CN112966895A - Dynamic calibration method and device for crowd risk area based on internal energy and information entropy - Google Patents

Dynamic calibration method and device for crowd risk area based on internal energy and information entropy Download PDF

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CN112966895A
CN112966895A CN202110142459.5A CN202110142459A CN112966895A CN 112966895 A CN112966895 A CN 112966895A CN 202110142459 A CN202110142459 A CN 202110142459A CN 112966895 A CN112966895 A CN 112966895A
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赵荣泳
王妍
贾萍
刘琼
李翠玲
张智舒
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Abstract

The invention relates to a dynamic calibration method and a dynamic calibration device for a crowd risk area based on internal energy and information entropy, wherein the method comprises the following steps: constructing a crowd movement fluid dynamic model, and performing pedestrian flow simulation based on the model; according to the simulation result, the internal energy of the crowd is calculated based on the enthalpy principle; and calibrating the crowd risk area based on the thermal entropy principle according to the crowd internal energy and the simulation result. Compared with the prior art, the method has the advantages of high risk assessment accuracy and the like.

Description

Dynamic calibration method and device for crowd risk area based on internal energy and information entropy
Technical Field
The invention relates to a crowd risk area judgment method, in particular to a dynamic crowd risk area calibration method and device based on internal energy and information entropy.
Background
Any disaster occurs because of the outbreak of potential risks in the system, and accurate risk assessment is carried out on the control places, so that the system has great significance for timely and effectively controlling the flow of large-scale pedestrians. Existing research methods are broadly divided into three categories: 1) statistical analysis based on experience. According to the method, various factors influencing the risk of the crowd are provided through a large amount of literature collection, and various factors are comprehensively considered, so that risk assessment is given. Such methods are basically a qualitative determination of risk. 2) State evaluation method based on experiments. The method mainly extracts and analyzes the flow data of the crowd on site, or simulates the flow of the crowd in a simulation mode, extracts the state characteristics of the speed, the density and the like of the crowd and carries out risk assessment and management and control guidance. 3) The quantitative determination method based on theoretical derivation quantifies the crowd movement indexes to form a risk criterion according to mature analysis theory.
To date, there are several deficiencies in determining the risk area of a population: 1) at present, most of the risk judgment and research of crowd evacuation stays on the theoretical basis, and the actual achievement falls to the ground. And the crowd gathering risk is defined from the theoretical derivation point of view, and a recognized and effective judgment method is not formed. 2) Overcrowding of people, i.e., high crowd density or fast movement of people, is generally considered to be a major factor in the development of pedaling events. However, it is not comprehensive to consider only the density and the speed, and it is difficult to accurately determine the risk region.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide a dynamic crowd risk area calibration method and device based on internal energy and information entropy.
The purpose of the invention can be realized by the following technical scheme:
a dynamic calibration method for a crowd risk area based on internal energy and information entropy comprises the following steps:
constructing a crowd movement fluid dynamic model, and performing pedestrian flow simulation based on the model;
according to the simulation result, the internal energy of the crowd is calculated based on the enthalpy principle;
and calibrating the crowd risk area based on the thermal entropy principle according to the crowd internal energy and the simulation result.
Further, the crowd motion fluid dynamics model is represented as:
Figure BDA0002929375830000021
Figure BDA0002929375830000022
Figure BDA0002929375830000023
Figure BDA0002929375830000024
where ρ ist(x, y, t) represents pedestrian flow density,
Figure BDA0002929375830000025
indicates the pedestrian flow, Ue(x, y, ρ) represents a monotonically decreasing function of pedestrian stream density, c (x, y, t) is a cost function, and φ (x, y, t) is a cost bit function.
Further, the calculation of the crowd internal energy based on the enthalpy principle specifically includes:
dividing a research scene into networks, and calculating the energy of each grid by taking the grid as a unit, wherein the calculation formula is as follows:
Figure BDA0002929375830000026
wherein, R is the grid crowd internal energy, and rho and v respectively represent the crowd density and the crowd speed in the grid.
Further, the calibrating the crowd risk area based on the principle of thermal entropy specifically comprises:
calculating the motion entropy of the crowd in each grid, obtaining a crowd risk value by adopting a crowd aggregation risk evaluation model, and calibrating a crowd risk area, wherein the crowd aggregation risk evaluation model is expressed as follows:
Figure BDA0002929375830000027
wherein, High _ Ri,jFor the calculated crowd risk value, i and j are pedestrian coordinates corresponding to x and y axes, rho and v respectively represent the crowd density and the crowd speed in the grid, R is the internal energy of the crowd in the grid, and R is the internal energy of the crowd in the gridmaxIs the maximum of the internal energy, α + β ═ 1, p (d)1),p(d2),…,p(dn) For the crowd in the grid along 8 directions d1,d2,…,d8Probability of movement, Ei,jmax is the maximum value of the population motion entropy.
Further, the calculation formula of the crowd motion entropy is as follows.
Figure BDA0002929375830000028
The invention also provides a dynamic calibration device for the crowd risk area based on the internal energy and the information entropy, which comprises the following components:
the simulation module is used for constructing a crowd movement fluid dynamic model and carrying out pedestrian flow simulation based on the model;
the internal energy calculation module is used for calculating the internal energy of the crowd based on the enthalpy principle according to the simulation result;
and the calibration module is used for calibrating the crowd risk area based on the thermal entropy principle according to the crowd internal energy and the simulation result.
Further, in the simulation module, the constructed crowd movement fluid dynamic model is represented as:
Figure BDA0002929375830000031
u(x,y，t):＝Ue(x，y，ρ)
Figure BDA0002929375830000032
Figure BDA0002929375830000033
where ρ ist(x, y, t) represents pedestrian flow density,
Figure BDA0002929375830000034
indicates the pedestrian flow, Ue(x, y, ρ) represents a monotonically decreasing function of pedestrian stream density, c (x, y, t) is a cost function, and φ (x, y, t) is a cost bit function.
Further, in the internal energy calculation module, the internal energy of the crowd calculated based on the enthalpy principle is specifically:
dividing a research scene into networks, and calculating the energy of each grid by taking the grid as a unit, wherein the calculation formula is as follows:
Figure BDA0002929375830000035
wherein, R is the grid crowd internal energy, and rho and v respectively represent the crowd density and the crowd speed in the grid.
Further, in the calibration module, calibrating the crowd risk region based on the principle of thermal entropy specifically includes:
calculating the motion entropy of the crowd in each grid, obtaining a crowd risk value by adopting a crowd aggregation risk evaluation model, and calibrating a crowd risk area, wherein the crowd aggregation risk evaluation model is expressed as follows:
Figure BDA0002929375830000036
wherein, High _ Ri,jFor the calculated crowd risk value, i and j are pedestrian coordinates corresponding to x and y axes, rho and v respectively represent the crowd density and the crowd speed in the grid, R is the internal energy of the crowd in the grid, and R is the internal energy of the crowd in the gridmaxIs the maximum value of internal energy, α + β ═1，p(d1),p(d2),…,p(dn) For the crowd in the grid along 8 directions d1,d2,…,d8Probability of movement, Ei,jmax is the maximum value of the population motion entropy.
Further, the calculation formula of the crowd motion entropy is as follows:
Figure BDA0002929375830000037
compared with the prior art, the invention has the following beneficial effects:
(1) an effective crowd gathering risk judgment method does not exist in the prior art, and factors considered by the existing risk evaluation model are not comprehensive enough. According to the invention, a more comprehensive risk dynamic evaluation model is established, so that the large-scale crowd risk area dynamic calibration is carried out, the reliability is high, and the application value is very high. The invention has certain research significance for further mining the internal mechanism of the movement of the crowd in the public place, can provide real-time guidance for crowd evacuation, and is more beneficial to timely managing and controlling the risk crowd.
(2) The crowd asphyxia degree is defined by applying the system internal energy principle, the three-dimensional characteristics of crowd density distribution, speed and movement direction are fully considered, the crowd enthalpy is provided to represent the crowd movement internal energy on the basis of the enthalpy, and the risk assessment accuracy is improved.
(3) High density populations can experience trample disasters and at relatively low densities, excessive chaotic population movements can also create dangerous events. The method is based on the information entropy principle, defines the motion entropy to express the chaos degree of the crowd motion, and improves the accuracy of risk assessment.
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FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic diagram of a grid for high risk area calibration of a population in accordance with the present invention;
fig. 3 is a schematic diagram of an evacuation layout of a waiting hall of a railway station according to an embodiment of the present invention;
FIG. 4 is a conventional population aggregation risk distribution based on population density distribution in an embodiment of the present invention;
FIG. 5 is a diagram illustrating a population group risk distribution based on a hybrid risk assessment model according to an embodiment of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
The invention provides a dynamic crowd risk area calibration method based on internal energy and information entropy, which takes a time axis as a driving shaft, reflects the change relation of plane distribution of crowd risks along with time, can set and calculate simulation step length according to specific precision requirements, and provides crowd enthalpy based on thermodynamic enthalpy to represent crowd density degree; motion entropy is provided based on thermodynamic entropy to represent the degree of crowd motion disorder, and the method comprises the following steps: constructing a crowd movement fluid dynamic model, and performing pedestrian flow simulation based on the model; according to the simulation result, the internal energy of the crowd is calculated based on the enthalpy principle; and calibrating the crowd risk area based on the thermal entropy principle according to the crowd internal energy and the simulation result.
The method comprises the following steps: crowd moving fluid dynamics modeling
In a macroscopic model of crowd evacuation, pedestrian flow is analogized into fluid, based on the assumption of continuous media, a mass conservation equation of density, speed and flow changing along with time and space is established, a partial differential equation continuously dependent on time and space is obtained, and a pedestrian flow model based on hydrodynamics is formed, wherein the model consists of two parts: continuity equations and pedestrian path selection conditions. The pedestrian path selection is to select the total instantaneous travel cost to the destination to be the minimum according to a reactive dynamic balance distribution principle, so that in a path selection balance state, a defined cost function meets an Eikonal equation, and pedestrians advance at the fastest speed reduced by a cost bit function along the direction of the negative gradient of the cost function. The constructed crowd movement fluid dynamic model is represented as:
Figure BDA0002929375830000051
u(x,y,t):＝Ue(x,y,ρ) (2)
Figure BDA0002929375830000052
Figure BDA0002929375830000053
where ρ ist(x, y, t) represents pedestrian flow density,
Figure BDA0002929375830000054
indicates the pedestrian flow, Ue(x, y, ρ) represents a monotonically decreasing function of pedestrian flow density, c (x, y, t) is a cost function representing local travel cost per unit distance of travel, depending on the pedestrian operating conditions of the study scenario itself, and φ (x, y, t) is a cost bit function representing the total instantaneous travel cost to the destination.
Step two: crowd internal energy calculation based on enthalpy principle
Enthalpy is an important state parameter for characterizing the system energy of a substance in thermodynamics, and population enthalpy can be calculated based on enthalpy definition to indicate that population accumulation energy is high:
Figure BDA0002929375830000055
wherein E iskCan be used
Figure BDA0002929375830000056
The calculation is carried out, namely, the kinetic energy is used for replacing the internal energy,
Figure BDA0002929375830000057
instead of the internal pressure, F is the force and A is the area, then
Figure BDA0002929375830000058
In the above formula, the average weight m of the pedestrians in different countries is 65kg, and the maximum speed v of the crowd is 1.34m/s2In grid units. In the formula, ρ is the population density of the unit grid (1m × 1m), and V and a are the volume and area of the unit grid, respectively, and are both set to 1. Therefore, the formula for applying the enthalpy formula to the crowd energy calculation is as follows:
Figure BDA0002929375830000059
it represents the enthalpy of each grid, and when the energy in the crowd is too high, the system is easy to trample once disturbance occurs. According to the definition of the enthalpy, the internal energy distribution of the crowd is calculated, and the energy size of the internal energy distribution is related to the crowd density, the crowd speed and the crowd acceleration size in the grid.
Step three: crowd risk calibration based on thermal entropy principle
Entropy is derived from thermodynamics and is later introduced into the science of information, where entropy is often used to measure uncertainty and disorder in random variables. The thermal entropy theorem of the invention provides a linear combination of crowd energy and the chaos degree of crowd movement directions, and a crowd risk value is obtained by adopting a crowd aggregation risk evaluation model to calibrate a crowd risk area.
According to the crowd flow model, the direction of crowd movement depends on the decreasing direction of the gradient of the cost function. Fig. 1 is a schematic diagram of a grid calibrated in a high risk region of a population, wherein the gray value of the grid represents the cost function value of the grid, and the larger the gray value, the higher the cost function value. The movement direction of the crowd is divided into 8 directions d1，d2，…，d8The probability of occurrence is labeled as p (d)1)，p(d2),…，p(dn) Therefore, the formula for calculating the motion entropy of the crowd is (8), and the cost function is d3The descending gradient of the direction is greatest. On the basis, the invention provides a more scientific and comprehensive crowdAnd (4) aggregating the risk assessment model as shown in formula (11).
Figure BDA0002929375830000061
Figure BDA0002929375830000062
Figure BDA0002929375830000063
Figure BDA0002929375830000064
Where α + β is 1, and α and β are empirical values selected according to different scene characteristics. High _ Ri，jThe calculated population risk value is in the range of (0, 1), and a larger value represents a larger risk of population aggregation, and a dangerous event such as treading is likely to occur. In this embodiment, the crowd risk value is higher than 0.8, namely, the crowd risk value is calibrated to be a high risk area, and important management and control and continuous observation are required.
The above functions, if implemented in the form of software functional units and sold or used as a separate product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another embodiment, a dynamic calibration apparatus for a crowd risk area based on internal energy and information entropy is provided, which includes: the simulation module is used for constructing a crowd movement fluid dynamic model and carrying out pedestrian flow simulation based on the model; the internal energy calculation module is used for calculating the internal energy of the crowd based on the enthalpy principle according to the simulation result; and the calibration module is used for calibrating the crowd risk area based on the thermal entropy principle according to the crowd internal energy and the simulation result.
Examples
In the embodiment, the effectiveness of the method is verified by taking a train station waiting hall scene as a case. The area of the waiting hall of the railway station in the embodiment is about 11340 square meters, 1 ten thousand waiting people can be accommodated at the same time, and the arrangement of the waiting hall for evacuation is shown in fig. 3.
In the waiting hall scene, the initial number of people is 10000, the people are uniformly distributed in three waiting areas and are evacuated to eight emergency exits, and the simulation step length is 200. Fig. 4 is a result of a conventional crowd gathering risk model based on crowd density distribution, and fig. 5 is a result of a mixed risk assessment model based on internal energy and information entropy proposed by the present invention. By contrast, the population density near obstacles, intersections, and exits is not very high, but the population risk values in these areas are high due to the confusion of the population movement direction and speed.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.

Claims (10)

1. A crowd risk area dynamic calibration method based on internal energy and information entropy is characterized by comprising the following steps:
constructing a crowd movement fluid dynamic model, and performing pedestrian flow simulation based on the model;
according to the simulation result, the internal energy of the crowd is calculated based on the enthalpy principle;
and calibrating the crowd risk area based on the thermal entropy principle according to the crowd internal energy and the simulation result.
2. The dynamic crowd risk region calibration method based on internal energy and information entropy of claim 1, wherein the crowd motion fluid dynamics model is expressed as:
Figure FDA0002929375820000011
u(x,y，t):＝Ue(x，y，ρ)
Figure FDA0002929375820000012
Figure FDA0002929375820000013
where ρ ist(x, y, t) represents pedestrian flow density,
Figure FDA0002929375820000014
indicates the pedestrian flow, Ue(x, y, ρ) represents a monotonically decreasing function of pedestrian stream density, c (x, y, t) is a cost function, and φ (x, y, t) is a cost bit function.
3. The dynamic crowd risk region calibration method based on internal energy and information entropy as claimed in claim 1, wherein the calculating the internal energy of the crowd based on the enthalpy principle specifically comprises:
dividing a research scene into networks, and calculating the energy of each grid by taking the grid as a unit, wherein the calculation formula is as follows:
Figure FDA0002929375820000015
wherein, R is the grid crowd internal energy, and rho and v respectively represent the crowd density and the crowd speed in the grid.
4. The dynamic calibration method for the crowd risk area based on the internal energy and the information entropy as claimed in claim 1, wherein the calibrating the crowd risk area based on the principle of the thermal entropy is specifically as follows:
calculating the motion entropy of the crowd in each grid, obtaining a crowd risk value by adopting a crowd aggregation risk evaluation model, and calibrating a crowd risk area, wherein the crowd aggregation risk evaluation model is expressed as follows:
Figure FDA0002929375820000016
wherein, High _ Ri,jFor the calculated crowd risk value, i and j are pedestrian coordinates corresponding to x and y axes, rho and v respectively represent the crowd density and the crowd speed in the grid, R is the internal energy of the crowd in the grid, and R is the internal energy of the crowd in the gridnaxIs the maximum of the internal energy, α + β ═ 1, p (d)1),p(d2),…,p(dn) For the crowd in the grid along 8 directions d1,d2,…,d8Probability of movement, Ei,jmax is the maximum value of the population motion entropy.
5. The dynamic crowd risk region calibration method based on internal energy and information entropy as claimed in claim 4, wherein the calculation formula of the crowd motion entropy is as follows:
Figure FDA0002929375820000021
6. the utility model provides a crowd risk area developments calibration device based on internal energy and information entropy which characterized in that includes:
the simulation module is used for constructing a crowd movement fluid dynamic model and carrying out pedestrian flow simulation based on the model;
the internal energy calculation module is used for calculating the internal energy of the crowd based on the enthalpy principle according to the simulation result;
and the calibration module is used for calibrating the crowd risk area based on the thermal entropy principle according to the crowd internal energy and the simulation result.
7. The dynamic calibration device for crowd risk areas based on internal energy and information entropy of claim 6, wherein in the simulation module, the constructed crowd movement fluid dynamic model is represented as:
Figure FDA0002929375820000022
u(x,y,t):＝Ue(x,y,ρ)
Figure FDA0002929375820000023
Figure FDA0002929375820000024
where ρ ist(x, y, t) represents pedestrian flow density,
Figure FDA0002929375820000025
indicates the pedestrian flow, Ue(x, y, ρ) represents a monotonically decreasing function of pedestrian stream density, c (x, y, t) is a cost function, and φ (x, y, t) is a cost bit function.
8. The dynamic crowd risk region calibration device based on internal energy and information entropy of claim 6, wherein in the internal energy calculation module, the calculation of the internal energy of the crowd based on the enthalpy principle is specifically as follows:
dividing a research scene into networks, and calculating the energy of each grid by taking the grid as a unit, wherein the calculation formula is as follows:
Figure FDA0002929375820000026
wherein, R is the grid crowd internal energy, and rho and v respectively represent the crowd density and the crowd speed in the grid.
9. The dynamic calibration device for the crowd risk area based on the internal energy and the information entropy as claimed in claim 6, wherein in the calibration module, calibrating the crowd risk area based on the principle of the thermal entropy specifically includes:
calculating the motion entropy of the crowd in each grid, obtaining a crowd risk value by adopting a crowd aggregation risk evaluation model, and calibrating a crowd risk area, wherein the crowd aggregation risk evaluation model is expressed as follows:
Figure FDA0002929375820000027
wherein, High _ Ri,jFor the calculated crowd risk value, i and j are pedestrian coordinates corresponding to x and y axes, rho and v respectively represent the crowd density and the crowd speed in the grid, R is the internal energy of the crowd in the grid, and R is the internal energy of the crowd in the gridmaxIs the maximum of the internal energy, α + β ═ 1, p (d)1),p(d2),…,p(dn) For the crowd in the grid along 8 directions d1,d2,…,d8Probability of movement, Ei,jmax is the maximum value of the population motion entropy.
10. The dynamic calibration device for crowd risk areas based on internal energy and information entropy of claim 9, wherein the calculation formula of the crowd motion entropy is as follows:
Figure FDA0002929375820000031
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