A dynamic casualty assessment method for earthquake falling objects considering evacuation behavior

Abstract

The present invention provides a method for dynamic casualty assessment of earthquake falling objects that takes into account people's evacuation behavior, and relates to the field of civil engineering computer visualization technology. The method comprises: constructing a population distribution model within the area to be assessed, refining the area to be assessed into multiple grid areas using a Kriging interpolation algorithm, determining the population size within each grid area, and establishing an intelligent agent model within each grid area based on the population size; establishing a building model of the area to be assessed, determining the potential energy weight of each grid based on the distance field between each grid area and the building, and setting a sparse spatial potential energy perception behavior specification for the intelligent agent model; establishing a falling object model for the area to be assessed, and simulating the distribution of the falling object after the falling object model moves based on contact dynamics; determining the impact energy of the falling object within each grid area, and performing a casualty assessment based on the impact energy and the distribution of people within each time step. Thus, the casualty assessment takes evacuation behavior into account.

Description

Technical Field

[0001] The present invention relates to the technical field of civil engineering computer visualization, and in particular to a method for dynamic casualties assessment of earthquake falling objects taking into account the evacuation behavior of personnel. Background Art

[0002] Earthquakes can trigger a variety of secondary disasters that can lead to casualties, including building collapse, secondary fires, and falling objects. Accurately predicting casualties not only helps mitigate the impact of earthquake disasters but also improves the efficiency and effectiveness of rescue operations. Predicting potential casualties allows for more specific and targeted emergency response plans. Compared to traditional casualty assessment methods, considering people's movement trajectories allows for dynamic assessment of casualties during earthquakes, resulting in more accurate casualty data.

[0003] However, on the one hand, most casualty assessment methods calculate the number of casualties based on regional population data combined with empirical formulas (Samardjieva E, Badal J. Estimation of the expected number of casualties caused by strong earthquakes [J]. Bulletin of the Seismological Society of America, 2002, 92 (6): 2310-2322.). However, this method cannot accurately quantify the impact of falling objects on casualties. On the other hand, when an earthquake occurs, people in the scene will take emergency shelters out of fear. A common method for simulating group behavior is through multi-agent (Guo Tianyu. Research on dynamic evacuation model of underground commercial buildings under fire conditions [D]. China University of Mining and Technology, 2023.; Battegazzorre E, Bottino A, Domaneschi M, et al. IdealCity: A hybrid approach to seismic evacuation modeling [J]. Advances inEngineering Software, 2021, 153: 102956.; Castro S, Poulos A, Herrera JC, et al. Modeling the impact of earthquake-induced debris on tsunami evacuationtimes of coastal cities [J]. Earthquake Spectra, 2019, 35(1): 137-158.; Trivedi A, Rao S. Agent-based modeling of emergency evacuations considering human panic behavior [J]. IEEE Transactions on Computational Social Systems, 2018, 5(1): 277-288.). However, most simulations only consider the organized evacuation behavior of people, but do not take into account the panic evacuation behavior caused by fear, and do not combine it with the analysis of falling object hazards.

[0004] In summary, there is a need to propose a dynamic casualty assessment method for earthquake falling objects that takes evacuation behavior into consideration. Summary of the Invention

[0005] In order to solve the technical problem that existing casualty assessment methods do not take evacuation behavior into account, the present invention provides a method for dynamic casualty assessment of earthquake falling objects that takes evacuation behavior into account. The technical solution is as follows:

[0006] In one aspect, a method for dynamic casualty assessment of earthquake falling objects taking into account evacuation behavior is provided, the method comprising:

[0007] Constructing a population distribution model within the area to be evaluated, subdividing the area to be evaluated into multiple grid areas using a Kriging interpolation algorithm, determining the population size within each grid area, and establishing an agent model within each grid area based on the population size;

[0008] Establishing a building model of the area to be evaluated, determining the potential energy weight of each grid based on the distance field between each grid area and the building, and setting sparse spatial potential energy perception behavior specifications for the intelligent agent model;

[0009] Establishing a falling object model of the area to be evaluated, and simulating the distribution of falling objects after the falling object model moves based on contact dynamics;

[0010] The impact energy of the falling object in each grid area is determined, and casualties are assessed based on the impact energy and the distribution of personnel in each time step.

[0011] Optionally, constructing a population distribution model within the area to be assessed includes:

[0012] Construct personnel distribution point data with a grid scale of 80m based on mobile phone signaling;

[0013] The personnel distribution point data is converted into grid data, and the grid data inherits the attributes of the personnel distribution point data through a spatial mapping method.

[0014] Optionally, the method of using a Kriging interpolation algorithm to subdivide the area to be assessed into a plurality of grid areas and determining the population size in each grid area includes:

[0015] The area to be assessed is refined into multiple grid areas with a grid size of 10m;

[0016]

[0017] Determine the population in each grid area, where P i is the population in each grid area, T is the total outdoor population in the area to be evaluated, and R i is the result of the Kriging interpolation algorithm.

[0018] Optionally, determining the potential energy weight of each grid according to the distance field between each grid area and the building includes:

[0019] Determine the distance field between each grid and the building;

[0020] A potential energy weight is assigned to each grid according to the distance field. The weight calculation formula of each grid is: ω=1 / dis, where dis is the distance field between each grid and the building, and ω is the grid weight.

[0021] Optionally, the sparse space potential energy perception behavior specification is to move towards a direction with a smaller potential energy weight.

[0022] Optionally, establishing the dropped object model of the area to be assessed includes:

[0023] Calculating structural response data of the urban building complex in the area to be assessed under earthquake action using a multi-degree-of-freedom shear model;

[0024] For each floor, when the inter-story displacement angle is determined to be greater than the damage limit based on the structural response data, a falling object model is established.

[0025] Optionally, determining the impact energy of the falling object in each grid area includes:

[0026] For each time step, the formula Determine the impact energy of the falling object in each grid area, where I is the impact energy of the falling object in the current grid area within the time step, t1 is the start time of the current time step, t2 is the end time of the current time step, and g is the acceleration due to gravity;

[0027] The casualty assessment is performed based on the impact energy and the distribution of personnel in each time step, including:

[0028] By formula n c =P(C|I)×n to determine the number of casualties, where n c is the number of casualties in the current grid area within the current time step, P(C|I) is the probability of casualties when the impact energy is I, and n is the number of people in the current grid area.

[0029] In another aspect, a device for casualty assessment is provided. The device for casualty assessment is used to implement the method for dynamic casualty assessment of earthquake falling objects that takes into account people's evacuation behavior provided in an embodiment of the present invention. The device includes:

[0030] A construction module is used to construct a population distribution model within the area to be evaluated, and to refine the area to be evaluated into multiple grid areas through the Kriging interpolation algorithm, and to determine the population size within each grid area, and to establish an agent model within each grid area based on the population size;

[0031] a determination module, configured to establish a building model of the area to be evaluated, determine the potential energy weight of each grid based on the distance field between each grid area and the building, and set a sparse spatial potential energy perception behavior specification for the intelligent agent model;

[0032] A simulation module, configured to establish a falling object model in the area to be evaluated, and simulate the distribution of falling objects after the falling object model moves based on contact dynamics;

[0033] The evaluation module is used to determine the impact energy of falling objects in each grid area and perform casualty assessment based on the impact energy and the distribution of people in each time step.

[0034] In another aspect, a device for casualty assessment is provided, the device for casualty assessment comprising:

[0035] processor;

[0036] A memory having computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed by the processor, implement the method provided by the embodiment of the present invention.

[0037] On the other hand, a computer-readable storage medium is provided, in which a program code is stored. The program code can be called by a processor to execute the method provided by the present invention.

[0038] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0039] An embodiment of the present invention constructs an intelligent agent model through the Kriging interpolation algorithm, sets sparse spatial potential energy perception behavior specifications for the intelligent agent model, and constructs a falling object model of the area to be evaluated. Then, based on the impact energy of the falling object in the area to be evaluated and the distribution of people in each time step, a casualty assessment is performed, thereby taking evacuation behavior into consideration when performing casualty assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in 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 creative work.

[0041] Figure 1 This is a flow chart of a method for dynamic casualties assessment of earthquake falling objects taking into account people's evacuation behavior, provided by an embodiment of the present invention;

[0042] Figure 2The present invention takes urban buildings as an example to establish an urban building model;

[0043] Figure 3 The casualty results obtained by the dynamic casualty assessment method of the present invention;

[0044] Figure 4 The 80m population grid established for the present invention;

[0045] Figure 5 A 10m population grid established for the present invention;

[0046] Figure 6 The distribution of people within the time steps of 0-5s, 10-15s, 30-35s, and 55-60s after the onset of the earthquake provided by the present invention;

[0047] Figure 7 The distribution results of falling objects in building complexes simulated by the present invention are as follows;

[0048] Figure 8 is a block diagram of a device for casualty assessment provided by an embodiment of the present invention;

[0049] Figure 9 1 is a schematic structural diagram of a device for casualty assessment provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0051] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0052] In the embodiments of the present invention, the terms "image" and "picture" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same. The terms "of," "corresponding," and "corresponding" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same.

[0053] In the embodiments of the present invention, sometimes a subscript such as W1 may be mistakenly written as a non-subscript form such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0054] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0055] In order to solve the technical problem that existing casualty assessment methods do not take evacuation behavior into account, the present invention provides a method for dynamic casualty assessment of earthquake falling objects that takes evacuation behavior into account. The technical solution is as follows:

[0056] like Figure 1 As shown, a method for dynamic casualties assessment of earthquake falling objects taking into account the evacuation behavior of people is provided, the method comprising:

[0057] S1. Construct a population distribution model within the area to be evaluated. Use the Kriging interpolation algorithm to refine the area to be evaluated into multiple grid areas, determine the population size within each grid area, and establish an agent model within each grid area based on the population size.

[0058] S2. Establish a building model of the area to be evaluated, determine the potential energy weight of each grid based on the distance field between each grid area and the building, and set sparse spatial potential energy perception behavior specifications for the intelligent agent model;

[0059] S3. Establish a falling object model for the area to be evaluated, and simulate the distribution of falling objects after the falling object model moves based on contact dynamics;

[0060] S4. Determine the impact energy of the falling object in each grid area, and perform casualty assessment based on the impact energy and the distribution of personnel in each time step.

[0061] Optionally, constructing a population distribution model within the area to be assessed includes:

[0062] Construct personnel distribution point data with a grid scale of 80m based on mobile phone signaling;

[0063] The personnel distribution point data is converted into grid data, and the grid data inherits the attributes of the personnel distribution point data through a spatial mapping method.

[0064] Optionally, the method of using a Kriging interpolation algorithm to subdivide the area to be assessed into a plurality of grid areas and determining the population size in each grid area includes:

[0065] The area to be assessed is refined into multiple grid areas with a grid size of 10m;

[0066] According to the formula

[0067] Determine the population in each grid area, where P iis the population in each grid area, T is the total outdoor population in the area to be evaluated, and R i is the result of the Kriging interpolation algorithm.

[0068] Optionally, determining the potential energy weight of each grid according to the distance field between each grid area and the building includes:

[0069] Determine the distance field between each grid and the building;

[0070] A potential energy weight is assigned to each grid according to the distance field. The weight calculation formula of each grid is: ω=1 / dis, where dis is the distance field between each grid and the building, and ω is the grid weight.

[0071] Optionally, the sparse space potential energy perception behavior specification is to move towards a direction with a smaller potential energy weight.

[0072] Optionally, establishing the dropped object model of the area to be assessed includes:

[0073] Calculating structural response data of the urban building complex in the area to be assessed under earthquake action using a multi-degree-of-freedom shear model;

[0074] For each floor, when the inter-story displacement angle is determined to be greater than the damage limit based on the structural response data, a falling object model is established.

[0075] Optionally, determining the impact energy of the falling object in each grid area includes:

[0076] For each time step, the formula Determine the impact energy of the falling object in each grid area, where I is the impact energy of the falling object in the current grid area within the time step, t1 is the start time of the current time step, t2 is the end time of the current time step, and g is the acceleration due to gravity;

[0077] The casualty assessment is performed based on the impact energy and the distribution of personnel in each time step, including:

[0078] By formula n c =P(C|I)×n to determine the number of casualties, where n c is the number of casualties in the current grid area within the current time step, P(C|I) is the probability of casualties when the impact energy is I, and n is the number of people in the current grid area.

[0079] In some embodiments, S1 is implemented as follows:

[0080] S11, constructing personnel distribution point data with a grid scale of 80m according to mobile phone signaling, converting the point data into grid data, and the grid data inherits the attributes of the personnel distribution point data through a spatial mapping method.

[0081] S12, based on the number of people per unit area in the building given by the seismic code, calculate the number of people in the building at that moment according to the building function, so as to obtain the total number of people in the building at that moment. The number of outdoor people is calculated by subtracting the total number of people in the building from the total number of people in the area.

[0082] S13, using the Kriging interpolation algorithm, the grid with a grid size of 80m is refined into a grid with a grid size of 10m. It should be noted that the result R obtained by the Kriging interpolation calculation i It reflects the transition linear change trend between the two data points, so it is necessary to calculate the population P in a grid with a grid size of 10m according to the weight of each data point. i , the number of people in the grid is calculated based on the total outdoor population T of the area and the interpolation formula:

[0083]

[0084] S14, establish the intelligent agent model of the region, through the grid mapping matrix N mesh And the initial calibration matrix N0 converts the geographic coordinate data P into the grid coordinate data Q in the agent scene; the mapping formula is:

[0085] Q=PN mesh -N0

[0086] According to the population size within a grid with a grid scale of 10m, an intelligent agent model is established for the corresponding grid in the scene.

[0087] Next, in step S2 above, to achieve accurate casualty assessment, a two-dimensional regional building model and agent model are established in the scene. A pathfinding algorithm is used to calculate the distance field dis between each grid and the building. Based on the distance field between the grid and the building, the grid is assigned a potential energy weight. The potential energy weight calculation formula is:

[0088] ω=1 / dis

[0089] In order to enable personnel to move to open spaces, based on the grid potential energy weight, each agent moves towards the direction with smaller surrounding potential energy weight at the beginning of the evacuation, and the volume parameter 1m is set for the agent. 2 , to avoid excessive aggregation of agents inside the grid, where the grid refers to a grid with a grid size of 10m.

[0090] Finally, the above S3 and S4 specifically include the following steps:

[0091] S31, calculates the structural response data of urban buildings in the area to be evaluated under earthquake action through a multi-degree-of-freedom shear model. Based on the structural response data, when the inter-story displacement angle is greater than the damage limit, a falling object model is established on that floor, and rigid body mechanics and dynamic properties are set for all falling object models. The movement process of the falling object model in the rigid body environment is calculated based on contact dynamics, and the distribution of falling objects is simulated.

[0092] S32. A method for dynamic casualty assessment may be to use a time step of 5 seconds, perform a superimposed analysis of the distribution of personnel and falling objects in each time step, and simultaneously integrate the results into a grid scenario.

[0093] S33, calculate the impact energy of falling objects in each grid, calculate the number of injured and dead people in the grid due to the impact of falling objects based on human body injuries and impact energy, repeat the above steps for each time step data to achieve dynamic casualties assessment of earthquake falling objects.

[0094] The following is an explanation with reference to specific implementation cases.

[0095] In some embodiments, the area to be evaluated is the CBD area of ​​a city, and a building model of the CBD area of ​​the city is constructed, such as Figure 2 As shown in the figure; and the dynamic casualty assessment in earthquake falling object scenario is realized by multi-agent technology, and the assessment results are shown in the figure. Figure 3 shown.

[0096] In S1, as Figure 4 As shown in the figure, based on mobile phone signaling, the population distribution point data with a grid scale of 80m is constructed. In order to make a more accurate assessment of casualties, it is necessary to reduce the resolution of the data. Based on the Kriging interpolation algorithm, the transition trend between each data point is calculated, and a grid map of the population distribution data is fitted. The values ​​are extracted from the raster map at a resolution of 10m to calculate the population number of each point, as shown in the figure. Figure 5 shown.

[0097] In order to avoid the impact of indoor occupants on the assessment, the population data is reduced. The reduction method is as follows: based on the number of people per unit area in the building given by the seismic code, the number of people in the building at that moment is calculated according to the building function, thereby obtaining the total number of people in the building at that moment. The outdoor population is calculated by subtracting the total number of people in the building from the total number of people in the area.

[0098] It should be noted that the result R obtained by Kriging interpolation calculation i It reflects the transition linear change trend between the two data points, so it is necessary to calculate the population P in the small grid according to the weight of each data point. i The number of people in the grid is calculated based on the total outdoor population T of the area and the interpolation formula:

[0099]

[0100] In S2, a two-dimensional plane model of the building is established, and the scene is divided into grids based on the resolution of the population distribution data. Grid properties are defined based on whether the grid is covered by a building. Those covered by a building are called obstacles, and those uncovered are called spaces. A diffusion pathfinding algorithm is used to find the nearest obstacle grid for each space, and the distance between the two is recorded as dis. A potential energy weight ω is assigned to each grid based on the distance field between the grid and the building. The potential energy weight is calculated as follows:

[0101] ω=1 / dis

[0102] In order to realize the movement of personnel to open space, based on the grid potential energy weight, each agent moves towards the direction with smaller surrounding potential energy weight at the beginning of the refuge, such as Figure 6 As shown, the volume parameter 1m is set for the intelligent body 2 , to avoid excessive aggregation of agents inside the grid.

[0103] In S3 and S4, the structural response data of the urban building complex in the area to be assessed under earthquake action are calculated using a multi-degree-of-freedom shear model. Based on the structural response data, when the inter-story displacement angle is greater than the damage limit, a falling object model is established on that floor, and rigid body mechanics and dynamic properties are set for all falling object models. The motion process of the falling object model in the rigid body environment is calculated based on contact dynamics, and the distribution of the falling objects is simulated. The results are as follows: Figure 7 As shown;

[0104] Based on the association mapping method, the distribution of falling objects and personnel with location information are superimposed on the same grid in the geographic information platform for analysis. Based on the multi-criteria joint assessment method, the impact energy I of all falling objects falling into a certain grid within the current time step is calculated according to the gravitational acceleration g of the falling objects and the start time t1 and end time t2 of the current time step. The calculation formula is:

[0105]

[0106] Estimate the number of casualties caused by falling objects based on the probability of casualties and the number of people n in the grid c , the calculation formula is;

[0107] n c =P(C|I)×n

[0108] Where P(C|I) is the probability of casualties when the impact energy is I.

[0109] Taking 5 seconds as a time step, all casualties in each time step are added up and summed to obtain the dynamic casualty assessment results of earthquake falling objects in this scenario.

[0110] On the other hand, Figure 8 As shown, a device for casualty assessment is provided. The device for casualty assessment is used to implement the earthquake falling object dynamic casualty assessment method considering the evacuation behavior of people provided in an embodiment of the present invention. The device includes:

[0111] Construction module 801 is used to construct a population distribution model within the area to be evaluated. The area to be evaluated is divided into multiple grid areas using the Kriging interpolation algorithm, and the population size within each grid area is determined. Based on the population size, an agent model is established within each grid area.

[0112] Determination module 802 is used to establish a building model of the area to be evaluated, determine the potential energy weight of each grid based on the distance field between each grid area and the building, and set sparse spatial potential energy perception behavior specifications for the intelligent agent model;

[0113] The simulation module 803 is used to establish a falling object model of the area to be evaluated, and simulate the distribution of falling objects after the falling object model moves based on contact dynamics;

[0114] The evaluation module 804 is used to determine the impact energy of the falling object in each grid area, and perform casualty assessment based on the impact energy and the distribution of people in each time step.

[0115] An embodiment of the present invention constructs an intelligent agent model through the Kriging interpolation algorithm, sets sparse spatial potential energy perception behavior specifications for the intelligent agent model, and constructs a falling object model of the area to be evaluated. Then, based on the impact energy of the falling object in the area to be evaluated and the distribution of people in each time step, a casualty assessment is performed, thereby taking evacuation behavior into consideration when performing casualty assessment.

[0116] Figure 9 FIG. 1 is a schematic diagram of a structure for casualty assessment provided by an embodiment of the present invention. Figure 9 As shown, optionally, the device 910 for casualty assessment may include a first processor 2001 .

[0117] Optionally, the device 910 for casualty assessment may further include a memory 2002 and a transceiver 2003 .

[0118] The first processor 2001, the memory 2002 and the transceiver 2003 may be connected via a communication bus.

[0119] The following combination Figure 9The components of the casualty assessment device 910 are described in detail:

[0120] The first processor 2001 is the control center of the casualty assessment device 910 and can be a single processor or a collective term for multiple processing elements. For example, the first processor 2001 can be one or more central processing units (CPUs), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs) or one or more field programmable gate arrays (FPGAs).

[0121] Optionally, the first processor 2001 may perform various functions of the device 910 for casualty assessment by running or executing a software program stored in the memory 2002 and calling data stored in the memory 2002 .

[0122] In a specific implementation, as an embodiment, the first processor 2001 may include one or more CPUs, such as Figure 9 CPU0 and CPU1 are shown in FIG.

[0123] In a specific implementation, as an embodiment, the device 910 for casualty assessment may also include multiple processors, such as Figure 9 1 and 2. The first processor 2001 and the second processor 2004 are shown in FIG. Each of these processors can be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). A processor herein can refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0124] The memory 2002 is used to store the software program for executing the solution of the present invention, and is controlled by the first processor 2001 for execution. The specific implementation method can refer to the above method embodiment and will not be repeated here.

[0125] Alternatively, the memory 2002 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, a random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and capable of being accessed by a computer, but not limited thereto. The memory 2002 may be integrated with the first processor 2001 or exist independently and accessed through the interface circuit ( Figure 9 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0126] The transceiver 2003 is used to communicate with a network device or a terminal device.

[0127] Optionally, the transceiver 2003 may include a receiver and a transmitter ( Figure 9 (not shown separately in the figure). The receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0128] Optionally, the transceiver 2003 may be integrated with the first processor 2001, or may exist independently and communicate with the first processor 2001 through the interface circuit ( Figure 9 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0129] It should be noted that Figure 9 The structure of the device 910 for casualty assessment shown in the figure does not constitute a limitation on the router. The actual knowledge structure recognition device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0130] In addition, the technical effects of the device 910 for casualty assessment can refer to the technical effects of the order prediction method for personalized customization described in the above method embodiment, and will not be repeated here.

[0131] It should be understood that the first processor 2001 in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0132] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0133] The above embodiments can be implemented in whole or in part through software, hardware (such as circuits), firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via infrared, wireless, microwave, or other means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, or a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0134] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0135] In this disclosure, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0136] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0137] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0138] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0139] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of the device or unit, which can be electrical, mechanical or other forms.

[0140] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0141] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0142] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0143] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for dynamic casualty assessment of earthquake falling objects taking into account people's evacuation behavior, characterized by: The method comprises: Constructing a population distribution model within the area to be evaluated, subdividing the area to be evaluated into multiple grid areas using a Kriging interpolation algorithm, determining the population size within each grid area, and establishing an agent model within each grid area based on the population size; Establishing a building model of the area to be evaluated, determining the potential energy weight of each grid based on the distance field between each grid area and the building, and setting sparse spatial potential energy perception behavior specifications for the intelligent agent model; Establishing a falling object model of the area to be evaluated, and simulating the distribution of falling objects after the falling object model moves based on contact dynamics; Determine the impact energy of the falling object within each grid area, and perform casualty assessment based on the impact energy and the distribution of personnel within each time step; The step of determining the impact energy of the falling object in each grid area includes: For each time step, the formula Determine the impact energy of the falling object in each grid area, where I is the impact energy of the falling object in the current grid area within the time step, t1 is the start time of the current time step, t2 is the end time of the current time step, and g is the acceleration due to gravity; The casualty assessment is performed based on the impact energy and the distribution of personnel in each time step, including: By formula n c =P(C|I)×n to determine the number of casualties, where n c is the number of casualties in the current grid area within the current time step, P(C|I) is the probability of casualties when the impact energy is I, and n is the number of people in the current grid area.

2. The method according to claim 1, characterized in that The constructing of a population distribution model within the area to be assessed includes: Construct personnel distribution point data with a grid scale of 80m based on mobile phone signaling; The personnel distribution point data is converted into grid data, and the grid data inherits the attributes of the personnel distribution point data through a spatial mapping method.

3. The method according to claim 1, characterized in that The Kriging interpolation algorithm is used to refine the area to be evaluated into multiple grid areas, and the population size in each grid area is determined, including: The area to be assessed is refined into multiple grid areas with a grid size of 10m; According to the formula Determine the population in each grid area, where P i is the population in each grid area, T is the total outdoor population in the area to be evaluated, and R i is the result of the Kriging interpolation algorithm.

4. The method according to claim 1, wherein Determining the potential energy weight of each grid based on the distance field between each grid area and the building includes: Determine the distance field between each grid and the building; A potential energy weight is assigned to each grid according to the distance field. The weight calculation formula of each grid is: ω=1 / dis, where dis is the distance field between each grid and the building, and ω is the grid weight.

5. The method according to claim 1, wherein The sparse space potential energy perception behavior specification is to move in a direction with a smaller potential energy weight.

6. The method according to claim 1, characterized in that The step of establishing the falling object model of the area to be assessed includes: Calculating structural response data of the urban building complex in the area to be assessed under earthquake action using a multi-degree-of-freedom shear model; For each floor, when the inter-story displacement angle is determined to be greater than the damage limit based on the structural response data, a falling object model is established.

7. A device for casualty assessment, the device for casualty assessment being used to implement the method for dynamic casualty assessment of earthquake falling objects taking into account evacuation behavior of people as described in any one of claims 1 to 6, characterized in that: The device comprises: A construction module is used to construct a population distribution model within the area to be evaluated, and to refine the area to be evaluated into multiple grid areas through the Kriging interpolation algorithm, and to determine the population size within each grid area, and to establish an agent model within each grid area based on the population size; a determination module, configured to establish a building model of the area to be evaluated, determine the potential energy weight of each grid based on the distance field between each grid area and the building, and set a sparse spatial potential energy perception behavior specification for the intelligent agent model; A simulation module, configured to establish a falling object model in the area to be evaluated, and simulate the distribution of falling objects after the falling object model moves based on contact dynamics; The evaluation module is used to determine the impact energy of falling objects in each grid area and perform casualty assessment based on the impact energy and the distribution of people in each time step.

8. A device for casualty assessment, characterized in that The equipment used for casualty assessment includes: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program code, which can be called by a processor to execute the method according to any one of claims 1 to 6.

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