtering conditions to delete edges that do not meet the conditions. Based on the filtered complete graph, the design problem of the layout of crowd control facilities in high-speed railway stations is expressed as an optimization problem of solving multidimensional binary encoded vectors. Historical passenger flow data of high-speed railway stations is obtained. Based on the historical passenger flow data and optimization problem, a simulation model of the high-speed railway station scenario is constructed. The simulation model is optimized by discrete competitive particle swarm optimization algorithm to obtain the final crowd control route. The obstacles in the high-speed railway station scenario include gates, support columns, and elevators. The process of representing the high-speed rail station scene as a complete graph based on the layout information includes: Node set obtained from obstacles in the high-speed rail station scene ; According to the node set Connect any two nodes to construct a complete graph. ;in, Represents the complete graph edge set, edge set elements in Representing a complete graph An edge in the node represents an edge in the node. and nodes A fence between them; For elements have: The step of filtering edges in the complete graph according to preset filtering conditions includes: Set the upper limit for the side length. ; Complete map The length of the middle section exceeds the upper limit of the side length. By deleting edges, we obtain the optimized edge set. : In the formula, Represents a node and nodes The distance between them; Based on the filtered complete graph, the design problem of the layout of crowd control facilities at high-speed railway stations is simplified into an optimization problem of solving multidimensional binary encoded vectors, including: set up Each particle in the population has one Dimensional position: express A candidate solution for the placement of obstacles on each edge. Only take , indicating the first Discrete binary encoding to determine whether a stripe is passable.

2. The method for optimizing crowd control routes at high-speed railway stations according to claim 1, characterized in that, The optimization of the simulation model using the discrete competitive particle swarm optimization algorithm to obtain the final crowd control route includes: Randomly initialize the positions of population particles, population Includes A randomly initialized particle, For evolutionary algebra index; Each particle initializes one 1D velocity vector: Based on the position of the particle Input the simulation model, which then provides the simulated crowding degree of this candidate solution, as the fitness value for each particle. ; Based on fitness Obtain the final crowd control route.

3. The method for optimizing crowd control routes at high-speed railway stations according to claim 2, characterized in that, For a given graph middle The value of is determined by solving the graph at this time in the following way. Simulated congestion : in, For maximum simulation time, The number of passengers in the high-speed rail station; , and In simulation Passengers at all times and The angle between their directions of travel, their respective radii, and the Euclidean distance between them; It is a hyperparameter that controls the optimal ratio of homogeneous and heterogeneous passenger crowding.

4. The method for optimizing crowd control routes at high-speed railway stations according to claim 2, characterized in that, According to fitness value Obtain the final crowd control routes, including: A1. Randomly divide the population particles into... Groups, and perform a fitness test between the two particles in each group. In a comparison, based on the superiority or inferiority of the comparison results, the two particles are labeled as the winner and the loser, respectively; among them, Represents the number of particles in the population; A2, the first The first generation In this comparison, the positions and speeds of the winner and loser are denoted as follows: and , ; Update the speed of the losers in sequence: Update the position vector of the losing particle: in, For the three in the The generation Three random vectors generated after comparison and learning. It is the average position of related particles, representing the current consensus of a particle. It is control The parameters that affect it; It is the dimensional index of the location; A3. Re-evaluate all losing particles and update the global optimal solution. If the evolution has reached the maximum number of evaluations, proceed to step A4; otherwise, return to step A2 to enter the next round of evolution. A4. Decoding The optimal layout of obstacles is obtained and used as the final crowd evacuation route.

5. A crowd control route optimization system for high-speed railway stations, used to implement the method described in any one of claims 1-4, characterized in that, include: Layout and composition module; This is used to obtain the layout information of the high-speed rail station, and to represent the high-speed rail station scene as a complete graph based on the layout information; where obstacles in the high-speed rail station scene are used as nodes of the complete graph, and the edge between any two nodes is used to represent a fence that prohibits pedestrian passage. The edge set filtering module is used to filter edges in the complete graph according to preset filtering conditions, so as to delete edges that do not meet the conditions. The problem representation module is used to represent the design problem of the layout of crowd control facilities in high-speed railway stations as an optimization problem of solving multidimensional binary encoded vectors, based on the filtered complete graph. The model optimization module is used to acquire historical passenger flow data of high-speed railway stations, construct a simulation model of the high-speed railway station scenario based on the historical passenger flow data and the optimization problem, and optimize the simulation model through discrete competitive particle swarm optimization algorithm to obtain the final crowd control route.

6. A crowd control route optimization device for high-speed railway stations, characterized in that, include: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method of any one of claims 1-4.

7. A computer-readable storage medium storing a processor-executable program, characterized in that, The processor-executable program, when executed by the processor, is used to perform the method as described in any one of claims 1-4.

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