An airport crowd route guidance scheme optimization method and device and a storage medium

Abstract

The application discloses an airport crowd route guidance scheme optimization method and device and a storage medium, and the method comprises the following steps: acquiring layout information of an airport, and abstracting an airport scene into a complete graph according to the layout information; screening edges in the complete graph according to a preset screening condition, so as to delete edges that do not meet the condition; abstracting an airport crowd route guidance scheme optimization problem into a combination optimization problem according to the screened complete graph; and using a discrete differential evolution algorithm to optimize the combination optimization problem, so as to obtain a final optimization result. The application performs mathematical modeling on an airport departure layer scene, and automatically generates and optimizes an airport crowd route guidance scheme by using a discrete differential evolution algorithm, so that a high-quality solution can be obtained, and the guidance of the crowd route in the airport can be effectively assisted. The application can be widely applied to the fields of intelligent transportation and evolutionary calculation.

Description

TECHNICAL FIELD

[0001] The present application relates to the fields of intelligent transportation and evolutionary computation, and in particular, to an airport crowd route guidance scheme optimization method and device and a storage medium. BACKGROUND

[0002] Airports are important hubs in urban transportation systems, carrying a large portion of the traffic flow. Airports are usually connected to other elements in the transportation system, such as subways, buses, etc. With the continuous increase in the size of the city and the population of the city, the passenger flow of the airport during special holidays will reach a peak, and may even exceed its passenger flow carrying capacity, causing congestion, passenger misconnection, etc. Therefore, to improve the quality of airport operation, a reasonable crowd route guidance scheme is needed to cope with the large passenger flow during peak hours in real time to avoid congestion. SUMMARY

[0003] To at least partially solve one of the technical problems existing in the prior art, the purpose of the present application is to provide an airport crowd route guidance scheme optimization method, device and storage medium.

[0004] The technical solution adopted by the present application is:

[0005] An airport crowd route guidance scheme optimization method, comprising the following steps:

[0006] Obtaining layout information of the airport, and abstracting the airport scene into a complete graph according to the layout information; wherein the obstacle objects in the airport scene are taken as vertices of the complete graph, and the edges between any two vertices are used to represent a fence that prohibits the flow of people;

[0007] According to a preset screening condition, the edges in the complete graph are screened to delete the edges that do not meet the condition;

[0008] According to the screened complete graph, the airport crowd route guidance scheme optimization problem is abstracted into a combinatorial optimization problem;

[0009] A discrete differential evolution algorithm is used to optimize the combinatorial optimization problem to obtain the final optimization result.

[0010] Further, the obstacle objects in the airport scene include security checkpoints, service counters, entrances and exits, and obstacles in the airport;

[0011] According to the layout information, the airport scene is abstracted into a complete graph, comprising:

[0012] According to the obstacle objects in the airport scene, a vertex set V = {v1, v2,...v n} is obtained;

[0013] By connecting any two vertices in vertex 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 edge set E is a vertex. ij Let v represent an edge in a complete graph G, where v is located at vertex v. i and vertex 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 an upper limit LU for edge length; delete edges in the complete graph G that exceed the upper limit LU to obtain the optimized edge set E':

[0018] E'={e ij ||e ij |≤LU,i≠j}

[0019] Furthermore, based on the filtered complete graph, the airport crowd route guidance optimization problem is abstracted into a combinatorial optimization problem, including:

[0020] Number all the edges after the filtering and optimization to obtain the edge set E' = {e1, e2, ..., e m}, m represents the total number of all possible fences;

[0021] Using a vector x consisting of 0s and 1s i =(x i1 ,x i2 ...,x im Let x represent an optimization scheme; where x is the number of solutions. ij =0 indicates that in the i-th optimization scheme, e is not placed. j The fence that represents; x ij =1 indicates that in the i-th optimization scheme, place e j The fence it represents;

[0022] Based on the combination X of all vectors, the optimization problem of airport crowd route guidance scheme is abstracted into a combinatorial optimization problem.

[0023] Furthermore, the use of a discrete differential evolution algorithm to optimize the combinatorial optimization problem and obtain the final optimization result includes:

[0024] Initialize the parameters required for the differential evolution algorithm, determine and initialize the population;

[0025] Construct the objective function, optimize the population according to the objective function, and obtain the final optimization result;

[0026] Wherein, before calculating the objective function value, the continuous numerical value in the differential evolution algorithm is mapped into a discrete decision vector, and the mapping method is as follows:

[0027]

[0028] Wherein, x represents a crowd route guidance scheme, x i The value of an element in the scheme is 0, which means that the scheme does not need to consider the element, and the value is 1, which means that the scheme needs to consider the element; p is a random number uniformly distributed in (0, 1), and the function S(x i ) is a sigmoid function.

[0029] Further, the optimization goal is to make the expected value of the mutual congestion degree of all passengers at all times as small as possible;

[0030] The expression of the objective function is as follows:

[0031]

[0032] Wherein, X is the set of all schemes, x is one of the schemes, and the fitness value of x is calculated by putting the x scheme into the simulation system for once airport crowd flow simulation; T is the maximum simulation time of one simulation, and C is the number of existing passengers in the airport; Taking an airport passenger as the center, the space occupied by the airport passenger can be approximately regarded as a circle, is the sum of the space radii occupied by passengers i and j at time t; and are the Euler distance and the angle between the travel direction of passenger i and passenger j at time t, respectively, and the angle between the travel direction is in the range of [0, π]; when i and j take all possible values, d ij -r ij When the value of η is 0, the objective function only considers the non-homogeneous passengers, that is, only the congestion degree of passengers with inconsistent travel directions is optimized. When the value of η is 1, the objective function only considers the static congestion degree in the current scene, that is, only the distance is considered and the travel direction of the passenger is ignored.

[0033] Further, the population is optimized according to the target function to obtain a final optimization result, comprising:

[0034] A child is obtained by using a mutation and a crossover operator of the differential evolution algorithm.

[0035] The child is mapped into a discrete value, and an adaptive value of the child is calculated by using the target function.

[0036] The adaptive values of the child and the parent are compared, and the smaller one is selected to enter a next generation population until an end condition of the algorithm is executed.

[0037] Further, in the mutation operation, it is assumed that an individual is from X i to X i .

[0038] The binomial crossover is used to cross the mutated individual X i and the individual X i to obtain X i , and the expression is as follows:

[0039]

[0040] In the formula, Cr represents a crossover rate. The crossover rate Cr controls the crossover result of each dimension, and the value is between 0 and 1. The crossover rates suitable for different application scenarios are inconsistent. In addition, the binomial crossover ensures that at least one dimension adopts the result in X i in the crossing process of an individual. In the selection operation, according to the adaptive values of the parent individual X i and the child individual X i , the winner is selected as a new individual X i to enter the next generation population.

[0041] Another technical solution adopted by the application is:

[0042] An airport crowd route guidance scheme optimization device, comprising:

[0043] At least one processor;

[0044] At least one memory for storing at least one program;

[0045] When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

[0046] Another technical solution adopted by the application is:

[0047] A computer-readable storage medium having stored therein a program executable by a processor for performing the method as described above when executed by the processor.

[0048] The beneficial effect of the present application is that the present application mathematically models the airport departure layer scene, and automatically generates and optimizes the airport crowd route guidance scheme using the discrete differential evolution algorithm, can obtain a higher quality solution, and can effectively assist the guidance of the crowd route in the airport. BRIEF DESCRIPTION OF DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following introduces the drawings of the related technical solutions in the embodiments of the present application or the prior art. It should be understood that the drawings in the following introduction are only for the convenience of clearly describing part of the embodiments of the technical solutions of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0050] Figure 1 is a step flow chart of an airport crowd route guidance scheme optimization method in an embodiment of the present application;

[0051] Figure 2 is a plan view of an airport departure layer in an embodiment of the present application;

[0052] Figure 3 is a flow chart of a differential evolution algorithm optimizing an airport crowd guidance scheme in an embodiment of the present application. DETAILED DESCRIPTION

[0053] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the drawings, wherein the same or similar reference signs represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the drawings are exemplary and are only used to explain the present application, and cannot be understood as a limitation of the present application. For the step numbers in the following embodiments, they are only set for the convenience of description and explanation, and the order between the steps is not limited in any way, and the execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0054] In the description of the present application, it should be understood that the orientation description, such as the orientation or position relationship indicated by up, down, front, back, left, right, etc. is based on the orientation or position relationship shown in the drawings, and is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation of the present application.

[0055] In the description of the present application, the meaning of one or more is one or more, the meaning of multiple is more than two, greater than, less than, more than, etc. Understand as not including the number, above, below, within, etc. Understand as including the number. If it is described to the first, the second is only used for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features or implicitly indicating the sequence of indicated technical features.

[0056] In the description of the present application, unless otherwise explicitly limited, the words such as setting, installation, connection, etc. should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meaning of the above words in the present application in combination with the specific content of the technical scheme.

[0057] The automatic generation and optimization of the airport crowd route guidance scheme are abstracted into a combinatorial optimization problem. There are generally three methods to solve combinatorial optimization problems: linear programming, heuristic algorithm and meta-heuristic algorithm. Linear programming method generally has high complexity and is only suitable for solving small-scale problems, and loses applicability for actual engineering problems. Therefore, the most commonly used combinatorial optimization method is still heuristic and meta-heuristic method. Common heuristic algorithms include artificial neural network, tabu search algorithm, etc. Heuristic algorithms rely on the characteristics of the problem and intuitive prior knowledge, and cannot guarantee to obtain the optimal solution of the problem. Because of the too greedy nature of the heuristic algorithm, it is easy to fall into local optimization and difficult to obtain the global optimal solution. Meta-heuristic algorithm is an improvement of heuristic algorithm, which is a "black box" technology independent of the problem to be optimized. Evolutionary algorithm is a meta-heuristic algorithm without any problem characteristics, which searches the solution space of the problem by using random search and local search techniques, and can find the approximate optimal solution of the problem with a high probability. Differential evolution algorithm is a kind of evolutionary algorithm, which is simple to implement, has few parameters, and can find the approximate optimal solution of the problem within a reasonable time. Therefore, differential evolution algorithm is very suitable for generating and optimizing the airport crowd route guidance scheme.

[0058] As shown in Figure 1 The embodiment provides an airport crowd route guidance scheme optimization method, which comprises the following steps:

[0059] S1, obtaining the layout information of the airport, and abstracting the airport scene into a complete graph according to the layout information; wherein the obstacle objects in the airport scene are taken as the vertices of the complete graph, and the edges between any two vertices are used to represent a fence for prohibiting the passage of people flow.

[0060] The airport scene is abstracted into a complete graph: the obstacles, service counters, security check points, entrances and exits in the airport can be taken as the vertices of the complete graph. The edges between the vertices represent a fence for prohibiting the passage of people flow.

[0061] S2. Filter the edges in the complete graph according to the preset filtering conditions to delete the edges that do not meet the conditions.

[0062] The complete graph is optimized based on the actual airport layout, removing unnecessary edges. Edges that can be optimized include: excessively long edges, edges where obstacles cross other obstacles, and edges that do not conform to reality. In the optimized graph, each edge represents a possible fence placement, and each edge is numbered. A feasible solution is (x1, x2, ... x...). n For each edge x i The value can be either 0 or 1. A value of 0 means that the fence is not placed, while a value of 1 means that the fence is placed.

[0063] S3. Based on the filtered complete graph, the airport crowd route guidance optimization problem is abstracted into a combinatorial optimization problem.

[0064] S4. The discrete differential evolution algorithm is used to optimize the combinatorial optimization problem and obtain the final optimization result.

[0065] Specifically, step S4 includes the following steps S41-S47:

[0066] S41. Initialize the parameters required for the differential evolution algorithm, determine the upper and lower limits of population change, and initialize the population.

[0067] The parameters required for the differential evolution algorithm are initialized, and the population is initialized based on problem modeling. The population is a set of vectors, and a function maps continuous values ​​to 0 and 1 to represent the placement of fences within the airport scenario.

[0068] S42. Since the crowd route guidance scheme is a discrete sequence of 0s and 1s, and the differential evolution algorithm is an optimization method for continuous numerical values, it is necessary to map the continuous numerical values ​​into discrete decision vectors before calculating the objective function value. The mapping method is as follows:

[0069]

[0070] Where x represents a crowd route guidance scheme, x i The value of an element in the proposed solution is represented by 0, where 0 indicates that the element does not need to be considered, and 1 indicates that the element needs to be considered. p is a random number uniformly distributed between (0,1), and the function S(x) represents the element's value. i ) is a sigmoid function:

[0071]

[0072] S43. Calculate the fitness value for each individual. The fitness function is:

[0073]

[0074] Where X is the set of all schemes, x is one of them, the fitness of x is calculated by putting x into the simulation system and running it for one time. T is the maximum simulation time of one simulation, C is the number of all passengers in one simulation. The space occupied by passengers can be approximated as a circle with the passenger as the center, is the sum of the radius of the space occupied by passenger i and passenger j at time t. and are the angle between the Euler distance of passenger i and passenger j and the direction of travel at time t, respectively. The range of the angle between the direction of travel is [0, π]. When i and j take all possible values, d ij -r ij When the value of r is large, it is considered that the passenger distribution in the airport under scheme x is sparse, and the effect of route guidance of the crowd is good. On the contrary, it is considered that the airport under scheme x is crowded. The hyperparameter η is used to control the proportion of homogeneous passengers and non-homogeneous passengers in the optimization objective, and its value range is [0, 1]. When the value of η is 0, the objective function only considers the non-homogeneous passengers, that is, only the congestion degree of passengers with inconsistent travel directions is optimized. When the value of η is 1, the objective function only considers the static congestion degree in the current scene, that is, only the distance is considered and the travel direction of the passenger is ignored. The optimization objective is to make the expected value of the mutual congestion degree of all passengers at all times in one simulation as small as possible.

[0075] S44, the mutation and crossover operators of the differential evolution algorithm are used to obtain offspring.

[0076] S45, the offspring is mapped into a discrete value using the method in step S42 and the fitness of the offspring is calculated using the formula in step S43.

[0077] S46, the fitness of the offspring individual and the parent individual is compared, and the one with smaller fitness enters the next generation population.

[0078] S47, if the end condition of the algorithm execution is reached, the program ends. Otherwise, return to step S44.

[0079] Since the differential evolution algorithm is simple to implement, has few parameters, and has good optimization effect, it has been widely applied. However, the differential evolution algorithm is mostly used for continuous optimization, so the airport route guidance scheme is modeled as a combinatorial optimization problem in the present application. A method for mapping continuous values to discrete values is introduced in the present application, a discrete differential evolution algorithm is realized, and good results are obtained.

[0080] The above method will be explained and described in detail in combination with the drawings and specific embodiments.

[0081] Before optimizing a crowd route guidance scheme of an airport, a departure floor plan of the airport is acquired, historical passenger flow data of each entrance and exit of the departure floor is counted, and then a real airport departure floor scene simulation model is established according to the collected data and a virtual crowd simulation model. The simulation model is used to simulate crowd flow of the airport departure floor in a period of time, and the quality of an optimization scheme is judged by expected values of congestion degrees of all passengers at each time of the simulation model.

[0082] A simple departure floor plan of an airport is shown as Figure 2 The embodiment of the application performs mathematical modeling on the airport departure floor distribution and optimization scheme by the following steps:

[0083] Step 1, the security gate and other obstacles such as service counter, airport departure floor building, rest area and the like in the plan are counted, and the obstacles are abstracted as a node set V = {v1, v2,... v n}.

[0084] Step 2, the node set V constructed in the previous step is connected into a complete graph G, that is, G = (V, E). Wherein E represents an edge set of the complete graph, and an element e ij represents an edge of the graph G, and also represents a fence between the obstacles of v i and v j . For e ij , there are:

[0085] e ij ∈ {(v i ,v j )|i≠j,(v i ,v j )∈V 2}

[0086] Step 3, the complete graph G is optimized to obtain G' = (V, E'). Two parts need to be optimized: unreasonable edges and edges with too long length. Referring to Figure 2 , the edges between the obstacles No. 1-11 and the obstacles No. 12-17 are unreasonable edges, that is, the fence cannot be artificially added between the obstacles No. 1-11 and the obstacles No. 12-17. Therefore, the edges need to be deleted from the edge set E. In addition, an upper limit LU of the edge length is used to control the length of the artificial fence. The edges with too long length are also deleted from the edge set E. The optimized edge set E' satisfies:

[0087] E' = {e ij ||e ij |≤LU,i≠j}

[0088] Step 4, as described in step 2, an edge e ijThis represents a possible fence placement. Numbering all optimized edges yields E' = {e1, e2, ..., e...} m}, m represents the total number of possible fences. The optimal solution is represented by a vector of 0s and 1s, i.e., x i =(x i1 ,x i2 ...,x im ), where x ij =0 indicates that in the i-th optimization scheme, e is not placed. j The fence representing the area. ij =1 means that in the i-th optimization scheme, place e j The fence represents the area. X represents the population of the evolutionary algorithm, i.e., all candidate solutions. Thus, the airport crowd route guidance optimization problem is abstracted into a combinatorial optimization problem.

[0089] Based on the established combinatorial optimization mathematical model, this invention employs a discrete differential evolution algorithm as the optimization method to address this problem. The specific algorithm steps are as follows: Figure 3 As shown. In mutation operations, the scalar F typically takes values ​​between [0.4, 1]. Let the individual start from X... i Mutate into X i The differential evolution algorithm used in this invention employs a binomial crossover method to mutate the individual X. i 'and individual X i X is obtained by crossover. i This means crossing each bit of a decision vector once. The specific operation is as follows:

[0090]

[0091] The crossover rate Cr controls the crossover result for each dimension, taking a value between (0,1). Different application scenarios may have different applicable crossover rates. Furthermore, the binomial crossover method ensures that at least one dimension uses X during the crossover process for an individual. i The results in '. In the selection operation, based on the comparison with parent individual X. i The offspring individual X formed after crossover i The fitness value of "X", the winner as a new individual X i They will enter the next generation of the population.

[0092] Experiments have shown that using the discrete differential evolution algorithm to solve the automatic generation and optimization of crowd route guidance schemes in airports is relatively efficient in terms of time and performs well in simulation models.

[0093] This embodiment provides an airport crowd route guidance optimization device, including:

[0094] at least one processor;

[0095] at least one memory for storing the at least one program;

[0096] when the at least one program is executed by the at least one processor, the at least one processor implements Figure 1 the method.

[0097] The airport crowd route guidance scheme optimization device of the embodiment can execute the airport crowd route guidance scheme optimization method provided by the method embodiment of the application, can execute the implementation steps of any combination of the method embodiment, and has the corresponding functions and beneficial effects of the method.

[0098] The embodiment of the application further discloses a computer program product or a computer program, which comprises 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 the processor executes the computer instructions, so that the computer device executes Figure 1 the method.

[0099] The embodiment further provides a storage medium, which stores instructions or programs that can execute the airport crowd route guidance scheme optimization method provided by the method embodiment of the application. When the instructions or programs are executed, implementation steps of any combination of the method embodiment can be executed, and the corresponding functions and beneficial effects of the method are possessed.

[0100] In some alternative embodiments, the functions / operations mentioned in the block diagram can not occur in the order mentioned in the operation diagram. For example, depending on the functions / operations involved, two blocks shown in succession can actually be executed substantially simultaneously or the blocks can sometimes be executed in reverse order. In addition, the embodiments presented and described in the flowcharts of the application are provided by way of example, with the purpose of providing a more comprehensive understanding of the technology. The disclosed method is not limited to the operations and logical flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of larger operations are independently executed.

[0101] Furthermore, although the present application is described in the context of functional modules, it is to be understood that one or more of the described functions and / or features can be integrated in a single physical device and / or software module, or one or more functions and / or features can be implemented in separate physical devices or software modules. It will also be appreciated that detailed discussion of the actual implementation of each module is not necessary to an understanding of the application. Rather, the actual implementation of the modules, in combination with their attributes, functions, and internal relationships, are to be understood within the context of the devices disclosed herein. Thus, those skilled in the art with access to patents, scientific journals, and other public sources known by those skilled in the art will be able, using ordinary skill, to practice the application as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative and that the scope of the present application is to be determined by the entire scope of the claims, along with all equivalents of the claims and their equivalents.

[0102] If the functions are implemented in software, the functions can be stored in or implemented as one or more computer program products, which can be incorporated into a computer- readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor- containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. In the context of this document, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-readable medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium.

[0103] The logic and / or steps represented in the flowcharts and / or described herein, for example, can be considered as a sequence of instructions to implement logic functions, and can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor- containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. In the context of this specification, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

[0104] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can also be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example, via optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.

[0105] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the above embodiments, various steps or methods can be implemented in software or firmware which are stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any of the following technologies, known in the art, or their combinations can be used: discrete logic circuitry having logic gates for implementing logic functions upon an application of data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), and the like.

[0106] In the above description of the present specification, the description referring to the terms "one embodiment", "another embodiment", or "certain embodiments" or the like means that a specific feature, structure, material or characteristic described in connection with the embodiments or examples is included in at least one embodiment or example of the present application. The illustrative expressions of the above terms do not necessarily refer to the same embodiment or example in the present specification. Also, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0107] Although the embodiments of the present application have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, alternatives and variations to these embodiments can be made without departing from the principles and spirit of the application, the scope of which is defined by the claims and their equivalents.

[0108] The above is a specific description of the preferred embodiments of the present application, but the present application is not limited to the above-described embodiments, and those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present application, and these equivalent modifications or substitutions are included in the scope defined by the claims of the present application.

Claims

1. A method for optimizing airport crowd route guidance, characterized in that, Includes the following steps: Obtain the layout information of the airport and abstract the airport scene into a complete graph based on the layout information; where the obstacle objects in the airport scene are used as vertices of the complete graph, and the edge between any two vertices 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 airport crowd route guidance optimization problem is abstracted into a combinatorial optimization problem; A discrete differential evolution algorithm is used to optimize the combinatorial optimization problem and obtain the final optimization result. Based on the filtered complete graph, the airport crowd route guidance optimization problem is abstracted into a combinatorial optimization problem, including: Number all the edges after filtering and optimization to obtain the edge set. , This indicates the total number of fences that can be placed. Using a vector consisting of 0s and 1s x i = ( x i1 ,x i2 ,...,x im ) represents an optimization scheme; where, x ij =0 indicates the first In the optimization scheme, no placement is required. The fence that represents; x ij =1 indicates that in the first... In the optimization scheme, place The fence it represents; Based on the combination of all vectors The optimization problem of airport crowd route guidance scheme is abstracted into a combinatorial optimization problem.

2. The airport crowd route guidance optimization method according to claim 1, characterized in that, The obstacles in the airport scenario include security gates, service counters, entrances and exits, and other obstacles within the airport. Based on the layout information, the airport scene is abstracted into a complete graph, including: Obtain the vertex set based on obstacles in the airport scene. ; According to the vertex set Connect any two vertices to construct a complete graph. ;in, E Represents the complete graph G edge set, edge set E elements in Representing a complete graph An edge in the diagram represents the edge at the vertex. and vertex A fence between them; For elements have: 。 3. The airport crowd route guidance optimization method according to claim 2, characterized in that, 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. : 。 4. The airport crowd route guidance optimization method according to claim 1, characterized in that, The discrete differential evolution algorithm is used to optimize the combinatorial optimization problem and obtain the final optimization result, including: Initialize the parameters required for the differential evolution algorithm, and determine and initialize the population; Construct an objective function, optimize the population based on the objective function, and obtain the final optimization result; Before calculating the objective function value, the continuous numerical values ​​in the differential evolution algorithm are mapped into discrete decision vectors, as follows: in, This represents a crowd route guidance plan. The value represents the value of a certain element in the solution. A value of 0 means that the solution does not need to consider the element, while a value of 1 means that the solution needs to consider the element. To distribute evenly in Random numbers between, function It is sigmoid function.

5. The airport crowd route guidance optimization method according to claim 4, characterized in that, The optimization of the population based on the objective function to obtain the final optimization result includes: Offspring are obtained using the mutation and crossover operators of the differential evolution algorithm; The offspring are mapped to discrete values, and the fitness of the offspring is calculated using the objective function. The fitness values ​​of offspring and parents are compared, and the offspring with the lower fitness value is selected to enter the next generation of the population, until the algorithm reaches its termination condition.

6. The airport crowd route guidance optimization method according to claim 5, characterized in that, In mutation operations, suppose an individual starts from... Mutation ; Using a binomial crossover method to mutate individuals and individuals Obtain by cross The expression is as follows: In the formula, Cr represents the crossover rate.

7. An airport crowd route guidance optimization device, 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 according to any one of claims 1-6.

8. 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 1-6.

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