Disaster distribution and influence analysis platform

Abstract

The invention discloses a disaster distribution and influence analysis platform, and particularly relates to the field of big data evaluation. Comprising a system central processor module, a system operation database, a user information terminal, a disaster area division module, a disaster data information acquisition module, a data preprocessing module, a disaster influence evaluation module, a rescue resource allocation module and a visual feedback module. According to development and change of disasters, a rescue resource allocation scheme is dynamically adjusted, a visual feedback module feeds comprehensive influence coefficient values back to a user information end in a map layer superposition mode, basic information such as landforms, population distribution and infrastructures and disaster influence information are displayed at the same time, priorities are marked according to color shades, and the disaster influence information is displayed. A decision maker can intuitively know the real-time condition of a disaster and the rescue demand urgency degree of each area, so that rescue resources can be timely and accurately distributed to the most needed place, and the rescue efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of big data evaluation, and more specifically, to a disaster distribution and impact analysis platform. Background Art

[0002] The patent application with the application number CN202410911926.X discloses a method and system for analyzing the whole-chain information of county-level geological disasters based on big data. By the mining unit, the mining results of disaster elements in the geological disaster theme are subjected to feature integration to downsample the geological disaster theme, remove the secondary information in the geological disaster theme, and save the subsequent calculation amount; by associating the mining results of disaster elements in the geological disaster theme with several impact labels of the geological disaster impact attributes, the key features in the geological disaster theme corresponding to the geological disaster impact attributes are obtained, so that the key features include the impact labels, and the geological disaster impact attributes of the geological disaster theme are associated through the impact labels, making the geological disaster impact attributes of the analyzed geological disaster theme more accurate. When performing the whole-chain information analysis of geological disasters, the whole-chain information analysis results of geological disasters can be obtained more accurately, thereby ensuring people's life safety and property safety while.

[0003] The patent application with the application number CN202310278323.6 discloses a method and system for risk assessment in multi-disaster areas based on big data. By performing multivariate disaster impact analysis on historical disaster big data to obtain impact factors, using the impact factors as quantitative indicators for evaluating the susceptibility and danger of geological disasters and further constructing a geological disaster evaluation model, then evaluating and analyzing a preset area through the geological disaster evaluation model and generating a regional geological disaster susceptibility and danger zoning result map, and finally overlaying the regional geological disaster susceptibility and danger zoning result map based on a GIS platform to obtain a geological disaster risk zoning map. Through the geological disaster risk zoning map, the comprehensive assessment of risks in multi-disaster areas can be carried out accurately and comprehensively, thus helping with disaster prediction and repair in multi-disaster areas.

[0004] However, in actual use, there are still some drawbacks. For example, when traditional methods are used to process information on geological disaster themes, there are lacks of effective data integration and feature extraction means. Facing a large amount of complex disaster data, it is difficult to quickly and accurately extract key information from the geological disaster theme, resulting in inaccurate analysis of the relationship between disaster elements and impact attributes, affecting the timeliness and reliability of the analysis results, and being unable to meet the urgent information needs of actual disaster prevention and control work. At the same time, in the process of disaster assessment using traditional technologies, it is difficult to accurately measure the comprehensive impact of disasters on different regions. For example, when analyzing the damage situation of buildings, the combined effects of factors such as building structure types and population distribution may not be fully considered, leading to inaccurate judgment of the degree of disaster impact, and the assessment of the risk of casualties and property losses is often relatively rough, unable to accurately reflect the actual disaster situation in different regions, making the allocation of rescue resources lack a scientific basis. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a disaster distribution and impact analysis platform, which adopts the following solutions to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: A disaster distribution and impact analysis platform, including a system central processing unit module, a system operation database, a user information terminal, a disaster-affected area division module, a disaster data information acquisition module, a data preprocessing module, a disaster impact assessment module, a rescue resource allocation module, and a visualization feedback module;

[0007] The system operation database includes all data texts of the disaster distribution and impact analysis platform and real-time collects the information texts output by each module. The system central processing unit module is used to centrally control the information text instructions output by each module. The user information terminal is a device for receiving the information output by the risk early warning monitoring system of the disaster distribution and impact analysis platform;

[0008] The disaster-affected area division module is used to record the disaster-affected area as the target monitoring area, divide the target monitoring area into each sub-monitoring area according to the number of buildings, and sequentially label them as 1, 2, 3,..., i,..., n;

[0009] The disaster data information acquisition module includes a geographical environment information collection unit, a population distribution information collection unit, an infrastructure information collection unit, and a disaster data information collection unit, and is used to collect data for the sub-areas;

[0010] The data preprocessing module is used to convert the collected data from different sources and different formats into a unified data format, remove noise data, outliers, and duplicate data, and perform normalization processing on the data;

[0011] The disaster impact assessment module is used to establish mathematical models for the building damage level assessment coefficient, the casualty risk coefficient, the property loss coefficient, and the comprehensive impact coefficient based on the preprocessed data, and calculate the building damage level assessment coefficient value, the casualty risk coefficient value, the property loss coefficient, and the comprehensive impact coefficient value of the sub-monitoring area;

[0012] The rescue resource allocation module is used to prioritize the sub-monitoring areas according to the comprehensive impact coefficient value. The sub-monitoring area with a larger comprehensive impact coefficient value has a higher priority, and rescue is carried out on the sub-monitoring area with a higher priority according to the priority ranking;

[0013] The visualization feedback module is used to feedback the comprehensive impact coefficient value to the user information terminal.

[0014] Preferably, the data collected by the geographical environment information collection unit includes topographic and geomorphic data, geological structure data, land use type data, water system distribution data, etc., which are mainly obtained through geographical information system databases, satellite remote sensing image interpretation, and geological exploration reports;

[0015] The population distribution information collection unit obtains the population distribution information of each sub-monitoring area in real time through census data, mobile operator base station data, and social media location information;

[0016] The infrastructure information collection unit obtains different types of building information in the sub-monitoring area by collecting and sorting the infrastructure management files of government departments, geographical information systems, and databases of related industries through the Internet;

[0017] The disaster data information collection unit is used to obtain various types of disaster monitoring data in real time, such as seismic wave data of seismic monitoring networks, water level and flow data of monitoring stations, displacement and deformation data of geological disaster monitoring points, etc.; at the same time, it collects records of historical disaster events, including information such as the time, location, type, intensity, and losses caused by the disasters.

[0018] Preferably, the mathematical model of the building damage level assessment coefficient is specifically expressed as:

[0019]

[0020] Among them, P(D|PGA) is the building damage probability, PGA is the peak ground acceleration of the earthquake, PGA min and PGA max are the acceleration thresholds for the building to start to be damaged and completely damaged, θ and k are constants, BD is the building damage level assessment coefficient, 1 represents slight damage, 2 represents moderate damage, 3 represents severe damage, 4 represents collapse, and P1, P2, P3, P4 are the damage probability thresholds for dividing different damage levels.

[0021] Preferably, the θ and k are parameters related to the building structure type, which are determined by statistical analysis of seismic damage tests or historical seismic damage data of buildings with different structure types. The specific acquisition method is as follows:

[0022] Collect the damage data of buildings with different structure types in a large number of historical earthquakes through the disaster data information acquisition module. These data cover the basic information of the earthquake such as the earthquake occurrence time, location, magnitude, ground motion parameters, etc., the structure type of the building, and the geological conditions of the area where the building is located. And the damage degree of the building is divided into four grades: slight damage, moderate damage, severe damage, and collapse according to certain standards;

[0023] Sort and classify the collected historical earthquake damage data, classify them according to the structure type of the building, and group the building data of the same structure type into one group. For each group of data, further divide the interval according to the magnitude of the peak ground acceleration of the earthquake, and count the number of buildings with different damage degrees in each PGA interval and the total number of buildings in this interval;

[0024] According to the damage quantity and total number of buildings in different PGA intervals obtained by statistics, calculate the damage probability of buildings in each PGA interval. Taking PGA as the independent variable and the damage probability as the dependent variable, fit the data for each group of structure type data respectively, and fit the data into the model to determine the corresponding θ and k parameter values for each structure type of building.

[0025] Preferably, the specific determination methods of P1, P2, P3, and P4 are as follows:

[0026] Collect the damage data of buildings in a large number of historical earthquakes, including the magnitude, intensity, ground motion parameters of the earthquake, the detailed information of the building such as the structure type, construction year, number of floors, usage function, etc. and the corresponding building damage grade, and classify them according to slight damage, moderate damage, severe damage, and collapse. For example, obtain the relevant data of various buildings in multiple earthquakes that occurred in different regions in the past few decades from channels such as earthquake disaster databases, research reports of scientific research institutions, and disaster records of government departments. Classify and sort these data according to factors such as the structure type of the building for subsequent analysis.

[0027] For each type of building, such as brick-concrete structure residential buildings, frame structure office buildings, etc., calculate the proportion of the damage degree of this type of building when the building damage probability reaches a certain value. For example, count the proportion of the number of slightly damaged brick-concrete structure residential buildings to the total number at a certain building damage probability. If this proportion is greater than 50%, determine this building damage probability value as P1; when the proportion of the number of moderately damaged brick-concrete structure residential buildings to the total number is greater than 50%, determine this building damage probability value as P2, and similarly determine P3 and P4.

[0028] Preferably, the mathematical model of the risk coefficient of casualties is specifically expressed as:

[0029] P(C|BD ij ,P ij )=P injury (BD ij )×P death (BD ij )×P presence (P ij );

[0030]

[0031] Where P(C|BD ij ,P ij ) is the probability of casualties, N(C) is the risk coefficient of casualties, P injury (BD ij ) is the probability of people being injured under the building damage level BD of this type of building, P death (BD ij ) is the probability of people dying under the building damage level BD of this type of building; P presence (P ij ) is the probability of people being in the jth floor of the ith building, u is the number of buildings, v is the number of floors in each building, and N ij is the number of people on the jth floor of the ith building.

[0032] Preferably, the obtaining methods of the said P injury (BD ij ) and P death (BD ij ) are specifically as follows:

[0033] Collect historical earthquake casualty data of different regions, magnitudes, and building types in a large number of historical earthquakes through the disaster data information acquisition module. These data cover the basic information of earthquakes such as the occurrence time, location, magnitude, ground motion parameters, etc., the structural type of buildings, and the geological conditions of the regions where the buildings are located. The damage degree of buildings is divided into four levels according to certain criteria: slight damage, moderate damage, severe damage, and collapse. Classify and organize the collected data according to the damage level of buildings, and group the buildings with the same damage level and their corresponding casualty data together. Within each damage level group, further subdivide according to the structural type and number of floors of the buildings to analyze the influence of different factors on the probability of casualties. For each damage level group, calculate the probability of injury and the probability of death of personnel. The specific calculation method is as follows:

[0034]

[0035] Preferably, the mathematical model of the property loss coefficient is specifically expressed as:

[0036]

[0037] Among them, BV is the property loss coefficient, ρ BD is the loss ratio of the asset value of this type of building under the building damage level BD, w is the number of building components, C k is the unit volume cost of the k-th component, A k is the volume of the k-th component, d k is the depreciation rate of the k-th component, and t is the service life of the building.

[0038] Preferably, the mathematical model of the comprehensive influence coefficient is specifically expressed as:

[0039]

[0040] Among them, is the comprehensive influence coefficient, N(C) is the casualty risk coefficient, and BV is the property loss coefficient.

[0041] Preferably, the value of the comprehensive influence coefficient is displayed by overlaying map layers, simultaneously showing basic information such as topography, population distribution, and infrastructure, as well as disaster impact information, and marking the priority with the depth of color. The areas with darker colors have higher priorities.

[0042] The technical effects and advantages of the present invention:

[0043] 1. The present invention constructs mathematical models such as building damage level assessment coefficients, casualty risk coefficients, property loss coefficients, and comprehensive impact coefficients to comprehensively and accurately evaluate the impact of disasters on each sub-monitoring area. In the assessment of building damage levels, based on a large amount of historical earthquake data, detailed analysis is carried out on buildings of different structural types to determine parameters related to the building structure, so as to accurately judge the damage situation of buildings in disasters. The comprehensive impact coefficient comprehensively considers factors such as casualties and property losses, and through reasonable weight allocation, can accurately reflect the severity of disasters in each area, providing a scientific and accurate basis for the allocation of rescue resources;

[0044] 2. The present invention dynamically adjusts the rescue resource allocation plan according to the development and change of disasters. The visualization feedback module feeds back the comprehensive impact coefficient value to the user information terminal in the form of map layer superposition, and at the same time displays basic information such as topography, population distribution, and infrastructure and disaster impact information, and marks the priority with the depth of color, enabling decision-makers to intuitively understand the real-time situation of disasters and the urgency of rescue needs in each area. This can ensure that rescue resources can be timely and accurately allocated to the places where they are most needed, improving rescue efficiency;

[0045] 3. In the construction of the mathematical models of building damage level assessment coefficients, casualty risk coefficients, property loss coefficients, and comprehensive impact coefficients, the present invention fully considers various factors. In the building damage level assessment model, through a large amount of historical earthquake data, classification analysis is carried out on buildings of different structural types to determine parameters related to the building structure, making the model more targeted and accurate, and better reflecting the damage situation of buildings in actual disaster situations. This is an important improvement to the traditional risk assessment model. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a schematic diagram of the overall structure of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0048] As shown in the attached Figure 1 A disaster distribution and impact analysis platform includes a system central processor module, a system operation database, a user information terminal, a disaster area division module, a disaster data information acquisition module, a data preprocessing module, a disaster impact assessment module, a rescue resource allocation module, and a visualization feedback module;

[0049] The system operation database includes all data texts of the disaster distribution and impact analysis platform, and real-time collects the information texts output by each module. The system central processing unit module is used to centrally control the information text instructions output by each module. The user information terminal is a device for receiving the information output of the risk early warning monitoring system of the disaster distribution and impact analysis platform;

[0050] The output end of the affected area division module is connected to the input end of the disaster data information acquisition module by telecommunications. The output end of the disaster data information acquisition module is connected to the input end of the data preprocessing module by telecommunications. The output end of the data preprocessing module is connected to the input end of the disaster impact assessment module by telecommunications. The output end of the disaster impact assessment module is connected to the input end of the rescue resource allocation module by telecommunications. The output end of the rescue resource allocation module is connected to the input end of the visualization feedback module by telecommunications.

[0051] The affected area division module is used to record the affected area as the target monitoring area, divide the target monitoring area into each sub-monitoring area according to the number of buildings, and sequentially mark them as 1, 2, 3, … i …, n;

[0052] The disaster data information acquisition module includes a geographical environment information collection unit, a population distribution information collection unit, an infrastructure information collection unit, and a disaster data information collection unit, and is used to collect data for the sub-areas;

[0053] In a preferred technical solution of the present application, the data collected by the geographical environment information collection unit includes landform data, geological structure data, land use type data, water system distribution data, etc., which are mainly obtained through the geographical information system database, satellite remote sensing image interpretation, and geological exploration reports, etc. No specific limitation is made in this embodiment;

[0054] The population distribution information collection unit obtains the population distribution information of each sub-monitoring area in real time through census data, mobile operator base station data, and social media positioning information;

[0055] The infrastructure information collection unit collects and collates different types of building information of the sub-monitoring area through the infrastructure management files of government departments, geographical information systems, and databases of related industries on the Internet;

[0056] The disaster data information collection unit is used to obtain various types of disaster monitoring data in real time, such as seismic wave data of the seismic monitoring network, water level and flow data of the monitoring station, displacement and deformation data of geological disaster monitoring points, etc.; at the same time, it collects records of historical disaster events, including information such as the time, location, type, intensity, and losses caused by the disaster;

[0057] The data preprocessing module is used to convert the data collected from different sources and in different formats into a unified data format, remove noise data, outliers and duplicate data, and perform normalization processing on the data;

[0058] The disaster impact assessment module is used to establish mathematical models for the building damage level assessment coefficient, the casualty risk coefficient, the property loss coefficient, and the comprehensive impact coefficient based on the preprocessed data, and calculate the building damage level assessment coefficient value, the casualty risk coefficient value, the property loss coefficient, and the comprehensive impact coefficient value of the sub-monitoring area;

[0059] Specifically in this embodiment, the mathematical model of the building damage level assessment coefficient is specifically expressed as:

[0060]

[0061]

[0062] Among them, P(D|PGA) is the building damage probability, PGA is the peak ground acceleration of the earthquake, PGA min and PGA max are the acceleration thresholds for the building to start to be damaged and completely damaged, θ and k are constants, BD is the building damage level assessment coefficient, 1 represents slight damage, 2 represents moderate damage, 3 represents severe damage, 4 represents collapse, and P1, P2, P3, P4 are the damage probability thresholds for dividing different damage levels;

[0063] Specifically in this embodiment, θ and k are parameters related to the building structure type, which are determined by statistical analysis of earthquake damage tests or historical earthquake damage data of buildings of different structure types. The specific acquisition method is as follows:

[0064] Collect a large amount of damage data of buildings of different structure types in historical earthquakes through the disaster data information acquisition module. These data cover the basic information of the earthquake such as the earthquake occurrence time, location, magnitude, ground motion parameters, etc., the structure type of the building, and the geological conditions of the area where the building is located, etc., and divide the damage degree of the building into four levels: slight damage, moderate damage, severe damage, and collapse according to certain standards. For example, collect the damage situations of various types of buildings such as brick-concrete structures and frame structures in multiple earthquakes that occurred in a certain area in the past few decades, including the specific location of each building, the PGA value during the earthquake estimated through seismic station records or ground motion attenuation relationships, the damage level of the building, and other detailed information;

[0065] Sort and classify the collected historical earthquake damage data. Classify them according to the structural types of buildings, and group the building data of the same structural type into one group. For each group of data, further divide it into intervals according to the magnitude of the peak ground acceleration of the earthquake. For example, divide it into intervals such as PGA < 0.1g, 0.1g ≤ PGA < 0.2g, 0.2g ≤ PGA < 0.3g, etc., where g is the acceleration due to gravity. Count the number of buildings with different damage degrees within each PGA interval and the total number of buildings within that interval;

[0066] According to the number of damaged buildings and the total number of buildings within different PGA intervals obtained from the statistics, calculate the damage probability of buildings within each PGA interval. Taking PGA as the independent variable and the damage probability as the dependent variable, respectively fit the data for each group of structural type data, and fit the data into the model to determine the θ and k parameter values corresponding to each structural type of building.

[0067] It should be specifically noted in this embodiment that the specific determination methods of the above P1, P2, P3, and P4 are as follows:

[0068] Collect a large amount of historical earthquake damage data of buildings, including the magnitude, intensity, and ground motion parameters of the earthquake, the detailed information of the buildings such as the structural type, construction year, number of floors, and usage function, and the corresponding building damage grades. Classify them according to slight damage, moderate damage, severe damage, and collapse. For example, obtain the relevant data of various buildings in multiple earthquakes that occurred in different regions in the past few decades from channels such as earthquake disaster databases, research reports of scientific research institutions, and disaster records of government departments. Classify and organize these data according to factors such as building structural types for subsequent analysis.

[0069] For each type of building, such as brick-concrete structure residential buildings, frame structure office buildings, etc., calculate the proportion of the damage degree of this type of building when the building damage probability reaches a certain value. For example, count the proportion of the number of times of slight damage of brick-concrete structure residential buildings to the total number of times when the building damage probability is a certain value. If this proportion is greater than 50%, determine this building damage probability value as P1; when the proportion of the number of times of moderate damage of brick-concrete structure residential buildings to the total number of times is greater than 50%, determine this building damage probability value as P2. Similarly, determine P3 and P4;

[0070] It should be specifically noted in this embodiment that the mathematical model of the casualty risk coefficient is specifically expressed as:

[0071] P(C|BD ij ,P ij ) = P injury (BD ij ) × P death (BDij ) × P presence (P ij );

[0072]

[0073] where P(C|BD ij , P ij ) is the probability of casualties, N(C) is the risk coefficient of casualties, and P injury (BD ij ) is the probability of people being injured under the building damage level BD, and P death (BD ij ) is the probability of people dying under the building damage level BD; P presence (P ij ) is the probability of a person being in the j-th floor of the i-th building, u is the number of buildings, v is the number of floors in each building, and N ij is the number of people on the j-th floor of the i-th building.

[0074] It should be specifically noted in this embodiment that P injury (BD ij ) and P death (BD ij ) are obtained as follows:

[0075] Collect a large amount of historical earthquake casualty data of different regions, different magnitudes, and different building types in historical earthquakes through the disaster data information acquisition module. These data cover the basic information of the earthquake such as the earthquake occurrence time, location, magnitude, ground motion parameters, etc., the structural type of the building, and the geological conditions of the area where the building is located. The damage degree of the building is divided into four grades: slight damage, moderate damage, severe damage, and collapse according to certain standards. Classify and sort the collected data according to the building damage level, group the buildings with the same damage level and their corresponding casualty data together, and further subdivide according to the structural type and number of floors of the building within each damage level group to analyze the influence of different factors on the probability of casualties. For each damage level group, calculate the probability of people being injured and the probability of people dying. The specific calculation method is as follows:

[0076]

[0077] It should be specifically noted in this embodiment that the mathematical model of the property loss coefficient is specifically expressed as:

[0078]

[0079] where BV is the property loss coefficient, ρ BDρ is the loss ratio of the asset value of this type of building under the building damage level BD, w is the number of building components, C k is the cost per unit volume of the k-th component, A k is the volume of the k-th component, d k is the depreciation rate of the k-th component, and t is the service life of the building;

[0080] It should be specifically noted in this embodiment that ρ BD is obtained based on the average asset value loss ratio of this type of building under the damage level BD in the statistical historical data, so it is not specifically limited in this embodiment;

[0081] It should be specifically noted in this embodiment that the mathematical model of the comprehensive influence coefficient is specifically expressed as:

[0082]

[0083] Among them, is the comprehensive influence coefficient, N(C) is the risk coefficient of casualties, and BV is the property loss coefficient;

[0084] The rescue resource allocation module is used to rank the sub-monitoring areas according to the comprehensive influence coefficient value. The sub-monitoring area with a larger comprehensive influence coefficient value has a higher priority, and rescue is carried out on the sub-monitoring area with a higher priority according to the priority ranking;

[0085] The visualization feedback module is used to feedback the comprehensive influence coefficient value to the user information terminal;

[0086] It should be specifically noted in this embodiment that the comprehensive influence coefficient value simultaneously displays basic information such as topography, population distribution, and infrastructure and disaster impact information through the method of map layer superposition, and marks the priority with the depth of color. The area with a darker color has a higher priority;

[0087] Secondly: In the attached drawings of the disclosed embodiments of the present invention, only the structures related to the disclosed embodiments are involved. For other structures, reference can be made to the general design. Without conflict, the same embodiment and different embodiments of the present invention can be combined with each other;

[0088] Finally: The above are only the preferred embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A disaster distribution and impact analysis platform, characterized in that, Including: System central processor module, system operation database, user information terminal, disaster area division module, disaster data information acquisition module, data preprocessing module, disaster impact assessment module, rescue resource allocation module, and visualization feedback module; The system operation database includes all data texts of the disaster distribution and impact analysis platform, and collects information texts output by each module in real time. The system central processor module is used to control the information text instructions output by each module. The user information terminal is a device for receiving the information output of the risk early warning monitoring system of the disaster distribution and impact analysis platform; The disaster area division module is used to record the disaster area as the target monitoring area, divide the target monitoring area into each sub-monitoring area according to the number of buildings, and sequentially mark them as 1, 2, 3, … i …, n; The disaster data information acquisition module includes a geographical environment information acquisition unit, a population distribution information acquisition unit, an infrastructure information acquisition unit, and a disaster data information acquisition unit, and is used to collect data for the sub-areas; The data preprocessing module is used to convert the collected data from different sources and different formats into a unified data format, remove noise data, outliers, and duplicate data, and perform normalization processing on the data; The disaster impact assessment module is used to establish mathematical models for the building damage level assessment coefficient, the personnel casualty risk coefficient, the property loss coefficient, and the comprehensive impact coefficient based on the preprocessed data, and calculate the building damage level assessment coefficient value, the personnel casualty risk coefficient value, the property loss coefficient, and the comprehensive impact coefficient value of the sub-monitoring area; The rescue resource allocation module is used to rank the sub-monitoring areas according to the comprehensive impact coefficient value. The sub-monitoring area with a larger comprehensive impact coefficient value has a higher priority, and rescue is carried out for the sub-monitoring area with a higher priority according to the priority ranking; The visualization feedback module is used to feedback the comprehensive impact coefficient value to the user information terminal.

2. The disaster distribution and impact analysis platform according to claim 1, characterized in that: The mathematical model of the building damage level assessment coefficient is specifically expressed as: Among them, P(D|PGA) is the building damage probability, PGA is the peak ground acceleration of the earthquake, PGA min and PGA max are the acceleration thresholds for the start and complete damage of the building, θ and k are constants, BD is the building damage level evaluation coefficient, 1 represents slight damage, 2 represents moderate damage, 3 represents severe damage, 4 represents collapse, and P1, P2, P3, and P4 are the damage probability thresholds for dividing different damage levels.

3. The disaster distribution and impact analysis platform according to claim 1, wherein: The mathematical model of the personnel casualty risk coefficient is specifically expressed as: P(C|BD ij , P ij ) = P injury (BD ij ) × P death (BD ij ) × P presence (P ij ); where P(C|BD ij , P ij ) is the probability of casualties, N(C) is the casualty risk coefficient, P injury (BD ij ) is the probability of people being injured under the building damage level BD of this type, P death (BD ij ) is the probability of people dying under the building damage level BD of this type; P presence (P ij ) is the probability of people being on the j-th floor of the i-th building, u is the number of buildings, v is the number of floors in each building, N ij is the number of people on the j-th floor of the i-th building.

4. A disaster distribution and impact analysis platform according to claim 1, characterized in that: The mathematical model of the property loss coefficient is specifically expressed as: Among them, BV is the property loss coefficient, ρ BD is the loss ratio of the asset value of this type of building under the building damage level BD, w is the number of building components, C k is the unit volume cost of the k-th component, A k is the volume of the k-th component, d k is the depreciation rate of the k-th component, and t is the service life of the building.

5. The disaster distribution and impact analysis platform according to claim 1, wherein: The mathematical model of the comprehensive impact coefficient is specifically expressed as: Among them, is the comprehensive influence coefficient, N(C) is the risk coefficient of casualties, and BV is the property loss coefficient.

6. The disaster distribution and impact analysis platform according to claim 1, wherein: The specific acquisition methods of the θ and k are as follows: Collect the damage data of buildings of different structural types in a large number of historical earthquakes through the disaster data information acquisition module, organize and classify the collected historical earthquake damage data, classify it according to the structural type of the building, group the building data of the same structural type into one group, for each group of data, divide the interval according to the magnitude of the peak ground acceleration of the earthquake, count the number of buildings with different damage degrees in each PGA interval and the total number of buildings in this interval, according to the damage number and total number of buildings in different PGA intervals obtained by statistics, calculate the damage probability of buildings in each PGA interval, take PGA as the independent variable and the damage probability as the dependent variable, and fit each group of data of the structural type respectively, and fit the data into In the model, the θ and k parameter values corresponding to each structural type of building are determined.

7. The disaster distribution and impact analysis platform according to claim 1, characterized in that: The said P injury (BD ij ) and P death (BD ij ) are obtained as follows: Collect a large amount of historical earthquake casualty data in different regions, with different magnitudes, and different building types during historical earthquakes through the disaster data information acquisition module. Divide them into four levels according to the damage degree of the buildings: slight damage, moderate damage, severe damage, and collapse. Classify and organize the collected data according to the building damage level, and group the buildings with the same damage level and their corresponding personnel casualty data. Within each damage level group, further subdivide according to factors such as the building structure type and the number of floors, and analyze the influence of different factors on the personnel casualty probability. For each damage level group, calculate the personnel injury probability and the personnel death probability. The specific calculation methods are as follows:

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