CN107704667B - Crowd movement simulation method, device and system for simulating clustering - Google Patents
Crowd movement simulation method, device and system for simulating clustering Download PDFInfo
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Abstract
The invention relates to a crowd movement simulation method for simulating clustering, which comprises the following steps: setting scene information, randomly setting individuals in a barrier-free area of the scene information, and generating individual information, wherein the individual information comprises position coordinates of the individuals, speed directions and speed values of the individuals; setting a clustering influence factor representing the clustering concentration, and calculating a clustering direction change value of each individual according to the clustering influence factor and the individual information; and carrying out vector combination on the individual clustering direction change value and the individual speed direction to obtain an updated individual speed direction, and generating crowd movement simulation according to the updated individual speed direction, the individual position coordinate and the individual speed value.
Description
Technical Field
The invention relates to a crowd movement simulation method, device and system for simulating clustering.
Background
The rapid and safe evacuation of people is a hot problem for the research in the safety field because of the frequent emergency of large public places. The crowd movement simulation has wide application in the aspects of guiding crowd evacuation, making emergency plans, scene design, movie and television entertainment and the like. At present, most of group simulation is focused on how to avoid collision of large-scale groups and how to improve simulation efficiency, and modeling of social characteristics of group movement, such as clustering performance, is ignored. Although the video-based method extracts the individual motion behaviors of the group from the real video, the overall motion morphology of the whole group cannot be described. The clustering is a social characteristic ubiquitous in group sports, and is represented by the clustering of individuals, so that the individual will gradually disappear and follow the behavior of the clustered will, and the group interior presents the characteristics of self organization, self coordination, mutual fusion and consistent movement. Therefore, how to simulate clustering becomes a research hotspot for simulating group motion behaviors.
Disclosure of Invention
In order to solve the problems, the invention provides a simulation method for simulating the cluster crowd movement, which constructs a simulation cluster crowd movement framework, establishes a cluster model for the crowd movement, and couples the cluster model with the crowd movement to obtain the simulation method capable of simulating the cluster crowd movement.
In order to achieve the purpose, the invention adopts the following scheme:
a crowd movement simulation method for simulating clustering comprises the following steps:
setting scene information, randomly setting individuals in a barrier-free area of the scene information, and generating individual information, wherein the individual information comprises position coordinates of the individuals, speed directions and speed values of the individuals;
setting a clustering influence factor representing the clustering concentration, and calculating a clustering direction change value of each individual according to the clustering influence factor and the individual information;
and carrying out vector combination on the individual clustering direction change value and the individual speed direction to obtain an updated individual speed direction, and generating crowd movement simulation according to the updated individual speed direction, the individual position coordinate and the individual speed value.
Further, calculating a clustering direction change value of each individual according to the clustering influence factor and the individual information includes:
first, calculating a first clustering direction change value between an individual and an adjacent individual, obtaining a second clustering direction change value between the individual and a next adjacent individual from the first clustering direction change value according to a propagation effect between the individuals, repeating iteration until obtaining an Nth clustering direction change value between the individual and a boundary individual, and calculating the expectation of the N clustering direction change values to obtain a clustering direction change value of the individual.
Further, calculating a first clustering direction change value between an individual and an adjacent individual comprises:
setting a circular area with a unit distance as a radius, wherein the circular area comprises a unique central individual and a plurality of adjacent individuals, calculating the vector difference of the speed direction between all the adjacent individuals and the central individual, obtaining the expectation of the vector difference of the speed direction on all the individuals in the circular area, and multiplying the expectation by the clustering influence factor to obtain a first clustering direction change value.
Further, calculating a second clustering direction change value between the individual and the next adjacent individual comprises:
setting a circular area with 2 times of unit distance as a radius, wherein the circular area comprises a unique central individual, a plurality of adjacent individuals and a plurality of times of adjacent individuals, calculating first speed direction vector differences between all adjacent individuals and the central individual, second speed direction vector differences between all times of adjacent individuals and each adjacent individual, adding the first speed direction vector differences and the second speed direction vector differences to obtain third speed direction vector differences, representing the speed direction vector differences between all times of adjacent individuals and the central individual, obtaining expectations of the third speed direction vector differences on all individuals in the circular area, and multiplying the expectations by a clustering influence factor to obtain a second clustering direction change value.
The clustering influence factor in the above technical solution decreases with the increase of the inter-individual distance, and is used to indicate that the clustering concentration gradually decreases with the increase of the inter-individual distance.
Further, the speed value is obtained as follows:
setting an expected speed, a maximum speed and a maximum acceleration, calculating all candidate speeds of an individual by using the maximum speed and the maximum acceleration, and comparing the expected speed with all the candidate speeds to obtain an optimal speed serving as a speed value.
Further, comparing the expected speed with all the candidate speeds, and obtaining the optimal speed as the speed value includes:
setting the expected collision time of each individual, setting a user-defined weight value for the expected collision time, obtaining the punishment measurement of the individual according to the expected collision time and the individual expected speed of the individual under the user-defined weight value, and determining the optimal speed according to the minimum value of the punishment measurement of the individual.
In the technical scheme, the time step can be set, the individual speed direction, the individual position coordinate and the individual speed value are updated once in each time step to generate animation frames, and each animation frame forms crowd motion simulation.
The invention also provides a storage device, which stores a plurality of instructions, wherein the instructions are loaded by a processor and execute the following processing:
setting scene information, randomly setting individuals in a barrier-free area of the scene information, and generating individual information, wherein the individual information comprises position coordinates of the individuals, speed directions and speed values of the individuals;
setting a clustering influence factor representing the clustering concentration, and calculating a clustering direction change value of each individual according to the clustering influence factor and the individual information;
and carrying out vector combination on the individual clustering direction change value and the individual speed direction to obtain an updated individual speed direction, and generating crowd movement simulation according to the updated individual speed direction, the individual position coordinate and the individual speed value.
The invention also provides a crowd movement simulation system for simulating the clustering, which comprises a display, a simulation module and a control module, wherein the display is used for displaying simulation animation; a processor to implement instructions; and storage means for storing a plurality of instructions, the instructions being loaded by the processor and performing the following:
setting scene information, randomly setting individuals in a barrier-free area of the scene information, and generating individual information, wherein the individual information comprises position coordinates of the individuals, speed directions and speed values of the individuals;
setting a clustering influence factor representing the clustering concentration, and calculating a clustering direction change value of each individual according to the clustering influence factor and the individual information;
and carrying out vector combination on the individual clustering direction change value and the individual speed direction to obtain an updated individual speed direction, and generating crowd movement simulation according to the updated individual speed direction, the individual position coordinate and the individual speed value.
The invention has the beneficial effects that:
the invention provides a simulation method and a system for simulating crowd movement clustering, which integrate the clustering characteristics in the real crowd movement process into crowd movement simulation, and construct a crowd movement framework for simulating the clustering: and establishing a clustering model of the crowd movement, coupling the clustering model with the crowd movement, and finally rendering and outputting the simulation result through realistic rendering. The method can simulate the crowd movement more truly, embodies the cluster characteristics in the crowd movement process, and enhances the reality of the crowd simulation result.
Drawings
FIG. 1 is a simplified flow diagram of the present invention.
The specific implementation mode is as follows:
the invention is further described with reference to the following figures and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
As shown in fig. 1, an exemplary embodiment of the present invention is a method for simulating crowd movement of a simulated crowd, which mainly includes the following steps:
firstly, initializing scene information, and then constructing a cluster crowd motion model; and combining the clustering crowd movement model with the individual information, updating the individual information, generating crowd movement simulation, and obtaining simulation with realistic rendering.
The initializing of the scene information mainly includes initializing the scene information and initializing the individual information.
The position information of the scene obstacle is initialized, and the position coordinates of the obstacle are shown. Secondly, scene semantics are initialized according to the obstacle information drawing, and a Roadmap of the scene is drawn. The method approximately describes scene semantic information by using the Roadmap, and an area without obstacles in a scene is represented into a Graph (Graph) structure through the Roadmap for describing a topological Graph bypassing the obstacles. The topological graph consists of vertexes and edges, wherein V represents a set of vertexes, and the Roadmap vertex is a random coordinate point generated in a scene by avoiding an obstacle; e represents a set of Roadmap sides, and any two random points with a distance of l and connecting lines without passing through obstacles are connected to form a Roadmap side, and is represented as E (v)1,v2) E, vertex v1And vertex v2Are the two end points of the edge e.
The individual information comprises an individual speed direction, an individual position coordinate and an individual speed value, wherein the individual position coordinate is the position coordinate of an individual randomly generated in a scene range by avoiding an obstacle.
Then, calculating the clustering direction change value of each individual according to the clustering influence factors and the individual information:
first, calculating a first clustering direction change value between an individual and an adjacent individual, obtaining a second clustering direction change value between the individual and a next adjacent individual from the first clustering direction change value according to a propagation effect between the individuals, repeating iteration until obtaining an Nth clustering direction change value between the individual and a boundary individual, and calculating the expectation of the N clustering direction change values to obtain a clustering direction change value of the individual.
The first clustering direction change value is obtained by setting a circular area with a unit distance as a radius, wherein the circular area comprises a unique central individual and a plurality of adjacent individuals, calculating the speed direction vector difference between all the adjacent individuals and the central individual, obtaining the expectation of the speed direction vector difference on all the individuals in the circular area, and multiplying the expectation by a clustering influence factor.
A second cluster direction change value between the individual and the next adjacent individual is then calculated: setting a circular area with 2 times of unit distance as a radius, wherein the circular area comprises a unique central individual, a plurality of adjacent individuals and a plurality of times of adjacent individuals, calculating first speed direction vector differences between all adjacent individuals and the central individual, second speed direction vector differences between all times of adjacent individuals and each adjacent individual, adding the first speed direction vector differences and the second speed direction vector differences to obtain third speed direction vector differences, representing the speed direction vector differences between all times of adjacent individuals and the central individual, obtaining expectations of the third speed direction vector differences on all individuals in the circular area, and multiplying the expectations by clustering influence factors to obtain a second clustering direction change value.
In this embodiment, the following steps can be implemented:
the first step is as follows: in different scenarios, crowd motion exhibits different clustering capabilities. The clustering influence factor z is defined and initialized, the value of the clustering influence factor z is set in (0,1), and the clustering phenomenon of the crowd is controlled by adjusting the value of z to be consistent with the real motion by combining the actual scene.
The second step is that: an adjacency distance L is defined for describing the relationship of the surrounding other individuals to the central individual. For any individual i, a circular area with the position of i as the center of a circle and the distance d as the radius is defined as the neighborhood of i, and a neighbor set N of i is obtained in the neighborhoodL(i) And (L1), wherein the adjacent distance between the neighbor individuals in the neighborhood range and i is L1.
The third step: the speed direction change value x is defined, and when the adjacency distance L is 1, the neighbor individual j ∈ N is calculated according to the formula (1)L(i) The influence value on the speed direction of i.
The fourth step: when an individual k is not a neighbor of i, the patent defines that it is indirectly related to i through the transmission between neighboring individuals, and the neighbor distance is i to kNumber of individuals passed in the course of the transfer, L>1. With NL(i) (L > 1) represents a set of all individuals having an adjacent distance L from the individual i. Because the clustering property is attenuated in the transfer process and the complexity of layer-by-layer transfer is comprehensively considered, the maximum adjacent distance of the transfer is set to be L-5, and it is considered that when L is equal to 5>At 5, the effect of the individual on the central i individual is 0. Calculating the neighbor set N of all L distances when L is less than or equal to 5L(i)。
The fifth step: when L is more than 1 and less than or equal to 5, calculating the cluster direction change value of the indirect influence according to the formula (2)
And a sixth step: the influence of all L distance neighbor individuals on the central individual i is integrated, and the final speed direction change value x of i is calculated according to the formula (3)i。
And 3, process: and (4) crowd movement calculation, wherein clustering is added into the crowd movement calculation in the process, and the overall movement form of the crowd is controlled.
The clustering influence factor can be set to be reduced along with the increase of the distance between individuals, and is used for representing that the clustering concentration degree is gradually reduced along with the increase of the distance between the individuals, so that the realistic rendering is embodied more.
The speed value in the above embodiment adopts the following specific calculation method:
v for all individualspref、ω、VmaxAnd amaxCarrying out initialization, wherein VprefIs the desired speed, V, of the individualmaxIs the maximum speed of the individual, amaxThe maximum acceleration of the individual is shown, and omega represents the user-defined weight and is used for measuring the optimal candidate speed of the individual.
1) Using maximum speed VmaxAnd maximum acceleration amaxCalculating the candidate velocity set V of the individual according to the formula (4)cand。
Wherein, ViWhich represents the current speed of the individual i,
represents the jth candidate speed for individual i,
representing the magnitude of the jth candidate speed for individual i,
indicating the magnitude of the change value of the candidate speed with respect to the current speed, t indicating the time step,
representing the maximum speed variation per unit time. In the formula, the magnitude of the candidate velocity is influenced by the maximum velocity VmaxAnd maximum acceleration amaxTo the next step. Therefore, the temperature of the molten metal is controlled,
should be greater than 0 and less than the maximum velocity value VmaxAnd its speed variation value
Should also be less than
(2) According to the change value x of the speed direction of the individual i calculated in the second processiTo calculate a new desired speed direction Dpref
(3) Updating the desired speed direction according to equation (5)
(4) Using desired speed VprefUpdating the new velocity v at the next moment in time according to equation (6)t+1。
Where, penalty is the penalty factor, ω is the user-defined weight,
a time of the expected collision is indicated,
representing the amount of change between the desired speed and the candidate speed. In this step, we select the candidate speed that minimizes the penalty factor penalty
New velocity v as the next momentt+1。
Judging whether the individual reaches the target, if not, returning to the second step, and continuously calculating the new speed at the next moment; otherwise, the individual reaches the target and stops exercising.
The invention can set time steps with fixed time as intervals, individual information is updated in each time step to generate an animation frame, the crowd movement calculation result is generated by the animation frame, and the crowd movement calculation result is rendered to obtain the crowd evacuation simulation animation by combining with a realistic rendering technology.
The invention provides a simulation method for simulating crowd movement clustering, which integrates the clustering characteristics in the real crowd movement process into crowd movement simulation. In order to realize the method, the invention constructs a crowd movement framework simulating the clustering: and establishing a clustering model of the crowd movement, coupling the clustering model with the crowd movement, and finally rendering and outputting the simulation result through realistic rendering. The method can simulate the crowd movement more truly, embodies the cluster characteristics in the crowd movement process, and enhances the reality of the crowd simulation result.
In order to implement the method, the invention further provides a storage device, which stores a plurality of instructions, wherein the instructions are loaded by a processor and execute the following processing:
setting scene information, randomly setting individuals in a barrier-free area of the scene information, and generating individual information, wherein the individual information comprises position coordinates of the individuals, speed directions and speed values of the individuals;
setting a clustering influence factor representing the clustering concentration, and calculating a clustering direction change value of each individual according to the clustering influence factor and the individual information;
and carrying out vector combination on the individual clustering direction change value and the individual speed direction to obtain an updated individual speed direction, and generating crowd movement simulation according to the updated individual speed direction, the individual position coordinate and the individual speed value.
In the specific application process, the display connected with the processor can display simulation animations, and realistic rendering is carried out on the crowd motion simulation by adopting an artistic rendering component.
The invention constructs a crowd movement framework simulating clustering: and establishing a clustering model of the crowd movement, coupling the clustering model with the crowd movement, and finally rendering and outputting the simulation result through realistic rendering. The method can simulate the crowd movement more truly, embodies the cluster characteristics in the crowd movement process, and enhances the reality of the crowd simulation result.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (9)
1. A crowd movement simulation method for simulating clustering is characterized by comprising the following steps:
setting scene information, randomly setting individuals in a barrier-free area of the scene information, and generating individual information, wherein the individual information comprises position coordinates of the individuals, speed directions and speed values of the individuals;
setting a clustering influence factor representing the clustering concentration, and calculating a clustering direction change value of each individual according to the clustering influence factor and the individual information;
carrying out vector combination on the individual clustering direction change value and the individual speed direction to obtain an updated individual speed direction, and generating crowd movement simulation according to the updated individual speed direction, the individual position coordinate and the individual speed value;
calculating a clustering direction change value of each individual according to the clustering influence factor and the individual information includes:
first, calculating a first clustering direction change value between an individual and an adjacent individual, obtaining a second clustering direction change value between the individual and a next adjacent individual from the first clustering direction change value according to a propagation effect between the individuals, repeating iteration until obtaining an Nth clustering direction change value between the individual and a boundary individual, and calculating the expectation of the N clustering direction change values to obtain a clustering direction change value of the individual.
2. The method of claim 1, wherein calculating a first clustering direction change value between an individual and an adjacent individual comprises:
setting a circular area with a unit distance as a radius, wherein the circular area comprises a unique central individual and a plurality of adjacent individuals, calculating the vector difference of the speed direction between all the adjacent individuals and the central individual, obtaining the expectation of the vector difference of the speed direction on all the individuals in the circular area, and multiplying the expectation by the clustering influence factor to obtain a first clustering direction change value.
3. The method of claim 1, wherein calculating a second clustering direction change value between an individual and a next adjacent individual comprises:
setting a circular area with 2 times of unit distance as a radius, wherein the circular area comprises a unique central individual, a plurality of adjacent individuals and a plurality of times of adjacent individuals, calculating first speed direction vector differences between all adjacent individuals and the central individual, second speed direction vector differences between all times of adjacent individuals and each adjacent individual, adding the first speed direction vector differences and the second speed direction vector differences to obtain third speed direction vector differences, representing the speed direction vector differences between all times of adjacent individuals and the central individual, obtaining expectations of the third speed direction vector differences on all individuals in the circular area, and multiplying the expectations by a clustering influence factor to obtain a second clustering direction change value.
4. The method of claim 1, wherein the clustering impact factor decreases with increasing inter-individual distance, representing a gradual decrease in cluster concentration with increasing inter-individual distance.
5. Method according to claim 1, characterized in that the speed value is obtained as follows:
setting an expected speed, a maximum speed and a maximum acceleration, calculating all candidate speeds of an individual by using the maximum speed and the maximum acceleration, and comparing the expected speed with all the candidate speeds to obtain an optimal speed serving as a speed value.
6. The method of claim 5, wherein comparing the desired speed with all candidate speeds, and wherein obtaining the optimal speed as the speed value comprises:
setting the expected collision time of each individual, setting a user-defined weight value for the expected collision time, obtaining the punishment measurement of the individual according to the expected collision time and the individual expected speed of the individual under the user-defined weight value, and determining the optimal speed according to the minimum value of the punishment measurement of the individual.
7. The method according to any one of claims 1 to 6, wherein time steps are set, the individual speed direction, the individual position coordinates and the speed value are updated once in each time step, animation frames are generated, and a crowd movement simulation is formed by each animation frame.
8. A memory device storing a plurality of instructions, the instructions being loaded by a processor and performing the following:
setting scene information, randomly setting individuals in a barrier-free area of the scene information, and generating individual information, wherein the individual information comprises position coordinates of the individuals, speed directions and speed values of the individuals;
setting a clustering influence factor representing the clustering concentration, and calculating a clustering direction change value of each individual according to the clustering influence factor and the individual information;
carrying out vector combination on the individual clustering direction change value and the individual speed direction to obtain an updated individual speed direction, and generating crowd movement simulation according to the updated individual speed direction, the individual position coordinate and the individual speed value;
calculating a clustering direction change value of each individual according to the clustering influence factor and the individual information includes:
first, calculating a first clustering direction change value between an individual and an adjacent individual, obtaining a second clustering direction change value between the individual and a next adjacent individual from the first clustering direction change value according to a propagation effect between the individuals, repeating iteration until obtaining an Nth clustering direction change value between the individual and a boundary individual, and calculating the expectation of the N clustering direction change values to obtain a clustering direction change value of the individual.
9. A simulation system for simulating the cluster population movement is characterized by comprising a display, a simulation module and a control module, wherein the display is used for displaying simulation animation; a processor to implement instructions; and storage means for storing a plurality of instructions, the instructions being loaded by the processor and performing the following:
setting scene information, randomly setting individuals in a barrier-free area of the scene information, and generating individual information, wherein the individual information comprises position coordinates of the individuals, speed directions and speed values of the individuals;
setting a clustering influence factor representing the clustering concentration, and calculating a clustering direction change value of each individual according to the clustering influence factor and the individual information;
carrying out vector combination on the individual clustering direction change value and the individual speed direction to obtain an updated individual speed direction, and generating crowd movement simulation according to the updated individual speed direction, the individual position coordinate and the individual speed value;
calculating a clustering direction change value of each individual according to the clustering influence factor and the individual information includes:
first, calculating a first clustering direction change value between an individual and an adjacent individual, obtaining a second clustering direction change value between the individual and a next adjacent individual from the first clustering direction change value according to a propagation effect between the individuals, repeating iteration until obtaining an Nth clustering direction change value between the individual and a boundary individual, and calculating the expectation of the N clustering direction change values to obtain a clustering direction change value of the individual.
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| CN111165395B (en) * | 2020-01-08 | 2021-04-30 | 清华大学 | Experimental method for simulating crowd flow by using zebra fish |
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