CN107665269B - Rapid crowd evacuation simulation method and device based on geographic information - Google Patents

Rapid crowd evacuation simulation method and device based on geographic information Download PDF

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CN107665269B
CN107665269B CN201710684884.0A CN201710684884A CN107665269B CN 107665269 B CN107665269 B CN 107665269B CN 201710684884 A CN201710684884 A CN 201710684884A CN 107665269 B CN107665269 B CN 107665269B
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张桂娟
黄丽君
张成铭
陆佃杰
刘弘
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Shandong Normal University
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Abstract

The invention discloses a rapid crowd evacuation simulation method and device based on geographic information, wherein the method comprises the following steps: acquiring a two-dimensional map of a scene; extracting geographic information based on the two-dimensional map, and reconstructing a road model and a building group model; establishing a path semantic topological graph according to the topological relation between the road skeleton data and the road; carrying out crowd movement calculation based on a relative speed obstacle method of normal distribution and scene semantics; and (4) combining a realistic rendering technology, rendering the crowd movement calculation result and the road and building group model to obtain the crowd evacuation simulation animation. The method provided by the invention has low complexity and can efficiently carry out crowd evacuation simulation.

Description

Rapid crowd evacuation simulation method and device based on geographic information
Technical Field
The invention relates to the field of three-dimensional simulation, in particular to a rapid crowd evacuation simulation method and device based on geographic information.
Background
In recent years, the economic society of China is continuously developed, the population-intensive places are continuously increased, and the problem of safe evacuation of people in public places becomes a very important concern in the current society. When an emergency safety accident happens, how to guide people to evacuate quickly, safely and orderly is achieved, the safety of the people is guaranteed to the maximum extent, and the method becomes a research hotspot in the field of public safety. The computer simulation technology is utilized to construct a scene model and carry out crowd evacuation simulation, which is one of the key methods for solving the public safety problems. In order to provide timely decision support, the virtual scene model is used for crowd evacuation simulation, and the high efficiency and timeliness are particularly important. Therefore, how to efficiently construct a crowd simulation model and perform rapid crowd movement simulation becomes a hot problem of research. In the research of the crowd evacuation simulation method at present, the crowd evacuation simulation method mostly cannot quickly construct a scene model and extract scene semantics to carry out efficient crowd evacuation simulation.
How to improve the efficiency of crowd evacuation simulation is a technical problem which needs to be urgently solved by technical personnel in the field at present.
Disclosure of Invention
In order to solve the problems, the invention provides a rapid crowd evacuation simulation method based on geographic information. The method comprises the steps of constructing a scene model by extracting geographic information data in a two-dimensional map, extracting scene semantics, calculating crowd movement by combining the scene semantics and global path navigation through a relative speed obstacle method based on normal distribution, realizing rapid crowd evacuation simulation, and providing timely decision support for solving the public safety problem.
In order to achieve the purpose, the invention adopts the following technical scheme:
a rapid crowd evacuation simulation method based on geographic information comprises the following steps:
step 1: a two-dimensional map of a scene is acquired.
Step 2: and extracting geographic information based on the two-dimensional map, and reconstructing a road model and a building group model.
And step 3: and establishing a path semantic topological graph according to the topological relation between the road skeleton data and the road.
And 4, step 4: and calculating the crowd movement based on a relative speed obstacle method of normal distribution and scene semantics.
And 5: and (4) combining a realistic rendering technology, rendering the crowd movement calculation result and the road and building group model to obtain the crowd evacuation simulation animation.
Further, the step 2 specifically includes:
step 2.1: extracting road skeleton data based on the two-dimensional map, and finishing the reconstruction of a road model by adding road width information on the basis of the road skeleton;
step 2.2: generating a building group model of a scene by adopting a method based on a shape grammar;
step 2.3: and combining the road model and the building group model to obtain a scene model.
Further, the road model reconstruction includes:
based on the road skeleton data and the road width, a vector rotation method is adopted to obtain the peak of each formed road;
and respectively storing the obtained coordinate points on two sides of the road into a left _ edge and a right _ edge of the data set, and combining the coordinate points corresponding to the same road in the left _ edge and the right _ edge of the data set according to a counterclockwise sequence to obtain all road polygons and realize road model reconstruction.
Further, the step 3 specifically includes:
step 3.1: carrying out duplication removal preprocessing on coordinate points in the road skeleton data set, then carrying out coordinate transformation, and taking the coordinate points after the coordinate transformation as the vertexes of the path semantic topological graph;
step 3.2: and judging whether the distance between two points is smaller than r and whether a connecting line between the two points passes through an obstacle or not aiming at any two vertexes in the path semantic topological graph, and taking the connecting line of the two points as an edge of the path semantic topological graph if and only if the distance is smaller than r and does not pass through the obstacle.
Further, the step 4 specifically includes:
step 4.1: obtaining n velocity values V satisfying normal distribution according to formula (1)prefAnd respectively assigning values to the n individuals;
Figure BDA0001376450260000021
wherein the desired velocity of the individual i is Vi pref，μVMean value, σ, representing desired speedVDetermining the distribution amplitude of the expected speed;
step 4.2: obtaining the number of movements according to equation (2)Set V of all candidate speeds at next time in bodycandMaximum velocity V of individual ii maxAnd maximum acceleration
Figure BDA0001376450260000022
Controlling the candidate speed within a certain range;
Figure BDA0001376450260000023
wherein, ViWhich represents the current speed of the individual i,
Figure BDA0001376450260000024
represents the jth candidate speed of the individual i, and t represents a time step;
step 4.3: according to the path semantic topological graph, performing path planning by using a shortest path algorithm, and selecting a shortest path from the current position of an individual to a final target point, wherein the shortest path is composed of a series of path semantic topological graph vertexes;
step 4.4: selecting the vertex closest to the current position of the individual on the shortest path as the next temporary target point, and updating the expected speed V of the individual i in each time stepi prefThe direction is the direction of the next temporary target point; and selecting the penalty factor penalty to be minimized according to equation (3)
Figure BDA0001376450260000031
As the new velocity of the individual i at the next moment,
Figure BDA0001376450260000032
wherein, wiRepresents a weight, tci′(Vi cand) Indicating the desired time of collision.
According to another aspect of the present invention, there is also provided a computer apparatus for rapid crowd evacuation simulation based on geographical information, comprising a display, a graphics processor, a memory, and a processor, wherein,
selecting a two-dimensional map of a specified scene based on a human-machine interaction interface on the display;
the processor is configured to perform: receiving a two-dimensional map of a scene;
extracting geographic information based on the two-dimensional map, and reconstructing a road model and a building group model;
establishing a path semantic topological graph according to the topological relation between the road skeleton data and the road;
carrying out crowd movement calculation based on a normal distribution relative speed obstacle method and scene semantics, and transmitting the crowd movement calculation result, the road and the building model to a graphic processor;
the graphics processor renders the crowd movement calculation result and the road and building group model based on a realistic rendering technology, obtains crowd evacuation simulation animation and transmits the crowd evacuation simulation animation to the display for displaying.
Further, the reconstructing the road model and the building group model includes:
extracting road skeleton data based on the two-dimensional map, and finishing the reconstruction of a road model by adding road width information on the basis of the road skeleton;
generating a building group model of a scene by adopting a method based on a shape grammar;
and combining the road model and the building group model to obtain a scene model.
Further, the road model reconstruction includes:
based on the road skeleton data and the road width, a vector rotation method is adopted to obtain the peak of each formed road;
and respectively storing the obtained coordinate points on two sides of the road into a left _ edge and a right _ edge of the data set, and combining the coordinate points corresponding to the same road in the left _ edge and the right _ edge of the data set according to a counterclockwise sequence to obtain all road polygons and realize road model reconstruction.
Further, the establishing the path semantic topological graph comprises:
carrying out duplication removal preprocessing on coordinate points in the road skeleton data set, then carrying out coordinate transformation, and taking the coordinate points after the coordinate transformation as the vertexes of the path semantic topological graph;
and judging whether the distance between two points is smaller than r and whether a connecting line between the two points passes through an obstacle or not aiming at any two vertexes in the path semantic topological graph, and taking the connecting line of the two points as an edge of the path semantic topological graph if and only if the distance is smaller than r and does not pass through the obstacle.
Further, the crowd motion calculation comprises:
obtaining n velocity values V satisfying normal distribution according to formula (1)prefAnd respectively assigning values to the n individuals;
Figure BDA0001376450260000041
wherein the desired velocity of the individual i is Vi pref，μVMean value, σ, representing desired speedVDetermining the distribution amplitude of the expected speed;
obtaining a set V of all candidate speeds of the sports individual at the next moment according to the formula (2)candMaximum velocity V of individual ii maxAnd maximum acceleration
Figure BDA0001376450260000042
Controlling the candidate speed within a certain range;
Figure BDA0001376450260000043
wherein, ViWhich represents the current speed of the individual i,
Figure BDA0001376450260000044
represents the jth candidate speed of the individual i, and t represents a time step;
according to the path semantic topological graph, performing path planning by using a shortest path algorithm, and selecting a shortest path from the current position of an individual to a final target point, wherein the shortest path is composed of a series of path semantic topological graph vertexes;
selecting a vertex closest to the current position of the individual on the shortest path as a next temporary target point; within each time step, the desired speed V of the individual i is updatedi prefThe direction is the direction of the next temporary target point; and selecting the penalty factor penalty to be minimized according to equation (3)
Figure BDA0001376450260000045
As the new velocity of the individual i at the next moment,
Figure BDA0001376450260000046
wherein, wiRepresents a weight, tci′(Vi cand) Indicating the desired time of collision.
The invention has the beneficial effects that:
1. the invention designs a scene modeling method based on geographic information, which can acquire geographic coordinate information of roads from a two-dimensional map and quickly obtain a scene model by utilizing geometric transformation, patch construction and reality processing technologies;
2. the invention defines scene semantics based on geographic information, establishes a path topological graph by utilizing the geographic information acquired from the two-dimensional map to extract the scene semantics, and can provide more reasonable path planning and navigation for crowd movement calculation;
3. the invention provides a relative velocity barrier method based on normal distribution, normal distribution velocity is added into the relative velocity barrier method, algorithm complexity is low, and crowd simulation can be efficiently carried out.
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The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
FIG. 1 is a flow diagram of a rapid crowd evacuation simulation method based on geographic information;
FIG. 2 is a flow diagram of rapid scene modeling based on geographic information;
FIG. 3 is a quick modeling process and effect graph based on geographic information, wherein 3(a) interactive information collection of a road network skeleton; 3(b) a road network skeleton diagram; 3(c) road reconstruction; 3(d) a road network model; 3(e) building group models; 3(f) refining the model;
FIG. 4 is a schematic representation of road reconstruction, wherein 4(a) the road network skeleton; fig. 4(b) is a schematic diagram of a road reconstruction process.
Detailed Description
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. The embodiments and features of the embodiments in the present application may be combined with each other without conflict.
Example one
The embodiment provides a rapid crowd evacuation simulation method based on geographic information, as shown in fig. 1, including the following steps:
step 1: a two-dimensional map of a scene is acquired.
Step 2: and extracting geographic information based on the two-dimensional map, and reconstructing a road model and a building group model.
Step 2.1: extracting road skeleton data based on the two-dimensional map, and finishing the reconstruction of a road model by adding road width information on the basis of the road skeleton;
specifically, each vertex constituting the road polygon is obtained by vector rotation, as shown in fig. 4 (b). First, take a straight line p2p3Last point p to point p2A distance of
Figure BDA0001376450260000061
Wherein
Figure BDA0001376450260000062
d is the width of the road section. Secondly, the point p is rotated counterclockwise by an angle θ using the formula (1) to obtain the point p8(i.e., vector of interest)
Figure BDA0001376450260000063
Counterclockwise rotation), wherein
Figure BDA0001376450260000064
Is a rotation matrix in a two-dimensional coordinate system. Then, the point p is again expressed by the formula (1)8Rotate 180 degrees counterclockwise to obtain a point p5The coordinate values of (2). And sequentially processing all coordinate points in the road skeleton set X by using the method to respectively obtain the rotated coordinate points on the two sides of the corresponding road. Respectively storing the obtained coordinate points on two sides of the road into a data set left _ edge and a data set right _ edge, wherein left _ edge is { p }8i|2≤i≤n}，right_edge＝{p5iI is more than or equal to |2 and less than or equal to n }. Finally, combining the coordinate points corresponding to the same road in the sets left _ edge and right _ edge according to the anticlockwise sequence to form a road polygon riFinally, all r are calculatediConstituting a road R.
Figure BDA0001376450260000065
Step 2.2: generating a building group model of a scene by adopting a method based on a shape grammar;
the shape grammar is a computer aided design method, which can generate new shapes according to the design thought and requirement of people and certain rules. Specifically, in this example, the building is decomposed by analyzing the building's constituent components and the building's creation is described using CSG modeling. According to the generation process, specific rules of each generation process are defined in the form of shape grammar, the generation of the building is formalized, materialized and automated through operations of stretching, translation, zooming, intersection, combination, complementation and the like, and the batch generation of the large-scale building is realized by means of mel script language of Maya.
Step 2.3: and combining the road model and the building group model to obtain a scene model. In this embodiment, the road model and the building group model are imported into the same scene of Maya and edited to obtain a final scene model.
And step 3: and establishing a path semantic topological graph according to the topological relation between the road skeleton data and the road.
Specifically, the path semantic topology map (Roadmap map) is composed of vertices and edges, denoted G ═ V, E.
Wherein, V represents a set of vertexes, and is obtained by coordinate conversion of coordinate points in the road skeleton data set; specifically, the graph vertex used in the present embodiment is formed by coordinate point X in road network skeleton set X storing road geographic coordinate informationikThe coordinate transformation is performed on the longitudinal coordinate (longitude, latitude) and the longitudinal coordinate (longitude, latitude). As shown in table 1, since the extracted geographic coordinates are longitude and latitude coordinates, the change between the longitude and latitude coordinates in the scene is from the 2 nd position after the decimal point, and the change is very small and inconvenient to calculate, the coordinate point x needs to be calculatedikAnd (5) carrying out coordinate transformation. And (3) respectively carrying out coordinate transformation on the longitude and latitude coordinates according to formulas (2) and (3), wherein k represents the number of digits of the decimal part of the original coordinate, and l represents the integer number of digits of the new coordinate to be obtained. Using the vertex as a Roadmap map after coordinate transformation, expressed as equation (4), the transformed partThe coordinates are shown in table 2.
Figure BDA0001376450260000071
Figure BDA0001376450260000072
Figure BDA0001376450260000073
Figure BDA0001376450260000074
Figure BDA0001376450260000081
E represents a set of edges, each E (v)1,v2) E, where the vertex v1And v2Are the two end points of the edge e. Connecting any two vertexes with the distance r, and ensuring that the connecting line does not pass through any obstacle during connection. E represents a set of edges, each E (v)1,v2) E, where the vertex v1And v2Are the two end points of the edge e. Connecting any two vertexes with the distance r, and ensuring that the connecting line does not pass through any obstacle during connection.
Based on this, the step 3 may specifically include the following steps:
step 3.1: carrying out duplication removal preprocessing on coordinate points in the road skeleton data set, then carrying out coordinate transformation, and taking the coordinate points after the coordinate transformation as the vertexes of the path semantic topological graph;
step 3.2: aiming at any two vertexes in the path semantic topological graph, judging whether the distance between the two points is smaller than r or not and whether a connecting line between the two points passes through an obstacle or not, and taking the connecting line of the two points as the path semantic topological graph if the distance is smaller than r and the connecting line does not pass through the obstacle or notThe edge of (2). In particular, V is for any two points V in Vp，vqThe following determination is performed: (a) v. ofp，vqThe distance between is less than r; (b) v. ofp，vqThe connecting line between the two does not pass through the barrier; if both conditions (a) and (b) are satisfied, v is setp，vqIs stored as e (v)p,vq) And storing in E, and continuing to execute until all the vertexes in V are judged completely.
And 4, step 4: and (3) carrying out crowd movement calculation based on a Normal Distribution-based relative Velocity obstruction (ND-RVO) method and scene semantics.
In actual sports, people can present different movement speeds due to factors such as age, physical state and the like, the number of people with the speed between 1m/s and 1.3m/s is more, and the number of people with the rest speeds is less. Based on this, the expected velocity V of the population is approximately described herein using a normal distributionpref。
Step 4.1: at initialization, n V satisfying normal distribution are obtained according to formula (5)prefAnd respectively assigning values to the n individuals;
Figure BDA0001376450260000091
wherein the desired velocity of the individual i is Vi pref，μVMean value, σ, representing desired speedVDetermines the magnitude of the desired velocity profile.
Step 4.2: obtaining a set V of all candidate speeds of the sports individual at the next moment according to the formula (6)candMaximum velocity V of individual ii maxAnd maximum acceleration
Figure BDA0001376450260000092
Controlling the candidate speed within a certain range;
Figure BDA0001376450260000093
wherein, ViExpress anThe current velocity of the body i is,
Figure BDA0001376450260000094
represents the jth candidate speed of the individual i, and t represents a time step;
step 4.3: and planning a path by using a shortest path algorithm according to the path semantic topological graph, and selecting a shortest path from the current position of the individual to the final target point, wherein the shortest path consists of a series of path semantic topological graph vertexes. The shortest path algorithm in this embodiment is Dijkstra algorithm.
Step 4.4: selecting the vertex closest to the current position of the individual on the shortest path as the next temporary target point, and updating the expected speed V of the individual i in each time stepi prefThe direction is towards the next temporary target point; and selects the penalty factor penalty to be minimized according to equation (7)
Figure BDA0001376450260000095
As the new velocity of the individual i at the next moment,
Figure BDA0001376450260000096
wherein, wiRepresents a weight, tci′(Vi cand) Indicating the desired time of collision.
And 5: and (4) combining a realistic rendering technology, rendering the crowd movement calculation result and the road and building group model to obtain the crowd evacuation simulation animation.
Specifically, a road, a building group model and a generated crowd movement path are led in the realistic rendering platform, and the animation effect of crowd movement is generated. The realistic rendering platform is a real-time crowd motion simulation system supporting cross-platform and realized based on an XNA/MonoGame platform. The XNA Game Studio2013 is adopted, and Microsoft Visual Studio2013 is taken as a platform to simulate the movement of the crowd. The generated scene model, character model and path file are imported into the simulation platform, so that the crowd movement effect can be observed more visually, and the crowd movement condition can be analyzed.
Example two
A computer device for rapid crowd evacuation simulation based on geographical information, comprising a display, a graphics processor, a memory and a processor, the memory having stored therein a computer program, characterized in that,
selecting a two-dimensional map of a specified scene based on a human-machine interaction interface on the display;
the processor is configured to execute the computer program to: receiving a two-dimensional map of a scene;
extracting geographic information based on the two-dimensional map, and reconstructing a road model and a building group model;
establishing a path semantic topological graph according to the topological relation between the road skeleton data and the road;
carrying out crowd movement calculation based on a normal distribution relative speed obstacle method and scene semantics, and transmitting the crowd movement calculation result, the road and the building model to a graphic processor;
the graphics processor renders the crowd movement calculation result and the road and building group model based on a realistic rendering technology, obtains crowd evacuation simulation animation and transmits the crowd evacuation simulation animation to the display for displaying.
The reconstructing the road model and the building group model includes:
extracting road skeleton data based on the two-dimensional map, and finishing the reconstruction of a road model by adding road width information on the basis of the road skeleton;
generating a building group model of a scene by adopting a method based on a shape grammar;
and combining the road model and the building group model to obtain a scene model.
The road model reconstruction includes:
based on the road skeleton data and the road width, a vector rotation method is adopted to obtain the peak of each formed road;
and respectively storing the obtained coordinate points on two sides of the road into a left _ edge and a right _ edge of the data set, and combining the coordinate points corresponding to the same road in the left _ edge and the right _ edge of the data set according to a counterclockwise sequence to obtain all road polygons and realize road model reconstruction.
The establishing of the path semantic topological graph comprises the following steps:
carrying out duplication removal preprocessing on coordinate points in the road skeleton data set, then carrying out coordinate transformation, and taking the coordinate points after the coordinate transformation as the vertexes of the path semantic topological graph;
and judging whether the distance between two points is smaller than r and whether a connecting line between the two points passes through an obstacle or not aiming at any two vertexes in the path semantic topological graph, and taking the connecting line of the two points as an edge of the path semantic topological graph if and only if the distance is smaller than r and does not pass through the obstacle.
The crowd motion calculation comprises:
obtaining n velocity values V satisfying normal distribution according to formula (1)prefAnd respectively assigning values to the n individuals;
Figure BDA0001376450260000111
wherein the desired velocity of the individual i is Vi pref，μVMean value, σ, representing desired speedVDetermining the distribution amplitude of the expected speed;
obtaining a set V of all candidate speeds of the sports individual at the next moment according to the formula (2)candMaximum velocity V of individual ii maxAnd maximum acceleration
Figure BDA0001376450260000112
Controlling the candidate speed within a certain range;
Figure BDA0001376450260000113
wherein, ViWhich represents the current speed of the individual i,
Figure BDA0001376450260000114
to representThe jth candidate speed of the individual i, t representing a time step;
according to the path semantic topological graph, performing path planning by using a shortest path algorithm, and selecting a shortest path from the current position of an individual to a final target point, wherein the shortest path is composed of a series of path semantic topological graph vertexes;
selecting the vertex closest to the current position of the individual on the shortest path as the next temporary target point, and updating the expected speed V of the individual i in each time stepi prefThe direction is the direction of the next temporary target point; and selecting the penalty factor penalty to be minimized according to equation (3)
Figure BDA0001376450260000115
As the new velocity of the individual i at the next moment,
Figure BDA0001376450260000116
wherein, wiRepresents a weight, tci′(Vi cand) Indicating the desired time of collision.
The invention designs a scene modeling method based on geographic information, which can acquire geographic coordinate information of roads from a two-dimensional map and quickly obtain a scene model by utilizing geometric transformation, patch construction and reality processing technologies; a path topological graph is established by utilizing the geographic information acquired from the two-dimensional map so as to extract scene semantics, and more reasonable path planning and navigation can be provided for crowd movement calculation; and finally, a relative velocity barrier method based on normal distribution is adopted, normal distribution velocity is added into the relative velocity barrier method, the algorithm complexity is low, and crowd evacuation simulation can be efficiently carried out.
Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using general purpose computer means, or alternatively, they can be implemented using program code that is executable by computing means, such that they are stored in memory means and executed by computing means, or they are separately fabricated into individual integrated circuit modules, or multiple modules or steps of them are fabricated into a single integrated circuit module. The present invention is not limited to any specific combination of hardware and software.
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 (2)

1. A rapid crowd evacuation simulation method based on geographic information is characterized by comprising the following steps:
step 1: acquiring a two-dimensional map of a scene;
step 2: extracting geographic information based on the two-dimensional map, and reconstructing a road model and a building group model;
step 2.1: extracting road skeleton data based on the two-dimensional map, adding road width information on the basis of the road skeleton, and obtaining the peak of each formed road by adopting a vector rotation method;
respectively storing the obtained coordinate points on two sides of the road into a left _ edge and a right _ edge of a data set, and combining the coordinate points corresponding to the same road in the left _ edge and the right _ edge of the data set according to a counterclockwise sequence to obtain all road polygons and realize road model reconstruction;
step 2.2: generating a building group model of a scene by adopting a method based on a shape grammar;
step 2.3: merging the road model and the building group model to obtain a scene model;
and step 3: establishing a path semantic topological graph according to the topological relation between the road skeleton data and the road;
step 3.1: carrying out duplication removal preprocessing on coordinate points in the road skeleton data set, then carrying out coordinate transformation, and taking the coordinate points after the coordinate transformation as the vertexes of the path semantic topological graph;
step 3.2: aiming at any two vertexes in the path semantic topological graph, judging whether the distance between the two points is smaller than r and whether a connecting line between the two points passes through an obstacle, and taking the connecting line of the two points as an edge of the path semantic topological graph if and only if the distance is smaller than r and the connecting line does not pass through the obstacle;
and 4, step 4: carrying out crowd movement calculation based on a relative speed obstacle method of normal distribution and scene semantics;
step 4.1: obtaining n velocity values V satisfying normal distribution according to formula (1)prefAnd respectively assigning values to the n individuals;
Figure FDA0002694073670000011
wherein the desired velocity of the individual i is Vi pref，μVMean value, σ, representing desired speedVDetermining the distribution amplitude of the expected speed;
step 4.2: obtaining a set V of all candidate speeds of the sports individual at the next moment according to the formula (2)candMaximum velocity V of individual ii maxAnd maximum acceleration
Figure FDA0002694073670000012
Controlling the candidate speed within a certain range;
Figure FDA0002694073670000013
wherein, ViWhich represents the current speed of the individual i,
Figure FDA0002694073670000014
represents the jth candidate speed of the individual i, and t represents a time step;
step 4.3: according to the path semantic topological graph, performing path planning by using a shortest path algorithm, and selecting a shortest path from the current position of an individual to a final target point, wherein the shortest path is composed of a series of path semantic topological graph vertexes;
step 4.4: selecting the vertex closest to the current position of the individual on the shortest path as the next temporary target point, and updating the expected speed V of the individual i in each time stepi prefThe direction is towards the next temporary target point; and selecting the penalty factor penalty to be minimized according to equation (3)
Figure FDA0002694073670000021
As the new velocity of the individual i at the next moment,
Figure FDA0002694073670000022
wherein, wiDenotes weight, tc'i(Vi cand) Representing a desired time of collision;
and 5: and (4) combining a realistic rendering technology, rendering the crowd movement calculation result and the road and building group model to obtain the crowd evacuation simulation animation.
2. A computer device for rapid crowd evacuation simulation based on geographical information, comprising a display, a graphics processor, a memory and a processor, the memory having stored therein a computer program, characterized in that,
selecting a two-dimensional map of a specified scene based on a human-machine interaction interface on the display;
the processor is configured to execute the computer program to: receiving a two-dimensional map of a scene;
extracting road skeleton data based on the two-dimensional map, adding road width information on the basis of the road skeleton, and obtaining the peak of each formed road by adopting a vector rotation method;
respectively storing the obtained coordinate points on two sides of the road into a left _ edge and a right _ edge of a data set, and combining the coordinate points corresponding to the same road in the left _ edge and the right _ edge of the data set according to a counterclockwise sequence to obtain all road polygons and realize road model reconstruction;
generating a building group model of a scene by adopting a method based on a shape grammar;
merging the road model and the building group model to obtain a scene model;
establishing a path semantic topological graph according to the topological relation between the road skeleton data and the road;
the method for establishing the path semantic topological graph comprises the following steps:
carrying out duplication removal preprocessing on coordinate points in the road skeleton data set, then carrying out coordinate transformation, and taking the coordinate points after the coordinate transformation as the vertexes of the path semantic topological graph;
aiming at any two vertexes in the path semantic topological graph, judging whether the distance between the two points is smaller than r and whether a connecting line between the two points passes through an obstacle, and taking the connecting line of the two points as an edge of the path semantic topological graph if and only if the distance is smaller than r and the connecting line does not pass through the obstacle;
carrying out crowd movement calculation based on a normal distribution relative speed obstacle method and scene semantics, and transmitting the crowd movement calculation result, the road and the building model to a graphic processor;
the crowd motion calculation comprises:
obtaining n velocity values V satisfying normal distribution according to formula (1)prefAnd respectively assigning values to the n individuals;
Figure FDA0002694073670000031
wherein the desired velocity of the individual i is Vi pref，μVMean value, σ, representing desired speedVDetermining the distribution amplitude of the expected speed;
obtaining a set V of all candidate speeds of the sports individual at the next moment according to the formula (2)candMaximum velocity V of individual ii maxAnd maximum acceleration
Figure FDA0002694073670000032
Controlling the candidate speed to be oneWithin a fixed range;
Figure FDA0002694073670000033
wherein, ViWhich represents the current speed of the individual i,
Figure FDA0002694073670000034
represents the jth candidate speed of the individual i, and t represents a time step;
according to the path semantic topological graph, performing path planning by using a shortest path algorithm, and selecting a shortest path from the current position of an individual to a final target point, wherein the shortest path is composed of a series of path semantic topological graph vertexes;
selecting the vertex closest to the current position of the individual on the shortest path as the next temporary target point, and updating the expected speed V of the individual i in each time stepi prefThe direction is the direction of the next temporary target point; and selecting the penalty factor penalty to be minimized according to equation (3)
Figure FDA0002694073670000035
As the new velocity of the individual i at the next moment,
Figure FDA0002694073670000036
wherein, wiDenotes weight, tc'i(Vi cand) Representing a desired time of collision;
the graphics processor renders the crowd movement calculation result and the road and building group model based on a realistic rendering technology, obtains crowd evacuation simulation animation and transmits the crowd evacuation simulation animation to the display for displaying.
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