Fire-fighting effect evaluation method and platform under digital modeling

Abstract

The invention discloses a fire-fighting effect evaluation method and platform under digital modeling, and relates to the technical field of fire-fighting evaluation, and the method comprises the steps: carrying out the structural information collection of a target building, and obtaining the target structural information; performing digital modeling by adopting the target structure information to obtain a building three-dimensional model of the target building; performing interaction to obtain fire-fighting configuration data of the target building; performing fire-fighting equipment fitting on the building three-dimensional model to obtain a fire-fighting layout model; presetting a multi-stage fire risk scene, and obtaining a multi-stage fire protection simulation result; and performing fire-fighting response quantitative evaluation to obtain a fire-fighting effect evaluation result. The technical problems that in the prior art, the fire emergency capacity in a complex building environment is difficult to comprehensively evaluate, and the overall effect of a fire-fighting plan cannot be effectively verified are solved, and the technical effects that the spreading path and the smoke diffusion condition of a fire are accurately simulated, and the timeliness in an actual fire is effectively evaluated are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of fire protection assessment, and in particular to a fire protection effect assessment method and platform under digital modeling. Background Art

[0002] With the acceleration of urbanization, modern buildings are becoming increasingly large and complex in structure, and their functions are also diversified. These complex buildings include but are not limited to large commercial complexes, high-rise office buildings, hospitals, transportation hubs, etc. In these building environments, the occurrence of fire will not only pose a serious threat to the safety of people's lives, but also bring huge property losses and social impacts. At present, there are still many shortcomings in the evaluation technology for building fire emergency response capabilities. On the one hand, traditional evaluation methods often rely on simple theoretical analysis or limited field tests. Most theoretical analyses are based on idealized models, ignoring the actual factors of complex buildings such as irregular spatial layout, complex ventilation systems, and mixing of multiple types of flammable materials. On the other hand, although some existing simulation technologies can simulate fires to a certain extent, they are defective in the accuracy and comprehensiveness of the simulation.

[0003] Existing technologies have technical problems such as difficulty in comprehensively evaluating fire emergency response capabilities in complex building environments and inability to effectively verify the overall effectiveness of fire protection plans. Summary of the invention

[0004] The present application provides a firefighting effectiveness evaluation method and platform under digital modeling, which is used to solve the technical problems that the existing technology is difficult to comprehensively evaluate the fire emergency response capabilities in complex building environments and cannot effectively verify the overall effectiveness of the firefighting plan.

[0005] In view of the above problems, the present application provides a fire protection effectiveness evaluation method and platform under digital modeling.

[0006] In a first aspect of the present application, a fire protection effect evaluation method under digital modeling is provided, the method comprising:

[0007] Structural information of the target building is collected to obtain the target structural information; digital modeling is performed using the target structural information to obtain a three-dimensional building model of the target building; firefighting configuration data of the target building is interactively obtained; firefighting equipment is fitted in the three-dimensional building model according to the firefighting configuration data to obtain a firefighting layout model; multi-level fire risk scenarios are preset, and firefighting simulation is performed in the firefighting layout model based on the multi-level fire risk scenarios to obtain multi-level firefighting simulation results; firefighting response quantitative evaluation is performed according to the multi-level firefighting simulation results to obtain firefighting effectiveness evaluation results.

[0008] The second aspect of the present application provides a fire protection effect evaluation platform under digital modeling, the platform comprising:

[0009] A target structure information acquisition module, the target structure information acquisition module is used to collect structure information of the target building and obtain target structure information; a building three-dimensional model acquisition module, the building three-dimensional model acquisition module is used to use the target structure information for digital modeling to obtain a building three-dimensional model of the target building; a fire configuration data acquisition module, the fire configuration data acquisition module is used to interactively obtain the fire configuration data of the target building; a fire layout model acquisition module, the fire layout model acquisition module is used to fit fire equipment in the building three-dimensional model according to the fire configuration data to obtain a fire layout model; a multi-level fire simulation result acquisition module, the multi-level fire simulation result acquisition module is used to preset multi-level fire risk scenarios, and perform fire simulation in the fire layout model based on the multi-level fire risk scenarios to obtain multi-level fire simulation results; a fire effect evaluation result acquisition module, the fire effect evaluation result acquisition module is used to perform a quantitative evaluation of fire response according to the multi-level fire simulation results to obtain a fire effect evaluation result.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] Collect structural information of the target building to obtain target structural information; use the target structural information for digital modeling to obtain the three-dimensional building model of the target building; interactively obtain the fire configuration data of the target building; fit the fire equipment in the three-dimensional building model according to the fire configuration data to obtain the fire layout model; preset multi-level fire risk scenarios, and perform fire simulation in the fire layout model based on the multi-level fire risk scenarios to obtain multi-level fire simulation results; perform quantitative evaluation of fire response according to the multi-level fire simulation results to obtain fire effect evaluation results. The technical effect of accurately simulating the spread path of fire and smoke diffusion and effectively evaluating the timeliness in actual fires has been achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] 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.

[0013] Figure 1 A schematic diagram of a fire protection effectiveness evaluation method under digital modeling provided in an embodiment of the present application;

[0014] Figure 2 A schematic diagram of the structure of a fire protection effectiveness evaluation platform under digital modeling provided in an embodiment of the present application.

[0015] Explanation of the reference numerals: target structure information acquisition module 10, building three-dimensional model acquisition module 20, fire configuration data acquisition module 30, fire layout model acquisition module 40, multi-level fire simulation result acquisition module 50, fire effect evaluation result acquisition module 60. DETAILED DESCRIPTION

[0016] This application provides a firefighting effectiveness evaluation method and platform under digital modeling, which is used to solve the technical problems that the existing technology is difficult to comprehensively evaluate the fire emergency response capabilities in complex building environments and cannot effectively verify the overall effectiveness of firefighting plans.

[0017] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0018] Embodiment 1, as Figure 1 As shown, the present application provides a fire protection effect evaluation method under digital modeling, the method comprising:

[0019] Step S100: collecting structural information of a target building to obtain target structural information.

[0020] Specifically, comprehensive information collection is carried out on the target building. In terms of collecting building structure information, 3D laser scanning technology is used to emit laser pulses to the building. The laser reflects on the surface of the object and returns to the scanner. The distance to the surface of the object is calculated based on the round-trip time of the laser, so as to quickly obtain a large amount of 3D point cloud data. These point clouds can clearly present the details of the building structure, such as the outline and connection method of the wall, beam and column. At the same time, it is also combined with the building information model (BIM) technology to import the existing digital information such as architectural design drawings and CAD files to obtain the preliminary model of the building structure, and then merge it with the data obtained by laser scanning to further improve the building structure information. For the collection of area information, the total station measurement technology is used to determine the coordinates of each vertex of the building by measuring the angle and distance, and then the area of ​​each area is calculated using professional surveying and mapping software. In addition, drone photogrammetry technology is used to shoot the building from multiple angles in the air to obtain high-resolution images, and the area of ​​large areas such as roofs and outdoor venues is calculated through image analysis algorithms. In the collection of floor distribution information, a high-precision level is used to determine the floor height by measuring the elevation difference between different floors. At the same time, the weak current system in the building, such as the floor information in the elevator control system and the identification information in the stairwell, can be used to accurately obtain the number of floors, functional distribution and other information. Through these diverse technical means, the structural information of the target building can be comprehensively and accurately collected.

[0021] Step S200: digital modeling is performed using the target structure information to obtain a three-dimensional architectural model of the target building.

[0022] Specifically, for the building structure data obtained from the target structure information acquisition module, professional modeling software is used to convert the size, shape, and position information of structural elements such as walls, beams, and columns into solid models in three-dimensional space. These solid models are constructed based on precise geometric parameters, and the connections between them are consistent with the actual building structure, ensuring the accuracy of the model at the structural level. When processing area data, the spatial scale of the corresponding parts in the model is adjusted according to the area size of different areas, so that the size of each room, corridor, and other spaces is consistent with the actual building. The floor distribution information helps the module determine the vertical hierarchy of the model and accurately set the height and number of floors, as well as the location and form of vertical traffic elements such as stairs and elevators. At the same time, the data is optimized, such as reasonably simplifying the complex structural parts to improve the modeling efficiency, but ensuring that the key structural features are not lost. Through the comprehensive application and fine processing of the target structure information, the three-dimensional architectural model of the target building is finally constructed, providing a realistic virtual environment for the subsequent integration of fire configuration data and fire simulation.

[0023] Step S300: interactively obtaining fire protection configuration data of the target building.

[0024] Specifically, the fire configuration data of the target building is obtained through various channels. On the one hand, it is connected with the fire facility management system of the building to directly read relevant data from the database, including detailed parameters of various fire equipment, such as the type, capacity, and pressure of fire extinguishers, the specifications and location of fire hydrants, the nozzle distribution and water spray intensity of the automatic sprinkler system, and the detector type and coverage of the automatic fire alarm system. This connection method ensures the accuracy and timeliness of the data, because the data in the management system is updated and maintained in real time. On the other hand, it interacts with the personnel responsible for the maintenance of fire facilities. Through the specially designed human-computer interface, the maintenance personnel input some fire configuration information that has not been updated in time or in special circumstances in the system, such as newly installed fire equipment, temporarily adjusted equipment location, or the status of recently repaired equipment. At the same time, the module can also interact with fire design documents, acceptance reports and other data sources to extract the original design data of the fire configuration, ensure that the acquired fire configuration data is comprehensive and reliable, and provide sufficient data support for the subsequent construction of the fire layout model.

[0025] Step S400: fitting firefighting equipment in the three-dimensional building model according to the firefighting configuration data to obtain a firefighting layout model.

[0026] Specifically, the complex process of fire equipment fitting is started with the building 3D model as the basic framework and the fire configuration data as the guide. First, the fire configuration data is deeply interpreted to extract the key information of various fire equipment, such as the model, quantity and performance parameters of fire extinguishers, fire hydrants, sprinkler systems, fire alarm devices, etc. Then, based on these data, the fire equipment model is accurately placed at the corresponding position of the building 3D model. For fire hydrants, they will be accurately "installed" in the specified locations such as stairwells and corridors of the model according to their planned positions in the building; fire extinguishers will be placed in easily accessible locations such as rooms and passages according to the specified density and distribution rules. The layout of the sprinkler system nozzles in the model will strictly follow the spacing and coverage requirements specified by the fire configuration data to ensure that comprehensive fire prevention and control coverage can also be achieved in the virtual model. Fire alarm devices will also be reasonably distributed in various key areas of the building, including ceilings, stairways, etc. according to design parameters. Through meticulous fitting operations on each type of fire-fighting equipment, a fire-fighting layout model is constructed. This model vividly displays the actual layout of fire-fighting equipment in the target building, providing an accurate virtual scene for subsequent fire-fighting simulation and effect evaluation.

[0027] Step S500: Preset a multi-level fire risk scenario, and perform a fire simulation in the fire layout model based on the multi-level fire risk scenario to obtain a multi-level fire simulation result.

[0028] Specifically, in terms of presetting multi-level fire risk scenarios, fire dynamics theory and a large amount of historical fire data are first used for analysis. By mining data such as the frequency of fires in different building types and functional areas, and fire development patterns, multiple possible fire starting points are determined, covering various high-incidence areas in the building, from electrical equipment concentrations, flammable material storage points to densely populated areas. For the setting of the fire spread speed, the computational fluid dynamics (CFD) model is combined to consider factors such as ventilation conditions inside the building, obstacle distribution, and combustion characteristics of different materials, and accurately simulate the spread of fires of different intensities in different environments. At the same time, the fire intensity is quantified based on parameters such as heat release rate, and multiple levels are set from small-scale smoldering to large-scale intense burning. When conducting fire simulation based on multi-level fire risk scenarios, virtual reality (VR) and computer simulation technology are used. The preset fire scenario parameters are input into the fire simulation software, which is deeply integrated with the fire layout model. During the simulation process, the physically based rendering (PBR) technology is used to realistically present the flames and smoke effects generated by the fire, so as to accurately observe the spread of the fire. For the simulation of fire-fighting equipment, the built-in equipment behavior models are called, which are built based on the working principles of real fire-fighting equipment. For example, the simulation of the automatic sprinkler fire-fighting system calculates the time, coverage and fire-fighting effect of its water spraying based on parameters such as the flow coefficient, working pressure and response characteristics of the thermistor. The fire alarm system simulates the process of detecting fire signals and sounding alarms according to the type of detector (such as smoke, temperature, etc.) and sensitivity settings. Equipment such as fire hydrants and fire hoses simulate the fire-fighting capabilities of firefighters when used according to parameters such as flow and range. The simulation of evacuation passages takes into account factors such as personnel density and personnel walking speed to evaluate the efficiency of personnel evacuation. Through these complex and precise technical means, comprehensive multi-level fire simulation results are obtained to provide solid data support for firefighting effect evaluation.

[0029] Step S600: Perform a quantitative evaluation of fire response according to the multi-level fire simulation results to obtain a fire effect evaluation result.

[0030] Specifically, the fire response quantitative evaluation of firefighting equipment is carried out based on the multi-level firefighting simulation results to obtain the firefighting effect evaluation results. First, the data related to the firefighting equipment is extracted from the multi-level firefighting simulation results. For the automatic sprinkler fire extinguishing system, the time nodes of starting water spraying under different fire scenarios are analyzed, compared with the preset ideal start time, and the start time deviation value is calculated to quantify its start timeliness. The degree of fit between the water spray coverage and the fire spread area is observed, and the coverage effect is measured by calculating the overlap ratio of the two, the proportion of uncovered fire areas and other data. At the same time, the fire extinguishing effect is quantified according to the water spray intensity. For example, the degree of fire reduction within a certain period of time is statistically calculated, which is reflected by parameters such as the reduction ratio of heat release rate and the reduction amplitude of flame height. For the fire alarm system, the timeliness of the alarm is evaluated based on the interval between the time of the first alarm and the fire start time in the simulation results, combined with the speed of fire development in different scenarios. The propagation range and accuracy of the alarm signal are examined, and the proportion of areas that receive accurate alarm signals is statistically calculated to quantify the effective coverage of the alarm system. The number of false alarms and missed alarms of the alarm system is analyzed, and these data are included in the evaluation system. For equipment such as fire hydrants and fire hoses, the speed at which they are put into use in the simulated scenario is considered, including the total time it takes for firefighters to reach the fire hydrant, unfold the hose and start spraying water, and the response speed is quantified by comparing it with the standard operation time. The effects of parameters such as the range, flow rate and pressure of water spraying in actual firefighting are evaluated, and quantitative analysis is performed by comparing the changes in the fire before and after the fire hydrant is applied, such as the change in the direction of flame spread and the reduction in the speed of fire spread. Then, these quantitative data for different firefighting equipment are combined to assign corresponding weights to each equipment according to its importance in the firefighting system. For example, automatic sprinkler systems and fire hydrants may be given higher weights because they are directly involved in firefighting, and the alarm system's key role in early warning will also have a reasonable weight ratio. Through weighted calculation, the performance of each device is integrated into a comprehensive evaluation value to obtain the firefighting effect evaluation result, which clearly shows the efficiency level of firefighting equipment in the entire firefighting process, and provides a strong basis for the subsequent improvement, maintenance and optimization of firefighting strategies of firefighting equipment.

[0031] In a possible implementation, step S200 further includes:

[0032] Step S210: identifying repeated areas by comparing the target structure information to obtain multiple repeated building information groups.

[0033] Step S220: performing normalized modeling based on the multiple repeated building information groups to obtain multiple local independent models.

[0034] Step S230: Dynamically scheduling the multiple local independent models in the process of modeling using the target structure information to complete the construction of the three-dimensional building model.

[0035] Specifically, the identification of repeated areas is carried out by carefully comparing the target structure information. This requires in-depth analysis of the overall layout of the building to accurately identify similar or identical areas, such as hotel guest rooms, which often have a unified layout structure, as well as corridors connecting rooms and public areas between different floors. For each area with the same layout, only one of them needs to be modeled in detail in 3D. This method avoids the tedious work of repeated modeling of a large number of repeated areas and effectively improves modeling efficiency.

[0036] Based on the identified multiple repeated building information groups, normalized modeling is carried out. In this process, each repeated area group is modeled according to unified standards and parameters, and the characteristics and structure of each repeated area are reflected in the model in a standardized form, thereby obtaining multiple local independent models. These local independent models are prefabricated building modules, each representing the three-dimensional structure of a certain type of repeated area in the building.

[0037] When using the target structure information for comprehensive modeling, multiple local independent models are dynamically scheduled. In the process of modeling non-standardized areas, these local independent models are embedded in the corresponding positions in a timely manner as needed. Through this dynamic scheduling method, the local independent models are combined together to finally complete the construction of the entire building 3D model, which greatly reduces the time required for modeling.

[0038] In a possible implementation, step S400 further includes:

[0039] Step S410: performing primary aggregation of the firefighting configuration data based on the firefighting equipment type to obtain multiple firefighting configuration data sets of multiple sample firefighting equipment.

[0040] Step S420: performing secondary aggregation of the plurality of firefighting configuration data sets based on equipment selection to obtain a plurality of groups of sample configuration data of a plurality of sample firefighting equipment.

[0041] Step S430: construct a fire configuration model library using the multiple sample firefighting equipment and multiple groups of sample configuration data to obtain a firefighting configuration scheduling library.

[0042] Step S440: In the process of fitting the fire equipment orientation in the three-dimensional building model according to the fire configuration data, the fire equipment model is dynamically scheduled and configured from the fire configuration scheduling library to complete the construction of the fire layout model.

[0043] Specifically, we first conduct an in-depth analysis of the fire configuration data. The fire configuration data contains a large amount of information related to fire equipment, such as equipment specifications, parameters, installation locations, etc. Based on the key classification standard of fire equipment type, the data aggregation process is started. For the type of fire extinguisher, all data related to fire extinguishers are screened out from a large number of fire configuration data, including the type of extinguishing agent, capacity, pressure range, effective spray distance and other parameters of different types of fire extinguishers, and these data are integrated together to form a fire configuration data set specifically for fire extinguishers. Similarly, for fire hydrants, data such as pipe diameter, interface specifications, hose length and material, and spray gun type are collected to form a fire configuration data set for fire hydrants. Similarly, for various types of fire equipment such as automatic sprinkler systems, fire alarm devices, and fire emergency lighting, the corresponding information is extracted from the overall fire configuration data and aggregated according to their types, and finally multiple fire configuration data sets corresponding to multiple sample fire equipment are obtained. These sets lay the foundation for subsequent more refined aggregation and model construction.

[0044] For each fire configuration data set, secondary aggregation is carried out based on the important dimension of equipment selection. Taking fire extinguishers as an example, in the fire configuration data set of fire extinguishers, there are fire extinguisher data of different selections, such as dry powder fire extinguishers, carbon dioxide fire extinguishers, water-based fire extinguishers, etc. For dry powder fire extinguishers, the relevant data will be further aggregated according to the selection factors such as the composition and weight specifications of the fire extinguishing agent; carbon dioxide fire extinguishers will integrate the corresponding data based on their selection characteristics such as storage pressure and capacity. Similarly, for fire hydrant sets, the data will be aggregated again according to the selection conditions such as different interface forms, special materials and pressure resistance levels of water hoses, and different spray modes of spray guns. The automatic sprinkler fire extinguishing system will also classify the data according to the type of sprinkler (such as drooping type, upright type, side wall type, etc.), the response temperature range of the thermistor, the water spray flow rate and other selection information. Through this secondary aggregation method based on equipment selection, multiple sets of more targeted and professional sample configuration data are compiled for each sample fire-fighting equipment. These data can more accurately reflect the characteristics of different selected equipment and provide more detailed and accurate information for the subsequent construction of the fire-fighting configuration model library.

[0045] The fire configuration model library is constructed using multiple sample firefighting equipment and multiple sets of sample configuration data obtained in the previous steps. For each sample firefighting equipment, its multiple sets of sample configuration data are like a blueprint for building a model. Taking fire extinguishers as an example, according to the sample configuration data corresponding to different types and selections, fire extinguisher models containing parameters such as different types of extinguishing agents, different capacities and pressure ranges are constructed. These models accurately present the various characteristics and functions of fire extinguishers in practical applications. For fire hydrants, fire hydrant models are constructed based on their pipe diameters, interface specifications, hoses and spray guns. The structure and operation of fire hydrants are described in detail. Similarly, the automatic sprinkler fire extinguishing system constructs a model that accurately reflects its working status and fire extinguishing effect based on data such as nozzle type, thermal element parameters, and water spray flow; other firefighting equipment such as fire alarm devices and fire emergency lighting also construct corresponding models based on their respective sample configuration data. All the models built based on multiple sample fire equipment and their respective multiple sets of sample configuration data are integrated together to form a rich model library covering various types and selections of fire equipment, namely the fire configuration scheduling library. This provides accurate and dynamically callable model resources for the subsequent fire equipment orientation fitting in the building 3D model, thereby achieving efficient and accurate fire layout model construction.

[0046] When the fire equipment orientation is fitted in the three-dimensional model of the building according to the fire configuration data, the fire configuration data is interpreted in detail. For example, according to the planned location information of fire hydrants on each floor and corridor in the data, the fire hydrant model is accurately retrieved from the fire configuration scheduling library. This retrieval process is dynamic and can quickly match the appropriate fire hydrant model according to the specific fire configuration data requirements, including its pipe diameter, hose length and material, spray gun type and other parameters are fully in line with the requirements. For fire extinguishers, according to the rooms, passages and other locations specified by the fire configuration data, the fire extinguisher model of the corresponding type and capacity is called out from the fire configuration scheduling library. The same is true for the sprinkler model of the automatic sprinkler fire extinguishing system. According to its distribution plan in the building, the sprinkler model that meets its water spray flow, thermistor response temperature and other parameters is selected from the library and placed in the ceiling and other accurate locations of the three-dimensional model of the building. In this process, whether it is the distribution of fire alarm devices in various areas or the setting of fire emergency lighting equipment in stairwells, evacuation passages and other locations, it is realized by dynamically scheduling and configuring the fire equipment model from the fire configuration scheduling library. Each fire-fighting equipment model is accurately placed in the position corresponding to its fire-fighting configuration data in the three-dimensional building model. After all the fire-fighting equipment models are placed, the construction of the fire-fighting layout model is successfully completed. This model can truly and intuitively show the layout of fire-fighting equipment in the target building.

[0047] In a possible implementation, step S500 further includes:

[0048] Step S510: Perform fire point location analysis according to the application function of the target building and locate M fire feature points.

[0049] Step S520: presetting rescue speed parameters, and performing rescue behavior simulation on the three-dimensional building model according to the rescue speed parameters, to obtain M rescue speed parameters of the M fire feature points.

[0050] Step S530: After serializing the M fire feature points according to the M rescue speeds, the M fire feature points are equally divided to obtain a multi-level fire risk scenario.

[0051] Step S540: configuring fire spread parameters for the M fire feature points to obtain M fire spread scenario parameter sets.

[0052] Step S550: The M fire spread scenario parameter sets are stored in association with the multi-level fire risk scenarios to obtain the multi-level fire risk scenarios.

[0053] Specifically, the fire point location analysis is carried out based on the application function of the target building. Through in-depth understanding of the functional layout of the building, for example, if it is an office building, the focus is on the locations prone to fire, such as the concentration of electrical equipment and the storage of documents and materials; if it is a shopping mall, attention should be paid to areas with dense crowds and more flammable materials, such as the commodity display area and the dining area. After such analysis, M fire feature points are accurately located.

[0054] Next, the rescue speed parameters are preset, which cover many factors such as the speed of rescuers and the deployment speed of firefighting equipment. Based on this, rescue behavior simulation is carried out in the three-dimensional building model. During the simulation, for each fire feature point, the rescuers start from different positions and travel in different channels inside the building, and the time required to reach each fire feature point is calculated, so as to obtain the rescue speed parameters corresponding to each of the M fire feature points.

[0055] Then, the M fire feature points are serialized according to the M rescue speeds and arranged in a certain order. After that, the serialized M fire feature points are divided equally. This equal division comprehensively considers the spatial layout and functional zoning of the building, so that each part can represent a typical fire situation, thereby obtaining a multi-level fire risk scenario.

[0056] For these M fire feature points, the fire diffusion parameters are further configured, including key parameters such as flame propagation speed, smoke diffusion speed and temperature change. For each fire feature point, different flame propagation speeds, smoke diffusion speeds and temperature changes are set according to factors such as the surrounding building materials and ventilation conditions, thereby obtaining M fire diffusion scenario parameter sets.

[0057] Finally, the multi-level fire risk scenarios are associated with M fire diffusion scenario parameter sets and stored. For each fire point, multiple sets of flame propagation speed, smoke diffusion speed and temperature change parameter combinations are set according to its characteristics under different risk scenarios. In this way, multi-level fire risk scenarios are successfully obtained, providing a comprehensive and detailed fire scenario setting for subsequent fire simulation.

[0058] In a possible implementation, step S500 further includes:

[0059] Step S560: calling the first fire spread scenario parameters from the multi-level fire risk scenarios.

[0060] Step S570: After locating the first fire point corresponding to the first fire spread scenario parameter in the fire layout model, loading the first fire spread scenario parameter at the first fire point.

[0061] Step S580: When the fire layout model uses the first fire spread scenario parameters to simulate the disaster, the response behavior of the fire configuration is collected to obtain the first equipment response behavior.

[0062] Step S590: Similarly, the multi-level fire simulation result is obtained.

[0063] Specifically, the key operation of calling the first fire spread scenario parameters from the multi-level fire risk scenario is performed. The multi-level fire risk scenario is a complex and comprehensive set of fire scene information, which covers a variety of preset situations of different fire conditions, fire development paths and intensity changes. The first fire spread scenario parameters are the first set of key data in this set. Through precise indexing and identification mechanisms, the first fire spread scenario parameters can be quickly located and extracted from a large number of fire risk scenario data. These parameters include detailed information such as the initial heat release rate of the ignition point, the initial propagation direction and speed of the flame, the initial amount of smoke generated and the diffusion speed, etc., which provide the necessary data basis for the subsequent accurate simulation of the initial state and initial diffusion of the fire in the fire layout model.

[0064] By using the spatial coordinate indexing technology in the fire layout model, a large and accurate coordinate system is constructed by assigning a unique three-dimensional coordinate value to each point in the model. Based on this, according to the location information of the fire point contained in the first fire diffusion scenario parameters, a data matching algorithm is used to quickly search in the fire layout model. This algorithm will traverse each spatial point in the model, compare the location information in the parameters with the coordinate values ​​in the model one by one, and locate the first fire point with high speed and high precision. When the first fire point is accurately located, the parameter loading module is used to load the first fire diffusion scenario parameters. This module uses data injection technology to accurately transmit the flame propagation speed value, smoke diffusion speed value, temperature change value and other data in the parameters to the model data structure corresponding to the first fire point through the model interface. At the same time, the physical simulation engine of the model is used to initialize the physical properties of the fire point according to the loaded parameters. For example, the spread rate simulation rules of the combustion area are set according to the flame propagation speed, the diffusion direction and speed simulation conditions of the smoke particles are determined according to the smoke diffusion speed, and the simulation parameters of heat transfer and temperature field changes are set according to the temperature change value. Therefore, the fire spread simulation that conforms to the actual fire development law is started at the first fire point, so that the flame and smoke begin to dynamically diffuse in the fire protection layout model according to the set parameters.

[0065] When the fire layout model starts the disaster simulation according to the first fire spread scenario parameters, the entire simulation environment is like a real fire scene. In this process, a variety of advanced technical means are used to collect response behaviors for each link of the fire configuration. For the fire alarm system, by setting up virtual sensors in the model to simulate the working principles of real smoke detectors and temperature detectors, data such as the trigger time, signal strength changes, and the range and speed of alarm information transmission when the fire smoke spreads and the temperature rises are collected. These data can reflect whether the alarm system can respond to the disaster in a timely and accurate manner. In terms of the automatic sprinkler fire extinguishing system, the fluid simulation algorithm is used to record the time point when the sprinkler is started, the flow rate change of the water spray, the overlap between the water spray coverage and the fire spread area, etc. By analyzing these data, the effectiveness and timeliness of the sprinkler in the fire scene can be evaluated. The use of fire-fighting equipment such as fire hydrants is also recorded in detail. The action path and operation time of firefighters in the model are simulated, and parameters such as the speed of opening the fire hydrant valve, the duration of the water hose deployment, and the pressure and direction of the water flow sprayed by the water gun are collected to judge the operability and fire-fighting ability of the fire hydrant in responding to the fire. The fire emergency lighting system monitors the changes in brightness and lighting range in the fire smoke environment, and whether it can provide sufficient and effective lighting guidance for personnel evacuation. At the same time, the traffic conditions of the fire passage are simulated and analyzed, including the movement speed of personnel and rescue equipment in the passage, whether there is congestion, etc. By collecting the full range of response behaviors of these fire configurations, the first equipment response behavior is finally obtained, providing a key basis for the subsequent fire effect evaluation.

[0066] According to the process and method of steps S560 to S580, the operation is cyclically performed in sequence. For each set of fire spread scenario parameters in the multi-level fire risk scenario, the corresponding parameters are first called from them, and then the corresponding fire point is accurately located in the fire layout model, and these parameters are loaded to start the disaster simulation. During the simulation process, all response behaviors of the fire configuration in the scenario are carefully collected, including data on the start-up time, working status, effect, and personnel evacuation of various fire equipment. In this way, these steps are repeated for each fire risk scenario. With the continuous accumulation and integration of fire equipment and personnel response data in different scenarios, the multi-level fire simulation results are finally obtained in a comprehensive manner. These results fully present the overall performance of the fire protection system in a variety of complex fire scenarios, providing sufficient and valuable basis for fire protection effect evaluation.

[0067] In a possible implementation, step S580 further includes:

[0068] Step S581: Locate multiple associated firefighting equipment responsible for responding to the first fire point in the firefighting layout model.

[0069] Step S582: Preset a behavior data indicator set, and use the behavior data indicator set as a data collection constraint. During the disaster simulation process, collect the response behavior of the fire protection configuration to obtain multiple associated response data sets of the multiple associated fire protection equipment, wherein each associated response data set includes response start time, continuous response time, response intensity, and fire extinguishing efficiency.

[0070] Step S583: associate and store the multiple associated firefighting devices and the multiple associated response data sets to obtain the first device response behavior.

[0071] Specifically, a precise search is performed in the complex fire layout model to identify multiple associated firefighting equipment responsible for responding to the first fire point. By analyzing the layout information of the firefighting equipment in the fire layout model and the relationship between the fire point and the fire point, combined with the scope and functional characteristics of the firefighting equipment, such as the coverage radius of the fire hydrant, the protection area of ​​the sprinkler system nozzle, the sensing range of the fire alarm detector, etc., the firefighting equipment that should be involved in the response when the fire occurs at the first fire point can be accurately found.

[0072] A comprehensive and detailed set of behavioral data indicators is preset in advance. This indicator set covers multiple key dimensions, including response start time, continuous response time, response intensity, fire extinguishing efficiency, etc. It will serve as a constraint for data collection. In the process of disaster simulation, the response behavior of the fire protection configuration is collected based on this. For each associated fire protection equipment, its response start time is accurately recorded, that is, the time from the appearance of the fire signal at the fire point to the start of the equipment action; the continuous response time is the time the equipment continues to work effectively. The collection of response intensity involves multiple parameters, such as the water flow and pressure of the sprinkler head of the automatic sprinkler fire extinguishing system, the water flow rate of the fire hydrant, etc. The evaluation of fire extinguishing efficiency is determined by comparing the fire development before and after the equipment acts, such as the degree of reduction in the speed of fire spread and the reduction ratio of the flame coverage area. In this way, the associated response data sets corresponding to multiple associated fire protection equipment are obtained.

[0073] Multiple associated fire-fighting equipment and their respective multiple associated response data sets are stored in an associated manner. This associated storage establishes a close connection between the fire-fighting equipment and their behavioral performance when responding to the first fire point, thereby forming complete first-equipment response behavior information, providing a detailed and valuable data basis for subsequent in-depth evaluation of fire-fighting effectiveness.

[0074] In a possible implementation, step S600 further includes:

[0075] Step S610: pre-constructing a multi-level quantitative scoring table for the multi-level fire risk scenarios.

[0076] Step S620: calling a first-level quantitative scoring table in the multi-level quantitative scoring table according to the first fire spread scenario parameter.

[0077] Step S630: using the first device response behavior to traverse the first level quantitative score table to obtain multiple groups of associated response quantitative scores corresponding to the multiple associated response data sets.

[0078] Step S640: configuring a behavior data weight set according to the behavior data indicator set.

[0079] Step S650: After solving the mean of the plurality of groups of quantitative scores of associated responses, weighted solution is performed using the behavioral data weight set to obtain a first scenario assessment result.

[0080] Step S660: Similarly, multiple groups of scenario assessment results for the multi-level fire risk scenarios are obtained.

[0081] Step S670: storing the multiple groups of scenario assessment results and the multiple levels of fire risk scenarios in an associated manner as the fire protection effectiveness assessment result.

[0082] Specifically, to construct a multi-level quantitative scoring table for multi-level fire risk scenarios, first of all, a comprehensive analysis of the multi-level fire risk scenarios should be conducted, taking into account various factors such as the initial scale of the fire, the spread speed, the burning intensity, and the amount of smoke generated in different scenarios. For the initial scale of the fire, the number of fire points, the initial heat release rate of each fire point, etc. are graded and quantified; for the spread speed, the scoring standards corresponding to different speed intervals are determined in combination with the structural characteristics of the building, ventilation conditions, and the distribution of flammable materials. The burning intensity is divided into levels and assigned corresponding scores based on parameters such as flame temperature and thermal radiation range. The amount of smoke generated is quantitatively scored according to different degrees such as thick smoke, medium smoke, and small amount of smoke. At the same time, the impact of the duration of the fire on the overall risk must also be considered. There is a significant difference in the scoring of short-term fires and long-term fires. For the response of the fire protection system, the scoring standards are constructed from the dimensions of alarm timeliness, fire protection equipment startup speed, and fire extinguishing efficiency. The timeliness of alarm is graded according to the time from the occurrence of fire to the issuance of alarm signal; the activation speed of fire-fighting equipment is scored according to the time from the time when various equipment (such as water sprinkler system, fire hydrant, fire extinguisher, etc.) should be activated to the time when it is actually activated; the efficiency of fire-fighting is quantified by comparing the changes in fire intensity before and after the fire-fighting equipment is used, such as the reduction ratio of flame coverage area and the degree of heat release rate reduction, and a comprehensive evaluation is conducted. The quantitative standards of these different dimensions are integrated to form a systematic, multi-level quantitative scoring table for multi-level fire risk scenarios, providing a solid basis for the subsequent accurate evaluation of fire-fighting effects.

[0083] The first fire spread scenario parameters are analyzed in detail. These parameters contain a wealth of information, such as the location of the fire point, the initial fire size, the flame propagation speed, the smoke diffusion speed and other key elements. Then, these parameters are matched with the contents of the multi-level quantitative scoring table, which is a complex and comprehensive evaluation system that covers the quantitative scoring standards corresponding to different parameter ranges in various fire scenarios. By analyzing each element in the first fire spread scenario parameters, the most suitable part is searched in this huge multi-level quantitative scoring table. For example, if the first fire spread scenario parameters show a scene in a closed space with a medium fire and a fast spread speed, based on these characteristics, the first-level quantitative scoring table specifically set for such situations can be accurately found in the table. This table will provide an accurate scoring basis for the subsequent evaluation of the effectiveness of the fire response in this specific fire scenario.

[0084] The first-level quantitative score table is traversed using the first-level device response behavior, which contains response information of multiple associated fire-fighting equipment, such as response start time, continuous response time, response intensity, fire-fighting efficiency, etc. This information is compared with each standard in the first-level quantitative score table one by one, and the corresponding quantitative score is determined for each associated response data set based on different indicator ranges and corresponding scores, thereby obtaining multiple sets of associated response quantitative scores. These scores intuitively reflect the degree of fit between the performance of fire-fighting equipment in a specific fire scenario and the standard.

[0085] The behavioral data weight set is configured based on the behavioral data indicator set, which covers all aspects that are critical to the evaluation of firefighting effectiveness. Considering the relative importance of each indicator in the overall firefighting response, a reasonable weight is assigned to each indicator. For example, a higher weight is given to the key indicator of firefighting efficiency because it is directly related to the effect of fire control; and the response start time is also of great significance, and its weight needs to be determined by comprehensively considering factors such as the speed of fire development.

[0086] The average of multiple groups of quantitative scores of related responses is calculated to obtain a comprehensive intermediate evaluation value, which represents the average level of fire-fighting equipment in all aspects. After that, the configured behavioral data weight set is used to weight this intermediate value. Through this weighted method, the scores of each dimension are integrated according to their importance, so that the final evaluation result can more accurately reflect the real effect of fire-fighting equipment in this scenario, thereby obtaining the first scenario evaluation result.

[0087] According to the process of steps S630-S650 above, operations are performed in turn for each scenario in the multi-level fire risk scenario, and the equipment response behavior in each scenario is quantitatively scored, weighted and calculated, so as to obtain multiple groups of scenario evaluation results for the multi-level fire risk scenarios. These results comprehensively present the performance of the fire protection system in different fire scenarios.

[0088] It is clear that there is a close internal connection between multiple sets of scenario assessment results and multi-level fire risk scenarios. Multiple sets of scenario assessment results are quantitative evaluations of the response performance of the fire protection system under different fire risk scenarios. Each set of results accurately reflects the implementation effect of fire protection equipment-related fire protection measures in a specific fire scenario. The multi-level fire risk scenario is pre-set and contains fire scenarios with different danger levels, different fire conditions and development trends from low to high. When they are stored in association, a special data structure or database table is established, with the multi-level fire risk scenario as the index or primary key, and the corresponding scenario assessment results are closely linked. For example, for a high-risk, large-scale, rapidly spreading fire scenario, it is associated with the results of the evaluation of the timeliness of the response of the fire protection equipment and the efficiency of fire extinguishing in the scenario. In this way, after all the scenario assessment results and the multi-level fire risk scenarios are matched and stored, they form a complete fire protection effect evaluation result, which can clearly show the actual effectiveness of the fire protection system in various possible fire scenarios, and provide a strong data basis for subsequent fire protection system optimization, resource allocation adjustment and other work.

[0089] Embodiment 2, based on the same inventive concept as the fire protection effect evaluation method under digital modeling in the above embodiment, Figure 2 As shown, the present application provides a fire protection effect evaluation platform under digital modeling, and the platform and method embodiments in the embodiments of the present application are based on the same inventive concept. Among them, the platform includes:

[0090] The target structure information acquisition module 10 is used to collect structure information of the target building to obtain target structure information.

[0091] The building three-dimensional model acquisition module 20 is used to use the target structure information to perform digital modeling to obtain the building three-dimensional model of the target building.

[0092] The fire protection configuration data acquisition module 30 is used to interactively obtain the fire protection configuration data of the target building.

[0093] The fire protection layout model acquisition module 40 is used to perform fire protection equipment fitting on the three-dimensional building model according to the fire protection configuration data to obtain a fire protection layout model.

[0094] The multi-level fire simulation result acquisition module 50 is used to preset multi-level fire risk scenarios, and perform fire simulation in the fire layout model based on the multi-level fire risk scenarios to obtain multi-level fire simulation results.

[0095] The firefighting effect evaluation result acquisition module 60 is used to perform a firefighting response quantitative evaluation according to the multi-level firefighting simulation results to obtain a firefighting effect evaluation result.

[0096] Furthermore, the building three-dimensional model acquisition module 20 also includes:

[0097] A repeated building information group acquisition unit is used to identify repeated areas by comparing the target structure information to obtain multiple repeated building information groups.

[0098] A local independent model acquisition unit is configured to perform normalized modeling based on the multiple repeated building information groups to obtain multiple local independent models.

[0099] The building three-dimensional model construction unit is used to dynamically schedule the multiple local independent models in the process of modeling using the target structure information to complete the construction of the building three-dimensional model.

[0100] Furthermore, the fire protection layout model acquisition module 40 also includes:

[0101] A firefighting configuration data set acquisition unit is configured to perform primary aggregation of the firefighting configuration data based on firefighting equipment types to obtain multiple firefighting configuration data sets of multiple sample firefighting equipment.

[0102] A multiple-group sample configuration data acquisition unit is used to perform secondary aggregation of the multiple firefighting configuration data sets based on equipment selection to obtain multiple groups of sample configuration data of multiple sample firefighting equipment.

[0103] A fire configuration scheduling library acquisition unit is used to construct a fire configuration model library using the multiple sample fire equipment and multiple groups of sample configuration data to obtain a fire configuration scheduling library.

[0104] A fire layout model construction unit is used to dynamically schedule and configure the fire equipment model from the fire configuration scheduling library during the process of fitting the fire equipment orientation in the three-dimensional building model according to the fire configuration data, so as to complete the construction of the fire layout model.

[0105] Furthermore, the multi-level fire simulation result acquisition module 50 also includes:

[0106] A fire feature point positioning unit is used to perform fire point position analysis according to the application function of the target building and locate M fire feature points.

[0107] A rescue speed parameter acquisition unit is used to preset rescue speed parameters, and perform rescue behavior simulation on the three-dimensional building model according to the rescue speed parameters to obtain M rescue speed parameters of the M fire feature points.

[0108] A multi-level fire risk scenario acquisition unit is used to obtain a multi-level fire risk scenario by equally dividing the M fire feature points after serializing the M fire feature points according to the M rescue speeds.

[0109] A fire spreading scenario parameter set acquisition unit is used to configure fire spreading parameters for the M fire feature points to obtain M fire spreading scenario parameter sets.

[0110] A multi-level fire risk scenario acquisition unit is used to associate and store the M fire spread scenario parameter sets according to the multi-level fire risk scenarios to obtain the multi-level fire risk scenarios.

[0111] Furthermore, the multi-level fire simulation result acquisition module 50 also includes:

[0112] A fire spread scenario parameter calling unit is used to call a first fire spread scenario parameter from the multi-level fire risk scenario.

[0113] A fire spread scenario parameter loading unit, wherein the fire spread scenario parameter loading unit is used to load the first fire spread scenario parameter at the first fire point after the fire layout model locates the first fire point corresponding to the first fire spread scenario parameter.

[0114] The device response behavior acquisition unit is used to collect the response behavior of the fire protection configuration and obtain the first device response behavior during the disaster simulation process using the first fire spread scenario parameters by the fire protection layout model.

[0115] The fire simulation result acquisition unit obtains the multi-level fire simulation results by analogy.

[0116] Furthermore, the device response behavior acquisition unit also includes:

[0117] An associated fire fighting equipment locating unit is used to locate a plurality of associated fire fighting equipment responsible for responding to the first fire point in the fire fighting layout model.

[0118] An associated response data set acquisition unit is used to preset a behavior data indicator set, and use the behavior data indicator set as a data collection constraint. During the disaster simulation process, the response behavior of the firefighting configuration is collected to obtain multiple associated response data sets of the multiple associated firefighting equipment, wherein each associated response data set includes a response start time, a continuous response time, a response intensity, and a fire extinguishing efficiency.

[0119] An associated storage unit, wherein the associated storage unit is used to associate and store the plurality of associated firefighting equipment and the plurality of associated response data sets to obtain the first equipment response behavior.

[0120] Furthermore, the fire protection effect evaluation result acquisition module 60 also includes:

[0121] A quantitative scoring table construction unit is used to pre-construct a multi-level quantitative scoring table for the multi-level fire risk scenario.

[0122] A quantitative scoring table calling unit is used to call a first-level quantitative scoring table in the multi-level quantitative scoring table according to the first fire spread scenario parameter.

[0123] An association response quantitative score acquisition unit is used to traverse the first level quantitative score table using the first device response behavior to obtain multiple groups of association response quantitative scores corresponding to the multiple association response data sets.

[0124] A behavior data weight set configuration unit, wherein the behavior data weight set configuration unit is used to configure the behavior data weight set according to the behavior data indicator set.

[0125] The first scenario assessment result acquisition unit is used to perform weighted solution using the behavior data weight set after performing mean solution on the plurality of groups of associated response quantitative scores to obtain a first scenario assessment result.

[0126] A multiple-group scenario assessment result acquisition unit is used to obtain multiple groups of scenario assessment results for the multi-level fire risk scenarios by analogy.

[0127] A firefighting effectiveness evaluation result acquisition unit is used to associate and store the multiple groups of scenario evaluation results and the multiple levels of fire risk scenarios as the firefighting effectiveness evaluation result.

[0128] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0129] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0130] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A fire protection effect evaluation method under digital modeling, characterized in that: The method comprises: Collect structural information of the target building to obtain target structural information; Using the target structure information to perform digital modeling to obtain a three-dimensional architectural model of the target building; interactively obtaining fire protection configuration data of the target building; Fitting firefighting equipment to the three-dimensional building model according to the firefighting configuration data to obtain a firefighting layout model; Preset multiple fire risk scenarios, and perform fire simulation in the fire layout model based on the multiple fire risk scenarios to obtain multiple fire simulation results; A fire response quantitative evaluation is performed based on the multi-stage fire simulation results to obtain a fire effectiveness evaluation result.

2. A firefighting effectiveness evaluation method under digital modeling as claimed in claim 1, characterized in that: The target structure information is used for digital modeling to obtain a three-dimensional architectural model of the target building. The method includes: By comparing the target structure information to identify repeated regions, a plurality of repeated building information groups are obtained; Performing normalized modeling based on the multiple repeated building information groups to obtain multiple local independent models; In the process of modeling using the target structure information, the multiple local independent models are dynamically scheduled to complete the construction of the three-dimensional building model.

3. A firefighting effectiveness evaluation method under digital modeling as claimed in claim 2, characterized in that: According to the fire configuration data, fire equipment is fitted in the three-dimensional building model to obtain a fire layout model, and the method includes: Performing primary aggregation of the firefighting configuration data based on the firefighting equipment type to obtain multiple firefighting configuration data sets for multiple sample firefighting equipment; Perform secondary aggregation of the plurality of firefighting configuration data sets based on equipment selection to obtain a plurality of groups of sample configuration data of a plurality of sample firefighting equipment; Constructing a fire configuration model library using the plurality of sample fire fighting equipment and the plurality of sets of sample configuration data to obtain a fire configuration scheduling library; In the process of fitting the fire equipment position in the three-dimensional building model according to the fire configuration data, the fire equipment model is dynamically scheduled and configured from the fire configuration scheduling library to complete the construction of the fire layout model.

4. The firefighting effectiveness evaluation method under digital modeling as claimed in claim 1, characterized in that: Preset multiple levels of fire risk scenarios, the method comprising: According to the application function of the target building, the fire point location is analyzed and M fire feature points are located; Preset rescue speed parameters, and perform rescue behavior simulation on the three-dimensional building model according to the rescue speed parameters to obtain M rescue speed parameters of the M fire feature points; After serializing the M fire feature points according to the M rescue speeds, the M fire feature points are equally divided to obtain a multi-level fire risk scenario; Performing fire spreading parameter configuration on the M fire feature points to obtain M fire spreading scenario parameter sets; The M fire spread scenario parameter sets are stored in association with the multi-level fire risk scenarios to obtain the multi-level fire risk scenarios.

5. A firefighting effectiveness evaluation method under digital modeling as claimed in claim 4, characterized in that: Based on the multi-level fire risk scenario, a fire simulation is performed in the fire layout model to obtain a multi-level fire simulation result, and the method includes: calling a first fire spread scenario parameter from the multi-level fire risk scenario; After the fire layout model locates a first fire point corresponding to a first fire spread scenario parameter, loading the first fire spread scenario parameter at the first fire point; In the process of simulating the disaster situation using the first fire spreading scenario parameter in the fire layout model, collecting the response behavior of the fire configuration to obtain the first equipment response behavior; And so on, the multi-level fire simulation result is obtained.

6. A firefighting effectiveness evaluation method under digital modeling as claimed in claim 5, characterized in that: In the process of simulating the disaster using the first fire spreading scenario parameter in the fire layout model, collecting the response behavior of the fire configuration to obtain the first equipment response behavior, the method includes: locating a plurality of associated firefighting equipment responsible for responding to the first fire point in the firefighting layout model; Preset a behavior data indicator set, and use the behavior data indicator set as a data collection constraint. During the disaster simulation process, collect the response behavior of the firefighting configuration to obtain multiple associated response data sets of the multiple associated firefighting equipment, wherein each associated response data set includes a response start time, a continuous response time, a response intensity, and a fire extinguishing efficiency; The plurality of associated firefighting devices and the plurality of associated response data sets are stored in association to obtain the first device response behavior.

7. A firefighting effectiveness evaluation method under digital modeling as claimed in claim 6, characterized in that: A fire response quantitative evaluation is performed according to the multi-level fire simulation results to obtain a fire effect evaluation result, and the method includes: Pre-constructing a multi-level quantitative scoring table for the multi-level fire risk scenarios; Calling a first-level quantitative scoring table in the multi-level quantitative scoring table according to the first fire spread scenario parameter; Using the first device response behavior to traverse the first level quantitative score table, to obtain multiple groups of association response quantitative scores corresponding to the multiple association response data sets; Configuring a behavior data weight set according to the behavior data indicator set; After solving the mean of the plurality of groups of quantitative scores of associated responses, weighted solution is performed using the behavioral data weight set to obtain a first scenario assessment result; By analogy, multiple groups of scenario assessment results for the multi-level fire risk scenarios are obtained; The multiple groups of scenario assessment results and the multiple levels of fire risk scenarios are stored in association as the firefighting effectiveness assessment result.

8. A firefighting effectiveness evaluation platform based on digital modeling, characterized in that: The platform includes: A target structure information acquisition module, wherein the target structure information acquisition module is used to collect structure information of a target building to obtain target structure information; A building three-dimensional model acquisition module, the building three-dimensional model acquisition module is used to use the target structure information to perform digital modeling to obtain a building three-dimensional model of the target building; A fire configuration data acquisition module, the fire configuration data acquisition module is used to interactively obtain the fire configuration data of the target building; A fire layout model acquisition module, the fire layout model acquisition module is used to fit fire equipment in the three-dimensional building model according to the fire configuration data to obtain a fire layout model; A multi-level fire simulation result acquisition module, the multi-level fire simulation result acquisition module is used to preset a multi-level fire risk scenario, and perform a fire simulation in the fire layout model based on the multi-level fire risk scenario to obtain a multi-level fire simulation result; The firefighting effect evaluation result acquisition module is used to perform a quantitative evaluation of the firefighting response according to the multi-level firefighting simulation results to obtain a firefighting effect evaluation result.

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