A method of emergency evacuation of a passenger cabin containing a disabled passenger

Abstract

The application discloses a passenger cabin emergency evacuation method containing disabled passengers, which comprises the following steps: discretizing a single-channel passenger cabin environment by using different sizes of grids, and dividing the passenger cabin into multiple cells; constructing a passenger real-time speed model according to the position, action ability and individual difference of passengers; constructing a common passenger group behavior rule by using a field model; constructing a heterogeneous passenger group behavior rule by using the field model; setting cell automaton evolution rules and time rules; constructing a passenger cabin evacuation cell automaton model, simulating single-channel passenger cabin emergency evacuation according to the passenger load rate and cabin section; and based on the simulation results, analyzing the evacuation behavior of the heterogeneous passenger group under different passenger load rates and cabin section conditions, and further optimizing the passenger seat distribution and evacuation strategy. The application improves the evacuation efficiency and safety of the whole group by optimizing the passenger seat distribution and evacuation strategy under the consideration of the special situation of the disabled passengers.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of aviation safety technology, in particular to a passenger cabin emergency evacuation method containing disabled passengers. BACKGROUND

[0002] Considering the movement characteristics of disabled passengers in emergency situations, including the fact that disabled passengers may require additional space to move, wheelchair users in particular, who occupy a larger space, have an impact on crowd fluidity and exit options. Therefore, the behavior of disabled passengers will affect the overall evacuation program to some extent.

[0003] With the increasing proportion of disabled passengers, the crowd dynamics and flow distribution will also change, because different types of disabled individuals have different behavior patterns and reaction speeds in emergency situations. Therefore, it is particularly important to carry out passenger cabin emergency evacuation analysis considering disabled passengers. Currently, the research in the field of evacuation mostly uses cellular automata model to simulate group evacuation behavior, analyze the movement characteristics of heterogeneous passenger groups containing disabled passengers, and quantify the impact of evacuation behavior on overall efficiency. However, the current qualitative description of evacuation behavior in this specific environment (including the impact of specific factors such as seat layout, emergency exit location, special needs of disabled passengers, etc. on evacuation behavior) and quantitative analysis of disabled passenger behavior (including analysis of evacuation speed, reaction time, and movement ability of disabled passengers in emergency situations) are still insufficient. SUMMARY

[0004] The purpose of the present application is to provide a passenger cabin emergency evacuation method containing disabled passengers that can improve the efficiency and safety of passenger cabin emergency evacuation.

[0005] Technical solution: In order to achieve the above-mentioned purpose, the passenger cabin emergency evacuation method containing disabled passengers according to the present application comprises the following steps:

[0006] Step 1: Discretize the single-channel passenger cabin environment using different sizes of grids, divide the passenger cabin into multiple cells, each cell represents a grid unit, store its position and passenger cabin environment attribute information, and the neighborhood type of the cell is Moore neighborhood type;

[0007] Step 2: Construct a passenger real-time speed model, and based on the passenger occupied cell position information, passenger attributes, passenger movement ability and passenger cabin design factor data, update the speed of passengers in the passenger real-time speed model in real time;

[0008] Step 3: Use the field model to simulate the special behavior of ordinary passenger groups with certain social relationships in the evacuation process, and construct the behavior rules of ordinary passenger groups;

[0009] Step 4: Simulate the special behavior of the heterogeneous passenger group consisting of passengers with disabilities and assistants assisting passengers with disabilities in the evacuation process using the field model, and construct the heterogeneous passenger group behavior rule;

[0010] Step 5: Set the evolution rule and time rule of the cellular automaton;

[0011] Step 6: Based on the discrete single-channel cabin environment, passenger real-time speed model, general passenger group behavior rule, heterogeneous passenger group behavior rule, evolution rule and time rule, construct a cabin evacuation cellular automaton model, and simulate the single-channel cabin emergency evacuation under the condition of load factor and cabin section.

[0012] Step 7: Based on the simulation results, analyze the influence of the evacuation behavior of the heterogeneous passenger group under different load factors and cabin section conditions on the evacuation efficiency, and further optimize the passenger seat distribution and evacuation strategy.

[0013] The single-channel cabin environment of step 1 includes seats, obstacles, passages, cabin doors, and emergency exit locations and sizes, and the emergency exits include type-A, type-C, type-I, and type-III. The cabin environment attribute information stored in each cell includes size and type, where size refers to the size of seats, obstacles, passages, cabin doors, and emergency exits, and type refers to obstacles, passable areas, and exits.

[0014] The passenger attributes of step 2 include the waist circumference, gender, age, and psychological state of each passenger, and the cabin design factors include the passage width and leg space size.

[0015] The passenger real-time speed model of step 2 is constructed based on the location, mobility, and individual differences of the passengers, and is used to represent the speed of the passengers during the evacuation process. The passenger speed includes the average speed of passenger r passing through each size of emergency exit and the real-time speed of passenger r in each time step, where the average speed of passenger r passing through each size of emergency exit is:

[0016]

[0017]

[0018] The real-time speed is:

[0019]

[0020] In the formula, the emergency exit includes type-A, type-C, type-I, and type-III, L exit represents the cabin wall thickness, represents the time of passenger r passing through the type-III exit obtained according to statistical data, and Tx denotes the time of passenger r passing through Type-III exit under the condition of single influencing factor, ∑f x =1 represents the importance of influencing factor; real-time speed v r is determined by the speed of passenger r passing through Type-III exit and the proportion of passenger r occupying cell f location and the action ability of passenger r f disabled ; α, β, γ represent the influencing factors of the cell position f location occupied by passenger r.

[0021] The field model described in steps 3 and 4 includes a static field and a dynamic field, the static field is used to describe the behavior of passengers choosing the shortest path to leave the cabin, and the dynamic field is used to describe the special behavior of passenger groups in the evacuation process.

[0022] The special behavior of the general passenger group in the evacuation process described in step 3 is the behavior of combination or backtracking or following, that is, passengers with certain social relationships search for friends or family in the cabin instead of evacuating, and in the evacuation process, members of this passenger group tend to gather from the cell where they are to the cell where the leader is, and the leader guides the activities of members in the group; the movement state of the whole group is represented as:

[0023]

[0024] In the formula, S' i,j =k S *S i,j ; (i, j) is the position of the cell; is the distance between the cell where the other members are and the cell where the leader is, (i l ,j l ) is the cell where the leader is, (i m ,j m ) is the cell where the member is; l represents the leader, and m represents the other members; k S and k D are sensitivity parameters determining the weights of S i,j and D i,j ; S i,j and D i,j represent the influencing factors of the static field and the dynamic field; N is a coefficient for ensuring that the transition probability of all adjacent cells is 1; η i,j is used to judge whether the cell is occupied, and ξ i,j is used to judge whether the cell can pass at this time; k s ' is a sensitivity parameter of the member affected by the static field;

[0025] The leader stays in the waiting cell with probability μ for a period of time, so that the members find the leader in this period of time, and the members' behavior of approaching each other is described by introducing the feature exp(-k d d lm ) where k d is a sensitivity parameter about the degree of the members' willingness to approach the leader, and its value is greater than 0, which is used to guide other members to select the cell closer to the leader; k i ' is a sensitivity parameter about the degree of the members' trust in the leader; p I (i,j) is a direction adjustment coefficient, which is used to describe the behavior that all members in the same group tend to select the same exit, i.e., if the moving direction of other members is the same as the leader, then p I (i,j) = exp(k i ), and p I (i,j) = 1 for all cells in the remaining Moore neighborhood.

[0026] where the special behavior of the heterogeneous passenger group in the evacuation process in step 4 is that the disabled passengers and the assistant personnel are in the same state, and the moving state of the whole group is represented as:

[0027] DP i,j = Nξ i,j exp(k S S i,j )exp(k A A i,j )(1-η i,j );

[0028] where (i,j) is the position of the cell, k S and k A are sensitivity parameters that determine the weights of S i,j and A i,j ; S i,j and A i,j represent the influence factors of the static field and the dynamic field; η i,j is used to judge whether the cell is occupied or not, ξ i,j is used to judge whether the cell can pass at this time; and N is a coefficient that ensures that the transition probabilities of all adjacent cells are 1.

[0029] When the behavior rules of the disabled group are constructed, the cell parameter ξ i,j at the emergency exit with small size is set to be forbidden to pass, and the size of k A is adjusted to ensure that the disabled passengers and the assistant personnel remain undetached.

[0030] The evolution rule in step 5 comprises: passengers tend to choose the shortest path principle to select an exit, and are unwilling to change the decision even if the exit is crowded; the behavior decision of the passengers has high certainty and no randomness; the movement of the passengers presents a single-direction dense flow; the passengers will not enter the same cell in the same scene; the time rule comprises a time step division mechanism, an updating mechanism and a conflict mechanism, wherein the time rule is used to set the time step; the updating mechanism is synchronous updating; the conflict mechanism is that a cell can only be occupied by one passenger at the same time, if the target cell of the passenger is currently in an occupied state, the passenger must wait until the passenger in the target cell leaves before entering, so as to avoid overlapping between virtual passengers, simplify the passenger movement control in simulation, and if the target cells of multiple passengers in the same step are the same, it is considered that the passenger with high speed has a greater probability of entering the target cell;

[0031] The simulation process in step 6 is: in the cabin evacuation cellular automaton model, the passenger density and distribution are set according to different passenger load rates, and the cabin evacuation simulation model is run based on the passenger speed, the ordinary passenger group behavior rule, the heterogeneous passenger group behavior rule, the evolution rule and the time rule in steps 2-5, and the evacuation behavior of the passengers under different passenger load rates and cabin section conditions is observed.

[0032] The analysis of the evacuation behavior of the heterogeneous passenger group under different passenger load rates and cabin section conditions in step 7 comprises the evacuation path selection, speed updating and interaction behavior with other passengers of the heterogeneous passenger group;

[0033] The optimization of the passenger seat distribution comprises: seat distribution adjustment of the ordinary passenger group with social relations and seat distribution optimization of the heterogeneous passenger group; wherein the seat distribution adjustment of the ordinary passenger group with social relations comprises arranging the ordinary passenger group in adjacent positions to ensure the aggregation of the group and reduce the waiting time; arranging the ordinary passenger group in the end area of the cabin, such as selecting a position with a large space area according to different aircraft types to provide evacuation space suitable for the group; the seat distribution optimization of the heterogeneous passenger group comprises arranging the positions of the disabled passengers close to barrier-free facilities, close to flight attendants and close to cabin doors to ensure that the passengers can quickly and effectively move and timely obtain the help of the flight attendants during evacuation;

[0034] The optimization of the evacuation strategy comprises: setting guide signs at the simulation evacuation congestion point positions to help passengers quickly escape; adopting different evacuation modes according to different emergency situations and different passenger load rates; setting easily accessible safety areas in the cabin for the disabled passengers to temporarily take refuge during the evacuation process until they can safely evacuate; providing simulation evacuation training for the staff to ensure that they can skillfully perform the evacuation program in an emergency.

[0035] Beneficial effects: The present application has the following advantages: 1. The method first discretizes the cabin environment by using different sizes of grids, thereby providing accurate basic data for further simulation of the spatial structure and passenger distribution in the cabin;

[0036] 2. The method uses the collected cell information occupied by passengers, passenger attributes, passenger action ability and cabin design factor data to update the speed of passengers in real time, which can more accurately reflect the actual movement of passengers during evacuation and improve the accuracy of evacuation simulation;

[0037] 3. The method considers the special behavior of ordinary passenger groups and heterogeneous passenger groups composed of disabled passengers and their auxiliary personnel during evacuation, which helps to more realistically reflect the actual situation during emergency evacuation and provides an important basis for designing evacuation strategies considering disabled passengers;

[0038] 4. The method not only analyzes the influence of spatial structure distribution and behavior of different size groups during evacuation on evacuation efficiency, but also puts forward optimization suggestions for passenger seat distribution and evacuation strategy based on simulation results, thereby improving the evacuation efficiency and safety of the entire passenger group. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 is a flowchart of the method;

[0040] Figure 2 is a simulation diagram of the cabin evacuation simulation model;

[0041] Figure 3 is a diagram showing the influence of sensitivity parameters on total evacuation time. DETAILED DESCRIPTION

[0042] The technical solutions of the present application will be described in detail below in conjunction with the embodiments and drawings.

[0043] As shown in Figure 1 , the cabin emergency evacuation method comprising disabled passengers comprises the following steps:

[0044] Step 1: Discretize the single-channel cabin environment by using different sizes of grids, divide the cabin into a plurality of cells, each cell representing a grid unit, store the position and cabin environment attribute information of each cell, and the neighborhood type of the cell is the Moore neighborhood type;

[0045] Step 2: Construct a passenger real-time speed model, and update the speed of passengers in the passenger real-time speed model in real time based on the cell position information occupied by passengers, passenger attributes, passenger action ability and cabin design factor data;

[0046] Step 3: Simulate the special behavior of the ordinary passenger group with certain social relations in the evacuation process using the field model, and construct the behavior rules of the ordinary passenger group;

[0047] Step 4: Simulate the special behavior of the heterogeneous passenger group composed of disabled passengers and assisting personnel assisting disabled passengers in the evacuation process using the field model, and construct the behavior rules of the heterogeneous passenger group;

[0048] Step 5: Set the evolution rules and time rules of the cellular automaton;

[0049] Step 6: Based on the discrete single-channel cabin environment, the model of real-time passenger speed, the behavior rules of the ordinary passenger group, the behavior rules of the heterogeneous passenger group, the evolution rules and the time rules, construct the cabin evacuation cellular automaton model, and simulate the single-channel cabin emergency evacuation under different passenger load rates and cabin segment conditions;

[0050] Step 7: Based on the simulation results, analyze the influence of the evacuation behavior of the heterogeneous passenger group under different passenger load rates and cabin segment conditions on the evacuation efficiency, and further optimize the passenger seat distribution and evacuation strategy.

[0051] Based on the above method, a narrow-body all-economy cabin aircraft is selected as the evacuation simulation experiment scene. The current economy class seat width size is 0.45-0.53m, and the seat pitch is 0.79-0.86m. The size of the seat space is set to 0.5m x 0.5m, the space of the seat front leg is set to 0.5m x 0.3m, and the actual main aisle width of the cabin is set to 0.5m. The number of seats is 112, and the seat arrangement is single-channel three-row seats.

[0052] Set the experimental scheme: the experimental population includes groups of 2, 3 and 4 people with certain social relations and groups of ordinary isolated individuals; the disabled group using wheelchairs and the ordinary passenger group. The half-cabin and full-cabin conditions are set for different experimental groups. The three-cabin segment scene statistics are performed for the disabled group. Two kinds of disabled passenger position distribution are set to explore the influence of the time period of the disabled passenger joining the evacuation process on the evacuation efficiency. Through the simulation image, the spatial structure and special behavior of the group are observed, and the total evacuation time average, the number of remaining passengers in the cabin, the average distance between group members, and the dynamic field value of the whole cabin are calculated. At the same time, the k s , k d and k i sensitivity parameters are set to further explore the influence of the parameters on the total evacuation time, as shown in Table 1. Position one is located in the middle position away from both ends of the exit, and position two is located near the tail crew seat.

[0053] Table 1 Simulation experiment parameter table

[0054]

[0055]

[0056] As Figure 2 shown, the third group, the fourth group in the cabin evacuation simulation model is constructed, the yellow circle represents the ordinary passenger, the blue circle represents the auxiliary personnel, the red circle represents the disabled passenger.

[0057] According to the simulation experiment, the following conclusions are obtained: group behavior (including ordinary passenger group, simulation of heterogeneous passenger group composed of disabled passengers and auxiliary personnel assisting disabled passengers) will lead to individual evacuation delay and affect the overall evacuation efficiency. When the members of the group move, the moving speed and direction are affected by other members. The group maintains a certain speed to move, and the members with high moving speed will wait for the members with slow speed, maintain a certain spatial structure, and increase the evacuation time. In addition, this group maintaining spatial structure will interfere with the movement of other isolated individuals, and this phenomenon is particularly significant near the congestion area position. The negative effect of the group increases with the increase of the scale.

[0058] The ordinary passenger group with certain social relations has an impact on the evacuation efficiency. The simulation results show that the spatial structure of the 2-person group is more compact, and the spatial structure of the 3-person group and the 4-person group is relatively loose and more easily destroyed; the existence of the ordinary passenger group has a significant impact on the evacuation efficiency, and the existence of the group leads to the evacuation delay of ordinary individuals; the backtracking behavior of the members also leads to the increase of the evacuation time; the negative effect of the group increases with the increase of the scale. The behavior of the group members is controlled by the key parameters, and the sensitivity analysis of the parameters explores the group behavior mechanism. The decision of the members in the group depends on the decision of the leader and the static field of the scene, and reasonable setting of the parameters can determine the cohesion between the group members. Through the analysis of the influence of the sensitivity parameters on the total evacuation time, this method can explore the group behavior mechanism and understand how different factors affect the evacuation efficiency. And accordingly optimize the evacuation plan and strategy, improve the evacuation efficiency and safety.

[0059] The behavior of the disabled group affects the evacuation efficiency of the cabin. The seat distribution of the disabled group affects the stage of joining the evacuation process, and the earlier the disabled group joins the evacuation process, the smaller the impact on ordinary personnel. Reasonable setting of the position of the disabled group is conducive to the diversion of the disabled group and ordinary passengers. In terms of cabin section, there is a difference in evacuation time among each cabin section, and the narrow outlet in the narrow position of the middle cabin section is more prone to congestion, and the evacuation of the middle section plays a decisive role in the overall evacuation efficiency.

[0060] As Figure 3 shown, the results of simulation based on the fifth group in Table 1 are shown, in the field model, the group behavior is controlled by the key parameters, the key parameters affect the evacuation time and the evacuation efficiency, the sensitivity analysis of the parameters explores the group behavior mechanism, and theFigure 3 It can be seen that the members of the ordinary passenger group have a certain dependence on the leader, but are also affected by the static field; as k s increases, the members of the group have an increased dependence on the leader, and the probability of self-searching for the shortest path decreases, resulting in a decrease in evacuation efficiency.

[0061] The parameter k d reflects the degree of willingness of the members of the ordinary passenger group to approach the leader. When k d = 1, the behavior decision of the members of the group is similar to that of an isolated individual, and the group cannot maintain stability. The presence of other individuals and obstacles has no effect on the behavior of the group, which is contrary to the original intention of the group construction. However, a higher k d will result in an increase in the total evacuation time, and the members of the group will move together, which will significantly affect the evacuation efficiency. Therefore, a reasonable setting of k d is particularly important for the modeling of the group, and k d ensures the cohesion of the group formation.

[0062] The key parameter k i has a non-monotonic effect on the total evacuation time in the entire interval range. When k i < 4, the total evacuation time decreases with the increase of k i , and when k i is set in the entire interval, the members of the ordinary passenger group wander around the leader during the evacuation process, and the members do not reach a consensus on the moving direction, exhibiting individual behavior. When k i ≥ 4, the trend reverses, and the evacuation time increases with the increase of k i , which is similar to k d , that is, if the members of the group completely trust the leader and always make the same decision as the leader, moving in the same direction, the total evacuation time will be significantly prolonged.

Claims

1. A method of emergency evacuation of a passenger cabin containing a passenger with a disability, characterized in that, The method comprises the following steps: Step 1: discretize the single-channel cabin environment using different sizes of grids, divide the cabin into multiple cells, each cell represents a grid unit, store the location and cabin environment attribute information of each cell, and the neighborhood type of the cell is the Moore neighborhood type; Step 2: construct a passenger real-time speed model, and update the speed of the passenger in the passenger real-time speed model in real time based on the cell location information occupied by the passenger, the passenger attribute, the passenger action ability and the cabin design factor data; Step 3: simulate the special behavior of a group of ordinary passengers with certain social relationships in the evacuation process using the field model, and construct the behavior rules of the group of ordinary passengers; Step 4: simulate the special behavior of a group of heterogeneous passengers composed of disabled passengers and assisting personnel assisting the disabled passengers in the evacuation process using the field model, and construct the behavior rules of the group of heterogeneous passengers; Step 5: set the evolution rules and time rules of the cellular automaton; Step 6: based on the discrete single-channel cabin environment, the passenger real-time speed model, the behavior rules of the group of ordinary passengers, the behavior rules of the group of heterogeneous passengers, the evolution rules and the time rules, construct a cabin evacuation cellular automaton model, and simulate the single-channel cabin emergency evacuation under the condition of different passenger load rates and cabin sections; Step 7: based on the simulation results, analyze the influence of the evacuation behavior of the group of heterogeneous passengers under different passenger load rates and cabin section conditions on the evacuation efficiency, and further optimize the passenger seat distribution and evacuation strategy.

2. The method of claim 1, wherein, The single-channel cabin environment of step 1 includes the positions and sizes of seats, obstacles, passages, cabin doors and emergency exits in the cabin, and the emergency exits include type-A, type-C, type-I and type-III; the cabin environment attribute information stored in each cell includes the size and type of the grid, wherein the size refers to the size of the seat, obstacle, passage, cabin door and emergency exit, and the type refers to the obstacle, passable area and exit.

3. The method of claim 1, wherein, The passenger attributes of step 2 include the waist circumference, gender, age and psychological state of the passenger; and the cabin design factors include the passage width and leg space size.

4. The method of claim 1, wherein the passenger cabin is a passenger cabin of an aircraft. The passenger real-time speed model of step 2 is constructed according to the location, action ability and individual differences of the passenger, and is used to represent the speed of the passenger in the evacuation process; the passenger speed includes the average speed of the passenger r passing through each size of emergency exit and the real-time speed of the passenger r in each time step, wherein the average speed of the passenger r passing through each size of emergency exit is: The real-time speed is: In the formula, the emergency exit includes type-A, type-C, type-I, type-III, L exit represents the thickness of the cabin wall, represents the time of passenger r passing through the type-III exit according to statistical data, T x represents the time of passenger r passing through the type-III exit under the condition of a single influencing factor, ∑f x =1 represents the importance of the influencing factor; real-time speed v r is determined by the speed of passenger r passing through the type-III exit and the occupancy of passenger r in cell f location and the action ability f disabled of passenger r; α, β, γ represent the influencing factors of the cell position f location occupied by passenger r.

5. The method of claim 1, wherein the passenger cabin emergency evacuation method for passengers with disabilities, characterized by, The field model of steps 3 and 4 includes a static field and a dynamic field, the static field is used to describe the behavior of the passenger selecting the shortest path to leave the cabin, and the dynamic field is used to describe the special behavior of the passenger group in the evacuation process.

6. The method of claim 5, wherein, The special behavior of the group of ordinary passengers in the evacuation process of step 3 is the behavior of combination, backtracking or following, that is, the passengers with certain social relationships search for friends or family members in the cabin instead of evacuating, and in the evacuation process, the members of the passenger group tend to gather in the cell where the leader is located, and the leader guides the activities of the members in the group; the moving state of the whole group is represented as: where S i,j = k S * D i,j ; (i,j) is the position of the cell; is the distance between the cell where the other member is located and the cell where the leader is located, (i l ,j l ) is the cell where the leader is located, (i m ,j m ) is the cell where the member is located; l represents the leader and m represents the other member; k S and k D are sensitivity parameters that determine the weights of S i,j and D i,j ; S i,j and D i,j represent the influence factors of the static field and the dynamic field; N is a coefficient that ensures that the transition probabilities of all adjacent cells are 1. η i,j for determining whether the cell is occupied, ξ i,j for determining whether the cell is passable at this time; k s is a sensitivity parameter for members affected by the static field. The leader stays in a waiting member with probability μ for a period of time, such that the member finds the leader within this time period, the mutual approaching behavior between members is described by introducing a feature exp(-k d d lm ) where k d is a sensitivity parameter about the degree of the member's willingness to approach the leader, which is greater than 0, used to guide other members to select cells closer to the leader; p I (i,j) is a directional adjustment coefficient, used to describe the behavior that all members in the same group tend to select the same exit, such as the moving direction of other members is the same as the leader, then p I (i,j) = exp(k i ), and p I (i,j) = 1 for all cells in the remaining molar neighborhood; k' i is a sensitivity parameter about the degree of the member's trust in the leader.

7. The method of claim 5, wherein the passenger cabin is a passenger cabin of an aircraft. The special behavior of the heterogeneous passenger group in the evacuation process is that the disabled passengers and the assistant personnel are consistent in the state, and the moving state of the whole group is represented as: DP i,j = N i,j exp(k S S i,j )exp(k A A i,j )(1-η i,j ) where (i,j) is the position of the cell, k S and k A are the sensitivity parameters that determine the weights of S i,j and A i,j ; S i,j and A i,j represent the influence factors of the static field and the dynamic field, respectively. η i,j for determining whether the cell is occupied, ξ i,j for determining whether the cell is passable at this time; N is a coefficient for ensuring that the sum of the transition probabilities of all neighboring cells is 1; In the construction of the behavior rules of the disabled group, the cell parameter ξ at the small-sized emergency exit i,j is set to prohibit passage, while adjusting k A The size ensures that the disabled passengers and the assistant personnel remain inseparable.

8. The method of claim 1, wherein, The evolution rule described in step 5 includes: passengers tend to choose the shortest path principle to select the exit; the behavior decision of passengers has a high degree of certainty; the movement of passengers presents a single-direction dense flow; passengers will not enter the same scene into the cell that has been entered before; the time rule includes time step division mechanism, update mechanism and conflict mechanism, wherein the time rule is used to set the time step; the update mechanism is synchronous update; the conflict mechanism is that the cell can only be occupied by one passenger at the same time, if the target cell of the passenger is currently in the occupied state, the passenger must wait for the passenger in the target cell to leave before entering; the passenger movement control in the simplified simulation is simplified, if the target cells of multiple passengers in the same step are the same, it is considered that the passenger with higher speed has a greater probability of entering the target cell.

9. The method of claim 1, wherein, The simulation process of step 6 is: in the cabin evacuation cellular automaton model, according to different passenger load rates, the passenger density and distribution are set, and based on the passenger speed, the behavior rules of the ordinary passenger group, the behavior rules of the heterogeneous passenger group, the evolution rule and the time rule of steps 2-5, the cabin evacuation simulation model is run, and the evacuation behavior of passengers under different passenger load rates and cabin section conditions is observed.

10. The method of claim 1, wherein the passenger cabin emergency evacuation method for passengers with disabilities, characterized by, The analysis of the evacuation behavior of the heterogeneous passenger group under different passenger load rates and cabin section conditions described in step 7 includes the evacuation path selection, speed update and interaction behavior of the heterogeneous passenger group with other passengers; The optimization of passenger seat distribution includes: seat distribution adjustment of the ordinary passenger group with social relations, seat distribution optimization of the heterogeneous passenger group; wherein the seat distribution adjustment of the ordinary passenger group with social relations includes arranging the ordinary passenger group in adjacent positions; arranging the ordinary passenger group in the end area of the cabin; the seat distribution optimization of the heterogeneous passenger group includes arranging the positions of the disabled passengers near the barrier-free facilities, near the cabin attendants and near the cabin doors; The optimization of evacuation strategy includes: setting guide signs at the simulation evacuation congestion point positions to help passengers quickly escape; adopting different evacuation modes according to different emergency situations and different passenger load rates; setting easily accessible safety areas in the cabin for disabled passengers to temporarily seek refuge during the evacuation process until they can safely evacuate; providing simulation evacuation training for staff to ensure that they can skillfully perform the evacuation procedure in emergency situations. The optimization of evacuation strategy includes: setting guide signs at the simulation evacuation congestion point positions to help passengers quickly escape; adopting different evacuation modes according to different emergency situations and different passenger load rates; setting easily accessible safety areas in the cabin for disabled passengers to temporarily seek refuge during the evacuation process until they can safely evacuate; providing simulation evacuation training for staff to ensure that they can skillfully perform the evacuation procedure in emergency situations.

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