Crowd evacuation simulation method and system based on panic emotion propagation and hazard source influence

Abstract

The invention discloses a crowd evacuation simulation method and system based on panic emotion propagation and hazard source influence, and the method comprises the steps: setting a simulation scene, and modifying the infection rate of infectious disease dynamics into a state transition function affected by an emotion threshold value; after simulation is started, calculating a state transfer function for each individual in the crowd, and establishing a panic emotion propagation model; setting danger repulsive force exclusive to a danger source, establishing a social force model based on the panic emotion propagation model and the danger repulsive force, and simulating crowd evacuation based on the social force model. According to the method, the infection rate of infectious disease dynamics is changed into a dynamic parameter influenced by an emotion threshold value from a static parameter, the dynamic parameter is used as a state transition function of individuals, the function is used as a bridge, a social force model and panic emotion propagation are fused together, individual differences in crowd evacuation are deeply reflected, and the social force model and the panic emotion propagation are integrated. Therefore, simulation of crowd evacuation is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of crowd evacuation, and in particular to a crowd evacuation simulation method and system based on the spread of panic emotions and the influence of danger sources. Background Art

[0002] Traditional evacuation plan designs are no longer able to meet the needs of increasingly complex environments. They rely on pre-set routes and fixed time parameters, making them unable to adapt to real-time environmental changes. Their assumption of a homogeneous crowd contradicts the heterogeneity of individuals in reality. Crowd evacuation simulation, a key research area in public safety and emergency management, relies on multidisciplinary and multi-methodological approaches.

[0003] At present, traditional virtual simulation can be divided into macro models and micro models.

[0004] Macro models, such as fluid dynamics models, regard the crowd as a continuous medium and describe the evacuation behavior of the group through macro variables such as average density, velocity, and flow. However, they ignore the heterogeneity and initiative of individuals in the crowd, resulting in the inability to explain situations such as "herd behavior" and "shock wave phenomenon".

[0005] Microscopic models, such as cellular automata, social force models, and agent modeling, focus more on individual behavior and simulate pedestrian movement by analyzing the interactions among individuals.

[0006] Crowd evacuation is a complex problem. In an evacuation scenario, each individual may react differently to the same event due to differences in thinking and physical abilities. To capture these individual differences at a microscopic level, existing technologies have proposed social force models. These consider the collective behavior of groups during crowd evacuation and mechanically represent the interactions and behaviors among people during the evacuation process. These include the driving force generated by the desire to reach the exit; the repulsive forces that prevent collisions between people and the surrounding environment; and the friction generated when collisions occur between people and objects. Based on this model, by modifying the key parameters of each force, crowd evacuation simulation can be achieved while also capturing individual differences to a certain extent. However, the actual complexity of crowd evacuation scenarios makes it difficult to accurately simulate them using a single social force model, so optimization of the social force model is necessary.

[0007] Therefore, the prior art still has defects. Summary of the Invention

[0008] The technical problem to be solved by the present invention is to address the above-mentioned shortcomings of the prior art and provide a crowd evacuation simulation method and system based on the spread of panic emotions and the impact of dangerous sources. The technical solution adopted by the present invention is as follows:

[0009] In a first aspect, the present invention provides a crowd evacuation simulation method based on the spread of panic emotions and the influence of danger sources, wherein the method comprises:

[0010] Set up a simulation scenario and modify the infection rate of the epidemic dynamics into a state transition function affected by the emotion threshold;

[0011] After the simulation starts, the state transition function is calculated for each individual in the crowd, and a panic spread model is established;

[0012] A danger repulsion force dedicated to the danger source is set, a social force model is established based on the panic emotion propagation model and the danger repulsion force, and crowd evacuation is simulated based on the social force model.

[0013] In one implementation, setting a simulation scenario includes:

[0014] Set up walls, exits, obstacles, danger sources, crowd distribution, and the initial state of the crowd.

[0015] In one implementation, the calculation formula of the state transfer function is:

[0016]

[0017] Among them, α(t) is the state transition function; P i (t) is the emotional threshold of individual i at the current time t; ε is the panic threshold, and k is the transition intensity parameter, which reflects the intensity of the mutation of the value of α(t) near ε.

[0018] In one implementation, after calculating the state transfer function for each individual in the crowd, the following steps are performed:

[0019] determining the number of panicked people in the crowd at a next moment based on the state transition function;

[0020] The number of panickers in the crowd at the next moment is:

[0021] P(t+1)=P(t)+αP(t)S(t)-(1-α)P(t)

[0022] Among them, S(t) is the proportion of people who are prone to panic in the total population, P(t) is the proportion of people who panic in the total population, and α is the state transition function, which here represents the infection rate.

[0023] In one implementation, the calculation formula for the emotion threshold is:

[0024]

[0025] Among them, P i(t) is the emotional threshold of individual i at the current time t; P d is the emotion decline function; λ is the emotion infection coefficient, which affects the efficiency of emotion transmission; α et is the state transition function of other individuals, serving as a panic spreading factor; P D is the influence function of panic emotions caused by dangerous sources; P et is the emotional threshold of other individuals; S is the distance between individuals; R p is the propagation radius, P max is the maximum emotion threshold that a pedestrian can have, with a value of 1.

[0026] In one implementation, the calculation formula of the sentiment drop function is:

[0027]

[0028] The influence function of panic emotions caused by dangerous sources is:

[0029]

[0030] β is the emotion drop coefficient; d is the distance to the exit, k is the linear adjustment parameter; D is the distance between the individual and the danger source; D crit It is the impact radius of the hazard source.

[0031] In one implementation, the calculation formula of the danger repelling force is:

[0032]

[0033] Among them, σ is the linear adjustment parameter, r i is the radius of individual i, k p is the elasticity coefficient of the hazard source, d ip is the distance between individual i and hazard source p, n ip is the normal unit vector of the hazard source.

[0034] In a second aspect, an embodiment of the present invention further provides a crowd evacuation simulation system based on the spread of panic and the impact of dangerous sources, wherein the system is used to implement the steps of the crowd evacuation simulation method based on the spread of panic and the impact of dangerous sources described in any one of the above solutions, the method comprising:

[0035] A state transfer function determination module is used to set up simulation scenarios and modify the infection rate of the infectious disease dynamics into a state transfer function affected by the emotion threshold;

[0036] The panic emotion propagation model determination module is used to calculate the state transfer function for each individual in the crowd and establish a panic emotion propagation model after the simulation starts;

[0037] The crowd evacuation simulation module is used to set a danger repulsion force specific to the danger source, establish a social force model based on the panic emotion propagation model and the danger repulsion force, and simulate crowd evacuation based on the social force model.

[0038] In a third aspect, an embodiment of the present invention further provides a terminal, wherein the terminal includes a memory, a processor, and a crowd evacuation simulation program based on the spread of panic emotions and the influence of dangerous sources, which is stored in the memory and can be run on the processor. When the processor executes the crowd evacuation simulation program based on the spread of panic emotions and the influence of dangerous sources, the steps of the crowd evacuation simulation method based on the spread of panic emotions and the influence of dangerous sources in any one of the above-mentioned schemes are implemented.

[0039] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein a crowd evacuation simulation program based on the spread of panic emotions and the influence of dangerous sources is stored on the computer-readable storage medium, and the crowd evacuation simulation program based on the spread of panic emotions and the influence of dangerous sources implements the steps of the crowd evacuation simulation method based on the spread of panic emotions and the influence of dangerous sources described in any one of the above-mentioned schemes on the computer-readable storage medium.

[0040] Beneficial effects: Compared with the prior art, the present invention provides a crowd evacuation simulation method based on the spread of panic emotions and the influence of dangerous sources. The present invention first sets up a simulation scene and modifies the infection rate of infectious disease dynamics into a state transfer function affected by the emotional threshold. Then, after the simulation starts, the state transfer function is calculated for each individual in the crowd, and a panic emotion propagation model is established. Finally, a dangerous repulsion force specific to the dangerous source is set, and a social force model is established based on the panic emotion propagation model and the dangerous repulsion force, and the crowd evacuation is simulated based on the social force model. The present invention changes the infection rate of infectious disease dynamics from a static parameter to a dynamic parameter affected by the emotional threshold, and uses it as the state transfer function of an individual. Using this function as a bridge, the social force model and panic emotion propagation are integrated together, and the individual differences in crowd evacuation are deeply reflected, thereby realizing the simulation of crowd evacuation. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 The present invention provides a flowchart of a preferred embodiment of a crowd evacuation simulation method based on the spread of panic and the influence of dangerous sources.

[0042] Figure 2 A schematic diagram of a state transition function in a crowd evacuation simulation method based on panic spread and the influence of danger sources provided by an embodiment of the present invention.

[0043] Figure 3This is the state transition process between panic-prone people and panic-inducing people in the crowd evacuation simulation method based on panic spread and danger source influence provided by an embodiment of the present invention.

[0044] Figure 4 A schematic diagram of the architecture of a crowd evacuation simulation system based on panic spread and the impact of danger sources provided by an embodiment of the present invention.

[0045] Figure 5 This is a functional block diagram of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and effect of the present invention clearer and more specific, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0047] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents, operations, or steps, nor must they be executed in the order described. For example, some operations or steps may be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0048] It should be understood that the terms used in this specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the specification and appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0049] It should be understood that, to facilitate a clear description of the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, terms such as "first" and "second" are used to distinguish between identical or similar items with substantially the same functions and effects. For example, the first control information and the second control information are merely used to distinguish different control information and do not limit their order.

[0050] Those skilled in the art can understand that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit them to be different.

[0051] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0052] During a crowd evacuation in a crisis scenario, panic inevitably arises, and this panic spreads throughout the crowd. Because the spread of emotions is similar to the spread of infectious diseases, the dynamics of infectious disease can be used to simulate panic during crowd evacuations. In infectious disease dynamics, a population is divided into three categories: susceptible, infected, and recovered. These three groups are then linked by infection and recovery rates, and a state-transition mathematical model is established to simulate the spread and outcomes of infectious diseases within a population at a macro level. Consequently, many researchers have attempted to mathematically simulate the spread and outcomes of panic using the principles of infectious disease dynamics. However, because infectious disease dynamics is a macroscopic mathematical model, most research on the spread of panic based on infectious disease dynamics has remained limited to mathematical simulations and has failed to incorporate it into crowd evacuation simulations. During crowd evacuation, the actual panic spread process is difficult to accurately simulate mathematically at the macro level due to factors such as speed differences between individuals, crowd density distribution in different scenarios, the scope of panic spread, and the source of danger. Therefore, it is necessary to put the panic spread model at the micro level, take each independent individual as the object, and simulate it through real-time parameter changes. This can improve the accuracy of panic spread simulation.

[0053] To this end, this embodiment provides a crowd evacuation simulation method based on the spread of panic emotions and the impact of dangerous sources. This method integrates the social force model and the dynamics of infectious diseases, considers the spread of panic emotions during the crowd evacuation process under crisis conditions and its impact on the crowd evacuation, and considers the impact of dangerous sources on the spread of panic emotions and the evacuation process in the crowd evacuation scenario. The method of this embodiment can be applied to terminals, which can be intelligent product terminals such as computers, smart TVs, and mobile phones. Figure 1 As shown in , the crowd evacuation simulation method based on panic spread and danger source impact includes the following steps:

[0054] Step S100: Setting a simulation scenario, modifying the infection rate of the infectious disease dynamics into a state transfer function affected by the emotion threshold;

[0055] Step S200: After the simulation starts, a state transition function is calculated for each individual in the crowd, and a panic spread model is established;

[0056] Step S300: setting a danger repulsion force specific to the danger source, establishing a social force model based on the panic emotion propagation model and the danger repulsion force, and simulating crowd evacuation based on the social force model.

[0057] In terms of the social force model, since the basic social force model lacks consideration of the mechanical impact of dangerous sources on individuals in critical situations, this embodiment adds a new force dedicated to dangerous sources to the basic social force model, namely, the dangerous repulsion force. The dangerous repulsion force exerts a force on pedestrians within the influence radius of the dangerous source that significantly increases as the distance increases, and has almost no effect on pedestrians outside the influence radius, thereby achieving the effect of pedestrians actively avoiding dangerous sources in the simulation model.

[0058] Regarding the spread of panic, infectious disease dynamics mathematically simulates and predicts the development of infectious diseases at a macro level and cannot be directly integrated into the micro-level crowd evacuation simulations of panic spread in the basic social force model. Therefore, this embodiment first sets an emotion threshold for each individual, ranging from 0 to 1. Each individual's emotion threshold is updated based on the state transition function, the emotions of surrounding pedestrians, the source of danger, and the calm factor. Based on the Markov chain properties, a panic spread model is constructed. This panic spread model is then combined with the hazard repulsion force to establish a social force model, which is then used to simulate the crowd evacuation process.

[0059] Specifically, before the simulation begins, the simulation scene needs to be set up, including walls, exits, obstacles, sources of danger, crowd distribution, the initial state of the crowd, etc. After the simulation starts, before each pedestrian action, calculations must be performed to determine the pedestrian's movement direction, movement speed, panic value, state, etc. in the next step. In order to better reflect the spread of panic in the crowd and the different acceptance of panic by each person, we optimize based on the idea of infectious disease dynamics and modify the infection rate of infectious disease dynamics to a state transfer function affected by the emotional threshold. For each individual in the simulation process, when deciding the next movement, the state transfer function α of the individual is first calculated. The calculation formula is as follows:

[0060]

[0061] In the formula, α(t) is the state transition function; P i (t) is the emotional threshold of individual i at the current time t; ε is the panic threshold, and ε is generally set to 0.7 to be more in line with the actual situation; k is the transition strength parameter, which reflects the strength of the mutation of the value of α near ε. From the above formula, we can see that the state transition function α is a function of the emotional threshold P. i (t) function, when k = 20, ε = 0.7, its image is as follows Figure 2 As shown. It can be seen that at the emotion threshold P iWhen (t) approaches the panic threshold ε, the value of the state transition function α changes significantly, so it can be used to reflect the state transition process. Based on this function, this embodiment integrates the SIR (susceptible-infectious-recovered) model and the SIS (susceptible-infectious-susceptible) model to propose a panic emotion propagation model. The derivation process is as follows:

[0062] Assume that there are a total number of people N in a simulation scenario. The crowd is divided into three groups: those prone to panic, those who panic, and those who are calm. At time t, their proportions in the crowd are denoted by S(t), P(t), and R(t), respectively, such that S(t) + P(t) + R(t) = 1.

[0063] The initial value conditions are set as S(0)=S0, P(0)=P0, R(0)=R0. At a certain moment, the number of panic-stricken people is NP(t). Since the proportion of people prone to panic in the crowd is S(t), the probability of a panic-stricken person contacting a person prone to panic is S(t), and the number of people a panic-stricken person contacting a person prone to panic is NP(t)S(t). After the contact, the panic-stricken person turns the person prone to panic into a panic-stricken person with a probability of α. Then, at the next moment, the number of panic-stricken people will increase by αNP(t)S(t). In this embodiment, α is the state transition function, which here represents the infection rate. At this moment, the panic-stricken person may calm down and turn into a person prone to panic. In order to reflect the influence of the emotional threshold, the panic-stricken person calms down with a probability of 1-α, that is, at the next moment, the number of panic-stricken people will decrease by (1-α)NP(t). Therefore, the number of panic-stricken people at the next moment can be determined as:

[0064] NP(t+1)=NP(t)+αNP(t)S(t)-(1-α)NP(t)

[0065] Eliminating N on both sides of the above equation, we get:

[0066] P(t+1)=P(t)+αP(t)S(t)-(1-α)P(t).

[0067] Based on the number of panicked people mentioned above, the number of people who are prone to panic and the number of calm people can also be determined, and the Markov chain of the crowd state distribution transition in the simulation scenario can be obtained:

[0068] Number of people prone to panic: S(t+1)=S(t)-αP(t)S(t)+(1-α)P(t)

[0069] Number of panicked people: P(t+1)=P(t)+αP(t)S(t)-(1-α)P(t)

[0070] Number of calm people: R(t+1)=R(t)

[0071] Derivative of function S(t) That is its rate of change. From the Markov chain, we know that the rate of change of panic-prone people is -αP(t)S(t)+(1-α)P(t), from which we can get its differential form as follows:

[0072]

[0073] This set of equations reflects the state transition process between the panic-prone person S and the panic-prone person P, as follows: Figure 3 As shown in .

[0074] This embodiment can construct an SPR panic emotion propagation model based on the Markov chain properties. It can be seen from the model that the state transition function reflects the transition between various states in the form of probability. When the emotional threshold is close to the panic threshold, the state transition probability increases significantly. Individual i has a greater probability of changing from a panic-prone person S to a panic person P, and a smaller probability of changing from P to S. The calm person R is not affected by the emotional threshold and plays the role of a safety officer in the simulation scenario. Using probability as the transfer function also reflects to a certain extent the different acceptance of panic emotions among different individuals, which are distributed around the set panic threshold. During the simulation process, the state transition process is reflected in the infection radius R p In the network, each individual who has become a panic-taker P will send an infectious message to the surroundings at every moment, infecting each panic-prone individual S who receives the message with a probability α, turning it into a panic-taker P.

[0075] After calculating the state transfer function, due to the influence of the emotion threshold, the emotion threshold P is first calculated. i The calculation formula for (t) is as follows:

[0076]

[0077] In the formula, P i (t) is the emotional threshold of individual i at the current time t; P d is the emotion decline function; λ is the emotion contagion coefficient, which can affect the efficiency of emotion transmission; α et is the state transition function of other individuals, which serves as the panic propagation factor here; P D It is the panic caused by the danger source; et is the emotional threshold of other individuals; S is the distance between individuals; R p is the propagation radius, P max is the maximum emotional threshold that a pedestrian can have, and its value is 1. This formula reflects that the emotional threshold of an individual is affected by many factors, including self-calmness, the spread of other people's emotions, and the distance from the source of danger. etThe introduction of shows that individuals who are in panic (individuals with a large enough emotional threshold) have a more significant effect on the spread of panic, while individuals who are not in panic (individuals with a small emotional threshold) have a smaller effect on the spread of panic.

[0078] For the emotion drop function (self-calming function) P d , which is calculated as follows:

[0079]

[0080] In the formula, β is the emotion drop coefficient, and d is the individual's distance from the exit. The emotion drop function reflects that during the evacuation process, the process of pedestrians calming down is not only influenced by their natural calmness but also by their distance from the exit.

[0081] During the crowd evacuation process, pedestrians will actively avoid nearby danger sources, while nearby danger sources will intensify the panic of pedestrians. Considering the impact of danger sources on panic, in order to reflect this process, the danger source panic emotion influence function P is proposed. D , the formula is as follows:

[0082]

[0083] In the formula, k is a linear adjustment parameter, which is generally a sufficiently large number greater than 1; D is the distance between the individual and the hazard source; D crit is the impact radius of the danger source. This function shows that when an individual approaches a danger source, their panic will increase significantly, while when an individual moves away from the danger source, panic will hardly accumulate due to the danger source.

[0084] Finally, the calculation of the basic social force model that determines the direction and speed of individual movement is as follows:

[0085]

[0086] m i is the pedestrian mass; v i0 is the expected velocity of individual i; v it is the current speed of individual i; τ is the relaxation time. It pushes pedestrians to adjust their speed to the desired value, similar to the dynamic process of "moving towards equilibrium".

[0087] There is an interaction force between people, which is mainly composed of two parts. The first is psychological repulsion:

[0088]

[0089] A is the strength coefficient; B is the range of action; r ij=r i +r j , the sum of the radii of pedestrians i and j; d ij is the actual distance between the two; n ij is a unit vector pointing from j to i. The closer the distance, the more repulsive the force increases exponentially, simulating the pedestrian's instinct to avoid crowding. Then there is the physical contact force, also known as the social influence term:

[0090]

[0091] k is the elastic coefficient; μ is the sliding friction coefficient; θ(r ij -d ij ) is the heaviside function (valid only when the independent variable x>0, that is, when contact occurs); Δv t is the tangential velocity difference; t ij Is the tangent unit vector. It simulates body squeezing (normal) and friction resistance (tangential), and only takes effect when in contact.

[0092] There is an interaction force between people and walls (obstacles), which is similar to the interaction force between people:

[0093]

[0094] A w is the strength coefficient between the wall and the person, k w is the elastic coefficient between the wall and the person, d iw is the shortest distance from the pedestrian to the wall; n iw is the wall normal unit vector.

[0095] In summary, the total motion equation (determining the acceleration and direction of movement of pedestrian i) is obtained:

[0096]

[0097] Since the basic social force model fails to take into account many influences such as panic in crisis evacuation scenarios, it is necessary to revise the basic social force model.

[0098] Taking into account the impact of dangerous sources in emergency evacuation scenarios, a repulsive force specific to dangerous sources is added to the basic social force model, namely the dangerous source repulsive force. Its calculation formula is as follows:

[0099]

[0100] In the formula, σ and k are linear adjustment parameters, and k is generally a sufficiently large number greater than 1. This formula shows that when an individual approaches a danger source, the driving force of the danger source increases significantly, causing the path selection to be biased towards the direction away from the danger. p is the elasticity coefficient of the hazard source, dip is the distance between individual i and hazard source p, n ip is the normal unit vector of the hazard source.

[0101] Based on the aforementioned panic spread model and danger repulsion, the social force model of this embodiment is established. During a crisis scenario, due to the influence of panic, pedestrians' speed will increase. This is reflected in the social force model of this embodiment as an increase in expected speed. The formula for how expected speed is affected by the emotion threshold is as follows:

[0102] V i0 (t)=(1-α)v0+αv max

[0103] In the formula, v0 is the comfortable speed, that is, the expected speed when not panicking; v max is the maximum speed, i.e., the maximum speed an individual can achieve; α is the state transition function, which is the speed influencing factor here. This formula reflects that during a crisis scenario, the speed of pedestrians will increase significantly due to panic until reaching the maximum speed, which is consistent with reality. The revised driving force formula is:

[0104]

[0105] In addition to affecting the expected speed, the emotional threshold also affects the interaction between people, which is specifically expressed as the following formula:

[0106]

[0107] This formula reflects that during the evacuation of pedestrians, as panic accumulates, the repulsive force between different individuals will decrease, causing the group to become more crowded, which is consistent with the actual situation.

[0108] In summary, the corrected total motion equation is obtained:

[0109]

[0110] After the calculation is completed, the individual moves and waits for the next moment to repeat the above calculation until it reaches the exit, thus realizing the simulation of crowd evacuation.

[0111] As can be seen, this example transforms the infection rate of epidemic dynamics from a static parameter into a dynamic parameter influenced by emotional thresholds. This parameter serves as an individual's state transition function, an influencing parameter for emotional threshold propagation, and an influencing parameter for the social force model. Using this function as a bridge, we integrate the social force model with the spread of panic, and further capture individual differences in crowd evacuation.

[0112] Based on the above embodiments, the present invention further provides a crowd evacuation simulation system based on the spread of panic emotions and the influence of dangerous sources, and the system is used to implement the steps of the crowd evacuation simulation method based on the spread of panic emotions and the influence of dangerous sources described in the above method embodiment, such as Figure 4 As shown in , the system includes: a state transfer function determination module 10, a panic emotion propagation model determination module 20, and a crowd evacuation simulation module 30. Specifically, the state transfer function determination module 10 is used to set a simulation scenario and modify the infection rate of the infectious disease dynamics into a state transfer function affected by the emotion threshold. The panic emotion propagation model determination module 20 is used to calculate the state transfer function for each individual in the crowd after the simulation starts, and establish a panic emotion propagation model. The crowd evacuation simulation module 30 is used to set a hazard repulsion force specific to the hazard source, establish a social force model based on the panic emotion propagation model and the hazard repulsion force, and simulate crowd evacuation based on the social force model.

[0113] The working principles of each module in the crowd evacuation simulation system based on panic spread and danger source influence of this embodiment are the same as the principles of each step in the above method embodiment, and will not be repeated here.

[0114] Each module in the aforementioned crowd evacuation simulation system based on the spread of panic and the impact of dangerous sources can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in the terminal in hardware form, or stored in the terminal's memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0115] Based on the above embodiment, the present invention further provides a terminal, the principle block diagram of the terminal can be as follows: Figure 5 The terminal may include one or more processors 100 ( Figure 5 Only one is shown), memory 101, and computer program 102 stored in memory 101 and executable on one or more processors 100. For example, a crowd evacuation simulation program based on the spread of panic and the impact of dangerous sources. When one or more processors 100 execute computer program 102, each step in an embodiment of a crowd evacuation simulation method based on the spread of panic and the impact of dangerous sources can be implemented. Alternatively, when one or more processors 100 execute computer program 102, the functions of each module / unit in an embodiment of a crowd evacuation simulation system based on the spread of panic and the impact of dangerous sources can be implemented, without limitation herein.

[0116] In one embodiment, the processor 100 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0117] In one embodiment, the memory 101 may be an internal storage unit of an electronic device, such as a hard disk or memory of the electronic device. The memory 101 may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device. Furthermore, the memory 101 may include both an internal storage unit of the electronic device and an external storage device. The memory 101 is used to store computer programs and other programs and data required by the terminal. The memory 101 may also be used to temporarily store data that has been output or is about to be output.

[0118] Those skilled in the art will understand that Figure 5 The principle block diagram shown in the figure is only a block diagram of a partial structure related to the solution of the present invention, and does not constitute a limitation on the terminal to which the solution of the present invention is applied. The specific terminal may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0119] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, operating database or other media used in the embodiments provided by the present invention may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0120] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A crowd evacuation simulation method based on the spread of panic and the impact of danger sources, characterized by: The method comprises: Set up a simulation scenario and modify the infection rate of the epidemic dynamics into a state transition function affected by the emotion threshold; After the simulation starts, the state transition function is calculated for each individual in the crowd, and a panic spread model is established; A danger repulsion force dedicated to the danger source is set, a social force model is established based on the panic emotion propagation model and the danger repulsion force, and crowd evacuation is simulated based on the social force model.

2. The crowd evacuation simulation method based on panic spread and danger source influence according to claim 1 is characterized in that: Set up the simulation scenario, including: Set up walls, exits, obstacles, danger sources, crowd distribution, and the initial state of the crowd.

3. The crowd evacuation simulation method based on panic spread and danger source influence according to claim 1 is characterized in that: The calculation formula of the state transfer function is: Among them, α(t) is the state transition function; P i (t) is the emotional threshold of individual i at the current time t; ε is the panic threshold, and k is the transition intensity parameter, which reflects the intensity of the mutation of the value of α(t) near ε.

4. The crowd evacuation simulation method based on panic spread and danger source influence according to claim 3 is characterized in that: After calculating the state transfer function for each individual in the crowd, including: determining the number of panicked people in the crowd at a next moment based on the state transition function; The number of panickers in the crowd at the next moment is: P(t+1)=P(t)+αP(t)S(t)-(1-α)P(t) Among them, S(t) is the proportion of people who are prone to panic in the total population, P(t) is the proportion of people who panic in the total population, and α is the state transition function, which here represents the infection rate.

5. The crowd evacuation simulation method based on panic spread and danger source influence according to claim 3 is characterized in that: The calculation formula of the emotion threshold is: Among them, P i (t) is the emotional threshold of individual i at the current time t; P d is the emotion decline function; λ is the emotion infection coefficient, which affects the efficiency of emotion transmission; α et is the state transition function of other individuals, serving as a panic spreading factor; P D is the influence function of panic emotions caused by dangerous sources; P et is the emotional threshold of other individuals; S is the distance between individuals; R p is the propagation radius, P max is the maximum emotion threshold that a pedestrian can have, with a value of 1.

6. The crowd evacuation simulation method based on panic spread and danger source influence according to claim 5 is characterized in that: The calculation formula of the sentiment drop function is: The influence function of panic emotions caused by dangerous sources is: β is the emotion drop coefficient; d is the distance to the exit, k is the linear adjustment parameter; D is the distance between the individual and the danger source; D crit It is the impact radius of the hazard source.

7. The crowd evacuation simulation method based on panic spread and danger source influence according to claim 6 is characterized in that: The calculation formula of the dangerous repulsive force is: Among them, σ is the linear adjustment parameter, r i is the radius of individual i, k p is the elasticity coefficient of the hazard source, d ip is the distance between individual i and hazard source p, n ip is the normal unit vector of the hazard source.

8. A crowd evacuation simulation system based on the spread of panic and the impact of dangerous sources, characterized by: The system is used to implement the steps of the crowd evacuation simulation method based on panic spread and danger source influence according to any one of claims 1 to 7, the method comprising: A state transfer function determination module is used to set up simulation scenarios and modify the infection rate of the infectious disease dynamics into a state transfer function affected by the emotion threshold; The panic emotion propagation model determination module is used to calculate the state transfer function for each individual in the crowd and establish a panic emotion propagation model after the simulation starts; The crowd evacuation simulation module is used to set a danger repulsion force specific to the danger source, establish a social force model based on the panic emotion propagation model and the danger repulsion force, and simulate crowd evacuation based on the social force model.

9. A terminal, characterized in that: The terminal includes a memory, a processor, and a crowd evacuation simulation program based on the spread of panic and the influence of dangerous sources, which is stored in the memory and can be run on the processor. When the processor executes the crowd evacuation simulation program based on the spread of panic and the influence of dangerous sources, the steps of the crowd evacuation simulation method based on the spread of panic and the influence of dangerous sources are implemented according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a crowd evacuation simulation program based on the spread of panic emotions and the influence of dangerous sources. The crowd evacuation simulation program based on the spread of panic emotions and the influence of dangerous sources implements the steps of the crowd evacuation simulation method based on the spread of panic emotions and the influence of dangerous sources as described in any one of claims 1 to 7 on the computer-readable storage medium.

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