Passenger roller fire personnel evacuation simulation method and system

Abstract

The invention provides a passenger roller fire personnel evacuation simulation method and system, and the method comprises the steps: building a passenger cabin scene model, carrying out the numerical simulation of a passenger cabin fire, and obtaining the data of a fire product; analyzing a change rule of the fire product, and calculating a fire evacuation speed influence coefficient; improving a particle swarm algorithm, and fusing the fire product data into the improved particle swarm algorithm to obtain a personnel evacuation path optimization model under the fire condition; and simulating a personnel evacuation process in the passenger cabin scene model based on the personnel evacuation path optimization model to obtain an optimal personnel evacuation path under the fire condition. According to the method, the optimization space can be effectively expanded, the capacity of the algorithm for obtaining the globally optimal solution is improved, the optimization capacity of the algorithm in the initial stage is enhanced, and the high-level development capacity is kept in the final stage of search, so that the convergence speed and effect of the algorithm are remarkably improved, and the path optimization result is improved; and the optimal path is ensured to be the shortest path.

Description

Technical Field

[0001] The present invention relates to the technical field of evacuation route planning, and in particular to a method and system for simulating the evacuation of personnel in a passenger and vehicle ferry fire. Background Art

[0002] In recent years, passenger and vehicle ferry accidents have occurred frequently, causing huge losses to the country and individuals. With the continuous development of the global shipping industry, the number of passenger and vehicle ferry fire accidents has also been increasing. It is very important to analyze the evacuation of personnel after a passenger and vehicle ferry fire accident, including emergency disposal, emergency response and escape route selection.

[0003] In the prior art, evacuation simulation technology can be used to optimize the evacuation plan of a ro-ro passenger ship in the event of a fire, thereby reducing the cost of evacuation drills. For evacuation simulation of fire scenes, the fire information is often preset in the evacuation simulation model in advance to complete the evacuation simulation. However, the traditional ro-ro passenger ship fire simulation model does not comprehensively consider the impact of fire parameters such as smoke layer height, smoke temperature and CO concentration on personnel evacuation, resulting in a large difference between the fire evacuation simulation and the actual situation, and cannot provide scientific theoretical guidance for the formulation of evacuation plans for ro-ro passenger ship fires.

[0004] In view of the current demand for rapid evacuation of personnel in passenger and vehicle ferry fire accidents, it is urgent to invent a passenger and vehicle ferry fire personnel evacuation simulation method, system, equipment and storage medium to solve the shortcomings of existing problems, and at the same time provide scientific theoretical guidance for the formulation of passenger and vehicle ferry personnel evacuation plans, so as to improve the emergency response capability of the ship when a fire occurs and reduce casualties and property losses. Summary of the invention

[0005] According to the technical problems raised above, a simulation method for evacuating personnel from a passenger ferry fire is provided. The present invention uses Pyrosim (Thunderhead Engineering PyroSim) software to establish a fire physical model for the cabin fire scene of the passenger ferry, obtains numerical data of smoke layer height, smoke temperature and CO concentration at different positions of the passenger ferry at different times during the simulation time through simulation experiments, and analyzes the numerical data of three fire products, namely, smoke layer height, smoke temperature and CO concentration; introduces the influence coefficient of evacuation speed under the influence of smoke layer height, smoke temperature and CO concentration into the particle swarm algorithm, and at the same time, by flexibly adjusting the inertia weight ω in the particle swarm optimization method, uses the improved particle swarm algorithm to solve the shortest path for personnel to evacuate from the vicinity of the fire source to the assembly position at each stage during the simulation time under the fire scene.

[0006] The technical means adopted by the present invention are as follows:

[0007] A method for simulating evacuation of personnel from a passenger and vehicle ferry fire, comprising:

[0008] S1. Establish a cabin scene model, conduct numerical simulation of cabin fire, and obtain data on fire products;

[0009] S2. Analyze the changing rules of fire products and calculate the influence coefficient of fire evacuation speed;

[0010] S3, improve the particle swarm algorithm, and integrate the fire product data into the improved particle swarm algorithm to obtain the personnel evacuation path optimization model under fire conditions;

[0011] S4. Simulate the evacuation process of personnel in the cabin scene model based on the evacuation path optimization model to obtain the optimal evacuation path for personnel under fire conditions.

[0012] Furthermore, step S1 specifically includes:

[0013] S11. Select the seventh, eighth and ninth decks of the ro-ro passenger ship to construct a cabin model and divide the fire grids;

[0014] S12, monitoring the fire parameters of each fine grid according to a preset simulation cycle;

[0015] S13. Using fire simulation software to simulate the change of fire intensity when a fire occurs in the cabin area.

[0016] Furthermore, step S2 specifically includes:

[0017] S21. Consider the influence of these factors on walking speed separately and introduce the corresponding influence coefficients, namely the smoke layer height influence coefficient f1(k), temperature influence coefficient f2(T) and CO concentration influence coefficient f3(ρ);

[0018] S22. Introduce equivalent speed to quantify the impact of fire products on the human body as the impact on the walking speed of people.

[0019] Furthermore, step S21 specifically includes:

[0020] S211. Calculate the smoke layer height influence coefficient. The calculation formula is as follows:

[0021]

[0022] Where k represents the ratio of the difference between the ship's floor height and the smoke layer height to the height of the personnel themselves;

[0023] S212. Calculate the smoke impact coefficient. The calculation formula is as follows:

[0024]

[0025] Where v0 represents the evacuation speed under normal conditions, in m / s; v max represents the maximum evacuation speed in m / s; T represents the ambient temperature in the fire scene in °C; T1 represents the temperature at which people begin to feel uncomfortable; T2 represents the temperature at which substantial damage is caused to the human body; T dead Indicates lethal temperature;

[0026] S213. Fire contains a large amount of toxic and harmful gases, among which CO concentration poses the greatest threat to evacuees. Therefore, the influence coefficient formula of CO concentration is fitted; the influence coefficient of CO concentration is calculated as follows:

[0027]

[0028] Wherein, ρ represents the CO concentration in mol / mol; t represents the exposure time in min.

[0029] Furthermore, in step S22, the size of the introduced equivalent speed is the product of the walking speed of personnel under normal circumstances and the influence coefficients of the three fire products, and the calculation formula is as follows:

[0030] V=V0*f1(k)*f2(T)*f3(ρ)

[0031] Among them, V represents the equivalent speed, that is, the walking speed of people under the influence of fire products, in m / s; V0 represents the walking speed of people when no fire occurs, in m / s.

[0032] Furthermore, in step S3, the particle swarm algorithm is improved, specifically including:

[0033] The inertia weight ω is set as a nonlinear change mechanism, and the calculation formula of the inertia weight ω is as follows:

[0034] ω(t)=ω1+(ω1-ω2)(2t / Nmax-(t / Nmax) 2 )

[0035] Among them, ω1 represents the initial value, ω2 represents the final value; t represents the current number of iterations; N max Indicates the maximum number of iterations.

[0036] Further, step S4 specifically includes:

[0037] S41, initialization system: setting the initial values ​​of control variables and other parameters;

[0038] S42, randomly initialize to obtain the position and velocity of each particle in the particle swarm;

[0039] S43, evaluating each particle and calculating the fitness value of the particle;

[0040] S44, comparing the fitness value of each particle with the fitness value of the best position it has ever experienced, if it is better, then taking it as the individual historical optimal fitness value of the particle, and using the current position to update the individual historical best position;

[0041] S45. For each particle, compare the individual historical best fitness value with the fitness value of the best position in the history experienced by the group. If it is better, take it as the current global best position, otherwise, keep it unchanged.

[0042] S46, adjusting the particle speed and position according to step S43 and step S44;

[0043] S47, check whether the end condition is met, if so, end the iteration and output the optimal solution; otherwise, return to execute steps S43 and S44.

[0044] The present invention also provides a passenger ferry fire personnel evacuation simulation system implemented according to the above-mentioned passenger ferry fire personnel evacuation simulation method, comprising: a scene establishment module, a data analysis module, a path optimization module and a simulation module, wherein:

[0045] The scenario establishment module is used to establish a cabin scenario model, perform numerical simulation on cabin fire, and obtain data on fire products;

[0046] The data analysis module is used to analyze the changing rules of fire products and calculate the fire evacuation speed influence coefficient;

[0047] The path optimization module is used to improve the particle swarm algorithm and integrate the fire product data into the improved particle swarm algorithm to obtain an optimization model for the personnel evacuation path under fire conditions;

[0048] The simulation module is used to simulate the personnel evacuation process in the cabin scene model based on the personnel evacuation path optimization model to obtain the optimal personnel evacuation path under fire conditions.

[0049] Compared with the prior art, the present invention has the following advantages:

[0050] The present invention provides a method for simulating the evacuation of personnel in a passenger and vehicle ferry fire. The method introduces the influence coefficient of the evacuation speed under the influence of the smoke layer height, smoke temperature and CO concentration into the particle swarm algorithm. At the same time, by flexibly adjusting the inertia weight ω in the particle swarm optimization method and setting the inertia weight ω as a nonlinear change mechanism, the optimization space can be effectively expanded, the ability of the algorithm to obtain the global optimal solution is improved, the optimization ability of the algorithm in the initial stage and the ability to maintain a high level of development in the final stage of the search are enhanced, thereby significantly improving the convergence speed and effect of the algorithm, improving the path optimization result, and ensuring that the optimized path is the shortest path in time.

[0051] Based on the above reasons, the present invention can be widely promoted in the fields of evacuation route planning, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0053] Figure 1 The figure is a flow chart of the method of the present invention.

[0054] Figure 2 This is a flow chart of the algorithm of the particle swarm algorithm (PSO) for optimizing the evacuation path of personnel in an embodiment of the present invention.

[0055] Figure 3 This is a curve chart showing the number of iterations of the ant colony algorithm provided in an embodiment of the present invention.

[0056] Figure 4 A curve chart showing the change in the number of iterations of the whale group algorithm provided in an embodiment of the present invention.

[0057] Figure 5 This is a curve chart showing the number of iterations of the improved particle swarm algorithm provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0059] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0060] like Figure 1 As shown, the present invention provides a method for simulating evacuation of personnel from a passenger and vehicle ferry fire, comprising:

[0061] S1. Establish a cabin scene model, conduct numerical simulation of cabin fire, and obtain data on fire products;

[0062] S2. Analyze the changing rules of fire products and calculate the influence coefficient of fire evacuation speed;

[0063] S3, improve the particle swarm algorithm, and integrate the fire product data into the improved particle swarm algorithm to obtain the personnel evacuation path optimization model under fire conditions;

[0064] S4. Simulate the evacuation process of personnel in the cabin scene model based on the evacuation path optimization model to obtain the optimal evacuation path for personnel under fire conditions.

[0065] In this embodiment, a method for evacuating personnel from a passenger ferry fire is provided. First, a physical model of a cabin fire is established, a numerical simulation of the cabin fire is carried out, and data of fire products are obtained. Secondly, the change law of the fire products is analyzed, the fire evacuation speed influence coefficient is calculated, and the particle swarm algorithm is improved. In particular, the inertia weight ω is set as a nonlinear change mechanism, and the fire product data is integrated into the improved particle swarm algorithm to obtain a personnel evacuation path optimization model under fire conditions. Finally, based on the personnel evacuation path optimization model, the personnel evacuation process in the simulation scenario model is simulated. The method of this embodiment quantitatively considers the influence of fire products on the behavior of evacuated personnel by determining the fire evacuation speed influence coefficient corresponding to the fire products, which is closer to the actual situation; by improving the particle swarm algorithm, the inertia weight ω is particularly set as a nonlinear change mechanism, and the fire product data is integrated into the improved particle swarm algorithm, an evacuation path optimization model under fire conditions is obtained, and the evacuation process in the simulation scenario model is simulated based on the evacuation path optimization model, which can more accurately simulate the evacuation situation when a passenger vehicle car fire occurs, and provide scientific theoretical guidance for the formulation of passenger vehicle car car evacuation plans and the formulation and revision of passenger vehicle car car evacuation standards and specifications.

[0066] In specific implementation, as a preferred embodiment of the present invention, step S1 specifically includes:

[0067] S11. Select the seventh, eighth and ninth decks of the ro-ro passenger ship to construct a cabin model and divide the fire grids;

[0068] S12, monitoring the fire parameters of each fine grid according to a preset simulation cycle;

[0069] S13. Using fire simulation software to simulate the change of fire intensity when a fire occurs in the cabin area.

[0070] In this embodiment, as a specific embodiment, the seventh, eighth and ninth deck areas of the passenger ferry are selected, the cabin is divided into fine grids of 0.5m×0.5m×0.5m, and a detector is arranged every 1m in the model, totaling 44562 detectors, including: 14854 smoke detectors for measuring the height of the smoke layer, 14854 temperature detectors for recording temperature data, and 14854 CO detectors for obtaining CO concentration. Fire simulation software Pyrosim is used for fire simulation.

[0071] In specific implementation, as a preferred embodiment of the present invention, step S2 specifically includes:

[0072] S21. Consider the influence of these factors on walking speed separately and introduce the corresponding influence coefficients, namely the smoke layer height influence coefficient f1(k), temperature influence coefficient f2(T) and CO concentration influence coefficient f3(ρ);

[0073] S22. Introduce equivalent speed to quantify the impact of fire products on the human body as the impact on the walking speed of people.

[0074] In specific implementation, as a preferred embodiment of the present invention, in order to more comprehensively consider the influence of fire parameters on personnel evacuation, the evacuation influence coefficient corresponding to the fire product in step S21 includes the influence of smoke layer height, smoke temperature and CO concentration on personnel evacuation, specifically including:

[0075] S211. Calculate the smoke layer height influence coefficient. The calculation formula is as follows:

[0076]

[0077] Where k represents the ratio of the difference between the ship's floor height and the smoke layer height to the height of the personnel themselves;

[0078] S212. Smoke temperature also has a great influence on the evacuation process. The smoke influence coefficient is calculated using the following formula:

[0079]

[0080] Where v0 represents the evacuation speed under normal conditions, in m / s; v max It indicates the maximum evacuation speed in m / s, usually 4 m / s; T indicates the ambient temperature in the fire scene in °C; T1 indicates the temperature at which people start to feel uncomfortable, which is 30 °C; T2 indicates the temperature at which substantial damage is caused to the human body, which is 60 °C; T dead Indicates the lethal temperature, which is 120°C;

[0081] S213. Fire contains a large amount of toxic and harmful gases, among which CO concentration poses the greatest threat to evacuees. Therefore, the influence coefficient formula of CO concentration is fitted; the influence coefficient of CO concentration is calculated as follows:

[0082]

[0083] Wherein, ρ represents the CO concentration in mol / mol; t represents the exposure time in min.

[0084] In specific implementation, as a preferred embodiment of the present invention, in step S22, the size of the introduced equivalent speed is the product of the walking speed of personnel under normal circumstances and the influence coefficients of the three fire products, and the calculation formula is as follows:

[0085] V=V0*f1(k)*f2(T)*f3(ρ)

[0086] Among them, V represents the equivalent speed, that is, the walking speed of personnel under the influence of fire products, in m / s; V0 represents the walking speed of personnel when no fire occurs, in m / s, and its size depends on factors such as the state of the ship, the gender and age of the personnel.

[0087] In specific implementation, as a preferred embodiment of the present invention, in step S3, the particle swarm algorithm is improved, specifically including:

[0088] The inertia weight ω is set as a nonlinear change mechanism, and the calculation formula of the inertia weight ω is as follows:

[0089] ω(t)=ω1+(ω1-ω2)(2t / Nmax-(t / Nmax) 2 )

[0090] Among them, ω1 represents the initial value, ω2 represents the final value; t represents the current number of iterations; N max Indicates the maximum number of iterations.

[0091] In this embodiment, by flexibly adjusting ω, the optimization space can be effectively expanded and the ability of the algorithm to obtain the global optimal solution can be improved. When the value of ω is large, the algorithm exhibits excellent global search ability and can effectively avoid the risk of falling into the local optimal solution. When the value of ω is small, the algorithm exhibits good convergence effect and local optimization ability. Therefore, the algorithm can be adjusted by adjusting the inertia weight ω to enhance the optimization ability of the algorithm in the initial stage and maintain a high level of development ability in the final stage of the search, thereby significantly improving the convergence speed and effect of the algorithm.

[0092] When specifically implemented, as a preferred embodiment of the present invention, Figure 2 As shown, step S4 specifically includes:

[0093] S41, initialization system: setting the initial values ​​of control variables and other parameters;

[0094] S42, randomly initialize to obtain the position and velocity of each particle in the particle swarm;

[0095] S43, evaluating each particle and calculating the fitness value of the particle;

[0096] S44, comparing the fitness value of each particle with the fitness value of the best position it has ever experienced, if it is better, then taking it as the individual historical optimal fitness value of the particle, and using the current position to update the individual historical best position;

[0097] S45. For each particle, compare the individual historical best fitness value with the fitness value of the best position in the history experienced by the group. If it is better, take it as the current global best position, otherwise, keep it unchanged.

[0098] S46, adjusting the particle speed and position according to step S43 and step S44;

[0099] S47, check whether the end condition is met, if so, end the iteration and output the optimal solution; otherwise, return to execute steps S43 and S44.

[0100] Corresponding to the passenger and vehicle ferry fire personnel evacuation simulation method in the present application, the present application also provides a passenger and vehicle ferry fire personnel evacuation simulation system, including: a scene establishment module, a data analysis module, a path optimization module and a simulation module, wherein:

[0101] The scenario establishment module is used to establish a cabin scenario model, perform numerical simulation on cabin fire, and obtain data on fire products;

[0102] The data analysis module is used to analyze the changing rules of fire products and calculate the fire evacuation speed influence coefficient;

[0103] The path optimization module is used to improve the particle swarm algorithm and integrate the fire product data into the improved particle swarm algorithm to obtain an optimization model for the personnel evacuation path under fire conditions;

[0104] The simulation module is used to simulate the personnel evacuation process in the cabin scene model based on the personnel evacuation path optimization model to obtain the optimal personnel evacuation path under fire conditions.

[0105] As for the embodiments of the present invention, since they correspond to the embodiments above, the description is relatively simple. For the relevant similarities, please refer to the description of the above embodiments, which will not be described in detail here.

[0106] Example

[0107] The present invention selects the seventh, eighth and ninth decks of the cruise ship as experimental objects. The seventh deck of the passenger and vehicle ferry is mainly divided into the crew living area and the guest room area; the eighth deck is mainly divided into the main restaurant area, the leisure and entertainment area and the guest room area; the ninth deck is mainly the guest room area. Each deck is regarded as an independent space unit, and the corresponding nodes are set and numbered one by one. The nodes of each independent space unit are connected by using stair nodes. A total of 162 nodes are set: among which the exit node U = [14, 16, 28, 32, 77, 79, 91, 94], and the stair node S = [6, 7, 18, 19, 36, 37, 59, 60, 61, 62, 80, 81, 82, 83, 84, 97, 98, 99, 100, 107, 108, 123, 124, 136, 137, 138, 149, 150, 159]. Other nodes are locations that evacuees can reach, such as turns, corridors, and guest rooms.

[0108] like Figure 3 As shown in the figure, a large number of simulations and calculations were performed in the early stage. When the relevant parameters of the ant colony algorithm are: information importance factor α = 1, heuristic function importance factor β = 7, pheromone volatility factor ρ = 0.7, pheromone constant Q = 10, number of ants m = 100, maximum number of iterations N max =100, the simulation result is optimal. When performing the evacuation path optimization example analysis, the parameters are set according to this parameter combination. In addition, a total of 162 nodes are set: the exit node U = [14, 16, 28, 32, 77, 79, 91, 94], the stair node S = [6, 7, 18, 19, 36, 37, 59, 60, 61, 62, 80, 81, 82, 83, 84, 97, 98, 99, 100, 107, 108, 123, 124, 136, 137, 138, 149, 150, 159]. For the above model, Matlab 2018b is used for compilation and solution. After loading the above data into the program, simulation calculation is performed to solve the evacuation path and total evacuation time of each evacuation starting node.

[0109] like Figure 4 As shown, for the above model, Matlab2018b is used to compile and solve. First, the relevant parameters of the improved whale group algorithm are defined: the population size m = 100, the spiral coefficient b = 1, the selection probability p = 0.5, and the maximum number of iterations N max =100. In the early stage, a large number of simulation calculations have shown that when ω max =0.9,ω min=0.2, the simulation effect is the best. In addition, a total of 162 nodes are set: the exit node U = [14, 16, 28, 32, 77, 79, 91, 94], the stair node S = [6, 7, 18, 19, 36, 37, 59, 60, 61, 62, 80, 81, 82, 83, 84, 97, 98, 99, 100, 107, 108, 123, 124, 136, 137, 138, 149, 150, 159]. After loading the above data into the program, simulation calculation is performed to solve the evacuation path and total evacuation time of each evacuation starting node.

[0110] like Figure 5 As shown in the figure, the evacuation path optimization model of the particle swarm algorithm (PSO) is applied to a ship model for evacuation test simulation, and the iterative curve of the improved particle swarm algorithm is obtained. For the optimization model, Matlab2018b is used for compilation and solution. First, the relevant parameters of the improved particle swarm algorithm are defined: the population size m = 100, the acceleration coefficient c1 = c2 = 1.35, and the maximum number of iterations N max =100. A large number of simulation calculations in the early stage show that the simulation effect is best when the inertia weight ω1 = 0.9 and ω2 = 0.4. In addition, a total of 162 nodes are set: the exit node U = [14, 16, 28, 32, 77, 79, 91, 94], the stair node S = [6, 7, 18, 19, 36, 37, 59, 60, 61, 62, 80, 81, 82, 83, 84, 97, 98, 99, 100, 107, 108, 123, 124, 136, 137, 138, 149, 150, 159]. After loading the above data into the program, simulation calculations are performed to solve the evacuation path and total evacuation time of each evacuation starting node.

[0111] By comparing the ant colony algorithm, particle swarm algorithm and whale swarm algorithm, combining the fire product data and the shaking state of the ship, the fire product data was integrated into the path optimization algorithm, and the Matlab software was used for simulation calculation. The results were compared and found that the total length of the evacuation path of the improved ant colony algorithm was 10881m, and the total evacuation time was 113.1s; the total length of the evacuation path of the improved particle swarm algorithm was 9569m, and the total evacuation time was 109.2s; the total length of the evacuation path of the improved whale swarm algorithm was 10133m, and the total evacuation time was 117.6s. The total length of the evacuation path and the total evacuation time of the improved particle swarm algorithm are both the lowest, and the improved algorithm of the present invention has the best effect when optimizing the evacuation path of personnel under fire conditions.

[0112] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for simulating the evacuation of personnel from a passenger and vehicle ferry fire, characterized in that: include: S1. Establish a cabin scene model, conduct numerical simulation of cabin fire, and obtain data on fire products; S2. Analyze the changing rules of fire products and calculate the influence coefficient of fire evacuation speed; S3, improve the particle swarm algorithm, and integrate the fire product data into the improved particle swarm algorithm to obtain the personnel evacuation path optimization model under fire conditions; S4. Simulate the evacuation process of personnel in the cabin scene model based on the evacuation path optimization model to obtain the optimal evacuation path for personnel under fire conditions.

2. A method for simulating evacuation of personnel from a passenger and vehicle ferry fire according to claim 1, characterized in that: Step S1 specifically includes: S11. Select the seventh, eighth and ninth decks of the ro-ro passenger ship to construct a cabin model and divide the fire grids; S12, monitoring the fire parameters of each fine grid according to a preset simulation cycle; S13. Using fire simulation software to simulate the change of fire intensity when a fire occurs in the cabin area.

3. The method for simulating evacuation of personnel from a passenger and vehicle ferry fire according to claim 1, characterized in that: Step S2 specifically includes: S21. Consider the influence of these factors on walking speed separately and introduce the corresponding influence coefficients, namely the smoke layer height influence coefficient f1(k), temperature influence coefficient f2(T) and CO concentration influence coefficient f3(ρ); S22. Introduce equivalent speed to quantify the impact of fire products on the human body as the impact on the walking speed of people.

4. A method for simulating evacuation of personnel from a passenger and vehicle ferry fire according to claim 3, characterized in that: Step S21 specifically includes: S211. Calculate the smoke layer height influence coefficient. The calculation formula is as follows: Where k represents the ratio of the difference between the ship's floor height and the smoke layer height to the height of the personnel themselves; S212. Calculate the smoke impact coefficient. The calculation formula is as follows: Where v0 represents the evacuation speed under normal conditions, in m / s; v max It indicates the maximum evacuation speed in m / s; T indicates the ambient temperature in the fire scene in °C; T1 indicates the temperature at which people begin to feel uncomfortable; T2 indicates the temperature at which substantial damage is caused to the human body; T dead Indicates lethal temperature; S213. Fire contains a large amount of toxic and harmful gases, among which CO concentration poses the greatest threat to evacuees. Therefore, the influence coefficient formula of CO concentration is fitted; the influence coefficient of CO concentration is calculated as follows: Wherein, ρ represents the CO concentration in mol / mol; t represents the exposure time in min.

5. The method for simulating evacuation of personnel from a passenger and vehicle ferry fire according to claim 3, characterized in that: In step S22, the equivalent speed introduced is the product of the normal walking speed of personnel and the influence coefficients of the three fire products, and the calculation formula is as follows: V=V0*f1(k)*f2(T)*f3(ρ) Among them, V represents the equivalent speed, that is, the walking speed of people under the influence of fire products, in m / s; V0 represents the walking speed of people when no fire occurs, in m / s.

6. The method for simulating evacuation of personnel from a passenger and vehicle ferry fire according to claim 1, characterized in that: In step S3, the particle swarm algorithm is improved, specifically including: The inertia weight ω is set as a nonlinear change mechanism, and the calculation formula of the inertia weight ω is as follows: ω(t)=ω1+(ω1-ω2)(2t / Nmax-(t / Nmax) 2 ) Among them, ω1 represents the initial value, ω2 represents the final value; t represents the current number of iterations; N max Indicates the maximum number of iterations.

7. The method for simulating evacuation of personnel from a passenger and vehicle ferry fire according to claim 1, characterized in that: Step S4 specifically includes: S41, initialization system: setting the initial values ​​of control variables and other parameters; S42, randomly initialize to obtain the position and velocity of each particle in the particle swarm; S43, evaluating each particle and calculating the fitness value of the particle; S44, comparing the fitness value of each particle with the fitness value of the best position it has ever experienced, if it is better, then taking it as the individual historical optimal fitness value of the particle, and using the current position to update the individual historical best position; S45. For each particle, compare the individual historical best fitness value with the fitness value of the best position in the history experienced by the group. If it is better, take it as the current global best position, otherwise, keep it unchanged. S46, adjusting the particle speed and position according to step S43 and step S44; S47, check whether the end condition is met, if so, end the iteration and output the optimal solution; otherwise, return to execute steps S43 and S44.

8. A passenger ferry fire personnel evacuation simulation system implemented by a passenger ferry fire personnel evacuation simulation method according to any one of claims 1 to 7, characterized in that: include: Scenario building module, data analysis module, path optimization module and simulation module, including: The scenario establishment module is used to establish a cabin scenario model, perform numerical simulation on cabin fire, and obtain data on fire products; The data analysis module is used to analyze the changing rules of fire products and calculate the fire evacuation speed influence coefficient; The path optimization module is used to improve the particle swarm algorithm and integrate the fire product data into the improved particle swarm algorithm to obtain an optimization model for the personnel evacuation path under fire conditions; The simulation module is used to simulate the personnel evacuation process in the cabin scene model based on the personnel evacuation path optimization model to obtain the optimal personnel evacuation path under fire conditions.

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