A simulation method for simulating safe evacuation of people in smart towns

Abstract

The present invention provides a simulation method that can simulate the safe evacuation of people in a smart town. The method includes: constructing a crowd evacuation movement model of a smart town, formulating several different safe evacuation strategies according to factors that affect the evacuation efficiency; using the crowd evacuation movement model to simulate the evacuation of people through different safe evacuation strategies. The system of the present invention can simulate the flow of people in a real town, and simulate and rehearse emergency evacuation events. Through data calculation, it provides a theoretical basis for town operators to optimize crowd safety management plans, thereby promoting the integrated and stable operation of smart towns with "high efficiency, high returns, and high safety". The method of the present invention can simulate the flow of people in a real town, and simulate and rehearse emergency evacuation events, and provide a theoretical basis for town operators to optimize crowd safety management plans, thereby promoting the integrated and stable operation of smart towns with "high efficiency, high returns, and high safety".

Description

Technical Field

[0001] The present invention relates to the technical field of crowd evacuation simulation, and in particular to a simulation method capable of simulating the safe evacuation of crowds in a smart town. Background Art

[0002] The social force model is a classic mathematical model used to simulate crowd evacuation. Its concept was first proposed by Lewin and implemented by Helbing and his colleagues. The social force model is based on Newtonian mechanics and assumes that individuals are affected by social forces when evacuating, thereby controlling the movement of the crowd. Social force is the general term for the social psychological force and physical force of the crowd. The social force model decomposes the force that each individual is subject to when moving into the repulsive force between people, between people and static obstacles, and the gravitational force between people and targets. It truly simulates the squeezing force and driving force when the crowd moves. Using Newtonian mechanics equations, the moving speed and direction of an individual are mathematically modeled, realistically simulating the evacuation process of the crowd in an emergency state.

[0003] Specifically, for a mass m i The social force model assumes that pedestrian i moves at the desired speed In a certain direction Move, at this time the person is at time τ i With speed v i At the same time, the pedestrian wants to keep a certain distance from other pedestrians j and the wall W. This can be achieved by the “interaction force” f ij and f iw To model. Based on Newton's mechanics equation, we can get the acceleration equation for the change of velocity in time t:

[0004]

[0005] And the position w of pedestrian i i (t) is determined by the speed v i (t) = dr i (t) / dt is obtained. The psychological tendency of two pedestrians i and j to move away from each other is represented by the repulsive interaction force:

[0006]

[0007] Among them A i and B i is a constant; d ij represents the scalar distance between two pedestrians, n ij =(r i -r j ) / d ij is the normalized vector from pedestrian j to pedestrian i, if their distance d ijsmaller than their radius and r ij =r i +r j , then the two people are considered to be in contact with each other. In this case, the social force model assumes that two other forces will be generated: body compression force kg (r ij -d ij )}n ij and sliding friction where t ij Yes ij The tangential direction of is the tangential velocity difference.

[0008] The repulsive force between pedestrians and walls can be expressed as:

[0009] f iW ={A i exp[r i -d iW ) / B i ]+kg(r i -d iW )}n iW +kg(r i -d iW )(v i ·t iW )t iW

[0010] where d iW represents the distance from the pedestrian to the wall W, n iW represents the direction perpendicular to the wall pointing to the pedestrian, t iW Indicates the direction tangent to it.

[0011] A* (pronounced as A Star) path search algorithm is a very commonly used path finding and graph traversal algorithm. It has good performance and accuracy. It can be considered as an extension of Dijkstra's algorithm. On the basis of Dijkstra's algorithm, the heuristic function of A* algorithm introduces the estimated cost of the current node and the end point (as shown in the following formula), so it has better performance.

[0012] F(n)=g(n)+h(n)

[0013] Among them, f(n) is the comprehensive priority of the nth node. When the algorithm selects the next node to traverse, it will always select the node with the highest comprehensive priority (smallest value). g(n) is the distance cost between the nth node and the starting point, and h(n) is the estimated distance cost between the nth node and the end point. In addition, the A* algorithm uses two sets to represent the nodes to be traversed (open_set) and the nodes that have been traversed (close_set).

[0014] In extreme cases, when the heuristic function h(n) is always 0, the priority of the node will be determined by g(n), and the algorithm will degenerate into the Dijkstra algorithm. If h(n) is always less than or equal to the cost from node n to the end point, the A* algorithm is guaranteed to find the shortest path. However, the smaller the value of h(n), the more nodes the algorithm will traverse, which will make the algorithm slower. If h(n) is exactly equal to the cost from node n to the end point, the A* algorithm will find the best path and it will be very fast. Unfortunately, this is not possible in all scenarios. Because before we reach the end point, it is difficult for us to calculate exactly how far we are from the end point. If the value of h(n) is greater than the cost from node n to the end point, the A* algorithm cannot guarantee to find the shortest path, but it will be very fast.

[0015] Compared with the A* path search algorithm, the ORCA local collision avoidance algorithm is a local navigation. The navigation goal is to navigate around the individual itself, so that the individual can avoid other individual targets and obstacles close to itself. ORCA can only perceive the situation around itself and has no information about the global environment, so it only avoids collisions or overlaps with other individual targets and obstacles around itself during navigation, but cannot find the shortest path between its starting point and target point. This is exactly the problem that A* pathfinding solves.

[0016] The following is a brief introduction to the problem definition and algorithm principle of the ORCA local collision avoidance algorithm.

[0017] First, for each agent, there are the following rules:

[0018] Each agent is circular in shape and has two properties: internal properties and external properties. The internal properties include the maximum speed that the agent can reach. and the agent's preferred speed External attributes include the agent's position coordinates p A , radius r A and the actual speed v A Internal properties are known only to the agent itself, while external properties can be observed by other agents.

[0019] Then, the movement for the agent has the following rules:

[0020] There are multiple agents and all agents can only move on a two-dimensional plane; it is guaranteed that no collision occurs within a time interval when the agents are moving; each agent has the same calculation rules for the collision problem, that is, when A and B are about to collide, A and B will make the same collision avoidance decision; during the movement of each agent, the external properties of other agents can be accurately observed, and their own update speed can be calculated according to certain rules To avoid collision with external objects within τ time.

[0021] The ORCA algorithm first introduced a velocity obstacle. The concept of is a region in the two-dimensional velocity space. When the positions of an agent A and another agent B are known, if the relative velocity of A to B belongs to this region at the next moment, A and B will collide within the time interval τ:

[0022]

[0023] Then collision avoidance was introduced The concept of is that given the speed of B, if the speed of agent A is in this speed range, A and B will not collide within the time interval τ:

[0024]

[0025] in Represents Minkowski and.

[0026] Based on the above concepts, when agentA and agentB are about to collide, the speed that satisfies collision avoidance for A is is an infinite set, and the speed that satisfies collision avoidance for B There should be countless sets, among which there are countless speed pairs V A and V B This can prevent A and B from colliding. Among these countless sets, select the one that contains the most pairs close to the optimal speed. and The set of and This is called optimal reciprocal collision avoidance. With the concept of the optimal reciprocal collision avoidance set, the next question is how to construct this set. First, let A and B both use the optimal speed. and when When A and B collide, let u be a Point to the speed barrier area The vector of the nearest point on the boundary:

[0027]

[0028] because and In the speed barrier area Inside, A and B will collide within τ time. Now, to prevent A and B from colliding within τ time, we must change and Or change both to make Move to speed barrier area In the previous regulations, it has been defined that A and B use the same calculation method to avoid collision, so now the collision responsibility is evenly distributed to the two agents A and B, that is, the new speed of A is The new velocity of B is so No longer in the speed barrier area This also avoids collisions. If n is a point Department The outer normal of the region boundary, then the optimal mutual collision set of agentA can be expressed as:

[0029]

[0030] Similarly for Agent B, the speed that ensures A and B do not collide can be obtained through the ORCA set.

[0031] Crowd evacuation behavior theories aim to detail how human behavior changes after an emergency occurs. All theories attempt to provide a framework to explain and ultimately predict empirically observed phenomena. The main ones include panic theory, emergency norm theory, social attachment theory, self-categorization theory, individualism theory, self-organization theory, etc.

[0032] Panic theory is a leading pioneering theory for understanding how humans react to physical threats, which posits that in a group that takes action in the face of a threat, individuals lose their individuality and become an anonymous part of the group. As a result, any action within the group is immediately and unquestioningly copied by others, creating a "herd mentality". The existence of panicked individuals can be explained by entrapment theory, in which people begin to ignore social rules and act in irrational, non-adaptive and competitive ways in very specific situations.

[0033] Emergent Norms Theory (ENT) proposes that people come together in emergencies, rather than "panic", and in ambiguous situations, define what is happening and that the best course of action develops from within the group through the generation of new social norms. The theory allows heterogeneous groups to begin acting in a homogeneous manner and has been used to inform conceptual models.

[0034] The social attachment model attempts to explain the empirically observed behavior of individuals seeking out "familiar" others in evacuated groups ("affiliative behavior") and has been examined for a variety of scenarios, including combat and fire. The theory provides a way to model the possible choices people make as they move in groups, particularly in evacuation scenarios.

[0035] Self-categorization theory (SCT) allows people to define themselves dynamically in emergency situations, showing how "the emergent properties of group processes can be explained by a shift in self-perceptions, from individual to social identity. Crucially, SCT provides a way to explain why strangers tend to help each other in emergencies."

[0036] The self-organization theory of crowd evacuation proposes that in the absence of external control or planning, pedestrians are affected by the local influence of neighboring individuals, resulting in the spontaneous formation of an overall orderly crowd.

[0037] In recent years, research on crowd evacuation has increasingly considered group behavior influenced by self-organization phenomena. The key to studying crowd self-organization behavior lies in the feedback of information between pedestrians and the pedestrian movement rules stipulated in the model. At the same time, in order to ensure the flexibility of the system, random perturbations are used to allow new behaviors to appear in the simulation. Finally, the interaction between pedestrians is simulated through micro models such as cellular automata and social forces, reflecting more complex self-organization behaviors in the macro group.

[0038] The crowd evacuation simulation scheme in the prior art is mainly aimed at evacuation scenarios in open spaces indoors and outdoors, and is not suitable for large and complex evacuation scenarios such as those in small towns with multiple roads and multiple exits.

[0039] The crowd evacuation simulation scheme in the prior art focuses on mathematical modeling of crowd evacuation behavior to simulate crowd evacuation. However, a single model often cannot reflect the social and psychological behavior of real people during evacuation. Summary of the invention

[0040] An embodiment of the present invention provides a simulation method that can simulate the safe evacuation of people in a smart town, so as to effectively perform simulation rehearsal of emergency evacuation events in the smart town.

[0041] In order to achieve the above object, the present invention adopts the following technical scheme.

[0042] A simulation method for simulating safe evacuation of people in a smart town, comprising:

[0043] Construct a crowd evacuation mobility model for smart towns.

[0044] Several different safe evacuation strategies were developed based on factors that affect evacuation efficiency;

[0045] The crowd evacuation mobility model is used to simulate the crowd evacuation through different safe evacuation strategies.

[0046] Preferably, the crowd evacuation mobility model for constructing a smart town includes:

[0047] A crowd evacuation mobility model for a smart town is constructed. The crowd evacuation mobility model divides the evacuated crowd into three categories according to their different behaviors during emergency evacuation: leaders, panickers, and followers. The crowd evacuation mobility model controls the movement of pedestrians by defining self-driving force, repulsion between pedestrians, and repulsion between pedestrians and obstacles, and adds evacuation exit attraction on the basis of the social force model.

[0048] Preferably, the several different safe evacuation strategies are formulated according to the factors affecting the evacuation efficiency, including:

[0049] The factors that affect evacuation efficiency include: the distance between pedestrians and evacuation exits, the density of people at evacuation exits, the capacity of evacuation exits and the degree of road congestion;

[0050] Setting up a safe evacuation strategy includes:

[0051] Evacuation strategy 1: choose the nearest evacuation exit;

[0052] Evacuation strategy 2, taking into account both the distance to the evacuation exit and the density of people at the evacuation exit;

[0053] Evacuation strategy 3, taking into account the distance to the evacuation exit, the density of the crowd at the evacuation exit, and the capacity of the evacuation exit;

[0054] Evacuation strategy 4 takes into account the distance to the evacuation exit, the density of people at the evacuation exit, the capacity of the evacuation exit and the road congestion.

[0055] Preferably, the crowd evacuation movement model is used to perform crowd evacuation simulations respectively through different safe evacuation strategies, including:

[0056] During the crowd evacuation simulation, the crowd distribution in the town is initialized after the evacuation simulation starts. When selecting the evacuation exit for all the guides for the first time, the priority of all the evacuation exits is calculated through the established strategy algorithm, and the optimal evacuation exit with the highest priority is notified to all the guides. Then, after a period of time, the priority of all the evacuation exits is recalculated, and the optimal evacuation exit with the highest priority recalculated is notified to all the guides.

[0057] The guide leads the panicked people and followers within sight to evacuate through the optimal evacuation exit.

[0058] Preferably, the crowd evacuation movement model is used to perform crowd evacuation simulations respectively through different safe evacuation strategies, including:

[0059] The calculation method of evacuation strategy 1 is as shown in formulas (4) and (5), f i represents the priority of the i-th evacuation exit and the distance d from the i-th evacuation exit i Inversely proportional:

[0060]

[0061]

[0062] (x ij ,y ij ) represents the coordinates of the path points in the two-dimensional map;

[0063] The calculation method of evacuation strategy 2 is shown in formula (6), f i represents the priority of the i-th evacuation exit, k i and w 1 represents the crowd density and density weight of the i-th evacuation exit, d i and w 2 Represents the distance and distance weight of the i-th evacuation exit:

[0064]

[0065] The calculation method of evacuation strategy 3 is shown in formula (7), f i represents the priority of the i-th evacuation exit, k i represents the crowd density at the evacuation exit, Cap i represents the capacity of the i-th evacuation exit, k i / Cap i and w 1 represents the crowd density capacity ratio of the i-th evacuation exit and the corresponding weight, d i and w 2 Represents the distance and distance weight of the i-th evacuation exit:

[0066]

[0067] The calculation method of evacuation strategy 4 is shown in formula (8), f i represents the priority of the i-th evacuation exit, k i represents the crowd density at the evacuation exit, Cap i represents the capacity of the i-th evacuation exit, k i / Cap i and w 1 represents the crowd density capacity ratio of the i-th evacuation exit and the corresponding weight, di and w 2 represents the distance and distance weight of the i-th evacuation exit, r i and w 3 Represents the number and weight of people on the road from each person's current location to the i-th evacuation exit:

[0068]

[0069] After calculating the priorities of all evacuation exits, they are compared using formula (9):

[0070] max ≠t (f 1 , f 2 , ..., f n ) / f t >Therehold (9)

[0071] where f t is the priority of the original evacuation exit in this calculation. Therehold is a preset comparison threshold, and its value represents the subjectivity of the guide. If the threshold is larger, the condition for re-changing the evacuation exit is more stringent, and the guide is more likely to choose the original evacuation exit. If the threshold is 0, the evacuation exit must be changed every time it is re-selected. If there is at least one other evacuation exit whose priority makes formula 4-9 valid, the corresponding evacuation exit of the guide is changed to the evacuation exit with the highest priority, otherwise the evacuation exit remains unchanged, and then continues to move for a period of time to determine whether it reaches the evacuation exit. If it does not reach the evacuation exit, the priority of all evacuation exits of the guide is recalculated until the guide leaves the smart town.

[0072] It can be seen from the technical solutions provided by the above-mentioned embodiments of the present invention that the simulation method of the present invention that can simulate the safe evacuation of people in a smart town can simulate the flow of people in a real town, and simulate and rehearse emergency evacuation events. Through data calculation, it provides a theoretical basis for town operators to optimize crowd safety management plans, thereby promoting the integrated and stable operation of smart towns with "high efficiency, high returns, and high safety".

[0073] Additional aspects and advantages of the present invention will be given in part in the following description, which will become obvious from the following description, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0075] Figure 1 A flow chart of dynamic crowd evacuation provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0076] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

[0077] It will be understood by those skilled in the art that, unless expressly stated, the singular forms "one", "said", and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, the "connection" or "coupling" used herein may include wireless connection or coupling. The term "and / or" used herein includes any unit and all combinations of one or more associated listed items.

[0078] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless defined as herein.

[0079] To facilitate understanding of the embodiments of the present invention, several specific embodiments will be further explained below with reference to the accompanying drawings, and each embodiment does not constitute a limitation on the embodiments of the present invention.

[0080] The present invention aims to propose a universal smart town crowd evacuation model, which can be used to simulate the safe evacuation of people in different smart towns, and provide a reliable reference for the customization of realistic evacuation plans through simulation. The several technical problems encountered mainly include: psychological modeling of the evacuated crowd, behavioral definition of the evacuated crowd, and customization of the evacuation strategy. In order to solve the above technical problems, the evacuation model proposed in the present invention mainly includes three parts: evacuation crowd classification design, evacuation strategy algorithm design, and crowd dynamic evacuation design.

[0081] In the design of crowd evacuation, the present invention divides the crowd into three categories according to their different behaviors during emergency evacuation: leaders, panickers and followers. The behavior of each type of crowd during evacuation is simulated through an improved social force model and an exit selection model; in the design of the evacuation strategy algorithm, the present invention analyzes several key factors that affect the efficiency of emergency evacuation in a small town, and studies four different strategy algorithms for selecting evacuation exits; in the design of dynamic crowd evacuation, the present invention proposes a simulation method in which the leader dynamically adopts an evacuation strategy to continuously adjust the evacuation exit according to the actual evacuation situation, aiming to improve the overall evacuation efficiency of the crowd in the small town under the constantly changing conditions during the evacuation process. The proposed dynamic evacuation simulation method is also closer to the objective fact that pedestrians will change their evacuation ideas according to the constantly changing environment in the small town.

[0082] According to the different behaviors shown during emergency evacuation, the present invention divides the evacuated crowd into three categories: guides, panickers and followers. During the evacuation process, the guide will select a certain exit according to the evacuation strategy plan designed by the present invention. Before reaching the exit during the evacuation process, it will not be affected by other pedestrians, and will only adjust the exit selection according to the dynamic evacuation rules; if the panicker does not see the guide in the field of vision, he will randomly choose an exit to escape and evacuate. When he sees the guide in the field of vision, he will evacuate with the guide; if the follower does not see the guide and the follower in the field of vision, he will move in the direction of the crowd. When he sees the panicker in the field of vision but does not see the guide, he will evacuate with the panicker. If he sees both the panicker and the guide in the field of vision, he will evacuate with the guide. In addition, the panickers and the followers also have self-subjective selectivity during evacuation, that is, if the panickers and the followers do not meet the guide at the beginning, but randomly select an exit, then the driving force of this choice will increase over time, and they may meet the guide later, and they will not change their original choice.

[0083] The crowd evacuation mobility model used in the present invention is a social force model improved based on the above crowd classification. In the social force model, several natural forces are defined, which jointly determine the speed and direction of pedestrian movement through Newtonian mechanics formulas, thereby controlling the movement of pedestrians. They are "self-driving force", "repulsion between pedestrians", "repulsion between pedestrians and obstacles", etc. The present invention adds "exit attraction" on the basis of the social force model, as shown in Formula 1:

[0084]

[0085] in It represents the self-driving force of pedestrians’ psychological factors (this item only exists when there are no panickers or guides around the follower, and he follows the direction with more people based on psychological factors). ij (t) is the repulsive force between people, which prevents pedestrians from colliding with each other, and f iw (t) is the repulsive force between pedestrians and obstacles, preventing pedestrians from passing through the wall, and f ik (t is the attraction of the pedestrian's current path to the exit, which only works when the pedestrian explicitly evacuates the exit. For the guide, f ik (t) The exit pointed to is the exit selected by the evacuation strategy formulated by the present invention. For panickers and followers, f ik The exit that (t) points to is determined by Formula 2 and Formula 3:

[0086] f panic =β p ×PERSONAL i +β s ×f leader (2)

[0087] f follower =β p ×PERSONAL i +β s ×f leader / panic (3)

[0088] Among them, PERSONAL i represents an exit randomly selected by pedestrians without the influence of others. This item increases with time and represents the subjective selectivity of pedestrians. leader It represents the impact of the exit chosen by the leader on the panickers and followers. In formula 3, when there is a panicker around the follower but no leader, the follower will be affected by the panicker. Once there is a leader around him, he will only be affected by the leader.

[0089] According to the structural layout of the town and previous evacuation theory research, this paper summarizes several important factors that affect evacuation efficiency:

[0090] 1. The distance between pedestrians and exits. Obviously, no matter which group of people, choosing the exit closest to them will definitely take less time to escape.

[0091] 2. Density of people at the exit. From an intuitive point of view, the shorter the distance, the less time it takes, but this is only true when the pedestrian speed does not decrease. If the density of people at the nearest exit is too high, the exit is congested, and the speed of pedestrian flow is significantly reduced, then choosing another exit that is farther away but has a lower density of people will often reduce the evacuation time.

[0092] 3. Exit capacity. However, we cannot only consider distance and crowd density. In some cases, even if the crowd density at a certain exit is very high, the flow rate will not decrease, because its capacity is also large, which can ensure that the flow rate will not decrease under the condition of high crowd density. At this time, if you choose the nearest exit, it is still the best choice, so the exit capacity is also a factor that should be considered.

[0093] 4. Road congestion. In addition to congestion at the exit, a large number of pedestrians may gather in some busy streets or pedestrian streets, causing congestion during the evacuation process. If pedestrians can be guided to avoid congested roads during the evacuation process, the overall evacuation time can often be reduced.

[0094] Although the above four influencing factors are all important for evacuation guidance, their importance varies in different situations. In order to explore which influencing factor is more important in various crowd distribution scenarios, the present invention proposes four strategies according to the importance of the four factors and different combinations:

[0095] Evacuation strategy 1: Choose the nearest exit. In most cases, evacuees will choose the nearest exit first. Therefore, the present invention only considers distance as an evacuation strategy, as shown in Formula 5, f i Represents the priority of the i-th exit and the distance d from the i-th exit i Inversely proportional:

[0096]

[0097]

[0098] Evacuation strategy 2: Consider both exit distance and exit crowd density. Strategy 2 takes the exit crowd density into account and gives priority to exits with low crowd density and short distance, as shown in Formula 6, f i represents the priority of the ith exit, k and w 1 represents the crowd density and density weight of the ith exit, di and w 2 Represents the distance and distance weight of the i-th exit:

[0099]

[0100] Evacuation Strategy 3 - Considering exit distance, exit crowd density and exit capacity at the same time. Strategy 3 takes exit capacity into account on the basis of Strategy 2. Compared with Strategy 2, it takes into account the influence of crowd density on crowd flow speed more comprehensively, as shown in Formula 7, f i represents the priority of the ith exit, k i represents the crowd density at the ith exit, Cap i represents the capacity of the ith exit, k i / Cap i and w 1 represents the crowd density capacity ratio of the i-th exit and the corresponding weight, d i and w 2 Represents the distance and distance weight of the i-th exit:

[0101]

[0102] Evacuation Strategy 4 - Considering the exit distance, exit crowd density and exit capacity at the same time. Strategy 4 takes into account the road congestion (expressed by the number of people on the road) based on Strategy 3, as shown in Formula 4-8, f i represents the priority of the ith exit, k i represents the crowd density at the ith exit, Cap i represents the capacity of the ith exit, k i / Cap i and w 1 represents the crowd density capacity ratio of the i-th exit and the corresponding weight, d i and w 2 represents the distance and distance weight of the i-th exit, r i and w 3 Represents the number and weight of people on the road from each person's current location to the i-th exit:

[0103]

[0104] Note that in the evacuation simulation of this platform, we assume that the guide has some global prior information (such as knowing the distance between himself and all exits, the density of people at each exit, the degree of road congestion, etc.). In fact, in reality, the town can also obtain the above information in real time through the existing big data Internet of Things and positioning technology. The focus of this invention is how to use this information to promote the effective implementation of emergency evacuation.

[0105] Finally, the dynamic evacuation process of the crowd during the evacuation process is modeled. During the evacuation simulation, the guide will select an optimal evacuation exit according to the established evacuation strategy and lead other pedestrians (panic-stricken people and followers) in the field of vision to evacuate together. It is worth emphasizing that in order to simulate the flexibility of pedestrians' subjective thinking in reality, the guide's choice is not static, but will be updated according to the strategy at regular intervals based on the evacuation situation in the town, so as to complete the dynamic evacuation of the crowd.

[0106] Figure 1 A crowd dynamic evacuation flow chart provided by the present invention includes the following processing procedures:

[0107] After starting the evacuation simulation, the town population distribution is initialized first (if entering the evacuation simulation from the normal simulation, no initialization is required). Then, when selecting an exit for all guides for the first time, the priority of all exits is calculated through the established strategy algorithm, and the exit with the highest priority is selected for evacuation. After a period of time, the priority of all exits is recalculated and compared using Formula 4-9:

[0108] max ≠t (f 1 , f 2 , ..., f n ) / f t >Therehold (9)

[0109] where f t is the priority of the original exit in this calculation, Therehold is a pre-set comparison threshold, and its value represents the subjectivity of the guide. If the threshold is larger, the conditions for re-changing the exit are more stringent, and the guide is more likely to choose the original evacuation exit. If the threshold is 0, the exit must be changed every time it is re-selected. If there is at least one other exit whose priority makes formula 4-9 valid, the corresponding exit of the guide is changed to the exit with the highest priority, otherwise the exit remains unchanged, and then continue to move for a while to determine whether it reaches the exit. If it does not reach the exit, the priorities of all exits of the guide are recalculated until it leaves the town.

[0110] In summary, the beneficial effects of the simulation method for simulating safe evacuation of people in a smart town according to the embodiment of the present invention are mainly reflected in the following two points:

[0111] (1) Practical significance

[0112] During the peak tourist season, the number of tourists in the smart town surges, and crowds and traffic jams are prone to occur in multiple locations. At this time, it is necessary to guide the crowd: let each customer group reach the place they want to go and carry out corresponding activities; the town's operating personnel need to dispatch on-site staff to control key locations and entrances and exits; for locations prone to chaotic events, it is necessary to set up plans in advance, and make arrangements in terms of security, temporary passages, crowd evacuation, and emergency event handling to ensure that the town is always in a safe and orderly operating state; in addition, when an emergency occurs in the town, large-scale crowd safety evacuation is required, and the relevant security personnel at each key location need to be able to quickly provide safe evacuation guidance according to the plan to minimize the risk and loss of accidents. The crowd behavior simulation platform of the present invention can simulate the flow of people in a real town, and simulate and rehearse emergency evacuation events. Through data calculation, it provides a theoretical basis for the town operator to optimize the crowd safety management plan, thereby promoting the integrated and stable operation of the "high efficiency, high return, and high safety" of the smart town.

[0113] (2) Academic significance

[0114] Crowd evacuation simulation is a technology that integrates multiple disciplines, including motion physics, computer image modeling, data analysis, machine learning, etc., and has always been a hot research direction. The crowd evacuation model proposed in this paper is based on previous research results and combines the crowd characteristics of smart towns to complete the modeling of crowd behavior without the need for real data support, which contributes to the combination of the theoretical basis of crowd evacuation simulation and the practice of safe evacuation in reality.

[0115] Those skilled in the art can understand that the accompanying drawings are only schematic diagrams of an embodiment, and the modules or processes in the accompanying drawings are not necessarily required to implement the present invention.

[0116] It can be known from the description of the above implementation methods that those skilled in the art can clearly understand that the present invention can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present invention or certain parts of the embodiments.

[0117] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0118] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A simulation method for simulating safe evacuation of people in a smart town, characterized in that: include: Construct a crowd evacuation mobility model for smart towns; Several different safe evacuation strategies were developed based on factors that affect evacuation efficiency; The crowd evacuation mobility model is used to simulate crowd evacuation through different safe evacuation strategies; The crowd evacuation movement model is: in represents the self-driving force of pedestrian psychological factors, f ij (t) is the repulsive force between people, f iw (t) is the repulsive force between the pedestrian and the obstacle, f ik (t) is the pedestrian’s current attraction to the road pointing to the exit, f ik The exit pointed by (t) is the exit selected by the evacuation strategy formulated by the crowd evacuation mobility model. For panickers and followers, f ik The exit that (t) points to is determined by Formula 2 and Formula 3: f panic =b p ×PERSONAL i +b s ×f leader (2) f follower =b p ×PERSONAL i +b s ×f leader / panic (3) Among them, PERSONAL i represents an exit randomly selected by pedestrians without the influence of others, f leader Represents the impact of the exit chosen by the leader on the panickers and followers. In formula 3, when there is a panicker around the follower but no leader, the follower will be affected by the panicker. Once there is a leader around the follower, the follower will only be affected by the leader. According to the factors that affect the evacuation efficiency, several different safe evacuation strategies have been formulated, including: The factors that affect evacuation efficiency include: the distance between pedestrians and evacuation exits, the density of people at evacuation exits, the capacity of evacuation exits and the degree of road congestion; Setting up a safe evacuation strategy includes: Evacuation strategy 1: choose the nearest evacuation exit; Evacuation strategy 2, taking into account both the distance to the evacuation exit and the density of people at the evacuation exit; Evacuation strategy 3, taking into account the distance to the evacuation exit, the density of the crowd at the evacuation exit, and the capacity of the evacuation exit; Evacuation strategy 4, taking into account the distance to the evacuation exit, the density of the crowd at the evacuation exit, the capacity of the evacuation exit and the road congestion; The crowd evacuation movement model is used to perform crowd evacuation simulation respectively through different safe evacuation strategies, including: The calculation method of setting evacuation strategy 1 is shown in formulas (4) and (5), where fi represents the priority of the i-th evacuation exit and the distance d from the i-th evacuation exit is i Inversely proportional: (x ij ,y ij ) represents the coordinates of the path points in the two-dimensional map; The calculation method of evacuation strategy 2 is shown in formula (6), f i represents the priority of the i-th evacuation exit, k i and w1 represent the crowd density and density weight of the ith evacuation exit, d i and w2 represent the distance and distance weight of the i-th evacuation exit: The calculation method of evacuation strategy 3 is shown in formula (7), f i represents the priority of the i-th evacuation exit, k i represents the crowd density at the i-th evacuation exit, Cap i represents the capacity of the i-th evacuation exit, k i / Cap i and w1 represent the crowd density capacity ratio and density weight of the ith evacuation exit, d i and w2 represent the distance and distance weight of the i-th evacuation exit: The calculation method of evacuation strategy 4 is shown in formula (8), f i represents the priority of the i-th evacuation exit, k i represents the crowd density at the i-th evacuation exit, Cap i represents the capacity of the i-th evacuation exit, k i / Cap i and w1 represent the crowd density capacity ratio and density weight of the ith evacuation exit, d i and w2 represent the distance and distance weight of the i-th evacuation exit, r i and w3 represent the number and weight of people on the road from each person’s current location to the i-th evacuation exit; After calculating the priorities of all evacuation exits, they are compared using formula (9): where f t is the priority of the original evacuation exit in this calculation. Therehold is a preset comparison threshold, and its value represents the subjectivity of the guide. If the threshold is larger, the condition for re-changing the evacuation exit is more stringent, and the guide is more likely to choose the original evacuation exit. If the threshold is 0, the evacuation exit must be changed every time it is re-selected. If there is at least one other evacuation exit whose priority makes formula 4-9 valid, the corresponding evacuation exit of the guide is changed to the evacuation exit with the highest priority, otherwise the evacuation exit remains unchanged, and then continues to move for a period of time to determine whether it reaches the evacuation exit. If it does not reach the evacuation exit, the priority of all evacuation exits of the guide is recalculated until the guide leaves the smart town.

2. The method according to claim 1, characterized in that The crowd evacuation mobility model for constructing a smart town includes: A crowd evacuation mobility model for a smart town is constructed. The crowd evacuation mobility model divides the evacuated crowd into three categories according to their different behaviors during emergency evacuation: leaders, panickers, and followers. The crowd evacuation mobility model controls the movement of pedestrians by defining self-driving force, repulsion between pedestrians, and repulsion between pedestrians and obstacles, and adds evacuation exit attraction on the basis of the social force model.

3. The method according to claim 2, characterized in that The crowd evacuation movement model is used to perform crowd evacuation simulation respectively through different safe evacuation strategies, including: During the crowd evacuation simulation, the crowd distribution in the town is initialized after the evacuation simulation starts. When selecting the evacuation exit for all the guides for the first time, the priority of all the evacuation exits is calculated through the established strategy algorithm, and the optimal evacuation exit with the highest priority is notified to all the guides. Then, after a period of time, the priority of all the evacuation exits is recalculated, and the optimal evacuation exit with the highest priority recalculated is notified to all the guides. The guide leads the panicked people and followers within sight to evacuate through the optimal evacuation exit.

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