A simulation training optimization method for building fires

Abstract

The present invention discloses a method for optimizing simulation training for building fires, which relates to the technical field of data processing. The method comprises: collecting structural data and surrounding environmental data of a target building; constructing a three-dimensional building simulation model based on computer simulation; extracting temperature distribution time series data, smoke time series data, fire point location data, and fire source type based on historical fire records of similar buildings of the target building; simulating N fire scenarios in the three-dimensional building simulation model based on parametric simulation; enabling real-time interaction and operation of training participants based on virtual reality to obtain behavioral data sets; and performing feedback optimization on fire simulation training based on analysis results. This method solves the technical problems of existing building fire simulation training, such as incomplete information acquisition and difficulty in realistically simulating and restoring complex fire scenarios, which results in insufficient efficiency and effectiveness in fire response. The method achieves the technical effect of improving the efficiency and effectiveness of building fire simulation training.

Description

Technical Field

[0001] The present application relates to data processing technology-related fields, and specifically to a simulation training optimization method for building fires. Background Art

[0002] With the acceleration of urbanization, complex buildings such as high-rise buildings and large commercial complexes have sprung up like mushrooms after rain, bringing great convenience to urban prosperity and people's lives. However, the complexity and high population density of these buildings have also brought unprecedented fire safety challenges. Once a fire occurs, how to quickly and effectively evacuate people, control the spread of fire, and protect people's lives and property has become an important issue that needs to be solved urgently. Traditional building fire simulation training has also gradually exposed some problems. The complexity and realism of the simulation scenes are limited, and it is difficult to fully restore the complexity and variability of real fires. Participants in simulation training are usually limited to the fire brigade, lacking effective coordination with other emergency departments and the public. The result analysis and optimization methods of simulation training are relatively simple, and it is difficult to adapt to the ever-changing fire situation.

[0003] Therefore, at the current stage, there are technical problems in the technologies related to building fire simulation training, such as incomplete information acquisition and difficulty in realistically simulating and restoring complex fire scenes, which leads to insufficient efficiency and effectiveness in fire response. Summary of the Invention

[0004] This application provides a method for optimizing simulation training for building fires. By adopting technical means such as constructing three-dimensional simulation models and parametric simulation, it solves the technical problems existing in existing building fire simulation training, such as incomplete information acquisition and difficulty in realistically simulating and restoring complex fire scenes, resulting in insufficient efficiency and effectiveness in fire response, thereby achieving the technical effect of improving the efficiency and effectiveness of building fire simulation training.

[0005] The present application provides a method for optimizing simulation training for building fires, the method comprising: collecting building structure data and building surrounding environment data of a target building using a deployed sensor array, the sensor array comprising multiple sensors inside and outside the target building; constructing a three-dimensional building simulation model based on computer simulation according to the building structure data and the building surrounding environment data; extracting multiple temperature distribution time series data, multiple smoke time series data, multiple fire point location data, and multiple fire source types based on historical fire records of similar buildings of the target building; simulating N fire scenarios in the three-dimensional building simulation model based on parametric simulation using the multiple temperature distribution time series data, the multiple smoke time series data, the multiple fire point location data, and the multiple fire source types, wherein N is a positive integer greater than or equal to 1; arranging training participants in the simulated target building fire scenario, enabling the training participants to interact and operate in real time based on virtual reality, collecting behavioral data of the training participants in the simulation training, and obtaining a behavioral data set; analyzing the behavioral data set, and performing feedback optimization of the fire simulation training based on the analysis results.

[0006] In a possible implementation, the deployed sensor array is used to collect the building structure data and the building surrounding environment data of the target building, and the following processing is performed: the building surrounding environment data is obtained based on remote sensing technology; point cloud data of the surface of the target building is obtained using a laser scanner, and the internal image of the target building is obtained using a surveillance camera inside the target building; processing is performed based on the internal image to obtain the internal structure data of the target building; and the point cloud data and the internal structure data are integrated to form the building structure data.

[0007] In a possible implementation, a three-dimensional building simulation model is constructed based on computer simulation according to the building structure data and the building surrounding environment data, and the following processing is performed: based on the building structure data, a three-dimensional geometric model of the target building is established using three-dimensional modeling software; the building surrounding environment data is mapped on the three-dimensional geometric model; and the three-dimensional building simulation model is obtained by fusion.

[0008] In a possible implementation, the fusion obtains the three-dimensional building simulation model and further performs the following processing: obtaining real-time data of the target building, the real-time data including internal real-time data and external real-time data; extracting target building features based on the internal real-time data and the external real-time data; and updating the three-dimensional building simulation model based on the target building features to obtain an updated three-dimensional building simulation model.

[0009] In a possible implementation, based on historical fire records of similar buildings of the target building, multiple temperature distribution time series data, multiple smoke time series data, and multiple fire point location data are extracted, and the following processing is also performed: the historical fire records of similar buildings of the target building are screened out from the historical fire record database; based on the historical fire records, multiple temperature monitoring data and multiple smoke monitoring data at preset time intervals at different locations of the target building are extracted, each of the historical fire records corresponds to M fire point locations, where M is a positive integer; the multiple temperature monitoring data and the multiple smoke monitoring data are sorted to obtain the multiple temperature distribution time series data and the multiple smoke time series data, respectively.

[0010] In a possible implementation, the multiple temperature distribution time series data, the multiple smoke time series data, the multiple fire point location data, and the multiple fire source types are used to simulate N fire scenes in the three-dimensional building simulation model based on parametric simulation, wherein N is a positive integer greater than or equal to 1, and the following processing is also performed: analysis is performed based on the multiple temperature distribution time series data and the multiple smoke time series data to obtain fire analysis results; based on the fire analysis results, the fire size and the fire spread speed are evaluated; and the fire point location, the fire source type, the fire size, and the fire spread speed are used as fire scene setting parameters.

[0011] In a possible implementation, based on parametric simulation, N fire scenes are simulated in the three-dimensional building simulation model, and the following processing is also performed: based on parametric simulation, N fire scenes are simulated in the three-dimensional building simulation model; the N simulated fire scenes are run, multiple real-time temperature data and multiple smoke data are observed and recorded, and corresponding real-time temperature change distribution and real-time smoke change distribution are established.

[0012] In a possible implementation, the behavioral data set is analyzed, and feedback optimization of the fire simulation training is performed based on the analysis results. The following processing is also performed: feedback data of each training participant is generated based on the analysis of the behavioral data set; a simulation training optimization plan is generated according to the feedback data, and the simulation training optimization plan includes adjusting the fire scene and updating the fire scene setting parameters; and feedback optimization of the fire simulation training is performed based on the simulation training optimization plan.

[0013] The present application proposes a simulation training optimization method for building fires, which uses a sensor array to collect building structure data and building surrounding environment data of the target building, wherein the sensor array includes multiple sensors inside and outside the target building; a three-dimensional building simulation model is constructed based on computer simulation according to the building structure data and the building surrounding environment data; based on the historical fire records of similar buildings of the target building, multiple temperature distribution time series data, multiple smoke time series data, multiple fire point location data, and multiple fire source types are extracted; the multiple temperature distribution time series data, the multiple smoke time series data, the multiple fire point location data, and the multiple fire source types are used to Parametric simulation, simulating N fire scenes in the three-dimensional building simulation model, where N is a positive integer greater than or equal to 1; setting training participants in the simulated target building fire scene, enabling the training participants to interact and operate in real time based on virtual reality, collecting behavioral data of the training participants in the simulation training, and obtaining a behavioral data set; analyzing the behavioral data set, and performing feedback optimization of the fire simulation training based on the analysis results, thereby solving the technical problems of incomplete information acquisition and difficulty in realistically simulating and restoring complex fire scenes in existing building fire simulation training, resulting in insufficient efficiency and effectiveness of fire response, and achieving the technical effect of improving the efficiency and effectiveness of building fire simulation training. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the accompanying drawings of the embodiments of the present disclosure are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the methods according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. On the contrary, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0015] Figure 1 A flow chart of a building fire simulation training optimization method provided in an embodiment of the present application;

[0016] Figure 2 A schematic diagram of the process of constructing a three-dimensional building simulation model in a building fire simulation training optimization method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0018] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0019] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or that are inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0020] The present application embodiment provides a method for optimizing simulation training of building fires, such as Figure 1 As shown, the method includes:

[0021] Step S100 utilizes a deployed sensor array to collect structural data and environmental data about the target building. The sensor array includes multiple sensors located inside and outside the target building. The sensor array is a sensor network composed of multiple sensors strategically placed inside and outside the target building to collect various data related to the building structure and environment. Specifically, sensors inside the target building include surveillance cameras, environmental monitoring sensors, and electrical safety monitoring sensors. The surveillance cameras can monitor multiple images of the target building's interior; environmental monitoring sensors include temperature sensors, humidity sensors, and gas concentration sensors (such as smoke detectors); electrical safety monitoring sensors monitor the safety of the electrical system, such as current monitors, voltage monitors, and arc detectors; and sensors outside the target building include surveillance cameras and laser scanners to monitor and acquire environmental data about the target building. These sensors are connected to a central control system via wired or wireless means, transmitting the collected data in real time to a data processing center for analysis and processing. This data analysis provides real-time insights into the building's structural health, environmental conditions, and changes in the external environment.

[0022] In one possible implementation, step S100 further includes step S110 of acquiring data about the building's surrounding environment based on remote sensing technology. Remote sensing technology (such as satellite remote sensing or aerial remote sensing) is used to acquire various information data about the building's surrounding environment. The process also includes step S120 of acquiring point cloud data of the target building's surface using a laser scanner and acquiring an image of the target building's interior using a surveillance camera inside the target building. Using a laser scanner to acquire point cloud data on the surface of a target building, and using surveillance cameras inside the target building to acquire images of the target building's interior, are two different but complementary data acquisition methods used to obtain detailed three-dimensional information and internal conditions of the building. Specifically, a laser scanner emits a laser beam and receives the reflected signal to measure the three-dimensional coordinate information of the building's surface. The laser scanner can quickly scan the entire building, acquiring a large amount of point cloud data that accurately represents the building's shape, size, and surface features. Installing surveillance cameras inside the building captures and transmits real-time images of the interior, visually reflecting the building's layout, facilities, and personnel activities. Surveillance cameras can be installed in key locations within the building, such as corridors, rooms, and stairwells, to provide a comprehensive understanding of the building's interior. The system also includes step S130, processing the internal images to acquire internal structural data of the target building. It also includes step S140, integrating the point cloud data and internal structural data to form the building structural data. By converting point cloud data into a 3D mesh model and then overlaying or mapping internal structure data (such as images or videos) with the 3D mesh model, the external shape and internal structure information of the building can be integrated to form a complete building structure data.

[0023] Step S200 constructs a three-dimensional building simulation model based on the building structure data and the building surrounding environment data using computer simulation technology. Constructing a three-dimensional building simulation model based on the building structure data and the building surrounding environment data using computer simulation technology specifically involves integrating the collected building structure data (such as the location, size, and material information of beams, columns, and floor slabs) and the building surrounding environment data (such as terrain, vegetation, roads, and weather conditions) into a three-dimensional virtual environment through specific software and algorithms, thereby creating a highly realistic building simulation model.

[0024] In one possible implementation, Figure 2As shown, step S200 further includes step S210, which uses three-dimensional modeling software to establish a three-dimensional geometric model of the target building based on the building structure data. The integrated building structure data is converted into a visual three-dimensional model through professional three-dimensional modeling software. It also includes step S220, which maps the building surrounding environment data on the three-dimensional geometric model. The collected building surrounding environment data (such as topography, vegetation coverage, water distribution, land use, traffic roads, meteorological climate, etc.) are combined with the established three-dimensional geometric model so that these environmental data are presented in a visual manner in the three-dimensional model. It also includes step S230, which fuses to obtain the three-dimensional building simulation model. The mapped building surrounding environment data is fused with the three-dimensional geometric model and the building structure data to obtain a three-dimensional building simulation model.

[0025] In one possible implementation, step S230 further includes step S231 of acquiring real-time data of the target building, where the real-time data includes internal real-time data and external real-time data. It also includes step S232 of extracting target building features based on the internal real-time data and the external real-time data. By analyzing real-time data streams from inside and outside the target building, key features or attributes related to the building are identified and extracted. These may include video images captured by surveillance cameras, environmental parameters collected by sensors (such as temperature, humidity, light, air quality, etc.), human activity data, and equipment operating status. The purpose of extracting target building features is to provide useful information support for subsequent updates to the three-dimensional model. It also includes step S233 of updating the three-dimensional building simulation model based on the target building features to obtain an updated three-dimensional building simulation model. Using the key features or attributes extracted from the real-time data from inside and outside the target building, the existing three-dimensional building simulation model is modified, adjusted, or enhanced to more accurately reflect the actual status or changes of the target building.

[0026] Step S300 , based on historical fire records of similar buildings of the target building, extract multiple temperature distribution time series data, multiple smoke time series data, multiple fire point location data, and multiple fire source types. Temperature distribution time series data refers to the temperature data of various areas inside or outside the building (such as floors, rooms, corridors, etc.) at different time points (usually intervals of seconds, minutes or hours) when a fire occurs, reflecting the speed and direction of fire spread, as well as the intensity of the fire; smoke time series data records the changes in smoke concentration over time in various areas inside or outside the building when a fire occurs, reflecting the speed and range of smoke diffusion; fire point location data refers to the specific location of the fire, including floor, room number, specific coordinates, etc., which is an important basis for fire investigation and cause analysis, and is also key information for fire simulation and prevention and control. Extracting fire point location data helps determine the scope and severity of the fire; multiple fire source types refer to the source types of fires, such as electrical fires, gas leaks, arson, etc. Extracting data on multiple fire source types can help analyze the fire risks that the target building may face and formulate corresponding fire prevention and control strategies and plans. By analyzing the fire source types in historical fire records, we can understand the common fire source types and their characteristics in similar buildings, and provide targeted suggestions for fire prevention and control in the target building.

[0027] In one possible implementation, step S300 further includes step S310 of filtering historical fire records of similar buildings to the target building from a historical fire record database. Using specific search and filtering conditions, the historical fire record database is searched for historical fire records of other buildings similar to the target building in terms of type, structure, use, geographic location, and so on. The method further includes step S320 of extracting, based on the historical fire records, multiple temperature monitoring data and multiple smoke monitoring data at preset time intervals at different locations of the target building. Each historical fire record corresponds to M fire origin locations, where M is a positive integer. The preset time interval refers to the interval at which temperature and smoke data are recorded during a fire, such as every minute or every five minutes. The preset time interval can be determined based on the severity of the fire and the performance of the monitoring equipment. Temperature and smoke monitoring data are important criteria for assessing fire development and determining fire risk. Each historical fire record may correspond to one or more fire origin locations. The M fire origin locations indicate that each record in the historical fire record may correspond to a different number of fire origin locations. Different fires may occur at different locations due to different causes and conditions, and therefore the number of fire origin locations may vary. The method further includes step S330 of sorting the plurality of temperature monitoring data and the plurality of smoke monitoring data to obtain the plurality of temperature distribution time series data and the plurality of smoke time series data, respectively. The collected temperature monitoring data and smoke monitoring data are sorted according to their timestamps or recording order to form two independent time series data sets: temperature distribution time series data and smoke time series data.

[0028] Step S400, using the multiple temperature distribution time series data, the multiple smoke time series data, the multiple fire point location data, and the multiple fire source types, based on parametric simulation, simulate N fire scenes in the three-dimensional building simulation model, where N is a positive integer greater than or equal to 1. Using multiple temperature distribution time series data, multiple smoke time series data, multiple fire point location data and multiple fire source types, N fire scenarios are simulated in a three-dimensional building simulation model based on parametric simulation. Specifically, according to the extracted data, the fire simulation parameters are set. These parameters may include fire source type, fire source location, fire size, burning rate, spread speed, temperature distribution, smoke concentration, etc. Using the constructed three-dimensional building simulation model as a basis, the set fire parameters are applied to the three-dimensional building simulation model to simulate the fire. According to the number of fire scenarios N that need to be simulated, the above parameter setting and simulation steps are repeated to generate N different fire scenarios, each with different fire source location, fire source type, fire size and other parameters. The dynamic processes such as temperature distribution, smoke diffusion, and fire spread in each fire scenario are observed and recorded. The simulation results are deeply analyzed to understand the key information such as fire characteristics, spread trends, evacuation paths, etc. in different fire scenarios. Based on actual data, a variety of possible fire scenarios are simulated in the three-dimensional building simulation model, providing strong support and guidance for fire prevention and control and emergency rescue.

[0029] In one possible implementation, step S400 further includes step S410, analyzing the multiple temperature distribution time series data and the multiple smoke time series data to obtain fire analysis results. Using data analysis techniques and methods, these time series data are deeply processed and interpreted to extract key information and patterns related to the occurrence, development, and impact of the fire, thereby obtaining a comprehensive analysis and assessment of the fire. The process also includes step S420, assessing the fire size and spread rate based on the fire analysis results. Using key information and patterns extracted from multiple temperature distribution time series data and multiple smoke time series data, a quantitative or qualitative assessment of the severity and development of a fire is performed. The magnitude of a fire can generally be measured by factors such as flame height, thermal radiation intensity, and the type and quantity of burning materials. For example, the size and distribution of high-temperature areas can reflect the concentration and intensity of the fire. The fire spread rate refers to the rate at which a fire expands in time and space. By analyzing the temperature distribution time series data and smoke time series data in the fire analysis results, the spread of the fire at different time points can be understood. For example, observing changes in the temperature distribution graph can determine the direction and speed of the fire spread. The smoke diffusion graph can reflect the range and speed of smoke diffusion, thereby indirectly inferring the spread of the fire. The system also includes step S430, using the location of the fire origin, the type of fire source, the fire magnitude, and the fire spread rate as fire scene setting parameters. Different fire origin locations affect the fire's spread path, firefighters' rescue routes, and evacuation strategies. The choice of fire source type directly influences the fire's characteristics and spread rate. For example, a fire caused by a liquid fuel leak may spread quickly and intensify. The size of the fire determines its destructive power and impact on the surrounding environment. In simulation training, fires of different sizes can be set as needed to test firefighters' response capabilities under different fire intensities. The fire's spread rate is affected by a variety of factors, such as the nature and quantity of the combustibles, and ventilation conditions. By setting different fire spread rates in simulation training, different types of fire scenarios can be simulated, such as fast-spreading fires and slow-spreading fires, allowing firefighters to conduct targeted training and response. By properly setting these parameters, realistic and effective fire simulations can be created, helping firefighters improve their fire response capabilities and skills.

[0030] In one possible implementation, step S400 further includes step S440, simulating N fire scenarios in the three-dimensional building simulation model based on parametric simulation. Using parametric modeling technology, in the established three-dimensional building simulation model, by setting and adjusting different parameters and conditions, N different fire scenarios are simulated. The parametric simulation software simulates the occurrence, development, and spread of the fire through calculation and analysis based on the set parameters and conditions, including the spread of flames, the diffusion of smoke, temperature changes, etc. Through simulation, a three-dimensional fire scene visualization effect can be generated, allowing users to intuitively understand the propagation path, impact range, and potential risks of the fire in the building. It also includes step S450, running the simulated N fire scenarios, observing and recording multiple real-time temperature data and multiple smoke data, and establishing corresponding real-time temperature change distribution and real-time smoke change distribution. In the three-dimensional building simulation model, N fire scenarios previously set up through parametric simulation are run in sequence, and multiple temperature and smoke data in the fire scenarios are collected and recorded in real time, reflecting the development of the fire at different time points and different spatial locations. Based on the collected real-time temperature and smoke data, real-time temperature change distribution maps and smoke change distribution maps are generated and displayed, allowing an intuitive understanding of the temperature change pattern and heat spread trend of the fire, as well as the diffusion of the smoke generated by the fire inside or around the building.

[0031] Step S500: Set up training participants in a simulated target building fire scenario, enable the training participants to interact and operate in real time based on virtual reality, collect behavioral data of the training participants during the simulation training, and obtain a behavioral dataset. Using virtual reality (VR) technology, a highly realistic virtual environment is created for the target building fire scenario. Training participants are set up in the virtual environment. These participants can be simulated firefighters, emergency personnel, or the public. Each participant is assigned a specific role and task, such as evacuating the crowd, finding the fire source, and extinguishing the fire. Through VR equipment (such as head-mounted displays, handles, etc.), training participants can interact and operate with the virtual environment in real time. They can move, observe, select, and use virtual tools and equipment to complete their respective tasks. During the simulation training process, the training participants' behavioral data is collected in real time, which may include the participants' movement trajectory, operation records, response time, decision-making process, etc. The collected behavioral data is organized, cleaned, and annotated to construct a complete behavioral dataset. This dataset will contain the behavioral data of multiple training participants in multiple fire scenarios, as well as related labels and metadata.

[0032] Step S600: Analyze the behavioral dataset and perform feedback optimization of the fire simulation training based on the analysis results. Conduct an in-depth analysis of the collected behavioral dataset to understand the behavioral performance of the training participants in the simulated fire scenario, including analyzing the participants' decision-making process, reaction time, operational correctness, and collaboration ability. Based on the analysis results, formulate targeted feedback and optimization strategies, which may include providing additional training materials, increasing the difficulty and complexity of the simulation training, improving the simulation training environment and equipment, optimizing the training process and task settings, etc. Apply the formulated feedback and optimization strategies to the actual fire simulation training, provide direct feedback and guidance to the training participants, and adjust the parameters and settings of the simulation training. After implementing feedback and optimization, continuously monitor the behavioral performance of the training participants and collect new behavioral datasets. Through comparative analysis, evaluate the effectiveness of the feedback optimization, and further adjust and optimize the strategies as needed to form an iterative and cyclical feedback optimization mechanism to continuously improve the effectiveness and quality of the fire simulation training.

[0033] In one possible implementation, step S600 further includes step S610, analyzing the behavioral dataset to generate feedback data for each training participant. The collected user behavioral dataset is deeply processed and analyzed to extract key information and behavioral patterns related to the training participants, and personalized feedback data is generated for each training participant based on this information and patterns. The process also includes step S620, generating a simulation training optimization plan based on the feedback data. The simulation training optimization plan includes adjusting fire scenarios and updating fire scenario setting parameters. Based on the participants' actual performance and feedback, the fire scenarios and their setting parameters in the simulation training are adjusted and optimized to improve the effectiveness and relevance of the training. This includes adding new fire scenarios to cover more emergency situations and operating environments; modifying the design of existing scenarios to make them more realistic; adjusting the complexity and difficulty of scenarios to suit the needs of different participants; and updating scenario setting parameters based on the feedback data. These parameters may include fire source location, fire size, type and quantity of combustible materials, ventilation conditions, and environmental conditions (such as temperature, humidity, wind speed, etc.). By adjusting these parameters, the simulation results can be made closer to real-world conditions, improving the effectiveness of the simulation training. The process further includes step S630 of performing feedback optimization on the fire simulation training based on the simulation training optimization scheme. Based on the collected feedback data from the training participants, targeted improvements and adjustments are made to the original fire simulation training content and settings to improve the efficiency and effectiveness of the building fire simulation training.

[0034] The above specific embodiments do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. A building fire simulation training optimization method, characterized in that: The method comprises: Collecting architectural structural data and building surrounding environment data of a target building using a deployed sensor array, wherein the sensor array includes multiple sensors inside and outside the target building; Constructing a three-dimensional building simulation model based on computer simulation according to the building structure data and the building surrounding environment data; Based on the historical fire records of similar buildings of the target building, multiple temperature distribution time series data, multiple smoke time series data, multiple fire point location data, and multiple fire source types are extracted; Using the multiple temperature distribution time series data, the multiple smoke time series data, the multiple fire point location data, and the multiple fire source types, based on parametric simulation, simulate N fire scenarios in the three-dimensional building simulation model, where N is a positive integer greater than or equal to 1; Setting training participants in a simulated target building fire scene, enabling the training participants to interact and operate in real time based on virtual reality, collecting behavioral data of the training participants in the simulation training, and obtaining a behavioral data set; Analyzing the behavioral data set, and performing feedback optimization of the fire simulation training based on the analysis results; Analyzing the behavioral data set and performing feedback optimization of fire simulation training based on the analysis results, the method includes: Analyzing the behavioral data set to generate feedback data for each training participant; Generate a simulation training optimization plan based on the feedback data, wherein the simulation training optimization plan includes adjusting the fire scene and updating the fire scene setting parameters; Feedback optimization is performed on fire simulation training based on the simulation training optimization scheme.

2. A building fire simulation training optimization method according to claim 1, characterized in that: The method of collecting building structure data and building surrounding environment data of a target building by using a deployed sensor array includes: Acquiring environmental data surrounding the building based on remote sensing technology; Using a laser scanner to obtain point cloud data of the surface of the target building, and using a surveillance camera inside the target building to obtain an image of the interior of the target building; Processing the internal image to obtain internal structural data of the target building; The building structure data is formed by integrating the point cloud data and the internal structure data.

3. The method for optimizing building fire simulation training according to claim 1, wherein: According to the building structure data and the building surrounding environment data, a three-dimensional building simulation model is constructed based on computer simulation, the method comprising: Based on the building structure data, using three-dimensional modeling software, establish a three-dimensional geometric model of the target building; Mapping the building surrounding environment data on the three-dimensional geometric model; The three-dimensional building simulation model is obtained by fusion.

4. A building fire simulation training optimization method as claimed in claim 3, characterized in that: The method for fusing and acquiring the three-dimensional building simulation model includes: Acquiring real-time data of the target building, wherein the real-time data includes internal real-time data and external real-time data; Extracting target building features according to the internal real-time data and the external real-time data; Based on the target building features, the three-dimensional building simulation model is updated to obtain an updated three-dimensional building simulation model.

5. The method for optimizing building fire simulation training according to claim 1, wherein: Based on historical fire records of similar buildings to the target building, extracting multiple temperature distribution time series data, multiple smoke time series data, and multiple fire point location data, the method includes: Filtering the historical fire records of similar buildings to the target building from a historical fire record database; Extracting, based on the historical fire records, a plurality of temperature monitoring data and a plurality of smoke monitoring data at preset time intervals at different locations of the target building, each of the historical fire records corresponding to M fire starting locations, where M is a positive integer; The plurality of temperature monitoring data and the plurality of smoke monitoring data are sorted to obtain the plurality of temperature distribution time series data and the plurality of smoke time series data respectively.

6. The method for optimizing building fire simulation training according to claim 1, wherein: Using the multiple temperature distribution time series data, the multiple smoke time series data, the multiple fire point location data, and the multiple fire source types, based on parametric simulation, simulating N fire scenarios in the three-dimensional building simulation model, where N is a positive integer greater than or equal to 1, the method includes: Performing analysis based on the plurality of temperature distribution time series data and the plurality of smoke time series data to obtain a fire analysis result; Based on the fire analysis results, assess the fire size and fire spread rate; The fire starting point location, the fire source type, the fire size and the fire spread speed are used as fire scene setting parameters.

7. A building fire simulation training optimization method according to claim 6, characterized in that: Based on parametric simulation, N fire scenarios are simulated in the three-dimensional building simulation model, the method comprising: Simulating N fire scenarios in the three-dimensional building simulation model based on parametric simulation; The N simulated fire scenes are run, a plurality of real-time temperature data and a plurality of smoke data are observed and recorded, and corresponding real-time temperature change distribution and real-time smoke change distribution are established.

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