Tunnel group fire personnel evacuation analysis method based on cellular automaton

Abstract

The invention relates to the field of fire evacuation, in particular to a cellular automaton-based tunnel group fire personnel evacuation analysis method. The method comprises the following steps: S1, constructing a tunnel group digital model according to the actual structure and size of a tunnel group; s2, dividing grids, and establishing a two-dimensional cellular automaton model; s3, evacuation personnel parameters are set; s4, simulating a fire scene by using the FDS; s5, calculating a relative distance field and a dynamic field; s6, generating a personnel motion model; s7, completing movement of the personnel and judging the number of the personnel in the space; and S8, outputting the evacuation time and the evacuation success rate of the evacuated people. By using the cellular automaton model, the evacuation process of personnel in the tunnel group when a fire occurs can be simulated. According to the method, the tunnel group digital model is constructed, the space of the tunnel group digital model is divided into discrete cells with the same size, and the moving direction of personnel is calculated based on fire data, so that the actual behavior of evacuated personnel can be reflected more truly.

Description

Technical Field

[0001] The invention relates to the field of fire evacuation, and in particular to a tunnel group fire personnel evacuation analysis method based on cellular automata. Background Art

[0002] The tunnel structure is long and narrow in the longitudinal direction, with small lateral space and poor ventilation conditions. It is difficult to extinguish a fire in time, and once a fire occurs, the consequences are very serious. Compared with a single-hole tunnel, the smoke flow in a tunnel group is particularly complex due to special geometric boundaries such as cross-sectional mutation, bifurcation, contraction, and gradual expansion, which leads to many uncertainties in the selection of evacuation routes when a fire occurs. Therefore, when simulating the evacuation of personnel in a tunnel group fire, the impact of the fire development on the escape route and exit selection of personnel should be fully considered, which will help to accurately judge the evacuation scene of the tunnel group fire and scientifically formulate the evacuation plan for the tunnel group fire.

[0003] Existing fire evacuation analysis methods are mostly based on fluid dynamics or continuous medium models, but these methods often have difficulty in accurately simulating individual behaviors and decision-making processes when dealing with large-scale, discrete individual evacuation problems. Cellular automata, as a discrete event simulation tool, have been widely used in traffic flow simulation, urban planning and other fields due to their simplicity, efficiency and ease of implementation. In recent years, cellular automata models have also been introduced into evacuation simulation and smoke diffusion models, showing their unique advantages, especially in dealing with large-scale evacuation problems.

[0004] However, most of the current evacuation simulations establish fire smoke models and personnel evacuation models separately. The impact of fire on personnel evacuation is not sufficiently considered. In the simulation, personnel cannot respond to the development of the fire, and it is difficult to reflect the impact of the fire on personnel escape routes and exit choices. Summary of the invention

[0005] The present invention provides a tunnel group fire personnel evacuation analysis method based on cellular automata, aiming to solve the shortcomings of the current tunnel evacuation model.

[0006] The present invention provides a tunnel group fire personnel evacuation analysis method based on cellular automata, comprising the following steps: S1. Construct a digital model of the tunnel group based on the actual structure and size of the tunnel group; S2. Divide the grid and establish a two-dimensional cellular automaton model; S3. Set the parameters for evacuating personnel, set the movable network selection rules, environmental factors and conflict avoidance principles according to the characteristics of the tunnel group and the actual situation of the fire, and arrange the personnel in the movable cells; S4. Use FDS to simulate the fire scene, process the obtained data through Matlab, and load it into the cellular automaton model at the same time interval as the evacuation update to obtain the temperature field and smoke field that change with time steps, and simulate the impact of fire products on personnel evacuation; S5. Calculate the relative distance field and dynamic field. The relative distance field with obstacles is calculated by Dijkstra algorithm. The dynamic field represents the attraction and repulsion of the virtual track left by the personnel on other personnel, and is updated in real time. S6. Generate a personnel movement model, calculate the movement probability of each neighboring cell of the personnel at time t and the personnel's health index, sort the movement probabilities, and the personnel go to the location with the highest movement probability or stay where they are; S7. The personnel have completed the movement and the number of personnel in the space is determined. If there are personnel, the evacuation continues, the time step is +1, and the scene information is updated, and steps S4, S5, and S6 are repeated; if all personnel have reached the exit, there are no personnel in the movable space or the health index of the remaining personnel is less than 0, the evacuation is terminated; S8. Output the evacuation time and evacuation success rate of the evacuees.

[0007] As a further improvement of the present invention, in the digital model of the tunnel group constructed in step S1, walls, obstacles, movable spaces, and exits are distinguished by different numbers, and the numbers are distinguished by different colors.

[0008] As a further improvement of the present invention, the step S2 specifically includes: The two-dimensional cellular automaton model divides the digital model space of the tunnel group into discrete cells of equal size in the horizontal and vertical directions according to the cellular automaton theory and the average shoulder width of personnel. Each evacuated personnel occupies at least one cell, and each of the cells except the exit is in one of the three states of idle, occupied by personnel, and occupied by obstacles. When the cell is occupied by an obstacle, the personnel cannot move to the cell. When the cell is occupied by a person, other personnel cannot move to the cell. The distance between personnel is more than one cell, and each person searches for a path to move to the exit cell by cell.

[0009] As a further improvement of the present invention, in step S3, the movable network selection rule includes using Moore's neighborhood to stipulate that all evacuees can only move one cell at least in each time step, and can only select their domain cells, which are the adjacent cells below, below left, left, above, above right, right, and below right of the cell; The environmental factors include the attraction of the tunnel exit to the evacuated personnel, the attraction and repulsion of other personnel by the virtual tracks left by the personnel, and the repulsion of the personnel to the high temperature and smoke generated by the fire; The conflict avoidance principle includes that if more than two persons choose to move to the same cell at the same time, one person is randomly selected to move to that cell, and the other persons who fail in the competition choose to move to the suboptimal cell or stay where they are.

[0010] As a further improvement of the present invention, in step S5, the relative distance field with obstacles is calculated by the Dijkstra algorithm, and the relative distance field is a global relative distance field formed by multiple exits, which represents the shortest distance between each point and the exit. By setting the cells near the fire point as virtual obstacles, when the fire grows to a specified degree, personnel choose other routes away from the fire point to escape, and update the detour and escape paths around the fire scene.

[0011] As a further improvement of the present invention, in step S5, the calculation and real-time updating process of the dynamic field is as follows: When a person passes through the cellular space ( i , j ), the dynamic field D Add 1: , , Normalization: , In the formula d is the dynamic field diffusion coefficient, which represents the proportion of diffusion to the adjacent cells in the next step; l is the attenuation coefficient, which represents the ratio of the dynamic field attenuation in the next step; Represents a cell ( i,j ) at time step t The dynamic field value at time.

[0012] As a further improvement of the present invention, in step S6, the movement probability of the personnel in each direction is used to calculate the direction of the personnel's movement, and the center of mass of the personnel moves to each adjacent cell ( i,j ) can be expressed as: , , In the formula, P i,j Represents a cell ( i,j ) Personnel movement probability; K is a normalization parameter to ensure that the sum of the cell movement probabilities is 1; S i,j Represents a cell ( i,j ), D i,j Represents a cell ( i,j )’s dynamic field value,T i,j Represents a cell ( i,j ) temperature field value, V i,j Represents a cell ( i,j )’s visibility field value, G i,j Represents a cell ( i,j )’s guiding field value; C S and C D Represent the relative distance field parameters and dynamic field parameters respectively; C T and C V are the sensitivity parameters of temperature and smoke concentration respectively; C G represents the weight coefficient of the guiding field strength; g i,j and l i,j Represents the cell state. When the cell ( i,j ) When occupied by people g i,j =0, otherwise, g i,j =1, when the cell ( i,j ) is occupied by buildings, facilities, obstacles, etc. l i,j =0, otherwise, l i,j =1.

[0013] As a further improvement of the present invention, in step S6, H A Indicates the health index of personnel, H 0 Represents the initial value of the health index, minus the loss value affected by temperature and CO concentration at each time step. H A When it drops to 0, it means that the person has died and no longer moves to the surrounding cells. The health index of the person H A The calculation formula is: , , , In the formula I T and I C Respectively represent the damage coefficients of temperature and CO concentration to human body; T Indicates the temperature value at the unit cell, °C;C Represents the CO concentration value per unit cell, ppm.

[0014] As a further improvement of the present invention, step S7 specifically includes: According to the movement status of evacuees and the cellular automaton model, each grid node is in t The information at the moment, select the next moving direction of each evacuee, when the evacuee completes the move, determine the moving status of the evacuee t Whether the evacuation can continue at this moment, if the personnel health index is greater than 0, the evacuation continues, the time step is +1, and steps S4 to S6 are repeated until all personnel have completed the evacuation. If the personnel health index is less than or equal to 0 or there is no path to move, the evacuation cannot continue and the evacuation is terminated.

[0015] As a further improvement of the present invention, in step S8, the evacuation time of the evacuees is the time when the last person leaves the exit, wherein the last person refers to the person who takes the longest time among all the people in the tunnel, or if there are still remaining people trapped in the tunnel, it is the time taken by the last successful escaper; the evacuation success rate is the percentage of the number of people who successfully escape from the exit to the total number of people.

[0016] The beneficial effect of the present invention is that the cellular automaton model can be used to simulate the evacuation process of personnel in a tunnel group when a fire occurs. By constructing a digital model of the tunnel group, and according to the cellular automaton theory and the average shoulder width of personnel, the digital model space of the tunnel group is divided into discrete cells of equal size in the horizontal and vertical directions, and the movement direction of personnel is calculated based on fire data, so that the actual behavior of evacuated personnel can be more realistically reflected. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of the tunnel group fire personnel evacuation analysis method based on cellular automata of the present invention; Figure 2 is a schematic diagram of the Moore neighborhood used in the mobile network selection rule of the present invention; Figure 3 It is a specific flow chart of the implementation process of the tunnel group fire personnel evacuation analysis method based on cellular automata of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0019] like Figure 1 As shown, a tunnel group fire personnel evacuation analysis method based on cellular automata of the present invention comprises the following steps: S1. Construct a digital model of the tunnel group based on the actual structure and size of the tunnel group.

[0020] The digital model of the tunnel group uses different numbers to distinguish walls, obstacles, movable spaces, and exits, and uses different colors to distinguish the numbers to achieve a visualization effect.

[0021] S2. Divide the grid and establish a two-dimensional cellular automaton model.

[0022] The two-dimensional cellular automaton model divides the tunnel group digital model space into discrete cells of equal size in the horizontal and vertical directions according to the cellular automaton theory and the average shoulder width of personnel. Each evacuee occupies at least one cell, such as Figure 2 As shown, in this embodiment, each evacuee occupies four cells (2×2), and each cell except the exit may be in three states: idle, occupied by people, and occupied by obstacles. When a cell is occupied by an obstacle, people cannot move to the cell. When a cell is occupied by a person, other people cannot move to the cell, and a distance of more than one cell is maintained between people. Each person searches for a path to move to the exit cell by cell.

[0023] S3. Set the parameters for evacuating personnel, set the movable network selection rules, environmental factors and conflict avoidance principles according to the characteristics of the tunnel group and the actual situation of the fire, and arrange the personnel in the movable cells.

[0024] like Figure 2 As shown in the figure, the movable network selection rule uses Moore's neighborhood to stipulate that all evacuees can only move one grid per time step, and can only select cells in their area, thereby determining the movement trend and direction of the evacuees. For example, it is stipulated that all evacuees in the carriage can only move one cell every four time steps, and the evacuees after leaving the carriage can only move one cell every three time steps to simulate the speed reduction of the evacuees in the carriage due to congestion.

[0025] Among them, the personnel are located in the middle cell ( i,j ), when using Moore neighborhood to determine the movement trend and direction of evacuees, evacuees can move to their 8 domain positions, and the adjacent position is the middle position ( i , j ), and the movement probability of each adjacent position is the sum of the movement probabilities of the four cells at that location.

[0026] Environmental factors include the attraction of tunnel group exits to evacuees, the attraction and repulsion of other people by the virtual tracks left by people, and people's repulsion to the high temperature and smoke generated by the fire. The attraction of exits to evacuees is affected by their familiarity with the environment and the shortest distance between them and the exit. The attraction and repulsion of other people by the virtual tracks left by people are affected by the escapees' recognition and mastery of the virtual path information left by other escapees or the herd mentality. People's repulsion to the high temperature and smoke generated by the fire is based on the consideration that as the fire develops, the temperature in the tunnel group rises and visibility decreases, and evacuees will be reluctant to approach the fire scene, which will affect their choice of escape routes. The attraction of broadcast information to people is mainly related to the subjective reaction characteristics of people and the number of broadcasts.

[0027] The conflict avoidance principle means that if two or more people choose to move to the same cell at the same time, one person is randomly selected to move to that cell, and the other people fail in the competition and choose to move to the suboptimal cell direction (i.e. the second most likely cell) or stay where they are.

[0028] S4. Use FDS to simulate the fire scene, process the obtained data through Matlab, and load it into the cellular automaton model at the same time interval as the evacuation update to obtain the temperature field and smoke field that change with the time step, simulating the impact of fire products on personnel evacuation.

[0029] like Figure 3 As shown in the figure, Matlab processes fire data, that is, FDS is used to simulate the fire scene, and the Excel data measured by the detector is processed through the interpolation algorithm in Matlab, the fire data is expanded, the time dimension and the space dimension are unified, and it is loaded into the cellular automaton model at the same time interval as the evacuation update, and a three-dimensional matrix is ​​created to store the fire data of each time step, and the temperature field and smoke field that change with the time step are obtained. In the subsequent steps, the movement probability of personnel is affected to simulate the influence of fire products on personnel evacuation.

[0030] S5. Calculate the relative distance field and dynamic field. The relative distance field with obstacles is calculated by Dijkstra algorithm. People will avoid obstacles. The dynamic field represents the attraction and repulsion of the virtual track left by people on other people, and is updated in real time.

[0031] The relative distance field is a global relative distance field formed by multiple exits, which indicates the shortest distance between each point and the exit. The tunnel group has complex routes, and people can escape from multiple routes. The relative distance field of multiple exits with obstacles is calculated by the Dijkstra algorithm. When the fire grows to a certain extent, the high temperature and smoke will cause people to stay away from the vicinity of the fire point and choose other routes to escape, which will also affect the choice of exits. The present invention sets the vicinity of the fire point as a virtual obstacle and updates the relative distance field to enable people to detour around the fire and change the escape route. In this embodiment, the temperature is higher than 60°C and the visibility is lower than 10m, which are set as virtual obstacles.

[0032] The dynamic field value describes the attraction and repulsion of the virtual track left by a person on other people. Its value diffuses and decays as the person moves, and disappears after a period of time. The dynamic field is calculated according to the corresponding formula and updated in real time. When someone passes through the cellular space ( i,j ), the dynamic field D increases by 1: , , Normalization: , Where: d is the dynamic field diffusion coefficient, which represents the proportion of diffusion to the adjacent cells in the next step; l is the attenuation coefficient, which represents the ratio of the dynamic field attenuation in the next step; Represents a cell ( i,j ) at time step t The dynamic field value at time.

[0033] S6. Generate personnel movement model and calculate t The movement probability of each neighboring cell of the personnel at each moment and the personnel's health index are used to sort the movement probabilities, and the personnel move to the area with the maximum movement probability or stay where they are.

[0034] The movement probability of personnel in each direction is used to calculate the direction of personnel movement. The center of mass of personnel moves to each adjacent cell ( i,j ) can be expressed as: , , The probability of the person's final moving direction is the sum of the probabilities of the four cells at that location.

[0035] In the formula, P i,j Represents a cell ( i,j ) Personnel movement probability; K is a normalization parameter to ensure that the sum of the cell movement probabilities is 1; S i,jRepresents a cell ( i,j )'s relative distance field value; D i,j Represents a cell ( i,j )’s dynamic field value; T i,j Represents a cell ( i,j )’s temperature field value; V i,j Represents a cell ( i,j )’s visibility field value; G i,j Represents a cell ( i,j )’s guiding field value; C S and C D They represent the relative distance field parameter and the dynamic field parameter respectively, and the influence of the relative distance field and the dynamic field on the movement of personnel can be adjusted by these two parameters; C T and C V They are the sensitivity parameters of temperature and smoke concentration, respectively. By adjusting these two parameters, the influence of temperature and smoke concentration on the probability of personnel movement can be reflected. C G The weight coefficient of the guiding field strength reflects the influence of the guiding effect on the evacuation of personnel. C S Take 0.5, considering that the tunnel group lines are relatively complex and the personnel are not familiar with the tunnel group environment. C S The value of also reflects the personnel’s familiarity with the environment; g i,j and l i,j Represents the cell state. When the cell ( i, j ) When occupied by people g i,j =0, otherwise, g i,j =1; when the cell ( i,j ) is occupied by buildings, facilities, obstacles, etc. l i,j =0, otherwise, l i,j =1. In this method C S Take 0.5, considering that the tunnel group lines are relatively complex and the personnel are not familiar with the tunnel group environment. C S The value of also reflects the personnel's familiarity with the environment. This is used to simulate the evacuation of personnel in the tunnel group and their exit selection after the exit locations are announced by the radio when a fire occurs.

[0036] use H A Indicates the health index of personnel, H 0 represents the initial value of the health index, H 0 The value is 100, and the loss value affected by temperature and CO concentration is subtracted at each time step. H A When it drops to 0, it means that the person is dead and no longer moves to the surrounding cell units. H A The calculation formula is as follows: , , , In the formula, I T and I C Respectively represent the damage coefficients of temperature and CO concentration to human body; T Indicates the temperature value at the unit cell, °C; C Represents the CO concentration value per unit cell, ppm.

[0037] In summary, while using Moore's neighborhood to consider the free movement of evacuees, it is also necessary to consider the influence of environmental factors, and determine the probability of evacuees moving to each movable grid according to the magnitude of their influence, as well as the simulation object, that is, the evacuees also have a certain probability of staying in place and waiting until the next time step to recalculate the movable grid and movement probability.

[0038] S7. The personnel complete the movement and determine the number of personnel in the space. If there are personnel, the evacuation continues, the time step is +1, and the scene information (such as personnel position, dynamic field, etc.) is updated, and steps S4, S5, and S6 are repeated; if all personnel reach the exit, there are no personnel in the movable space or the health index of the remaining personnel is less than 0, the evacuation is terminated.

[0039] In this embodiment, according to the movement state of the evacuees and the random cellular automaton model, each grid node is in t The information at the moment selects the next moving direction of each evacuee. When the evacuee completes the move, the moving state of the evacuee is determined. t Whether the evacuation can continue at this moment, if the personnel health index is greater than 0, the evacuation continues, the time step is +1, and steps S4 to S6 are repeated until all personnel have completed the evacuation; if the personnel health index is less than or equal to 0 or there is no path to move, the evacuation cannot continue and the evacuation is terminated.

[0040] S8. Output the evacuation time of the group evacuees and calculate the total evacuation time and evacuation success rate.

[0041] During the evacuation process, evacuation time and evacuation success rate are key parameters for evaluating evacuation efficiency. Here, evacuation time is defined as the time when the last person leaves the exit, where the last person refers to the person who takes the longest time among all people in the tunnel, or if there are still people trapped in the tunnel, it is the time taken by the last successful escaper. The evacuation success rate is defined as the percentage of people who successfully escape from the exit among all people.

[0042] The present invention utilizes a cellular automaton model to simulate the evacuation and exit selection of personnel in a tunnel group after the location of the escape exit is broadcasted when a fire occurs. By constructing a digital model of a tunnel group, and according to the cellular automaton theory and the average shoulder width of personnel, the digital model space of the tunnel group is divided into discrete cells of equal size in the horizontal and vertical directions, and the movement direction of personnel is calculated based on fire data and the evacuation information broadcasted, so as to more realistically reflect the movement direction and actual behavior of evacuated personnel in a complex tunnel group.

[0043] The cellular automaton model has the characteristics of fast calculation speed, simple and efficient process, which enables the present invention to efficiently handle the evacuation problem of large-scale, discrete individuals. Compared with the traditional method based on fluid dynamics or continuous medium model, the present invention has higher calculation efficiency and accuracy when simulating large-scale evacuation.

[0044] When calculating the movement probability, the present invention quantifies the influence of smoke on the evacuation of personnel by introducing the temperature field and the smoke field, and shows the distance of personnel from the exits of the tunnel group by introducing the relative distance field, so that the simulation results are more in line with the actual situation and provide a reference for the safety design and emergency management of the tunnel group.

[0045] The present invention uses a loop program in Matlab to traverse each cell, finds the cell with a temperature higher than 60 degrees Celsius and a visibility lower than 10 meters in each time step, sets it as a virtual obstacle to represent the avoidance of personnel around the fire point, and updates the relative distance field. If the temperature drops below 60 degrees Celsius and the visibility recovers to more than 10 meters, the virtual obstacle at the cell changes back to a movable area, thereby being able to more realistically reflect the personnel's choice of exits in a multi-exit tunnel group and the change of escape routes.

[0046] The evacuees select and move paths based on the movement probability, which can provide a step-by-step iterative process for the evacuees to the tunnel exit or safe area, providing strong support for the scientific formulation of tunnel group fire evacuation plans.

[0047] The evacuation time and evacuation success rate of the evacuees output by the present invention can provide a reference for the safety design and emergency response of the tunnel group.

[0048] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. A tunnel group fire personnel evacuation analysis method based on cellular automata, characterized in that: The following steps are involved: S1. Construct a digital model of the tunnel group based on the actual structure and size of the tunnel group; S2. Divide the grid and establish a two-dimensional cellular automaton model; S3. Set the parameters for evacuating personnel, set the movable network selection rules, environmental factors and conflict avoidance principles according to the characteristics of the tunnel group and the actual situation of the fire, and arrange the personnel in the movable cells; S4. Use FDS to simulate the fire scene, process the obtained data through Matlab, and load it into the cellular automaton model at the same time interval as the evacuation update to obtain the temperature field and smoke field that change with time steps, and simulate the impact of fire products on personnel evacuation; S5. Calculate the relative distance field and dynamic field. The relative distance field with obstacles is calculated by Dijkstra algorithm. The dynamic field represents the attraction and repulsion of the virtual track left by the personnel on other personnel, and is updated in real time. S6. Generate personnel movement model and calculate t The movement probability of each neighboring cell of the personnel at each moment and the personnel's health index are used to sort the movement probabilities, and the personnel move to the place with the highest movement probability or stay where they are; S7. The personnel have completed the movement and the number of personnel in the space is determined. If there are personnel, the evacuation continues, the time step is +1, and the scene information is updated, and steps S4, S5, and S6 are repeated; if all personnel have reached the exit, there are no personnel in the movable space or the health index of the remaining personnel is less than 0, the evacuation is terminated; S8. Output the evacuation time and evacuation success rate of the evacuees.

2. The tunnel group fire personnel evacuation analysis method based on cellular automata according to claim 1 is characterized in that: In the digital model of the tunnel group constructed in step S1, walls, obstacles, movable spaces, and exits are distinguished by different numbers, and the numbers are distinguished by different colors.

3. The tunnel group fire personnel evacuation analysis method based on cellular automata according to claim 1 is characterized in that: The step S2 specifically includes: The two-dimensional cellular automaton model divides the digital model space of the tunnel group into discrete cells of equal size in the horizontal and vertical directions according to the cellular automaton theory and the average shoulder width of personnel. Each evacuated personnel occupies at least one cell, and each of the cells except the exit is in one of the three states of idle, occupied by personnel, and occupied by obstacles. When the cell is occupied by an obstacle, the personnel cannot move to the cell. When the cell is occupied by a person, other personnel cannot move to the cell. The distance between personnel is more than one cell, and each person searches for a path to move to the exit cell by cell.

4. The tunnel group fire personnel evacuation analysis method based on cellular automata according to claim 1 is characterized in that: In step S3, the movable network selection rule includes using Moore's neighborhood to stipulate that all evacuees move only one cell at least in each time step, and only their domain cells can be selected, and the domain cells are the adjacent cells below, below left, left, above, above right, right, and below right of the cell; The environmental factors include the attraction of the tunnel exit to the evacuated personnel, the attraction and repulsion of other personnel by the virtual tracks left by the personnel, and the repulsion of the personnel to the high temperature and smoke generated by the fire; The conflict avoidance principle includes that if more than two persons choose to move to the same cell at the same time, one person is randomly selected to move to that cell, and the other persons who fail in the competition choose to move to the suboptimal cell or stay where they are.

5. The method for analyzing personnel evacuation in tunnel group fire based on cellular automata according to claim 1 is characterized in that: In step S5, the relative distance field with obstacles is calculated by the Dijkstra algorithm. The relative distance field is a global relative distance field formed by multiple exits, which represents the shortest distance between each point and the exit. By setting the cells near the fire point as virtual obstacles, when the fire grows to a specified degree, personnel choose other routes away from the fire point to escape, and update the detour and escape paths around the fire scene.

6. The method for analyzing personnel evacuation in tunnel group fire based on cellular automata according to claim 1 is characterized in that: In step S5, the calculation and real-time updating process of the dynamic field is as follows: When a person passes through the cellular space ( i , j ), the dynamic field D Add 1: ， ， Normalization: , In the formula δ is the dynamic field diffusion coefficient, which represents the proportion of diffusion to the adjacent cells in the next step; λ is the attenuation coefficient, which represents the ratio of the dynamic field attenuation in the next step; Represents a cell ( i,j ) at time step t The dynamic field value at time.

7. The method for analyzing personnel evacuation in tunnel group fire based on cellular automata according to claim 1, characterized in that: In step S6, the movement probability of the personnel in each direction is used to calculate the direction of the personnel's movement, and the center of mass of the personnel moves to each adjacent cell ( i,j ) is expressed as: ， ， In the formula, P i,j Represents a cell ( i,j ) Personnel movement probability; K is a normalization parameter to ensure that the sum of the cell movement probabilities is 1; S i,j Represents a cell ( i,j ), D i,j Represents a cell ( i,j )’s dynamic field value, T i,j Represents a cell ( i,j ) temperature field value, V i,j Represents a cell ( i,j )’s visibility field value, G i,j Represents a cell ( i,j )’s guiding field value; C S and C D Represent the relative distance field parameters and dynamic field parameters respectively; C T and C V are the sensitivity parameters of temperature and smoke concentration respectively; C G represents the weight coefficient of the guiding field strength; ζ i,j and λ i,j Represents the cell state. When the cell ( i,j ) When occupied by people ζ i,j =0, otherwise, ζ i,j =1, when the cell ( i,j ) is occupied by buildings, facilities, obstacles, etc. λ i,j =0, otherwise, λ i,j =1.

8. The method for analyzing personnel evacuation in tunnel group fire based on cellular automata according to claim 1 is characterized in that: In step S6, H A Indicates the health index of personnel, H 0 Represents the initial value of the health index, minus the loss value affected by temperature and CO concentration at each time step. H A When it drops to 0, it means that the person has died and no longer moves to the surrounding cells. The health index of the person H A The calculation formula is: ， ， ， In the formula I T and I C Respectively represent the damage coefficients of temperature and CO concentration to human body; T Represents the temperature value at the unit cell, °C; C Represents the CO concentration value per unit cell, ppm.

9. The method for analyzing personnel evacuation in tunnel group fire based on cellular automation according to claim 1, characterized in that: The step S7 specifically includes: According to the movement status of evacuees and the cellular automaton model, each grid node is in t The information at the moment, select the next moving direction of each evacuee, when the evacuee completes the move, determine the moving status of the evacuee t Whether the evacuation can continue at this moment, if the personnel health index is greater than 0, the evacuation continues, the time step is +1, and steps S4 to S6 are repeated until all personnel have completed the evacuation. If the personnel health index is less than or equal to 0 or there is no path to move, the evacuation cannot continue and the evacuation is terminated.

10. The method for analyzing personnel evacuation in tunnel group fire based on cellular automata according to claim 1, characterized in that: In step S8, the evacuation time of the evacuees is the time when the last person leaves the exit, wherein the last person refers to the person who takes the longest time among all the people in the tunnel, or if there are still remaining people trapped in the tunnel, it is the time taken by the last successful escaper; the evacuation success rate is the percentage of the number of people who successfully escape from the exit to the total number of people.

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