Subway station fire evacuation simulation method and system

Abstract

This invention provides a subway station fire evacuation simulation method and system, belonging to the field of rail transit technology. Based on the lattice-Boltzmann method, the lattice-Boltzmann sublattice model obtained by large eddy simulation is integrated with an obstacle flow simulation model and a smoke diffusion model to construct a lattice-Boltzmann fire model. Sensory, psychological, and physiological models are constructed based on the lattice-Boltzmann fire model, and combined with a social force model to construct an evacuation model. The lattice-Boltzmann fire model is combined with the evacuation model to simulate evacuees' real-time avoidance of fire smoke. This invention achieves a substantial integration of the fire smoke evolution and evacuation processes, simulating their interaction and better recreating actual fire scenarios, providing certain benefits for engineering practice.

Description

Technical Field

[0001] The present invention relates to the field of rail transportation technology, and in particular to a subway station fire evacuation simulation method and system based on a lattice-Boltzmann method. Background Art

[0002] In the field of fire simulation in subway stations, a macroscopic approach is mostly adopted, generally using simulation software to model and restore the scene. Currently, fire simulations in engineering applications and theoretical research usually use commercial software based on computational fluid dynamics to complete the simulation of fire smoke evolution, as well as FDS, an open source tool developed by the National Institute of Standards and Technology of the United States specifically for fire simulation. Fire smoke simulation is generally divided into three simulation methods: regional simulation, network simulation, and field simulation. The common software and characteristics corresponding to these methods are shown in the table below. Among them, regional simulation divides a single room into several control volumes with uniform parameters and solves the equations to obtain the change of each control volume parameter over time; network simulation treats the entire building as a system and each room as a control volume; field simulation divides the calculation area into thousands of units, which can determine the changes in detailed parameters within the building and is widely used.

[0003] In the field of personnel evacuation, the microscopic methods usually used are roughly divided into continuous models and discrete models. The continuous model means that the position and time of individuals in the model are continuous, and the kinematic equations are constructed for the individual motion variables. Among them, the social force model is a relatively classic continuous model. The discrete model means that the entire evacuation environment is divided into grids. Within equal time steps, each grid can only be in a blank or occupied state. Under the action of the simulation mechanism, the evacuated individuals choose the cell to move to according to the evacuation environment and their own state, and choose the appropriate exit to escape. Among them, cellular automata are relatively classic discrete models. Based on these models, many commercial software have been developed, including Legion, Anylogic, Building Exdous, etc.

[0004] For fire evacuation simulations, the fire scene information obtained from the fire simulation is typically input into a personnel evacuation simulation model. For example, Fire Data Systems (FDS) is used to simulate the fire situation and then the simulation information is imported into Legion for personnel evacuation simulation. Alternatively, a grid model is used for personnel evacuation, combined with FDS fire simulation, to study the effects of high temperatures and low visual acuity caused by the fire on personnel evacuation. Movement speed is determined based on changes in the extinction coefficient.

[0005] Due to limitations in relevant computational models, computer-based simulations of passenger evacuation in subway stations under fire conditions currently face two technical challenges. First, current fire evolution simulations typically utilize commercial software based on macroscopic methods, lacking scalability and integration with evacuation simulation methods. Current fire simulations are typically performed using computational fluid dynamics software such as CFX, PHOENICS, and FLUENT, as well as the open-source tool FDS, specifically designed for fire simulation. These software tools all employ a macroscopic approach based on the Naverstock equations to simulate fluid flow, assuming the fluid is a continuous medium and ensuring that conservation of mass, momentum, and energy is satisfied for numerical solution. However, evacuation simulation models are microscopic and difficult to integrate into fire evolution simulation models, leading to a second technical challenge. Second, current simulations of evacuation and smoke evolution during fires are generally performed separately in both engineering applications and theoretical research. Because fire evolution simulation models and evacuation simulation models are difficult to integrate, in practice, fire simulation software is often used to first calculate fire and smoke evolution data. This data is then imported into the evacuation simulation model as environmental parameters to simulate pedestrian avoidance during the evacuation process. However, pedestrian movement in a fire scene cannot affect the spread of fire and smoke, making this approach unfeasible.

[0006] In short, most existing technical research focuses on one aspect, either fire simulation or personnel evacuation simulation. The two are not fully integrated or even independent of each other. Most technical methods first simulate the fire smoke flow through professional software, and then use the simulated data to import the evacuation model to simulate the personnel evacuation. This operation not only makes the modeling independent of each other, which increases the workload, but also fails to well reflect the impact of pedestrian evacuation activities on the spread of fire smoke. Existing technical research focuses on improving the personnel evacuation model while ignoring the construction of the fire smoke diffusion model. Most research is to improve and develop existing models from the perspective of the personnel evacuation model. For smoke diffusion simulation data, it is only obtained through simulation with existing software, and even a fixed range is used to simulate the impact of the fire. It is difficult to achieve the substantial integration and interactive influence of the fire smoke evolution and the personnel evacuation process. Summary of the Invention

[0007] The object of the present invention is to provide a method and system for simulating the evacuation of personnel from a subway station fire based on the lattice-Boltzmann method, which integrates a fire evolution model based on the mesoscopic model lattice-Boltzmann method, and integrates a microscopic pedestrian simulation model based on an intelligent agent, thereby realizing a comprehensive simulation of the interaction between personnel evacuation and fire evolution and being able to more realistically simulate the personnel evacuation and fire evolution processes, so as to solve at least one technical problem existing in the above-mentioned background technology.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] In one aspect, the present invention provides a method for simulating evacuation of personnel from a subway station fire, comprising:

[0010] Construct an obstacle flow simulation model based on the physical model of flow around a cylinder;

[0011] Construct a smoke diffusion model based on the semi-Lagrangian method;

[0012] Based on the lattice-Boltzmann method, the lattice-Boltzmann sub-lattice model obtained by large eddy simulation is integrated with the obstacle flow simulation model and the smoke diffusion model to construct a lattice-Boltzmann fire model.

[0013] Based on the lattice-Boltzmann fire model, sensory model, psychological model and physiological model are constructed, and combined with the social force model to construct a personnel evacuation model;

[0014] The lattice-Boltzmann fire model is combined with the personnel evacuation model to realize the real-time simulation of evacuees avoiding fire smoke.

[0015] Preferably, a simulation model of flow around an obstacle is constructed based on a physical model of flow around a cylinder, including:

[0016] The simulation of flow around obstacles adopts the physical model of flow around a cylinder. For the boundary treatment of circular obstacles, when migrating between fluid nodes and adjacent grid nodes in the cylinder, a processing method combining non-equilibrium extrapolation format and spatial interpolation is adopted to obtain the unknown distribution function on the grid nodes during the migration process, and thus obtain the simulation model of flow around obstacles.

[0017] Preferably, a smoke diffusion model is constructed based on a semi-Lagrangian method, including:

[0018] x at time t+Δt D The cloud of points is composed of x at time t-Δt O The cloud at t+Δt migrates here, then x D The physical quantity of the point cloud is equal to x at time t-Δt O Physical quantities of the cloud at:

[0019]

[0020] Where F represents the physical quantity of the cloud, α represents the distance the cloud moves within Δt, and α is solved by using the second-order Runge-Kutta method. (k+1) =Δt·U(x-α (k) ,t), and finally get the position of the smoke at the previous moment and the smoke density.

[0021] Preferably, It is the driving force of pedestrian movement, and its direction points to the target point D; is the psychological force exerted on pedestrian α by pedestrian β, which is directed from pedestrian β to pedestrian α and is expressed as a repulsive force; is the psychological force exerted on pedestrian α by the obstacle (wall), which is perpendicular to the obstacle (wall) surface and points toward the pedestrian, and is expressed as a repulsive force; is the attraction of other pedestrians or places to pedestrian α; ξ is a random force to simulate the random changes in pedestrian behavior during walking;

[0022] Therefore, the motion control equation of pedestrian α can be expressed as:

[0023]

[0024] Considering the impact of high temperature, smoke, and toxic gases on pedestrians in the lattice-Boltzmann fire model during pedestrian evacuation, the pedestrian sight distance in a smoke-filled space is calculated as:

[0025]

[0026] Among them, K m Indicates the specific dimmer coefficient, M S It represents the mass of smoke. If the object being viewed is reflected light, c is 3. If the object being viewed is reflected light, c is 8. Vol represents the volume of space.

[0027] Preferably, the sensory model is mainly composed of a visual system and a sensory system. Using the visual system, pedestrians can observe the smoke density at the grid nodes within the field of view and the density of the crowd within the field of view. The visual system's viewing distance will be adjusted according to the smoke density within the field of view according to the following formula:

[0028]

[0029] Where V represents the viewing distance. If the object is reflected light, c is 3; if the object is reflected light, c is 8; if it is a combustion process, K s Take 7.6, if it is a thermal decomposition process, then K s Take 4.4; ρ s Indicates the average smoke density within the viewing area;

[0030] The sensory system is mainly composed of a sensing range. Pedestrians can use the sensory system to sense the surrounding ambient temperature. The selection of walking direction is similar to the visual system. The sensing range is also divided into three areas evenly. Pedestrians compare the temperatures of the three areas and choose to walk in the direction with the smallest temperature.

[0031] Preferably, the psychological model uses panic factor C m To express the psychological state of the evacuees:

[0032]

[0033] C m =(C mT +C mS ) / 2

[0034] When building a physiological model, first establish the relationship between ambient temperature and human physiological factors:

[0035]

[0036] Establish the physiological model calculation formula:

[0037]

[0038] The impact of the fire environment on the physiological state of evacuees can be described by the following formula:

[0039]

[0040] In a second aspect, the present invention provides a subway station fire evacuation simulation system, comprising:

[0041] The first construction module is used to construct an obstacle flow simulation model based on the cylinder flow physical model;

[0042] The second building module is used to build a smoke diffusion model based on the semi-Lagrangian method;

[0043] The third building block is used to construct a lattice-Boltzmann fire model based on the lattice-Boltzmann method, integrating the lattice-Boltzmann sub-lattice model obtained by large eddy simulation, combining the obstacle flow simulation model and the smoke diffusion model;

[0044] The third building module is used to build sensory models, psychological models, and physiological models based on the lattice-Boltzmann fire model, and combine them with the social force model to build a personnel evacuation model;

[0045] The simulation module is used to combine the lattice-Boltzmann fire model with the personnel evacuation model to realize the real-time simulation of the evacuees' avoidance of fire smoke.

[0046] In a third aspect, the present invention provides a non-transitory computer-readable storage medium, which is used to store computer instructions. When the computer instructions are executed by a processor, the subway station fire personnel evacuation simulation method based on the lattice-Boltzmann method as described above is implemented.

[0047] In a fourth aspect, the present invention provides a computer program product, comprising a computer program, which, when executed on one or more processors, is used to implement the subway station fire evacuation simulation method based on the lattice-Boltzmann method as described above.

[0048] In a fifth aspect, the present invention provides an electronic device comprising: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes instructions for implementing the subway station fire personnel evacuation simulation method based on the lattice-Boltzmann method as described above.

[0049] The beneficial effects of the present invention are as follows: a lattice Boltzmann model based on a mesoscopic method is used to simulate the evolution process of fire smoke. The fluid is regarded as a large number of discrete particles, the flow field is discretized into a series of regular grids according to a certain spatial step, and time is also discretized into a time series. The fluid particles can only move along the grid lines, and each time step can only move one spatial step. This realizes the substantial integration of the fire smoke evolution and the personnel evacuation process, simulates the interaction between the two, better restores the actual fire scene, and provides certain help to engineering practice.

[0050] Additional advantages of the present invention will be more clearly given in the following description or learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0052] Figure 1 This is a technical circuit diagram of the subway station fire evacuation simulation method based on the lattice-Boltzmann method according to an embodiment of the present invention.

[0053] Figure 2 Schematic diagram of the physical model of flow around a cylinder according to an embodiment of the present invention.

[0054] Figure 3 This is a schematic diagram of curve boundary processing according to an embodiment of the present invention.

[0055] Figure 4 This is a schematic diagram of the social force model described in an embodiment of the present invention.

[0056] Figure 5 Schematic diagram of the relationship between fire scene temperature and fire scene intensity according to an embodiment of the present invention.

[0057] Figure 6 Schematic diagram of the change of cadence in fire smoke according to an embodiment of the present invention.

[0058] Figure 7 Schematic diagram of the visual system model according to an embodiment of the present invention.

[0059] Figure 8 This is a schematic diagram of the sensory system model described in an embodiment of the present invention.

[0060] Figure 9 Schematic diagram of the structure of the fusion model described in an embodiment of the present invention.

[0061] Figure 10 Schematic diagram of the pedestrian model according to an embodiment of the present invention.

[0062] Figure 11 Schematic diagram of the pedestrian-environment interaction mechanism according to an embodiment of the present invention.

[0063] Figure 12 This is a simplified schematic diagram of a fire scene according to an embodiment of the present invention. DETAILED DESCRIPTION

[0064] The embodiments of the present invention are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention and are not to be construed as limiting the present invention.

[0065] Those skilled in the art will understand that unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which this invention belongs.

[0066] It should also be understood that terms, such as those defined in commonly used dictionaries, should be understood to have a meaning consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless as defined herein.

[0067] Those skilled in the art will appreciate that, unless otherwise stated, the singular forms "a," "an," "said," and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the stated features, integers, steps, operations, elements, and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, and / or groups thereof.

[0068] In the description of this specification, reference to the terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. In addition, those skilled in the art may combine and integrate different embodiments or examples described in this specification, as well as features of different embodiments or examples, unless otherwise inconsistent.

[0069] To facilitate understanding of the present invention, the present invention is further explained below with reference to specific embodiments in conjunction with the accompanying drawings. However, the specific embodiments do not constitute a limitation on the embodiments of the present invention.

[0070] Those skilled in the art should understand that the drawings are merely schematic diagrams of embodiments, and the components in the drawings are not necessarily necessary for implementing the present invention.

[0071] Example 1

[0072] This embodiment 1 provides a subway station fire evacuation simulation system based on the lattice-Boltzmann method, including:

[0073] The first construction module is used to construct an obstacle flow simulation model based on the cylinder flow physical model;

[0074] The second building module is used to build a smoke diffusion model based on the semi-Lagrangian method;

[0075] The third building block is used to construct a lattice-Boltzmann fire model based on the lattice-Boltzmann method, integrating the lattice-Boltzmann sub-lattice model obtained by large eddy simulation, combining the obstacle flow simulation model and the smoke diffusion model;

[0076] The third building module is used to build sensory models, psychological models, and physiological models based on the lattice-Boltzmann fire model, and combine them with the social force model to build a personnel evacuation model;

[0077] The simulation module is used to combine the lattice-Boltzmann fire model with the personnel evacuation model to realize the real-time simulation of the evacuees' avoidance of fire smoke.

[0078] In this embodiment 1, the above-mentioned system is used to implement a subway station fire evacuation simulation method based on the lattice-Boltzmann method, including:

[0079] Construct an obstacle flow simulation model based on the physical model of flow around a cylinder;

[0080] Construct a smoke diffusion model based on the semi-Lagrangian method;

[0081] Based on the lattice-Boltzmann method, the lattice-Boltzmann sub-lattice model obtained by large eddy simulation is integrated with the obstacle flow simulation model and the smoke diffusion model to construct a lattice-Boltzmann fire model.

[0082] Based on the lattice-Boltzmann fire model, sensory model, psychological model and physiological model are constructed, and combined with the social force model to construct a personnel evacuation model;

[0083] The lattice-Boltzmann fire model is combined with the personnel evacuation model to realize the real-time simulation of evacuees avoiding fire smoke.

[0084] Among them, the obstacle flow simulation model is constructed based on the physical model of flow around a cylinder, including:

[0085] The simulation of flow around obstacles adopts the physical model of flow around a cylinder. For the boundary treatment of circular obstacles, when migrating between fluid nodes and adjacent grid nodes in the cylinder, a processing method combining non-equilibrium extrapolation format and spatial interpolation is adopted to obtain the unknown distribution function on the grid nodes during the migration process, and thus obtain the simulation model of flow around obstacles.

[0086] The smoke diffusion model is constructed based on the semi-Lagrangian method, including:

[0087] x at time t+Δt D The cloud of points is composed of x at time t-Δt O The cloud at t+Δt migrates here, then x D The physical quantity of the point cloud is equal to x at time t-Δt O Physical quantities of the cloud at:

[0088]

[0089] Where F represents the physical quantity of the cloud, α represents the distance the cloud moves within Δt, and α is solved by using the second-order Runge-Kutta method. (k+1) =Δt·U(x-α (k) ,t), and finally get the position of the smoke at the previous moment and the smoke density.

[0090] 4. The subway station fire evacuation simulation method according to claim 3 is characterized in that:

[0091] It is the driving force of pedestrian movement, and its direction points to the target point D; is the psychological force exerted on pedestrian α by pedestrian β, which is directed from pedestrian β to pedestrian α and is expressed as a repulsive force; is the psychological force exerted on pedestrian α by the obstacle (wall), which is perpendicular to the obstacle (wall) surface and points toward the pedestrian, and is expressed as a repulsive force; is the attraction of other pedestrians or places to pedestrian α; ξ is a random force to simulate the random changes in pedestrian behavior during walking;

[0092] Therefore, the motion control equation of pedestrian α can be expressed as:

[0093]

[0094] Considering the impact of high temperature, smoke, and toxic gases on pedestrians in the lattice-Boltzmann fire model during pedestrian evacuation, the pedestrian sight distance in a smoke-filled space is calculated as:

[0095]

[0096] Among them, K m Indicates the specific dimmer coefficient, M S It represents the mass of smoke. If the object being viewed is reflected light, c is 3. If the object being viewed is reflected light, c is 8. Vol represents the volume of space.

[0097] The sensory model is mainly composed of the visual system and the sensory system. Using the visual system, pedestrians can observe the smoke density at the grid nodes within the field of view and the density of the crowd within the field of view. The visual system's viewing distance will be adjusted according to the smoke density within the field of view according to the following formula:

[0098]

[0099] Where V represents the viewing distance. If the object is reflected light, c is 3; if the object is reflected light, c is 8; if it is a combustion process, K s Take 7.6, if it is a thermal decomposition process, then K s Take 4.4; ρ s Indicates the average smoke density within the viewing area;

[0100] The sensory system is mainly composed of a sensing range. Pedestrians can use the sensory system to sense the surrounding ambient temperature. The selection of walking direction is similar to the visual system. The sensing range is also divided into three areas evenly. Pedestrians compare the temperatures of the three areas and choose to walk in the direction with the smallest temperature.

[0101] The psychological model uses panic factor C m To express the psychological state of the evacuees:

[0102]

[0103] C m =(C mT +CmS ) / 2

[0104] When building a physiological model, first establish the relationship between ambient temperature and human physiological factors:

[0105]

[0106] Establish the physiological model calculation formula:

[0107]

[0108] The impact of the fire environment on the physiological state of evacuees can be described by the following formula:

[0109]

[0110] Example 2

[0111] This embodiment 2 provides a subway station fire evacuation simulation method based on the lattice-Boltzmann method. Figure 1 As shown in Figure 2, the technical framework for implementing this method is divided into three main parts: the lattice-Boltzmann fire model, the agent-based evacuation model, and model fusion. The following sections describe these two models in detail.

[0112] (1) Fire smoke scene simulation

[0113] First, the lattice-Boltzmann fire model is mainly based on the lattice-Boltzmann method, and integrates the lattice-Boltzmann sub-lattice model obtained by the large eddy simulation method. At the same time, obstacle flow simulation and smoke diffusion simulation are added to the sub-lattice model. LBM is derived from the lattice gas automaton (LGA) method, which directly uses the discretized Boltzmann equation to calculate the particle distribution function. The fully discretized Boltzmann equation is shown in Equation (1)

[0114]

[0115] At the same time, the fire model includes obstacle flow simulation and smoke diffusion simulation.

[0116] The simulation of flow around obstacles is carried out using Figure 2 Physical model of flow around a cylinder shown

[0117] The boundary processing of circular obstacles is as follows Figure 3 shown.

[0118] With r F Take point as an example, the point and the adjacent grid node r in the column S When migrating between nodes, you need to know that these nodes enter the fluid node r F The unknown distribution function f -3 ,f-4 ,f -7 , where f -i The distribution function f on the node in the column corresponding to the node in the fluid pointing to the node i The distribution function in the opposite direction. Then the boundary node r S The unknown distribution function on the fluid node r F The expression of is shown in formula (2)

[0119]

[0120] In the formula It is not determined during the collision process, so it needs to be processed after the collision process and before the migration process.

[0121] Here we use a method proposed by Guo et al. in 2002 that combines the non-equilibrium extrapolation format and spatial interpolation. S The distribution function of is divided into two parts: equilibrium and non-equilibrium. The equilibrium part is solved using formula (3), and the non-equilibrium part is obtained by interpolation, see formula (4).

[0122]

[0123] In the formula Replaced by the node values ​​in the adjacent fluid, Obtained by interpolation.

[0124]

[0125] The smoke diffusion model uses the semi-Lagrangian method, which is a calculation method widely used in fields such as numerical weather forecasting. This method is not restricted by stability conditions and has good calculation stability. The main idea of ​​this method is that x at time t+Δt D The cloud of points is composed of x at time t-Δt O The cloud at t+Δt migrates here, then x D The physical quantity of the point cloud is equal to x at time t-Δt O The physical quantity of the cloud cluster at is shown in Equation (5).

[0126]

[0127] Where F is the physical quantity of the cloud, and α is the distance the cloud moves within the time Δt.

[0128] Therefore, in order to solve the smoke density at a grid node at this moment, it is necessary to know where the smoke at the node migrated from at the previous moment; that is, it is necessary to determine the cloud migration distance α, which can be calculated as shown in formula (6).

[0129] α (k+1) =Δt·U(x-α (k) ,t) (6)

[0130] Then, the second-order Runge-Kutta method is used to solve Equation (6), and finally the position of the smoke at the previous moment and the smoke density are obtained.

[0131] (2) Personnel evacuation scenario simulation

[0132] The social force model is used in the personnel evacuation model, as shown in the schematic diagram. Figure 4 shown.

[0133] in, It is the driving force of pedestrian movement, and its direction points to the target point D; is the psychological force exerted on pedestrian α by pedestrian β, which is directed from pedestrian β to pedestrian α and is expressed as a repulsive force; is the psychological force exerted on pedestrian α by the obstacle (wall), which is perpendicular to the obstacle (wall) surface and points toward the pedestrian, and is expressed as a repulsive force; is the attraction of other pedestrians or places to pedestrian α; ξ is a random force to simulate the random changes in pedestrian behavior during walking.

[0134] Therefore, the motion control equation of pedestrian α can be expressed as Equation (7).

[0135]

[0136] The impact of high temperature, smoke and toxic gases on pedestrians was taken into consideration during the pedestrian evacuation process. The impact of high temperature on pedestrian evacuation is mainly manifested in two aspects: physical damage and psychological panic. Figure 5 This is the relationship diagram between fire scene temperature and fire scene intensity.

[0137] The impact of smoke from a fire on pedestrian evacuation is mainly reflected in the shading effect, which in turn shortens the pedestrian's sight distance and reduces the escape speed. The calculation of pedestrian sight distance in a smoke-filled space is shown in formula (8):

[0138]

[0139] Where K m ——Specific attenuation coefficient, unit is m 2 / g, 7.6 is taken during the combustion of wood and plastic, and 4.4 is taken during thermal decomposition;

[0140] M S ——smoke mass, in g;

[0141] c - depends on whether the object is reflecting light or emitting light, the former is 3, the latter is 8;

[0142] Vol——space volume.

[0143] The relationship between the extinction coefficient of smoke and the step frequency, such as Figure 6 As shown. The extinction coefficient is greater than 0.5m -1 The walking speed corresponding to the step frequency of 0.8Hz is 0.56m / s. The step frequency changes in the fire smoke are as follows Figure 6 shown.

[0144] The extinction coefficient and pedestrian evacuation speed have the relationship shown in formula (9).

[0145]

[0146] Where η = 0.706 m·s -1 ,τ=0.057m 2 ·s -1 .

[0147] In addition to the high temperatures mentioned above, another factor that can cause physiological harm to the human body in a fire environment is the toxicity of harmful gases. Because the burning materials in a fire environment contain a large amount of polymers and incomplete combustion, a variety of harmful gases are produced in a short period of time, causing physiological damage to people evacuated from the fire scene. This is often determined based on the toxic dose of different toxic gases to the human body. Table 1 shows the dose determination criteria.

[0148] Table 1 Gas dose standards for human health hazards

[0149]

[0150] Then, when constructing the intelligent model, we start from three dimensions: sensory model, psychological model, and physiological model. The sensory model is mainly composed of the visual system and the sensory system. The schematic diagram of the visual system model is as follows: Figure 7 As shown in Figure 1, using the visual system, pedestrians can observe the smoke density at the grid nodes within the field of view and the density of the crowd within the field of view. In addition, the visual system's viewing distance will be adjusted according to the smoke density within the field of view according to formula (10).

[0151]

[0152] Where V is the sight distance, in meters;

[0153] c - depends on whether the object is reflecting light or emitting light, the former is 3, the latter is 8;

[0154] K s ——Unit is m 2 / g, the combustion process takes 7.6, and the thermal decomposition process takes 4.4;

[0155] ρ s ——Average smoke density within the viewing area, in g / m 3 .

[0156] In this model, the visual angle of the visual system is uniformly taken as 180°, and then the visual angle is divided into three equal parts, such as Figure 7 As shown in (b), there are the left area L, the right area R, and the middle area M. Pedestrians compare the smoke density in the three areas and choose to walk in the direction with the smallest smoke density.

[0157] The sensory system mainly consists of the range of sensations, such as Figure 8 As shown in the figure, the shaded area represents the pedestrian's sensory range; the grid represents the LBM grid system used in the fire scenario simulation. Pedestrians can use their sensory systems to sense the surrounding ambient temperature. Unlike the visual system, the sensory system's range remains fixed and does not change with other factors, such as temperature or time.

[0158] The selection of walking direction is similar to that of the visual system, which also divides the sensory range into three areas. Figure 8 As shown, there are the left area L, the right area R, and the middle area M. Pedestrians compare the temperatures of the three areas and choose to walk in the direction with the smallest temperature.

[0159] The psychological model uses panic factor C m To express the psychological state of the evacuees, see formula (11).

[0160]

[0161] When constructing a physiological model, the relationship between ambient temperature and human physiological factors is first established as shown in formula (12).

[0162]

[0163] For CO analysis, the average cumulative lethal concentration was calculated to be 8.4% from Table 1. The model calculation formula is shown in Equation (13).

[0164]

[0165] In summary, the impact of the fire environment on the physiological state of evacuees can be described by formula (14).

[0166]

[0167] The above models are combined with the classic social force evacuation model to obtain the final evacuation model. The integration of these models is mainly reflected in their impact on the pedestrian evacuation speed.

[0168] In the visual model, the smoke density seen by pedestrians will reduce their evacuation speed. The relationship between this and the evacuation speed is determined by this model as shown in Equation (15).

[0169]

[0170] The impact of psychological panic and physiological injury on pedestrian evacuation speed is relatively direct and can be calculated by reducing the proportional coefficient, as shown in formula (16).

[0171] v=v0C m C p (16)

[0172] (3) Model fusion

[0173] This model program involves two main models, fire evolution and evacuation. To ensure low coupling of the program, the two models are processed separately in the core algorithm implementation process. The fire evolution adopts the lattice Boltzmann method. The biggest feature of this method is that the evolution process is very clear and the program implementation is very logical. The calculation process of the fire evolution model and the evacuation model is as follows: Figure 9 The pedestrian model composition and pedestrian-environment interaction mechanism are shown in Figure 10 and Figure 11 shown.

[0174] In summary, in this embodiment 2, a lattice Boltzmann model based on a mesoscopic method is used to simulate the evolution of fire smoke. This method is derived from the lattice gas automaton method. The fluid is regarded as a large number of discrete particles, the flow field is discretized into a series of regular grids according to a certain spatial step, and time is also discretized into a time series; in this model, fluid particles can only move along the grid lines, and each time step can only move one spatial step. This model has a similar modeling idea to the microscopic simulation model of personnel evacuation, especially the discrete model, and it is easy to achieve a substantial fusion of the two models, thereby achieving a substantial fusion of the fire smoke evolution and the personnel evacuation process, and simulating the interaction between the two.

[0175] In order to verify the effectiveness of the comprehensive model simulation, this technology constructs a simplified scenario, such as Figure 12 As shown in the figure, the scene contains a rectangular staircase and six square columns. The fire source is located on the left side of the staircase, the exit is on the right side, and the evacuees are located on the far left side of the scene. The fire source temperature is constant at 200°C, and the ambient temperature is constant at 20°C. The left side shows the smoke diffusion isotherm without the interference of evacuees, while the right side shows the fire scene isotherm with the influence of evacuees. Comparing the two images, it is clear that after 50 seconds, the evacuees' passage through the smoke will affect the smoke diffusion area. Compared with other models, this model better reproduces the actual fire scene, providing certain benefits for engineering practice.

[0176] Example 3

[0177] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium for storing computer instructions. When the computer instructions are executed by a processor, a subway station fire evacuation simulation method based on the lattice-Boltzmann method is implemented. The method includes:

[0178] Construct an obstacle flow simulation model based on the physical model of flow around a cylinder;

[0179] Construct a smoke diffusion model based on the semi-Lagrangian method;

[0180] Based on the lattice-Boltzmann method, the lattice-Boltzmann sub-lattice model obtained by large eddy simulation is integrated with the obstacle flow simulation model and the smoke diffusion model to construct a lattice-Boltzmann fire model.

[0181] Based on the lattice-Boltzmann fire model, sensory model, psychological model and physiological model are constructed, and combined with the social force model to construct a personnel evacuation model;

[0182] The lattice-Boltzmann fire model is combined with the personnel evacuation model to realize the real-time simulation of evacuees avoiding fire smoke.

[0183] Example 4

[0184] Embodiment 4 of the present invention provides a computer program (product), including a computer program. When the computer program is executed on one or more processors, the computer program is used to implement a subway station fire evacuation simulation method based on the lattice-Boltzmann method, the method comprising:

[0185] Construct an obstacle flow simulation model based on the physical model of flow around a cylinder;

[0186] Construct a smoke diffusion model based on the semi-Lagrangian method;

[0187] Based on the lattice-Boltzmann method, the lattice-Boltzmann sub-lattice model obtained by large eddy simulation is integrated with the obstacle flow simulation model and the smoke diffusion model to construct a lattice-Boltzmann fire model.

[0188] Based on the lattice-Boltzmann fire model, sensory model, psychological model and physiological model are constructed, and combined with the social force model to construct a personnel evacuation model;

[0189] The lattice-Boltzmann fire model is combined with the personnel evacuation model to realize the real-time simulation of evacuees avoiding fire smoke.

[0190] Example 5

[0191] Embodiment 5 of the present invention provides an electronic device, including: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory to cause the electronic device to execute instructions for implementing a subway station fire evacuation simulation method based on the lattice-Boltzmann method, the method including:

[0192] Construct an obstacle flow simulation model based on the physical model of flow around a cylinder;

[0193] Construct a smoke diffusion model based on the semi-Lagrangian method;

[0194] Based on the lattice-Boltzmann method, the lattice-Boltzmann sub-lattice model obtained by large eddy simulation is integrated with the obstacle flow simulation model and the smoke diffusion model to construct a lattice-Boltzmann fire model.

[0195] Based on the lattice-Boltzmann fire model, sensory model, psychological model and physiological model are constructed, and combined with the social force model to construct a personnel evacuation model;

[0196] The lattice-Boltzmann fire model is combined with the personnel evacuation model to realize the real-time simulation of evacuees avoiding fire smoke.

[0197] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0198] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0199] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0200] These computer program instructions can also be loaded onto a computer or other programmable data processing device, and a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide the functions for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0201] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solutions disclosed in the present invention without the need for creative work should be included in the scope of protection of the present invention.

Claims

1. A simulation method for evacuating people from a subway station fire, characterized in that: include: Construct an obstacle flow simulation model based on the physical model of flow around a cylinder; Construct a smoke diffusion model based on the semi-Lagrangian method; Based on the lattice-Boltzmann method, the lattice-Boltzmann sub-lattice model obtained by large eddy simulation is integrated with the obstacle flow simulation model and the smoke diffusion model to construct a lattice-Boltzmann fire model. Based on the Lattice-Boltzmann fire model, sensory, psychological, and physiological models were constructed, and combined with the social force model to construct a personnel evacuation model. The sensory model consists of the visual system and the sensory system. The visual system's viewing angle is uniformly set to 180°, and then the viewing angle is evenly divided into three parts: the left area L, the right area R, and the middle area M. Pedestrians compare the smoke density in the three areas and choose the direction with the smallest smoke density. Pedestrians use the visual system to observe the smoke density at the grid nodes within the field of view and the density of the crowd within the field of view. The visual system's viewing distance is adjusted according to the smoke density within the field of view: Where V represents the viewing distance. If the object is reflected light, c is 3; if the object is emitted light, c is 8; if it is a combustion process, K s Take 7.6, if it is a thermal decomposition process, then K s Take 4.4; ρ s Indicates the average smoke density within the viewing area; The sensory system consists of a sensory range. Pedestrians use the sensory system to sense the surrounding ambient temperature. Similar to the visual system, the sensory range is divided into three equal areas. Pedestrians compare the temperatures of the three areas and choose the direction with the smallest temperature. The psychological model uses panic factor C m To express the psychological state of the evacuees: C m =(C mT +C mS ) / 2 When building a physiological model, first establish the relationship between ambient temperature and human physiological factors: Establish the physiological model calculation formula: The impact of the fire environment on the physiological state of evacuees is described by the following formula: The above models are combined with the classical social force evacuation model to obtain the final evacuation model; the fusion of these models is mainly reflected in their impact on pedestrian evacuation speed; The impact of psychological panic and physical injury on pedestrian evacuation speed is relatively direct, and is calculated using the proportional coefficient reduction method: v=v0C m C p ; The lattice-Boltzmann fire model is combined with the personnel evacuation model to realize the real-time simulation of evacuees avoiding fire smoke.

2. The subway station fire evacuation simulation method according to claim 1, characterized in that: The obstacle flow simulation model is constructed based on the physical model of flow around a cylinder, including: The simulation of flow around obstacles adopts the physical model of flow around a cylinder. For the boundary treatment of circular obstacles, when migrating between fluid nodes and adjacent grid nodes in the cylinder, a processing method combining non-equilibrium extrapolation format and spatial interpolation is adopted to obtain the unknown distribution function on the grid nodes during the migration process, and thus obtain the simulation model of flow around obstacles.

3. The subway station fire evacuation simulation method according to claim 2, characterized in that: The smoke diffusion model is constructed based on the semi-Lagrangian method, including: x at time t+Δt D The cloud of points is composed of x at time t-Δt O The cloud at t+Δt migrates here, then x D The physical quantity of the point cloud is equal to x at time t-Δt O Physical quantities of the cloud at: Where F represents the physical quantity of the cloud, α represents the distance the cloud moves within Δt, and α is solved by using the second-order Runge-Kutta method. (k+1) =Δt·U(x-α (k) ,t), and finally get the position of the smoke at the previous moment and the smoke density.

4. The subway station fire evacuation simulation method according to claim 3, characterized in that: It is the driving force of pedestrian movement, and its direction points to the target point D; is the psychological force exerted on pedestrian α by pedestrian β, which is directed from pedestrian β to pedestrian α and is expressed as a repulsive force; is the psychological force exerted on pedestrian α by the obstacle, which is perpendicular to the obstacle surface and points toward the pedestrian, and is expressed as a repulsive force; is the attraction of other pedestrians or places to pedestrian α; ξ is a random force to simulate the random changes in pedestrian behavior during walking; Therefore, the motion control equation of pedestrian α can be expressed as: Considering the impact of high temperature, smoke, and toxic gases on pedestrians in the lattice-Boltzmann fire model during pedestrian evacuation, the pedestrian sight distance in a smoke-filled space is calculated as: Among them, K m Indicates the specific dimmer coefficient, M S Indicates the mass of smoke. If the object being viewed is reflected light, c is 3. If the object being viewed is emitted light, c is 8. Vol indicates the volume of space.

5. A subway station fire evacuation simulation system, characterized in that: include: The first construction module is used to construct an obstacle flow simulation model based on the cylinder flow physical model; The second building module is used to build a smoke diffusion model based on the semi-Lagrangian method; The third building block is used to construct a lattice-Boltzmann fire model based on the lattice-Boltzmann method, integrating the lattice-Boltzmann sub-lattice model obtained by large eddy simulation, combining the obstacle flow simulation model and the smoke diffusion model; The third construction module is used to construct sensory, psychological, and physiological models based on the Lattice-Boltzmann fire model, and combines them with the social force model to construct a personnel evacuation model. The sensory model is composed of the visual system and the sensory system. The visual system's viewing angle is uniformly set to 180 degrees. The viewing angle is then divided into three equal parts: the left area L, the right area R, and the middle area M. Pedestrians compare the smoke density in these three areas and choose the direction with the smallest smoke density. Pedestrians use the visual system to observe the smoke density at the grid nodes within the field of view and the density of the crowd within the field of view. The visual system's viewing distance is adjusted according to the smoke density within the field of view: Where V represents the viewing distance. If the object is reflected light, c is 3; if the object is emitted light, c is 8; if it is a combustion process, K s Take 7.6, if it is a thermal decomposition process, then K s Take 4.4; ρ s Indicates the average smoke density within the viewing area; The sensory system consists of a sensory range. Pedestrians use the sensory system to sense the surrounding ambient temperature. Similar to the visual system, the sensory range is divided into three equal areas. Pedestrians compare the temperatures of the three areas and choose the direction with the smallest temperature. The psychological model uses panic factor C m To express the psychological state of the evacuees: C m =(C mT +C mS ) / 2 When building a physiological model, first establish the relationship between ambient temperature and human physiological factors: Establish the physiological model calculation formula: The impact of the fire environment on the physiological state of evacuees is described by the following formula: The above models are combined with the classical social force evacuation model to obtain the final evacuation model; the fusion of these models is mainly reflected in their impact on pedestrian evacuation speed; The impact of psychological panic and physical injury on pedestrian evacuation speed is relatively direct, and is calculated using the proportional coefficient reduction method: v=v0C m C p ; The simulation module is used to combine the lattice-Boltzmann fire model with the personnel evacuation model to realize the real-time simulation of the evacuees' avoidance of fire smoke.

6. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by the processor, the subway station fire personnel evacuation simulation method according to any one of claims 1 to 4 is implemented.

7. A computer program product, characterized in that The invention comprises a computer program, which, when running on one or more processors, is used to implement the subway station fire personnel evacuation simulation method according to any one of claims 1 to 4.

8. An electronic device, characterized in that: include: A processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to execute instructions for implementing the subway station fire personnel evacuation simulation method as described in any one of claims 1 to 4.

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