Earthquake emergency information rapid visualization method and system

Abstract

The invention relates to the technical field of data visualization, in particular to an earthquake emergency information rapid visualization method and system. The method comprises the following steps: acquiring earthquake monitoring real-time data; based on earthquake monitoring real-time data grid division, earthquake region population density distribution and key facility positions are generated, and an earthquake region sensitivity matrix is generated; dynamically simulating a seismic wave propagation process based on the seismic monitoring real-time data and the seismic region sensitivity matrix, and generating fluctuation propagation time sequence data; and identifying epicenter topography and landform vector data of the earthquake monitoring real-time data, and respectively evaluating topographic relief and geological stability to obtain earthquake topographic relief index data and earthquake geological stability score data. According to the invention, rapid and accurate assessment and visualization of earthquake damage distribution are realized through a data visualization technology, so that the earthquake emergency response efficiency and the disaster rescue decision scientificity are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data visualization, and particularly to a method and system for rapid visualization of earthquake emergency information. Background Art

[0002] In the early stage, simple statistical analysis methods, rule bases, classical accidental error processing models, data clustering and other methods were mainly used for extraction and verification of partial data. However, most of these methods are aimed at the characteristics of problems in specific application fields, and there is little research on data extraction technology specifically for earthquake emergency information. At present, earthquake emergency information visualization is developing towards platformization and intelligence. For example, the earthquake product and information visualization platform uses big data analysis and various data visualization analysis means, and through flexible Portal portal technology, realizes multi-dimensional business analysis views. Its visualization display module uses two-dimensional GIS technology and HTML5 and other technologies to classify and visually display various earthquake information. However, the previous simulation of earthquake wave propagation did not well combine the actual geographical and population and other factors in the earthquake area. The earthquake wave is affected by various factors such as terrain and geological conditions during the propagation process, while the traditional method only simulates based on a simple theoretical model and does not dynamically consider these complex actual conditions, resulting in an error between the simulation result and the actual earthquake wave propagation situation. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide a method and system for rapid visualization of earthquake emergency information to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for rapid visualization of earthquake emergency information includes the following steps: Step S1: Obtain real-time earthquake monitoring data; divide the population density distribution and key facility locations in the earthquake area based on the real-time earthquake monitoring data grid to generate a sensitivity matrix of the earthquake area; dynamically simulate the earthquake wave propagation process based on the real-time earthquake monitoring data and the sensitivity matrix of the earthquake area to generate wave propagation time series data; Step S2: Identify the epicenter topographic and geomorphic vector data of the real-time earthquake monitoring data, and respectively evaluate the terrain undulation and geological stability to obtain earthquake terrain undulation index data and earthquake geological stability scoring data; perform spatial overlay analysis on the earthquake terrain undulation index data and the earthquake geological stability scoring data to generate a distribution map of high-risk earthquake damage areas; Step S3: Retrieve earthquake cases in a preset historical earthquake database based on the real-time earthquake monitoring data to construct a set of similar earthquake events; Step S4: Predict the earthquake damage distribution probability in the target area based on the sensitivity matrix of the earthquake area, the distribution map of high-risk earthquake damage areas and the set of similar earthquake events to generate a preliminary earthquake damage level evaluation result; Step S5: Collect the building damage status data at the epicenter based on the real-time earthquake monitoring data; evaluate the disaster level of the target area based on the preliminary earthquake damage level assessment results and the building damage status data, and generate an accurate earthquake damage distribution map. Step S6: Perform visual processing of the disaster situation information on the wave propagation time series data, the preliminary earthquake damage level assessment results, and the accurate earthquake damage distribution map, and perform layer combination matching for each user role based on the results of the visual processing of the disaster situation information to obtain a role-based display scheme.

[0005] Through step S1, the present invention divides the population density distribution and the locations of key facilities in the earthquake area based on the real-time data grid of earthquake monitoring, and generates a sensitivity matrix for the earthquake area. At the same time, based on the real-time data of earthquake monitoring and the sensitivity matrix of the earthquake area, the process of earthquake wave propagation is dynamically simulated to generate time-series data of wave propagation. This process realizes the accurate characterization of the sensitivity of the earthquake area and the accurate simulation of the dynamic process of earthquake wave propagation. The sensitivity matrix of the earthquake area can clearly reflect the population density and the distribution of key facilities in different regions of the earthquake area, providing key regional sensitivity basic data for subsequent earthquake damage assessment. The time-series data of wave propagation details the propagation characteristics of earthquake waves at different times and spatial positions, including information such as amplitude and frequency. This synergistic effect enables full consideration of the dynamic characteristics of earthquake wave propagation and the sensitivity differences of the earthquake area in subsequent earthquake damage prediction and assessment, thereby improving the accuracy and reliability of earthquake damage prediction. In step S2, the vector data of the epicenter topography and geomorphology of the real-time earthquake monitoring data is identified, and the terrain undulation and geological stability are respectively evaluated to obtain the earthquake terrain undulation index data and the earthquake geological stability scoring data. The earthquake terrain undulation index data and the earthquake geological stability scoring data are subjected to spatial overlay analysis to generate a distribution map of high-risk earthquake damage areas. This process can accurately locate high-risk earthquake damage areas through a detailed analysis of the epicenter topography and geomorphology, combined with two key factors: terrain undulation and geological stability. The earthquake terrain undulation index data reflects the complexity of the terrain, such as the impact of terrain features such as steep slopes and valleys on earthquake wave propagation and earthquake disasters; the earthquake geological stability scoring data evaluates the stability of the geological structure in different regions, such as the susceptibility of geological conditions such as loose soil layers and fault zones to earthquake disasters. Spatial overlay analysis comprehensively considers these two data, and the generated distribution map of high-risk earthquake damage areas can intuitively show which areas are more likely to suffer severe damage during an earthquake, providing clear guidance on high-risk areas for earthquake emergency rescue and disaster prevention, and helping to deploy rescue forces in advance and take targeted protective measures. Step S3 retrieves earthquake cases in the preset historical earthquake database based on the real-time data of earthquake monitoring to construct a set of similar earthquake events. This process retrieves the historical earthquake database and screens out historical earthquake cases similar to the current earthquake event in terms of magnitude, focal depth, earthquake wave propagation characteristics, etc., to construct a set of similar earthquake events. The construction of the set of similar earthquake events provides rich historical data support for earthquake damage prediction. By analyzing similar earthquake events, the distribution law and disaster degree of earthquake damage in different regions under similar earthquake conditions can be understood. These historical data, as a reference basis, can make the process of predicting the earthquake damage distribution probability of the target area based on the sensitivity matrix of the earthquake area, the distribution map of high-risk earthquake damage areas, and the set of similar earthquake events more scientific and reasonable. The accuracy and credibility of the preliminary earthquake damage level assessment result generated in step S4 are significantly improved due to the support of the set of similar earthquake events, providing a more reliable scientific basis for subsequent disaster assessment and emergency decision-making.In step S5, based on the real-time earthquake monitoring data, the building damage status data of the epicenter is collected. Combining the preliminary earthquake damage level assessment results generated in step S4 and the building damage status data, the disaster level of the target area is evaluated, and an accurate earthquake damage distribution map is generated. This process corrects and refines the preliminary earthquake damage level assessment results by collecting the actual damage status data of the buildings in the epicenter, such as the crack width and collapse degree of the buildings. The preliminary earthquake damage level assessment results are based on model predictions and historical data, while the building damage status data is the earthquake damage situation actually observed on site. Combining the two can more accurately evaluate the disaster level of the target area. The accurate earthquake damage distribution map can clearly show the earthquake damage degree of different regions, including detailed information such as building damage and casualties. This precise disaster assessment helps to reasonably allocate rescue resources, prioritize the rescue of the most severely affected areas, improve the rescue efficiency, and reduce casualties and property losses during earthquake emergency rescue. In step S6, the wave propagation time series data, the preliminary earthquake damage level assessment results, and the accurate earthquake damage distribution map are subjected to disaster situation information visualization processing, and based on the results of the disaster situation information visualization processing, the layer combination matching of each user role is performed to obtain a role-based display scheme. The disaster situation information visualization processing can display complex earthquake monitoring data, earthquake damage assessment results, etc. in the form of intuitive graphics, images, charts, etc., so that different user roles can quickly understand and master the disaster situation information. The role-based display scheme customizes the display of the visualization information according to the needs of different user roles, such as earthquake rescue personnel and affected people. For example, rescue personnel can see the detailed distribution of the affected areas and rescue path information; they can also see the overall disaster situation overview and resource allocation requirements; affected people can see information such as the location of the shelter and the rescue progress. This efficient information transmission method can ensure that during the earthquake emergency process, each user role can timely obtain the most useful information for themselves, improving the overall efficiency and coordination of earthquake emergency response.

[0006] Preferably, the present invention also provides an earthquake emergency information rapid visualization system for executing the above-mentioned earthquake emergency information rapid visualization method. The earthquake emergency information rapid visualization system includes: An earthquake data monitoring module, which is used to obtain real-time earthquake monitoring data; based on the real-time earthquake monitoring data, the population density distribution and key facility locations in the earthquake area are grid-divided to generate a sensitivity matrix of the earthquake area; based on the real-time earthquake monitoring data and the sensitivity matrix of the earthquake area, the earthquake wave propagation process is dynamically simulated to generate wave propagation time series data; An earthquake terrain detection module, which is used to identify the epicenter terrain and geomorphic vector data of the real-time earthquake monitoring data, and respectively evaluate the terrain undulation and geological stability to obtain earthquake terrain undulation index data and earthquake geological stability scoring data; perform spatial overlay analysis on the earthquake terrain undulation index data and the earthquake geological stability scoring data to generate a high-risk earthquake damage area distribution map; An earthquake event set construction module, which is used to retrieve earthquake cases in a preset historical earthquake database based on real-time earthquake monitoring data and construct a similar earthquake event set; A seismic damage level assessment module, which is used to predict the seismic damage distribution probability of a target area based on a seismic area sensitivity matrix, a seismic damage high-risk area distribution map, and a similar earthquake event set, and generate a preliminary seismic damage level assessment result; A seismic damage distribution map division module, which is used to collect building damage status data at the epicenter based on real-time earthquake monitoring data; evaluate the disaster level of the target area based on the preliminary seismic damage level assessment result and the building damage status data, and generate an accurate seismic damage distribution map; An emergency visualization module, which is used to visually process disaster situation information for the wave propagation time series data, the preliminary seismic damage level assessment result, and the accurate seismic damage distribution map, and perform layer combination matching for each user role based on the visually processed result of the disaster situation information to obtain a role-based display scheme.

[0007] This system of the present invention obtains real-time data through a seismic data monitoring module and generates a seismic area sensitivity matrix and wave propagation time series data. The seismic terrain detection module accurately locates the high-risk areas of seismic damage. The earthquake event set construction module provides historical data support. The seismic damage level assessment module and the seismic damage distribution map division module successively perform seismic damage prediction and accurate assessment. The emergency visualization module realizes the visualization of disaster situation information and role-based display. Through the collaborative effect of the whole process, it realizes the rapid and efficient conversion from earthquake monitoring to accurate assessment and visualization of disaster situation, provides comprehensive, accurate, and timely information support for earthquake emergency rescue and decision-making, and significantly improves the scientificity and effectiveness of earthquake emergency response. Description of the Drawings

[0008] By reading the detailed description of the non-restrictive embodiments with reference to the following drawings, other features, objectives, and advantages of the present invention will become more obvious: Figure 1 It is a schematic flowchart of the steps of a method for rapid visualization of earthquake emergency information according to the present invention; Figure 2 For Figure 1 a detailed schematic flowchart of step S1 in Detailed Embodiments

[0009] The technical method of the present invention will be clearly and completely described below with reference to the drawings. Obviously, the described embodiments are a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts fall within the protection scope of the present invention.

[0010] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0011] It should be understood that although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0012] To achieve the above object, please refer to Figures 1 to 2 , the present invention provides a method for rapid visualization of earthquake emergency information, and the method includes the following steps: Step S1: Obtain real-time earthquake monitoring data; based on the real-time earthquake monitoring data, divide the population density distribution and the locations of key facilities in the earthquake area into grids to generate a sensitivity matrix for the earthquake area; based on the real-time earthquake monitoring data and the sensitivity matrix of the earthquake area, dynamically simulate the process of earthquake wave propagation to generate wave propagation time series data; Step S2: Identify the vector data of the epicenter topography and geomorphology in the real-time earthquake monitoring data, and respectively evaluate the terrain undulation and geological stability to obtain earthquake terrain undulation index data and earthquake geological stability scoring data; perform a spatial overlay analysis on the earthquake terrain undulation index data and the earthquake geological stability scoring data to generate a distribution map of high-risk earthquake disaster areas; Step S3: Retrieve earthquake cases in a preset historical earthquake database based on the real-time earthquake monitoring data to construct a set of similar earthquake events; Step S4: Predict the earthquake disaster distribution probability in the target area based on the sensitivity matrix of the earthquake area, the distribution map of high-risk earthquake disaster areas, and the set of similar earthquake events to generate a preliminary earthquake disaster level assessment result; Step S5: Collect the building damage status data of the epicenter based on the real-time earthquake monitoring data; evaluate the disaster level of the target area based on the preliminary earthquake disaster level assessment result and the building damage status data to generate an accurate earthquake disaster distribution map; Step S6: Visualize the disaster situation information with the fluctuation propagation timing data, the preliminary earthquake damage level assessment results, and the accurate earthquake damage distribution map, and perform layer combination matching for each user role based on the visualized disaster situation information processing results to obtain a role-based display scheme.

[0013] In the embodiment of the present invention, referring to Figure 1 As shown, it is a schematic diagram of the step flow of a rapid visualization method for earthquake emergency information of the present invention. In this example, the rapid visualization method for earthquake emergency information includes the following steps: Step S1: Obtain real-time earthquake monitoring data; divide the population density distribution and key facility locations in the earthquake area based on the real-time earthquake monitoring data grid to generate a sensitivity matrix for the earthquake area; dynamically simulate the earthquake wave propagation process based on the real-time earthquake monitoring data and the sensitivity matrix of the earthquake area to generate fluctuation propagation timing data; In the embodiments of the present invention, first, real-time seismic monitoring data is obtained through a high-precision seismic monitoring network. The seismic monitoring network consists of multiple seismic monitoring stations distributed at different geographical locations. Each station is equipped with a three-component seismograph, which can collect the vibration information of the longitudinal wave (P-wave), transverse wave (S-wave), and surface wave of seismic waves in real time, including parameters such as amplitude, frequency, and arrival time. Based on the timestamp, the seismic data collected by each monitoring station is sampled at a preset time interval (for example, every 0.1 seconds) to form a real-time seismic monitoring dataset containing a time series. Each data point in the dataset contains specific seismic wave vibration parameters and the location information (longitude, latitude, and altitude) of the corresponding monitoring station. Subsequently, based on the real-time seismic monitoring data, grid division of the population density distribution and key facility locations in the earthquake area is carried out. Taking the earthquake epicenter as the origin, the earthquake area is divided into multiple regular grid cells according to a preset grid size (for example, 1 km × 1 km). Using geographic information system (GIS) technology, combined with the population statistics data and the geographical coordinate information of key facilities in the earthquake area, the population quantity and the location information of key facilities are mapped into each grid cell. For the population density distribution, the population density value of each grid cell is calculated according to the population quantity in the grid cell; for the location of key facilities, the grid cell number where each key facility is located and its specific location coordinates in the grid cell are recorded. These information are integrated to generate a sensitivity matrix for the earthquake area. The rows and columns of the matrix correspond to the numbers of the grid cells in the earthquake area respectively. Each element value in the matrix is obtained by adding the population density value of the grid cell and the weight value of the key facility (the weight coefficient is preset according to the importance of the facility), so as to quantify the sensitivity of each grid cell. Then, based on the real-time seismic monitoring data and the sensitivity matrix of the earthquake area, the process of seismic wave propagation is dynamically simulated. The finite difference method is used to numerically solve the seismic wave propagation equation. Taking the initial vibration parameters of the seismic wave in the real-time seismic monitoring data as the initial conditions of the simulation, the earthquake area is divided into discrete calculation grids consistent with the grid division. According to the propagation speed of seismic waves in different media (the geological medium parameters of different regions in the earthquake area are obtained through geological exploration data, such as rock density, elastic modulus, etc., and then the propagation speed of seismic waves in each medium is calculated) and the sensitivity of each grid cell in the sensitivity matrix of the earthquake area, corresponding boundary conditions and damping coefficients are set. During the simulation process, the propagation state of seismic waves in each calculation grid is gradually calculated at a preset time step (for example, 0.01 seconds), including information such as the amplitude and phase of the wave field. Finally, time series data of wave propagation is generated, which details the propagation of seismic waves at different positions and times in the earthquake area, providing basic data support for the subsequent visualization of earthquake emergency information.

[0014] Step S2: Identify the vector data of the epicentral topography and geomorphology of the real-time earthquake monitoring data, and separately evaluate the terrain undulation and geological stability to obtain the earthquake terrain undulation index data and the earthquake geological stability scoring data; perform a spatial overlay analysis on the earthquake terrain undulation index data and the earthquake geological stability scoring data to generate a distribution map of high-risk earthquake disaster areas; In the embodiment of the present invention, the vector data of the topography and geomorphology in the epicentral area is extracted from the real-time earthquake monitoring data. Using the digital elevation model (DEM) data source and combining the location information of the earthquake monitoring stations, the terrain area within a certain range around the epicenter is determined. Through the vectorization processing function of the geographic information system (GIS) software, the terrain and geomorphology data of this area is converted into a vector format, including vector elements such as contour lines, slopes, and aspect directions. Among them, the interval of the contour lines is set to 5 meters, and the calculation accuracy of the slope and aspect direction is 0.1 degree, generating an epicentral terrain and geomorphology vector data set. The data set contains detailed information such as the spatial position coordinates, elevation values, slope values, and aspect direction values of each vector element. Then, the terrain undulation and geological stability are evaluated separately. For the evaluation of terrain undulation, the terrain undulation degree calculation formula is used, that is, the terrain undulation degree is equal to the elevation difference between the highest point and the lowest point in the area divided by the planar area of the area. Taking a 1 square kilometer × 1 square kilometer grid as the evaluation unit, calculate the terrain undulation degree of each grid unit to obtain the earthquake terrain undulation index data. Each grid unit in the data corresponds to a terrain undulation degree value, and the value range is from 0 to 1000 m / km². For the evaluation of geological stability, based on the geological survey data, including parameters such as the shear strength of the rock and soil mass, lithology classification, and groundwater level, use the geological stability evaluation model (based on the pre-set geological parameter weights and evaluation criteria) to score each grid unit. The scoring range is from 0 to 100 points, and the higher the score, the worse the geological stability, obtaining the earthquake geological stability scoring data. Each grid unit in the data corresponds to a geological stability scoring value. Then, perform a spatial overlay analysis on the earthquake terrain undulation index data and the earthquake geological stability scoring data. In the GIS software, register the two sets of data in the same geographic coordinate system, and use the grid unit as the basic analysis unit to perform an overlay analysis using the weighted summation method. Set the weight of the terrain undulation index to 0.4 and the weight of the geological stability score to 0.6, calculate the comprehensive risk value of each grid unit, and the comprehensive risk value range is from 0 to 100 points. According to the magnitude of the comprehensive risk value, the earthquake area is divided into different risk level areas. For example, the comprehensive risk value greater than 80 points is a high-risk area, 60 to 80 points is a medium-risk area, and less than 60 points is a low-risk area, generating a distribution map of high-risk earthquake disaster areas. The distribution map uses different colors or legends to identify areas of different risk levels, intuitively showing the spatial distribution of high-risk earthquake disaster areas.

[0015] Step S3: Retrieve earthquake cases in a preset historical earthquake database based on real-time earthquake monitoring data, and construct a set of similar earthquake events; In the embodiment of the present invention, key feature parameters are extracted from real-time earthquake monitoring data, including the magnitude, focal depth, origin time, epicenter location (longitude, latitude) of the earthquake, and waveform characteristics of the main seismic waves (such as the initial arrival time difference between P waves and S waves, amplitude ratio, etc.). These parameters are collected by seismographs in the earthquake monitoring network and obtained through preliminary processing. Among them, the accuracy of the magnitude is 0.1 magnitude, the accuracy of the focal depth is 1 km, the accuracy of the origin time is at the second level, the accuracy of the epicenter location is 0.01 degree, and the extraction accuracy of the waveform feature parameters is 1%. Then, using database retrieval technology, similar earthquake cases are retrieved in the preset historical earthquake database based on these key feature parameters. The historical earthquake database adopts a relational database structure, which contains multiple fields, respectively storing information such as the magnitude, focal depth, origin time, epicenter location, and waveform characteristics of historical earthquakes. During retrieval, the matching threshold for the magnitude is set to ±0.5 magnitude, the matching threshold for the focal depth is ±5 km, the matching range for the origin time is 1 hour before and after, the matching range for the epicenter location is that the longitude and latitude differences are both less than 0.1 degree, and the matching threshold for the waveform feature parameters is that the relative errors of the initial arrival time difference and the amplitude ratio are both less than 10%. Through the above retrieval parameters, historical earthquake events that meet the matching conditions are screened out from the historical earthquake database, and the relevant data of these events are extracted, including the basic information of the earthquake (such as magnitude, focal depth, origin time, epicenter location), earthquake waveform data, and corresponding disaster impact data (such as affected area, number of casualties, economic losses, etc.). These data are integrated and constructed into a set of similar earthquake events. Each event exists in the set of similar earthquake events in the form of an independent record, and the record contains complete earthquake feature parameters and disaster impact data for subsequent analysis and application.

[0016] Step S4: Predict the earthquake disaster distribution probability in the target area based on the seismic area sensitivity matrix, the earthquake disaster high-risk area distribution map, and the set of similar earthquake events, and generate a preliminary earthquake disaster level assessment result; In the embodiments of the present invention, first, the seismic area sensitivity matrix, the distribution map of high-risk earthquake disaster areas, and the data in the similar earthquake event set are integrated and processed. The seismic area sensitivity matrix contains the sensitivity values of each grid unit in the seismic area. The distribution map of high-risk earthquake disaster areas identifies areas with different risk levels. The similar earthquake event set provides the earthquake disaster data of historical earthquakes. Through the Geographic Information System (GIS) technology, the seismic area sensitivity matrix and the distribution map of high-risk earthquake disaster areas are spatially registered according to a unified geographic coordinate system to ensure their spatial consistency. At the same time, the historical earthquake events in the similar earthquake event set that are closest to the characteristic parameters such as the magnitude and focal depth of the target area are extracted, and their earthquake disaster distribution data are recorded. Then, based on the spatial analysis technology, the probability prediction of the earthquake disaster distribution in the target area is carried out. In the GIS software, the sensitivity values of the seismic area sensitivity matrix are used as weight factors to perform weighted processing on the high-risk areas in the distribution map of high-risk earthquake disaster areas. For each grid unit, the frequency of the occurrence of the earthquake disaster level corresponding to the historical earthquake event in the similar earthquake event set is calculated as the earthquake disaster distribution probability of the grid unit. The specific calculation method is as follows: count the number of times the earthquake disaster level (such as slight, moderate, severe) corresponding to each grid unit in the similar earthquake event set appears, and divide it by the total number of similar earthquake events to obtain the earthquake disaster distribution probability value of the grid unit. For example, if a certain grid unit has 3 occurrences of slight earthquake disasters, 4 occurrences of moderate earthquake disasters, and 3 occurrences of severe earthquake disasters in 10 similar earthquake events, then the distribution probabilities of slight, moderate, and severe earthquake disasters are 0.3, 0.4, and 0.3 respectively. According to the calculated earthquake disaster distribution probability and combined with the preset earthquake disaster level division criteria (such as the probability of slight earthquake disaster greater than 0.6 is the slight earthquake disaster level, the probability of moderate earthquake disaster greater than 0.6 is the moderate earthquake disaster level, the probability of severe earthquake disaster greater than 0.6 is the severe earthquake disaster level), the earthquake disaster level of each grid unit in the target area is evaluated. If the earthquake disaster distribution probability of a certain grid unit does not meet the division criteria of a single earthquake disaster level, a comprehensive judgment is made according to the size of the probability value to generate a preliminary earthquake disaster level evaluation result. Finally, the preliminary earthquake disaster level evaluation result is visually displayed in the form of a map, and the areas with different earthquake disaster levels are distinguished by different colors or legends to intuitively present the earthquake disaster distribution situation of the target area.

[0017] Step S5: Collect the building damage status data of the epicenter based on the real-time earthquake monitoring data; evaluate the disaster level of the target area based on the preliminary earthquake disaster level evaluation result and the building damage status data, and generate an accurate earthquake disaster distribution map; In the embodiments of the present invention, the damage state data of buildings in the earthquake epicenter is collected by using UAV remote sensing technology and a ground sensor network. The UAV is equipped with a high-resolution optical camera and a lidar device, and flies at a low altitude along a preset flight path (covering the earthquake epicenter and its surrounding areas) at a speed of 1 m / s, with a flight altitude of 100 m, to capture the external images of the buildings, and the image resolution is not less than 0.1 m / pixel. At the same time, acceleration sensors and displacement sensors in the ground sensor network are installed at key parts of the buildings (such as the roof and the joints of the walls) to collect the vibration acceleration and displacement data of the buildings in real time, with a sampling frequency of 100 Hz and a data recording time length of 10 minutes after the earthquake occurs. The image data captured by the UAV and the vibration data collected by the ground sensors are integrated to form a building damage state data set, and the data set contains information such as the image number of the building, the shooting position coordinates, the peak value of the vibration acceleration, and the maximum displacement. Then, based on the preliminary earthquake damage level assessment results and the building damage state data, the disaster level of the target area is evaluated. The building damage state data is spatially matched with the preliminary earthquake damage level assessment results. Based on the geographical location coordinates of the buildings, the building damage state data is mapped into the grid cells of the preliminary earthquake damage level assessment results. For each building within a grid cell, according to the peak value of the vibration acceleration and the maximum displacement in the building damage state data, combined with the structural type of the building (such as reinforced concrete structure, brick-concrete structure, etc.), the damage degree of the building is evaluated by using a preset building damage assessment standard (for example, when the peak value of the vibration acceleration of a reinforced concrete structure is greater than 0.3g or the maximum displacement exceeds 10 cm, it is severely damaged), and the damage level (slight, medium, severe) of each building is obtained. Then, the damage level of the building is comprehensively analyzed with the preliminary earthquake damage level assessment results. For each grid cell, the distribution of the damage levels of the buildings within the cell is counted, and the proportion of the number of slightly, medium, and severely damaged buildings is calculated. If the proportion of the number of severely damaged buildings in a grid cell exceeds 50%, the disaster level of the grid cell is upgraded by one level; if the proportion of the number of medium-damaged buildings exceeds 70%, the original preliminary earthquake damage level is maintained; if the proportion of the number of slightly damaged buildings exceeds 80%, the disaster level of the grid cell is downgraded by one level. Through the above comprehensive analysis, an accurate earthquake damage distribution map is generated, and different disaster level areas are marked with different colors or legends in the map, accurately reflecting the earthquake damage distribution of the target area.

[0018] Step S6: Visualize the disaster situation information for the wave propagation timing data, the preliminary earthquake damage level assessment results, and the accurate earthquake damage distribution map, and perform combined matching of each user role layer based on the results of the disaster situation information visualization to obtain a role-based display scheme.

[0019] In the embodiments of the present invention, the visualization processing is performed on the time series data of wave propagation. By using the time series visualization tool in the Geographic Information System (GIS) software, information such as seismic wave amplitude, frequency, and propagation time in the time series data of wave propagation is dynamically displayed according to the time sequence and spatial position. The time step is set to 0.1 second, and the amplitude distribution of the seismic wave is represented in the form of contour lines with a contour interval of 0.1 unit amplitude; the frequency distribution is represented in the form of a color cloud map with the color range from blue (low frequency) to red (high frequency); the propagation path and time change of the seismic wave are displayed in the form of an animation with the animation playback speed being 1 / 100 times the real-time seismic wave propagation speed, generating a visualization layer of the time series data of wave propagation. Then, the visualization processing is performed on the preliminary earthquake damage level assessment results. In the GIS software, the grid cells in the preliminary earthquake damage level assessment results are classified according to the earthquake damage level, and the areas with slight, moderate, and severe earthquake damage levels are respectively marked with different colors. The slightly damaged area is represented by light yellow, the moderately damaged area is represented by orange, and the severely damaged area is represented by red. The boundaries of each grid cell are clearly visible, and the size of the grid cell is 1 km × 1 km, generating a visualization layer of the preliminary earthquake damage level assessment results. Next, the visualization processing is performed on the accurate earthquake damage distribution map. The accurate earthquake damage distribution map already contains detailed information on the building damage status. It is directly imported into the GIS software and color-coded according to the disaster level, maintaining the same color scheme as the visualization layer of the preliminary earthquake damage level assessment results. At the same time, detailed information annotations of the buildings are added, including building numbers, damage levels, and specific location coordinates, with the annotation font size being 8 and the color being black, generating a visualization layer of the accurate earthquake damage distribution map. Finally, based on the visualization processing results of the disaster situation information, the layer combinations are matched for each user role. According to the preset user roles (such as earthquake emergency commanders, rescue teams, affected people, etc.), different layer combinations are configured for each role. For earthquake emergency commanders, the visualization layer of the time series data of wave propagation, the visualization layer of the preliminary earthquake damage level assessment results, and the visualization layer of the accurate earthquake damage distribution map are combined to highlight high-risk areas and the locations of key facilities; for rescue teams, the visualization layer of the accurate earthquake damage distribution map and the building damage status annotation layer are combined to highlight the locations and damage levels of damaged buildings; for affected people, the visualization layer of the preliminary earthquake damage level assessment results and the safe area identification layer are combined to highlight safe areas and evacuation routes. Through the layer management function of the GIS software, the layer combinations of different user roles are saved, and corresponding role-based display schemes are generated. Each scheme contains detailed parameters such as layer names, display orders, transparency settings, and annotation information to meet the emergency information needs of different user roles.

[0020] As an embodiment of the present invention, refer to Figure 2 shown in Figure 1The detailed step - by - step process schematic diagram of step S1. In the embodiments of the present invention, step S1 includes the following steps: Step S11: Real - time collect the original seismic waveform data through a distributed seismic monitoring sensor network, and perform filtering calibration and standardization processing to generate real - time seismic monitoring data; Step S12: Adaptive grid - divide the earthquake area based on the real - time seismic monitoring data to obtain earthquake area grid cell data; Step S13: Conduct spatial interpolation analysis of population density based on the earthquake area grid cell data to obtain grid - based population density distribution data; Step S14: Conduct spatial query of the geographical information of key facilities based on the earthquake area grid cell data to obtain grid - based key facility location data; Step S15: Calculate the sensitivity weights for the earthquake area grid cell data based on the grid - based population density distribution data and the grid - based key facility location data to generate an earthquake area sensitivity matrix; Step S16: Numerically simulate the seismic wave propagation mechanism based on the real - time seismic monitoring data and the earthquake area sensitivity matrix to generate wave propagation time - history data.

[0021] In the embodiments of the present invention, the original seismic waveform data collected is subjected to filtering calibration and standardization processing. Digital filtering technology is adopted, and a band-pass filter is used to filter the data. The frequency range of the filter is set from 0.1 Hz to 10 Hz to remove high-frequency noise and low-frequency interference. The filtered data is calibrated through a calibration algorithm. The calibration parameters include the gain, phase delay, and zero drift of the sensor. The calibrated data is further subjected to standardization processing to normalize the amplitude data to the range of 0 to 1, generating real-time seismic monitoring data. The real-time seismic monitoring data is based on timestamps, and each data point contains specific seismic wave vibration parameters and the corresponding monitoring station location information (longitude, latitude, and altitude). Based on the real-time seismic monitoring data, the earthquake area is divided into grids using the adaptive grid division technology. First, the scope of the earthquake area is determined. A circular area with a radius of 100 km centered at the epicenter is used as the earthquake area. According to parameters such as the magnitude, focal depth, and seismic wave propagation speed in the real-time seismic monitoring data, the size and density of the grids are adaptively adjusted. Near the epicenter, the grid size is set to 1 km × 1 km, and as the distance from the epicenter increases, the grid size gradually increases to 5 km × 5 km. Through Geographic Information System (GIS) software, the earthquake area is divided into multiple regular grid cells, each grid cell having a unique number and spatial location information (longitude, latitude, and area), generating earthquake area grid cell data. The spatial interpolation analysis of population density is carried out using the earthquake area grid cell data. First, the population statistics data of the earthquake area is obtained, including the population quantity and geographical boundaries of each administrative region. The population statistics data is imported into the GIS software, and combined with the earthquake area grid cell data, the Kriging interpolation method is used for the spatial interpolation analysis of population density. The parameters of Kriging interpolation are set as the spherical model for the semi-variance model, the search radius is 10 km, and the interpolation accuracy is 0.01 person / km². Through the interpolation analysis, the population density value within each grid cell is calculated, generating grid-based population density distribution data, which contains the population density value and its spatial location information of each grid cell. The spatial query of the geographical information of key facilities is carried out based on the earthquake area grid cell data. First, the geographical information data of key facilities in the earthquake area is collected, including the location coordinates and attribute information of facilities such as hospitals, schools, hydropower stations, and bridges. These data are imported into the GIS software and spatially matched with the earthquake area grid cell data. Through the spatial query function, the grid cell number where each key facility is located is determined, and its specific location coordinates within the grid cell are recorded. Grid-based key facility location data is generated, which contains the number, type, and specific location coordinates of key facilities within each grid cell. Based on the grid-based population density distribution data and the grid-based key facility location data, the sensitivity weight calculation is carried out for the earthquake area grid cell data. First, the weight coefficient of population density is set to 0.6, and the weight coefficient of key facilities is set to 0.4.For each grid cell, calculate its sensitivity weight value using the formula: Sensitivity weight value = Population density value × Population density weight coefficient + Number of key facilities × Key facility weight coefficient. For example, if the population density of a certain grid cell is 100 people per square kilometer and the number of key facilities is 2, then the sensitivity weight value of this grid cell is (100 × 0.6) + (2 × 0.4) = 60.8. Assign the calculated sensitivity weight value to each grid cell to generate a seismic area sensitivity matrix. The rows and columns of the matrix correspond to the numbers of the grid cells in the seismic area, and each element value in the matrix is the sensitivity weight value of the corresponding grid cell. Based on the real-time earthquake monitoring data and the seismic area sensitivity matrix, use numerical simulation technology to simulate the seismic wave propagation mechanism. Numerically solve the seismic wave propagation equation using the finite difference method, and divide the seismic area into discrete computational grids consistent with the grid division. Use the initial vibration parameters of the seismic wave in the real-time earthquake monitoring data as the initial conditions for the simulation, and set the propagation speed of the seismic wave in different media (obtain the geological medium parameters of different regions in the seismic area through geological exploration data, such as rock density, elastic modulus, etc., and then calculate the propagation speed of the seismic wave in each medium). At the same time, according to the sensitivity weight values of each grid cell in the seismic area sensitivity matrix, set the corresponding damping coefficient. The range of the damping coefficient is from 0.1 to 1.0. The higher the sensitivity weight value, the smaller the damping coefficient. During the simulation process, calculate the propagation state of the seismic wave in each computational grid step by step according to the preset time step (0.01 seconds), including information such as the amplitude and phase of the wave field, and finally generate wave propagation time series data, which details the propagation of the seismic wave at different positions and times in the seismic area.

[0022] Preferably, in step S2, identify the epicenter topographic and geomorphic vector data of the real-time earthquake monitoring data, and respectively evaluate the terrain undulation and geological stability, including: Retrieve multi-source remote sensing images based on the real-time earthquake monitoring data, and perform geometric correction and orthorectification to obtain the seismic area topographic image data; Extract the topographic boundary line based on the seismic area topographic image data, and construct a triangular network model of the seismic area to obtain the digital elevation model of the seismic area; Calculate the slope, aspect, and curvature of the digital elevation model of the seismic area, and perform enhancement processing on the calculation results to obtain enhanced topographic feature data; Statistically analyze the local elevation changes based on the digital elevation model of the seismic area, and perform standardization and grading processing to obtain seismic terrain undulation index data; Spatially match the enhanced topographic feature data with a preset geological structure database to obtain the geological structure distribution map of the seismic area; perform historical earthquake response analysis on the geological structure distribution map of the seismic area to obtain the geological historical stability score; Evaluate the fault activity based on the real-time earthquake monitoring data and the geological historical stability score to obtain the seismic geological stability score data.

[0023] In the embodiments of the present invention, based on the epicenter location and the affected area information in the real-time earthquake monitoring data, high-resolution optical images and radar images covering the earthquake area are retrieved from a multi-source remote sensing image database. The resolution of the optical images is not lower than 0.5 meters per pixel, and the resolution of the radar images is not lower than 1 meter per pixel. Using geographic information system (GIS) software and image processing software, geometric correction is performed on the retrieved remote sensing images. The polynomial transformation model is adopted, and the accuracy of the control points is set to 0.1 pixel. The geometric accuracy error of the corrected images is controlled within 1 pixel. Then orthorectification is carried out. Based on the digital elevation model (DEM) data, the projection method is set to UTM projection, and the ellipsoid model is WGS-84. The accuracy of the corrected images in the horizontal direction reaches 1 meter, obtaining the earthquake area terrain image data, which includes the pixel values, spatial resolution, projection information, and geographic coordinate information of the images. Using the earthquake area terrain image data, the terrain boundary line is extracted through an edge detection algorithm. The Canny edge detection algorithm is adopted, with the low threshold set to 50 and the high threshold set to 150, and a clear terrain boundary line is extracted. Then, based on the extracted terrain boundary line and the elevation information in the terrain image data, the Delaunay triangulation method is used to construct the earthquake area triangular network model. The minimum angle of the triangular network is set to not less than 20 degrees, and the maximum side length does not exceed 50 meters to ensure the accuracy and stability of the model. Through interpolation calculation, a digital elevation model (DEM) of the earthquake area is generated. The grid spacing of the DEM is set to 1 meter × 1 meter, and the elevation accuracy reaches 0.1 meter. The model includes the elevation values and geographic coordinate information of each grid point. Based on the digital elevation model of the earthquake area, the terrain analysis tools in the GIS software are used to calculate the slope, aspect, and curvature. The Horn algorithm with a 3×3 neighborhood is used for slope calculation, and the calculation accuracy of the slope is 0.1 degree; the 8-direction coding method is used for aspect calculation, and the calculation accuracy of the aspect is 1 degree; the quadratic polynomial fitting method is used for curvature calculation, and the calculation accuracy of the curvature is 0.01. Enhancement processing is performed on the calculated slope, aspect, and curvature data. The histogram equalization method is adopted to enhance the contrast and readability of the data, obtaining enhanced terrain feature data, which includes the slope, aspect, and curvature values of each grid point and their enhanced pixel values. Local elevation change statistical analysis is performed on the digital elevation model of the earthquake area. The moving window method is adopted, with the window size set to 3×3 grids, and the elevation standard deviation within each window is calculated. The calculated elevation standard deviation data is normalized. The Z-score normalization method is adopted to convert the data into normalized data with a mean of 0 and a standard deviation of 1. Then classification processing is carried out. According to the normalized elevation standard deviation values, the earthquake area is divided into three levels: slightly undulating (-1 to 1), moderately undulating (-2 to -1 and 1 to 2), and severely undulating (less than -2 and greater than 2), obtaining the earthquake terrain undulation index data, which includes the terrain undulation level of each grid point and its corresponding normalized elevation standard deviation value.Using enhanced terrain feature data, it is matched with a preset geological structure database through spatial matching technology. The geological structure database contains geological structure information of the earthquake area and its surrounding areas, such as the geographical locations, types, and attributes of faults, folds, etc. By using spatial analysis tools, based on the slope, aspect, and curvature values in the terrain feature data as the matching criteria, the matching thresholds are set as the slope difference less than 5 degrees, the aspect difference less than 10 degrees, and the curvature difference less than 0.1. Spatial queries and matches are performed on the geological structure information in the geological structure database to obtain the geological structure distribution map of the earthquake area. Different types of geological structures and their spatial positions are identified in the distribution map with different colors or legends. Historical earthquake response analysis is carried out on the geological structure distribution map of the earthquake area, and historical earthquake data of the earthquake area and its surrounding areas are collected, including information such as the magnitude, focal depth, occurrence time, and epicenter position of the earthquake. By using the spatial analysis tools in GIS software, the response of geological structures in historical earthquakes is analyzed, such as the activity of faults and the deformation degree of folds. According to the response intensity of geological structures in historical earthquakes, a preset scoring standard is used for scoring, with the scoring range from 0 to 100 points. The higher the score, the worse the historical stability of the geological structure. The geological historical stability score is obtained, and the scoring data includes the stability score value of each geological structure unit and its spatial position information. Combining the seismic waveform characteristics (such as the initial arrival time difference and amplitude ratio of P waves and S waves) in the real-time earthquake monitoring data and the geological historical stability score, the activity of faults in the earthquake area is evaluated. By using the seismic waveform characteristic parameters and through a preset fault activity identification algorithm, the active faults in the earthquake area are identified. Then, combining the geological historical stability score, a weighted comprehensive evaluation method is adopted. The weight of the seismic waveform characteristic parameters is set as 0.6, and the weight of the geological historical stability score is set as 0.4. The seismic geological stability score of each fault unit is calculated, with the scoring range from 0 to 100 points. The higher the score, the worse the geological stability. The seismic geological stability score data is obtained, and the data includes the stability score value of each fault unit and its spatial position information.

[0024] Preferably, the spatial overlay analysis of the seismic terrain undulation index data and the seismic geological stability score data in step S2 includes: Performing spatial data overlay operations on the earthquake area based on the seismic terrain undulation index data and the seismic geological stability score data to obtain a comprehensive risk score matrix; Performing threshold segmentation and clustering analysis on the comprehensive risk score matrix to obtain risk level partition data; Based on the risk level partition data, extracting high-risk areas and identifying boundaries in the earthquake area to obtain a distribution map of high-risk earthquake damage areas.

[0025] In the embodiments of the present invention, the seismic terrain undulation index data and the seismic geological stability score data are imported into a Geographic Information System (GIS) software. Ensure that the spatial resolutions of these two sets of data are consistent, both being grid data of 1 meter × 1 meter. Perform a spatial data overlay operation on these two sets of data using the method of weighted summation. Set the weight of the seismic terrain undulation index data to 0.5 and the weight of the seismic geological stability score data to 0.5. The calculation formula is: Comprehensive Risk Score = (Seismic Terrain Undulation Index Data × 0.5) + (Seismic Geological Stability Score Data × 0.5). Calculate the comprehensive risk score value of each grid point through this formula to generate a comprehensive risk score matrix. Each element value in the matrix represents the comprehensive risk score of the corresponding grid point, and the scoring range is from 0 to 100 points. Perform threshold segmentation processing on the comprehensive risk score matrix, and set three thresholds, which are 33 points, 66 points, and 100 points respectively. Divide the comprehensive risk score into four levels: low risk (0 to 33 points), medium risk (34 to 66 points), high risk (67 to 100 points), and extremely high risk (100 points). Then, perform cluster analysis on the grid points within each risk level. Use the K-means clustering algorithm, set the number of cluster centers to 4, the number of iterations to 100 times, and the convergence condition to that the change of the cluster centers is less than 0.01. Through cluster analysis, group the grid points with similar risk characteristics into the same cluster area to obtain risk level partition data, which includes the boundary information, risk level, and the number of grid points included in each cluster area of each cluster area. Extract the high-risk areas (including high-risk and extremely high-risk areas) according to the risk level partition data. Use the spatial analysis tool in the GIS software to identify the boundaries of the high-risk areas. Adopt the morphological boundary extraction method, set the structural element as a 3×3 square, and perform dilation and erosion operations to extract the precise boundaries of the high-risk areas. Save the extracted high-risk area boundaries in the form of a vector layer and overlay and display them with the risk level partition data. Generate a high-risk area distribution map of earthquake disasters. In the map, areas with different risk levels are identified by different colors. High-risk areas are represented by red, and extremely high-risk areas are represented by dark red. The boundaries are clearly visible, visually showing the distribution of high-risk areas in the earthquake area.

[0026] Preferably, step S3 includes the following steps: Step S31: Parametrically query the high-performance historical earthquake data server based on the real-time data of earthquake monitoring, and perform screening and sorting to obtain a candidate dataset of similar earthquake events; Step S32: Calculate the similarity of the candidate dataset of similar earthquake events, and perform weight assignment and threshold screening of candidate events based on the similarity calculation results to obtain a preliminary screening result of similar earthquake events; Step S33: Extract historical earthquake damages based on the preliminary screening results of similar earthquake events, and perform structured integration and metadata annotation on the extraction results to obtain a set of similar earthquake events.

[0027] In the embodiments of the present invention, first, key parameters are extracted from real-time earthquake monitoring data, including magnitude (M), focal depth (D), epicenter location (longitude Lon, latitude Lat), and origin time (T). The accuracy of magnitude is 0.1 magnitude, the accuracy of focal depth is 1 km, the accuracy of epicenter location is 0.01 degree, and the accuracy of origin time is at the second level. Query statements are constructed using these parameters and queried through the parameterized query interface of a high-performance historical earthquake data server. The query conditions are set as follows: the magnitude range is M±0.5 magnitude, the focal depth range is D±10 km, the epicenter location range is that the longitude and latitude differences are both less than 0.1 degree, and the origin time range is 1 hour before and after T. The server returns historical earthquake event data that meet the conditions, and the data fields include event number, magnitude, focal depth, epicenter location, origin time, and corresponding earthquake damage data. Next, the historical earthquake event data obtained from the query is filtered and sorted. The filtering condition is: exclude events with a magnitude less than 3.0 or a focal depth greater than 300 km. The sorting is based on the comprehensive similarity of magnitude and focal depth, and the calculation formula is: similarity = 1 - |M - M0| / M0 + 1 - |D - D0| / D0, where M0 and D0 are the magnitude and focal depth in the real-time earthquake monitoring data respectively. The historical earthquake event data is sorted from high to low according to the similarity to obtain a candidate set of similar earthquake events, and the data set contains event number, magnitude, focal depth, epicenter location, origin time, similarity value, and corresponding earthquake damage data. For each event in the candidate set of similar earthquake events, its similarity to the real-time earthquake monitoring data is further calculated. In addition to magnitude and focal depth, the similarity of epicenter location and origin time is also considered. The similarity calculation formula for epicenter location is: similarity = 1 - (ΔLon² + ΔLat²)^(1 / 2), where ΔLon and ΔLat are the longitude and latitude differences between the candidate event and the epicenter location of the real-time data respectively. The similarity calculation formula for origin time is: similarity = 1 - |ΔT| / 3600, where Σ The comprehensive similarity is the sum of the comprehensive similarities of all events in the candidate event set. Then, a similarity threshold of 0.7 is set, and events with a comprehensive similarity greater than or equal to 0.7 are selected to obtain the preliminary screening result of similar earthquake events. The data set contains event number, magnitude, focal depth, epicenter location, origin time, comprehensive similarity, weight value, and corresponding earthquake damage data. The historical earthquake damage data is extracted from the preliminary screening result of similar earthquake events, including information such as affected area, number of casualties, number of damaged buildings, and economic losses. The extracted historical earthquake damage data is structurally integrated and organized in a unified data format. The data table contains fields: event number, magnitude, focal depth, epicenter location, origin time, comprehensive similarity, weight value, affected area, number of casualties, number of damaged buildings, economic losses, etc. Next, the integrated data is annotated with metadata. The metadata includes information such as data source, data collection time, data processing method, and data quality assessment.The data source is labeled as "High-performance Historical Earthquake Data Server", the data collection time is labeled as the specific time of the query operation, the data processing method is labeled as "Similarity calculation and weight assignment", and the data quality assessment is labeled as "High", indicating that the data has been strictly screened and processed and has a high degree of credibility. Finally, a set of similar earthquake events is obtained. The dataset contains complete event information, earthquake damage data, and metadata annotations, providing a basis for subsequent analysis and applications.

[0028] Preferably, step S4 includes the following steps: Step S41: Perform data format standardization processing on the seismic area sensitivity matrix, earthquake damage high-risk area distribution map, and similar earthquake event set to obtain standardized input data for earthquake damage assessment; Step S42: Based on the standardized input data for earthquake damage assessment, perform geographical feature matching between the current seismic area and the similar earthquake event set, and perform optimization and calibration to obtain regional correspondence data; Step S43: Perform pattern recognition and feature extraction of historical earthquake damage on the similar earthquake event set to obtain a historical earthquake damage pattern feature library; Step S44: Based on the regional correspondence data, perform adaptive reconstruction on the historical earthquake damage pattern feature library to obtain the earthquake damage pattern of the current seismic area; Step S45: Perform quantitative analysis of the influence degree of terrain undulation and geological stability factors based on the earthquake damage high-risk area distribution map to obtain the terrain and geology influence coefficient; Step S46: Calculate the earthquake damage probability of the target area based on the earthquake damage pattern of the current seismic area, the seismic area sensitivity matrix, and the terrain and geology influence coefficient to obtain a preliminary assessment result of the earthquake damage distribution probability; Step S47: Perform threshold segmentation and regional clustering on the preliminary assessment result of the earthquake damage distribution probability to obtain an earthquake damage level regional division plan; Step S48: Based on the earthquake damage level regional division plan, perform target area level annotation and boundary optimization to obtain a preliminary earthquake damage level assessment result.

[0029] In the embodiments of the present invention, the data formats of the seismic area sensitivity matrix and the seismic disaster high-risk area distribution map are unified to ensure that their spatial references are consistent. The WGS-84 coordinate system and UTM projection are adopted. The coordinate transformation is performed on the geographical information fields (such as the epicenter location) in the similar earthquake event set, and they are unified into the WGS-84 coordinate system. The seismic disaster data in the similar earthquake event set is gridded. Centered on the epicenter, it is divided into grids of 1 km × 1 km to generate a gridded seismic disaster data layer, and the data format is GeoTIFF. Finally, the standardized input data for seismic disaster assessment is obtained, including the seismic area sensitivity matrix, the seismic disaster high-risk area distribution map, and the gridded seismic disaster data layer. Using the spatial analysis tools in GIS software, the geographical features of the standardized input data for seismic disaster assessment are matched. The geographical features of the current seismic area are extracted, including the terrain boundary line, main rivers, transportation arteries, etc. For each event in the similar earthquake event set, its corresponding geographical features are extracted. The spatial matching algorithm is used, and the terrain boundary line, main rivers, transportation arteries, etc. are used as the matching basis to calculate the geographical feature similarity between the current seismic area and each event in the similar earthquake event set. The similarity calculation formula is: similarity = 1 - (Δ boundary line length + Δ river length + Δ transportation artery length) / (boundary line length + river length + transportation artery length), where Δ represents the difference in the geographical feature lengths between the current seismic area and the similar earthquake event set. The matching results are optimized and calibrated. The iterative optimization algorithm is adopted, the number of iterations is set to 10 times, and the matching parameters are adjusted each time to optimize the matching results. Finally, the regional correspondence data is obtained, which includes the geographical feature similarity values, correspondence identifiers, and optimized matching parameters between the current seismic area and each event in the similar earthquake event set. Pattern recognition and feature extraction are performed on the historical seismic disaster data in the similar earthquake event set. The principal component analysis (PCA) method is used to extract the main features of the seismic disaster data. The cumulative variance contribution rate for principal component extraction is set to 95%, and the main components extracted include the affected area, the number of casualties, the number of damaged buildings, economic losses, etc. For each similar earthquake event, its eigenvector in the principal component space is calculated, and the dimension of the eigenvector is the same as the number of principal components. The clustering analysis method is used to cluster the extracted eigenvectors. The K-means clustering algorithm is adopted, the number of cluster centers is set to 5, the number of iterations is 100 times, and the convergence condition is that the change in the cluster centers is less than 0.01. The events in the similar earthquake event set are divided into 5 seismic disaster patterns, and each pattern corresponds to a cluster center. Finally, the historical seismic disaster pattern feature library is obtained, which includes the cluster center eigenvectors of each seismic disaster pattern, the corresponding event number set, and the description information of the seismic disaster pattern. According to the regional correspondence data, the historical seismic disaster pattern feature library is adaptively reconstructed. For the correspondence between the current seismic area and each event in the similar earthquake event set, the corresponding seismic disaster pattern eigenvectors are extracted.Calculate the comprehensive feature vector of the current earthquake area using the weighted average method, where the weight is the similarity value in the regional correspondence data. The calculation formula for the comprehensive feature vector is: Comprehensive feature vector = Σ(similarity value × feature vector of the corresponding event) / Σsimilarity value. Normalize the comprehensive feature vector to normalize the values of each principal component to the range of 0 to 1. Determine the earthquake damage pattern of the current earthquake area based on the normalized comprehensive feature vector. Compare the earthquake damage pattern of the current earthquake area with the patterns in the historical earthquake damage pattern feature library and calculate the similarity with each pattern. The similarity calculation formula is: Similarity = 1 - (Σ|comprehensive feature vector - pattern feature vector|) / (2 × number of dimensions). Select the pattern with the highest similarity as the earthquake damage pattern of the current earthquake area, obtaining the earthquake damage pattern of the current earthquake area, including the pattern feature vector, the corresponding earthquake damage pattern description information, and the similarity value. Use the earthquake damage high-risk area distribution map to quantitatively analyze the influence of terrain undulation and geological stability factors. Extract the terrain undulation index data and seismic geological stability score data within the high-risk areas. For each high-risk area, calculate the average value of the terrain undulation index and the average value of the seismic geological stability score. The formula for quantifying the influence of terrain undulation is: Influence of terrain undulation = 1 - (average terrain undulation index / maximum terrain undulation index). The formula for quantifying the influence of geological stability is: Influence of geological stability = 1 - (average geological stability score / maximum geological stability score). Combine the influence of terrain undulation and the influence of geological stability to obtain the terrain-geological influence coefficient. The comprehensive formula is: Terrain-geological influence coefficient = 0.6 × influence of terrain undulation + 0.4 × influence of geological stability. Calculate the terrain-geological influence coefficient for each high-risk area to obtain the terrain-geological influence coefficient data set, which contains the number of each high-risk area, the terrain-geological influence coefficient value, and the corresponding geographical coordinate information. Based on the earthquake damage pattern of the current earthquake area, the earthquake area sensitivity matrix, and the terrain-geological influence coefficient, calculate the earthquake damage probability of the target area. First, extract the feature vector of the earthquake damage pattern of the current earthquake area, including the principal component values such as affected area, number of casualties, number of damaged buildings, and economic losses. Use the sensitivity values in the earthquake area sensitivity matrix as weight factors to calculate the weighted earthquake damage probability for each grid point. The calculation formula is: Earthquake damage probability = sensitivity value × (earthquake damage pattern feature vector × terrain-geological influence coefficient). Calculate the earthquake damage probability for each grid point to obtain the preliminary evaluation result of the earthquake damage distribution probability. The result data set contains the earthquake damage probability value, geographical coordinate information, and the corresponding earthquake damage pattern feature vector and terrain-geological influence coefficient value for each grid point. The earthquake damage probability value ranges from 0 to 1, indicating the likelihood of an earthquake damage occurring at that grid point. Perform threshold segmentation on the preliminary evaluation result of the earthquake damage distribution probability, setting three thresholds, which are 0.3, 0.6, and 0.9 respectively.The seismic hazard distribution probability is divided into four levels: low risk (0 to 0.3), medium risk (0.3 to 0.6), high risk (0.6 to 0.9), and extremely high risk (0.9 to 1). Regional clustering analysis is performed on the grid points within each risk level. Using the DBSCAN clustering algorithm, the neighborhood radius is set to 10 meters and the minimum number of neighborhood points is set to 5. Through clustering analysis, the grid points with similar seismic hazard probabilities are grouped into the same clustering region, obtaining a seismic hazard level regional division scheme. The scheme includes the boundary information, seismic hazard level, and the number of grid points contained in each clustering region. According to the seismic hazard level regional division scheme, the target area is labeled with levels. Using the spatial analysis tool in GIS software, the boundary of each clustering region is optimized. The morphological boundary optimization method is adopted, with the structural element set as a 3×3 square, and dilation and erosion operations are performed to extract the optimized regional boundary. The optimized regional boundary is saved in the form of a vector layer and overlaid and displayed with the seismic hazard level regional division scheme.

[0030] Preferably, the building damage state data of the epicenter collected based on the real-time seismic monitoring data in step S5 includes: Plan the UAV inspection path based on the real-time seismic monitoring data, and scan the buildings in the epicenter area based on the planning result to obtain the building appearance image data and three-dimensional point cloud data; Enhance the building appearance image data and identify cracks, tilts, and structural deformations to obtain the building appearance damage feature data; Denoise the three-dimensional point cloud data, construct a three-dimensional building model, and perform structural integrity assessment and stress analysis on the three-dimensional building model to obtain the building structure damage assessment data; Scan the interior of the building through a thermal imaging sensor to generate indoor structure abnormal signal data, and perform feature extraction to generate the indoor damage state assessment result; Monitor the infrastructure status based on the UAV inspection path and perform a functional integrity score to obtain an infrastructure damage assessment report; Evaluate the damage state of the buildings and facilities in the earthquake area based on the building appearance damage feature data, the building structure damage assessment data, the indoor damage state assessment result, and the infrastructure damage assessment report to obtain the building damage state data.

[0031] In the embodiments of the present invention, first, the epicenter position and the affected area information are extracted from the real-time earthquake monitoring data to determine the geographical coordinate range of the epicenter area. Using Geographic Information System (GIS) software, combined with high-resolution terrain data and building distribution maps, the inspection path of the unmanned aerial vehicle (UAV) is planned. The path planning adopts the grid covering method, dividing the epicenter area into grids of 100 meters × 100 meters, and the UAV flies along a zigzag path to cover all grid cells. The flight altitude of the UAV is set at 100 meters, and the flight speed is 5 meters per second to ensure the acquisition accuracy of image and point cloud data. The UAV is equipped with a high-resolution optical camera and a lidar sensor, and scans the buildings in the epicenter area according to the planned path. The resolution of the optical camera is 4000×3000 pixels, and the shooting interval is 1 second to generate building exterior image data. The scanning frequency of the lidar is 100 Hz, and the point cloud density is 100 points per square meter to generate three-dimensional point cloud data. After the data acquisition is completed, the building exterior image data and the three-dimensional point cloud data are transmitted to the ground control center for processing. The building exterior image data is imported into image processing software for image enhancement processing. The histogram equalization method is used to enhance the contrast of the image, making the details of the building clearer. The gray scale range of the histogram equalization is set from 0 to 255, and the enhanced image can better highlight damage features such as cracks, inclinations, and structural deformations. Using image recognition algorithms, the enhanced image is recognized for cracks, inclinations, and structural deformations. For crack recognition, the edge detection algorithm is used, setting the low threshold of the Canny operator at 50 and the high threshold at 150 to detect the crack positions and lengths in the image. For inclination recognition, the Hough transform algorithm is used to detect the inclination angle of the building, setting the threshold of the Hough transform at 100 to detect the inclination direction and angle of the building. For structural deformation recognition, the template matching algorithm is used, setting the matching accuracy at 0.9 to detect the structural deformation area of the building. The recognition results are structurally integrated to generate building exterior damage feature data. The data includes crack positions, lengths, inclination angles, directions, and coordinate information of the structural deformation area, providing basic data for subsequent damage assessment. The three-dimensional point cloud data is denoised, adopting the statistical filtering method to remove outliers. The filtering parameters are set as follows: the neighborhood radius is 0.5 meters, and the minimum number of neighborhood points is 10, removing the point cloud data that does not conform to the statistical law to ensure the accuracy and integrity of the point cloud data. A three-dimensional model of the building is constructed using the point cloud data, adopting the multi-view geometric reconstruction method to convert the point cloud data into a triangular mesh model. The grid resolution is set to match the point cloud density to ensure the details and accuracy of the model. The generated three-dimensional model can visually display the exterior and structural features of the building. The structural integrity assessment and stress analysis of the three-dimensional building model are carried out. The finite element analysis method is adopted to apply seismic loads to the model and calculate the stress distribution and deformation of the building under earthquake action.Set the seismic load as the seismic waveform in the real-time seismic monitoring data, analyze the stress concentration areas and deformation degrees in the analysis model, and evaluate the structural integrity of the building. Structurally integrate the evaluation results to generate building structure damage assessment data. The data includes the locations and stress values of the stress concentration areas, as well as the quantitative indicators of the deformation degrees, providing a scientific basis for the structural safety assessment of the building. A drone is equipped with a thermal imaging sensor to scan the interior of the building. The resolution of the thermal imaging sensor is not less than 640×480 pixels, and the scanning range covers the interior walls, floors, and key structural parts of the building. The generated thermal imaging images can reflect the temperature distribution inside the building, and abnormal temperature changes may indicate structural damage. Extract the features of the thermal imaging images, use the region segmentation algorithm to identify the temperature abnormal areas. Set the temperature threshold as 1.2 times the room temperature, and extract the area, location, and shape features of the temperature abnormal areas. Evaluate the damage state of the indoor structure through comparative analysis with the normal temperature distribution. Structurally integrate the feature extraction results to generate the indoor damage state evaluation results. The data includes the locations, areas, and shape features of the temperature abnormal areas, as well as the corresponding damage types (such as cracks, leaks, etc.), providing a reference for the rapid assessment of the internal damage of the building. Along the drone inspection path, monitor the status of infrastructure (such as roads, bridges, water and electricity facilities, etc.). Use optical cameras and lidar sensors to collect the appearance images and three-dimensional point cloud data of the infrastructure. Through image recognition algorithms, identify the damage conditions of the infrastructure, such as road cracks, bridge deformations, and damage to water and electricity facilities. Score the functional integrity of the infrastructure, using a preset scoring standard to conduct a quantitative evaluation according to the damage degree and influence range. The scoring range is from 0 to 100 points, and the lower the score, the worse the functional integrity. Generate an infrastructure damage assessment report, which includes the type, location, damage condition, and functional integrity score of the infrastructure, providing decision-making support for the emergency repair of the infrastructure. Conduct a comprehensive analysis of the building appearance damage feature data, building structure damage assessment data, indoor damage state evaluation results, and infrastructure damage assessment report. Use the weighted summation method to comprehensively evaluate different types of damage data. Set the weight parameters as follows: the appearance damage weight is 0.3, the structure damage weight is 0.4, the indoor damage weight is 0.2, and the infrastructure damage weight is 0.1. Calculate the comprehensive damage score for each building, with the scoring range from 0 to 100 points, and the higher the score, the more serious the damage degree. Compare the comprehensive damage score with the preset damage level standard to divide the damage levels (such as minor, moderate, severe). Generate building damage state data, which includes the number, location, comprehensive damage score, damage level, and detailed damage feature description of each building. Provide comprehensive building facility damage information for emergency rescue and post-disaster reconstruction in the earthquake-stricken area.

[0032] Preferably, in step S5, the assessment of the disaster level of the target area based on the preliminary earthquake damage level assessment result and the building damage status data includes: Performing cluster analysis and level division on the building damage status data to obtain the classification result of the building damage level; Based on the classification result of the building damage level, performing spatial annotation and attribute association on the earthquake area map to obtain the spatial distribution map of building damage; Performing spatial matching on the spatial distribution map of building damage based on the preliminary earthquake damage level assessment result, and performing correlation analysis to obtain the earthquake damage assessment verification report; Assessing the secondary disaster risk based on the building damage status data and the earthquake damage assessment verification report, and performing spatio-temporal evolution simulation on the assessment result to obtain the prediction data of the earthquake damage diffusion trend; Based on the preliminary earthquake damage level assessment result, the building damage status data and the earthquake damage diffusion trend prediction model, performing comprehensive disaster level assessment of the target area to obtain the preliminary assessment result of the disaster level; Performing population impact analysis on the preliminary assessment result of the disaster level to obtain the prediction data of casualties; calculating the economic value loss of buildings and infrastructure based on the preliminary assessment result of the disaster level to obtain the economic loss assessment report; Based on the prediction data of casualties and the economic loss assessment report, calibrating and adjusting the preliminary assessment result of the disaster level to obtain the final assessment result of the disaster level; Based on the final assessment result of the disaster level, performing high-precision mapping and spatial visualization processing on the earthquake area to obtain the accurate earthquake damage distribution sketch; Optimizing the layer organization and symbol identification of the accurate earthquake damage distribution sketch to obtain the accurate earthquake damage distribution map.

[0033] In the embodiments of the present invention, the building damage status data is imported into data analysis software. The data fields include building number, location coordinates, comprehensive damage score, damage feature description, etc. The K-means clustering algorithm is used to perform clustering analysis on the building damage status data. The number of clustering centers is set to 3, corresponding to three levels of slight damage, moderate damage, and severe damage respectively. The number of iterations is set to 100 times, and the convergence condition is that the change in the clustering center is less than 0.01. Calculate the Euclidean distance between each building and the clustering center, and assign the building to the damage level corresponding to the nearest clustering center. Finally, the classification result of the building damage level is obtained. The data includes the building number, location coordinates, damage level, and the corresponding clustering center distance. Using Geographic Information System (GIS) software, spatially match the classification result of the building damage level with the earthquake area map. The earthquake area map contains the geographical location information and basic geographical information of the buildings. In the GIS software, according to the location coordinates of the buildings, mark the classification result of the damage level on the earthquake area map. Use different colors or symbols to identify buildings with different damage levels. Slight damage is represented by yellow, moderate damage by orange, and severe damage by red. At the same time, associate the damage feature description of the building as attribute information to the corresponding building location on the map. Generate a spatial distribution map of building damage, which visually shows the damage level and location distribution of the buildings. Spatially match the preliminary earthquake damage level assessment result with the spatial distribution map of building damage. The preliminary earthquake damage level assessment result is represented in the form of a grid, and each grid cell has earthquake damage level information. In the GIS software, overlay the spatial distribution map of building damage with the earthquake damage level grid map to ensure that their spatial references are consistent. Use spatial analysis tools to calculate the correlation between the earthquake damage level of the grid cell where each building is located and the building damage level. The correlation analysis uses the Pearson correlation coefficient. Combine the building damage status data and the earthquake damage assessment verification report to evaluate the secondary disaster risk. The secondary disaster risks include fire, landslide, building collapse, etc. For each building, calculate the secondary disaster risk index according to the damage level and damage features. The risk index calculation formula is: Risk Index = Damage Level Weight × Damage Feature Weight, where the damage level weight is set according to the damage level (0.3 for slight damage, 0.6 for moderate damage, 1.0 for severe damage), and the damage feature weight is set according to features such as crack width and tilt angle (weight is 1.0 for crack width greater than 10 mm, 0.5 for less than 10 mm; weight is 1.0 for tilt angle greater than 5 degrees, 0.5 for less than 5 degrees). Use a spatio-temporal evolution model to simulate the secondary disaster risk. The model considers factors such as the propagation speed of seismic waves and the structural stability of buildings. The simulation time step is 1 hour, and the simulation duration is 24 hours. Generate earthquake damage diffusion trend prediction data, which includes the distribution and diffusion trend of secondary disaster risks within each time step.Combined with the preliminary earthquake damage level assessment results, building damage status data, and earthquake damage spread trend prediction data, a comprehensive disaster level assessment is conducted for the target area. The weighted summation method is used to calculate the comprehensive disaster level score. The weights are set as follows: the weight of the preliminary earthquake damage level is 0.4, the weight of the building damage status is 0.3, and the weight of the earthquake damage spread trend is 0.3. The comprehensive disaster level score formula is: Comprehensive disaster level score = 0.4 × Preliminary earthquake damage level score + 0.3 × Building damage status score + 0.3 × Earthquake damage spread trend score. According to the comprehensive disaster level score, the target area is divided into low disaster levels (0 to 33 points), medium disaster levels (34 to 66 points), and high disaster levels (67 to 100 points). Generate the preliminary assessment results of the disaster level, which include the comprehensive disaster level score, disaster level, and corresponding geographical scope of the target area. Conduct a population impact analysis on the preliminary assessment results of the disaster level. Using the population distribution data in the earthquake area and combining with the disaster level distribution map, calculate the permanent resident population in each disaster level area. Estimate the casualty ratio according to the disaster level and building damage status. The casualty ratio in the minor disaster level area is 0.1%, in the medium disaster level area is 1%, and in the high disaster level area is 5%. The calculation formula is: Number of casualties = Permanent resident population × Casualty ratio. Generate the casualty prediction data, which includes the number of casualties and casualty ratio in each disaster level area. Based on the preliminary assessment results of the disaster level, calculate the economic value loss of buildings and infrastructure. For buildings and infrastructure in each disaster level area, estimate the economic loss according to the damage level and repair cost. The repair cost for minor damage is 10% of the building value, 30% for medium damage, and 60% for severe damage. Combining the casualty prediction data and the economic loss assessment report, calibrate and adjust the preliminary assessment results of the disaster level. Adopt a comprehensive assessment method, and use the number of casualties and economic loss as calibration indicators. Set the weight of the number of casualties as 0.6 and the weight of the economic loss as 0.4. The comprehensive calibration formula is: Comprehensive calibration score = 0.6 × Number of casualties + 0.4 × Economic loss. According to the comprehensive calibration score, adjust the disaster level. If the comprehensive calibration score exceeds the threshold of the current disaster level, the disaster level is upgraded; if it is lower than the threshold, the original level is maintained. Finally, obtain the final assessment results of the disaster level, which include the adjusted disaster level, comprehensive calibration score, and corresponding geographical scope. Using GIS software, conduct high-precision mapping and spatial visualization processing on the earthquake area based on the final assessment results of the disaster level. Use high-resolution terrain data and satellite images as the base map, and overlay the final assessment results of the disaster level on the earthquake area map in the form of layers. Use different colors and symbols to identify areas of different disaster levels. The minor disaster level is represented by light yellow, the medium disaster level by orange, and the high disaster level by red.Meanwhile, annotate the number of casualties and economic loss data, and generate a precise sketch of earthquake damage distribution, which visually shows the disaster level distribution, casualties, and economic losses in the earthquake-stricken area. Optimize the layer organization and symbol identification of the precise sketch of earthquake damage distribution. In GIS software, classify and organize the layers of different disaster levels to ensure the logic and readability of the layers. Optimize the symbol identification, and use gradient colors and transparency adjustment to enhance the visual differentiation between layers. Add a legend, scale, and geographical coordinate grid to improve the readability and practicality of the map.

[0034] Preferably, step S6 includes the following steps: Step S61: Convert the format of the wave propagation timing data, preliminary earthquake damage level assessment results, and precise earthquake damage distribution map, and perform standardization processing to obtain a visual standard data set; Step S62: Design a multi-dimensional expression of disaster situation information based on the visual standard data set, and perform interactive logic design and temporal dynamic processing on the design results to obtain a dynamic visual expression scheme; Step S63: Conduct research on the information needs and behavior analysis of each user role based on the dynamic visual expression scheme to obtain user role characteristics; Step S64: Differentially design the information presentation method and key content based on the user role characteristics to obtain a role-based information requirement matrix; Step S65: Classify the visual layers based on the dynamic visual expression scheme and the role-based information requirement matrix, and sort the classification results by priority to obtain a layer combination configuration library; Step S66: Calculate the optimal matching of user roles for the layer combination configuration library to generate a role-based display scheme.

[0035] In the embodiments of the present invention, the wave propagation time series data, the preliminary earthquake damage level assessment results, and the precise earthquake damage distribution map are imported into the data processing software. The wave propagation time series data is in CSV format and contains information such as timestamps, waveform amplitudes, frequencies, etc.; the preliminary earthquake damage level assessment results and the precise earthquake damage distribution map are in GeoTIFF format and contain information such as geographical coordinates, earthquake damage levels, etc. The format of the wave propagation time series data is converted, its timestamps are unified into the UTC time format, and the amplitude and frequency data are normalized, with the normalization range from 0 to 1. The preliminary earthquake damage level assessment results and the precise earthquake damage distribution map are standardized, the earthquake damage levels are converted into standardized values (slight damage is 0.3, moderate damage is 0.6, severe damage is 1.0), and the geographical coordinate system is unified into the WGS-84 coordinate system. Finally, a visual standard data set is generated. The data set contains the standardized wave propagation time series data, earthquake damage level assessment results, and earthquake damage distribution map, with a unified data format, facilitating subsequent visual processing. Using geographic information system (GIS) software and visualization design tools, a multi-dimensional expression design is carried out for the visual standard data set. Design the expression methods of disaster situation information, including map view, time series view, and 3D view. In the map view, different colors are used to identify areas with different earthquake damage levels; in the time series view, the dynamic changes of the wave propagation time series data are shown; in the 3D view, the three-dimensional effect of the earthquake damage distribution is shown in combination with terrain data. An interactive logic design is carried out for the design results, setting that users can control the dynamic changes of the time series through the time slider and view the detailed information of different areas by zooming in and dragging the map. The wave propagation time series data is processed for time series dynamicization, the animation playback speed is set to 1 / 100 times the real-time earthquake wave propagation speed, and a dynamic visualization expression scheme is generated. The scheme contains multi-dimensional expression methods, interactive logic, and time series dynamicization parameters. According to the dynamic visualization expression scheme, an information needs survey is carried out for different user roles (such as earthquake emergency command personnel, rescue teams, affected people, etc.). Through questionnaire surveys and interviews, the concerns, usage frequencies, and operation habits of users regarding disaster situation information are collected. Earthquake emergency command personnel are concerned about the overall earthquake damage distribution and the status of key facilities, rescue teams are concerned about the affected areas and rescue routes, and affected people are concerned about safe areas and evacuation routes. The collected data is analyzed for behavior, and the operation behavior patterns of user roles are extracted. For example, command personnel frequently use the time slider to view historical data, and rescue teams frequently zoom in on the map to view details. Finally, user role characteristics are obtained, which contain the information needs, concerns, and operation behavior patterns of user roles. According to the user role characteristics, the information presentation methods and key contents are designed differently.For earthquake emergency commanders, the earthquake damage level distribution map and the status of key facilities are highlighted, and a large-screen display method is adopted. The key contents include high-risk areas and facility damage conditions; for rescue teams, the affected areas and rescue routes are highlighted, and a mobile device display method is adopted. The key contents include the positions of rescue targets and route planning; for affected people, the safe areas and evacuation routes are highlighted, and a mobile application display method is adopted. The key contents include the positions of the nearest safe areas and evacuation routes. The design results are sorted into a role-based information requirement matrix, which includes user roles, information presentation methods, key contents, and display device types. According to the dynamic visualization expression scheme and the role-based information requirement matrix, the visualization layers are classified. The layers are divided into basic geographic information layers (such as terrain, roads, etc.), earthquake damage distribution layers (such as earthquake damage levels, building damages, etc.), dynamic data layers (such as wave propagation time series data), and thematic information layers (such as rescue routes, safe areas, etc.). The classification results are sorted by priority, and the priorities are set according to the needs and operation habits of user roles. For example, for earthquake emergency commanders, the earthquake damage distribution layer and the key facility status layer have the highest priorities; for rescue teams, the affected area and rescue route layers have the highest priorities. A layer combination configuration library is generated, which includes layer combinations, priority orders, and display parameters corresponding to different user roles. Using the layer combination configuration library, the optimal matching for user roles is calculated. By analyzing the operation behavior patterns and information requirements of user roles through algorithms, the most suitable layer combinations and display parameters are assigned to each user role. The parameters of the matching algorithm are set as the priority weights and operation frequencies of user roles, and the optimal matching results are calculated. A role-based display scheme is generated, which includes layer combinations, display parameters, and interaction logics for each user role. The role-based display scheme can automatically adjust the information presentation method and key contents according to different user roles, providing personalized visualization services.

[0036] Preferably, the present invention further provides an earthquake emergency information rapid visualization system for executing the above-mentioned earthquake emergency information rapid visualization method. The earthquake emergency information rapid visualization system includes: An earthquake data monitoring module, configured to obtain real-time earthquake monitoring data; divide the population density distribution and the positions of key facilities in the earthquake area based on the real-time earthquake monitoring data grid, and generate an earthquake area sensitivity matrix; dynamically simulate the earthquake wave propagation process based on the real-time earthquake monitoring data and the earthquake area sensitivity matrix, and generate wave propagation time series data; An earthquake terrain detection module, configured to identify the epicenter terrain and geomorphic vector data of the real-time earthquake monitoring data, and respectively evaluate the terrain undulation and geological stability to obtain earthquake terrain undulation index data and earthquake geological stability scoring data; perform spatial overlay analysis on the earthquake terrain undulation index data and the earthquake geological stability scoring data to generate an earthquake damage high-risk area distribution map; An earthquake event set construction module, which is used to retrieve earthquake examples in a preset historical earthquake database based on real-time earthquake monitoring data and construct a similar earthquake event set; A seismic damage level assessment module, which is used to predict the seismic damage distribution probability of a target area based on a seismic area sensitivity matrix, a seismic damage high-risk area distribution map, and a similar earthquake event set, and generate a preliminary seismic damage level assessment result; A seismic damage distribution map division module, which is used to collect building damage state data at the epicenter based on real-time earthquake monitoring data; evaluate the disaster level of the target area based on the preliminary seismic damage level assessment result and the building damage state data, and generate an accurate seismic damage distribution map; An emergency visualization module, which is used to perform visual processing of disaster situation information on wave propagation time series data, the preliminary seismic damage level assessment result, and the accurate seismic damage distribution map, and perform layer combination matching for each user role based on the visual processing result of the disaster situation information to obtain a role-based display scheme.

[0037] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is not limited by the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be included in the present invention.

[0038] The above are only specific embodiments of the present invention, which enable those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for rapid visualization of earthquake emergency information, characterized in that Including the following steps: Step S1: Obtain real-time earthquake monitoring data; based on the real-time earthquake monitoring data, divide the population density distribution and the locations of key facilities in the earthquake area into grids to generate a sensitivity matrix for the earthquake area; based on the real-time earthquake monitoring data and the sensitivity matrix of the earthquake area, dynamically simulate the earthquake wave propagation process to generate wave propagation time-series data; Step S2: Identify the vector data of the epicenter topography and geomorphology in the real-time earthquake monitoring data, and respectively evaluate the terrain undulation and geological stability to obtain earthquake terrain undulation index data and earthquake geological stability scoring data; perform spatial overlay analysis on the earthquake terrain undulation index data and the earthquake geological stability scoring data to generate a distribution map of high-risk earthquake damage areas; Step S3: Retrieve earthquake cases in the preset historical earthquake database based on the real-time earthquake monitoring data to construct a set of similar earthquake events; Step S4: Predict the earthquake damage distribution probability in the target area based on the sensitivity matrix of the earthquake area, the distribution map of high-risk earthquake damage areas, and the set of similar earthquake events to generate a preliminary earthquake damage level assessment result; Step S5: Collect building damage status data at the epicenter based on the real-time earthquake monitoring data; evaluate the disaster level of the target area based on the preliminary earthquake damage level assessment result and the building damage status data to generate an accurate earthquake damage distribution map; Step S6: Perform visualization processing on the wave propagation time-series data, the preliminary earthquake damage level assessment result, and the accurate earthquake damage distribution map for disaster situation information, and perform layer combination matching for each user role based on the visualization processing result of the disaster situation information to obtain a role-based display scheme.

2. The rapid visualization method of earthquake emergency information according to claim 1, characterized in that Step S1 includes the following steps: Step S11: Real-time collect raw earthquake waveform data through a distributed earthquake monitoring sensor network, and perform filtering calibration and standardization processing to generate real-time earthquake monitoring data; Step S12: Adaptively divide the earthquake area into grids based on the real-time earthquake monitoring data to obtain earthquake area grid cell data; Step S13: Perform spatial interpolation analysis of population density based on the earthquake area grid cell data to obtain grid-based population density distribution data; Step S14: Perform spatial query of the geographical information of key facilities based on the earthquake area grid cell data to obtain grid-based key facility location data; Step S15: Calculate the sensitivity weights for the earthquake area grid cell data based on the grid-based population density distribution data and the grid-based key facility location data to generate a sensitivity matrix for the earthquake area; Step S16: Numerically simulate the earthquake wave propagation mechanism based on the real-time earthquake monitoring data and the sensitivity matrix of the earthquake area to generate wave propagation time-series data.

3. The rapid visualization method of earthquake emergency information according to claim 1, wherein In step S2, identifying the vector data of the epicenter topography and geomorphology in the real-time earthquake monitoring data, and respectively evaluating the terrain undulation and geological stability includes: Retrieve multi-source remote sensing images based on the real-time earthquake monitoring data, and perform geometric correction and ortho-rectification to obtain earthquake area terrain image data; Extract the terrain boundary line based on the earthquake area terrain image data, and construct a triangular network model for the earthquake area to obtain a digital elevation model for the earthquake area; Calculate the slope, aspect, and curvature of the digital elevation model for the earthquake area, and perform enhancement processing on the calculation results to obtain enhanced terrain feature data; Statistically analyze the local elevation changes based on the digital elevation model for the earthquake area, and perform standardization and grading processing to obtain earthquake terrain undulation index data; Based on the spatial matching of enhanced terrain feature data with a preset geological structure database, a geological structure distribution map of the earthquake area is obtained; historical earthquake response analysis is carried out on the geological structure distribution map of the earthquake area to obtain a geological historical stability score. Based on real-time earthquake monitoring data and the geological historical stability score, the fault activity is evaluated to obtain earthquake geological stability score data.

4. The rapid visualization method of earthquake emergency information according to claim 1, wherein In step S2, the spatial overlay analysis of the earthquake terrain undulation index data and the earthquake geological stability score data includes: Based on the earthquake terrain undulation index data and the earthquake geological stability score data, spatial data overlay operation is carried out on the earthquake area to obtain a comprehensive risk score matrix. Threshold segmentation and clustering analysis are carried out on the comprehensive risk score matrix to obtain risk level zoning data. Based on the risk level zoning data, high-risk areas in the earthquake area are extracted and the boundaries are identified to obtain a distribution map of high-risk earthquake damage areas.

5. The rapid visualization method of earthquake emergency information according to claim 1, characterized in that Step S3 includes the following steps: Step S31: Parametrically query a high-performance historical earthquake data server based on real-time earthquake monitoring data, and perform screening and sorting to obtain a candidate dataset of similar earthquake events. Step S32: Calculate the similarity of the candidate dataset of similar earthquake events, and based on the similarity calculation results, perform weight assignment and threshold screening of the candidate events to obtain a preliminary screening result of similar earthquake events. Step S33: Extract historical earthquake damage based on the preliminary screening result of similar earthquake events, and perform structured integration and metadata annotation on the extraction result to obtain a set of similar earthquake events.

6. The rapid visualization method of earthquake emergency information according to claim 1, characterized in that Step S4 includes the following steps: Step S41: Perform data format unification processing on the earthquake area sensitivity matrix, the distribution map of high-risk earthquake damage areas, and the set of similar earthquake events to obtain standardized input data for earthquake damage assessment. Step S42: Based on the standardized input data for earthquake damage assessment, perform geographical feature matching between the current earthquake area and the set of similar earthquake events, and perform optimization and calibration to obtain regional correspondence data. Step S43: Perform pattern recognition and feature extraction of historical earthquake damage on the set of similar earthquake events to obtain a historical earthquake damage pattern feature library. Step S44: Based on the regional correspondence data, perform adaptive reconstruction on the historical earthquake damage pattern feature library to obtain the earthquake damage pattern of the current earthquake area. Step S45: Quantitatively analyze the influence degree of terrain undulation and geological stability factors based on the distribution map of high-risk earthquake damage areas to obtain a terrain and geology influence coefficient. Step S46: Calculate the earthquake damage probability of the target area based on the earthquake damage pattern of the current earthquake area, the earthquake area sensitivity matrix, and the terrain and geology influence coefficient to obtain a preliminary assessment result of the earthquake damage distribution probability. Step S47: Perform threshold segmentation and regional clustering on the preliminary assessment result of the earthquake damage distribution probability to obtain an earthquake damage level regional division plan. Step S48: Based on the earthquake damage level regional division plan, perform target area level annotation and boundary optimization to obtain a preliminary earthquake damage level assessment result.

7. The rapid visualization method of earthquake emergency information according to claim 1, wherein In step S5, the building damage status data of the earthquake epicenter is collected based on real-time earthquake monitoring data, including: Based on real-time earthquake monitoring data, plan the unmanned aerial vehicle (UAV) inspection path, and scan the buildings in the earthquake epicenter area based on the planning result to obtain building appearance image data and three-dimensional point cloud data. Perform image enhancement on the building appearance image data, identify cracks, inclinations, and structural deformations, and obtain the building appearance damage feature data; Denoise the three-dimensional point cloud data, construct a three-dimensional model of the building, and conduct structural integrity assessment and stress analysis on the three-dimensional model of the building to obtain the building structure damage assessment data; Scan the interior of the building through a thermal imaging sensor, generate indoor structure abnormal signal data, and perform feature extraction to generate the indoor damage status assessment result; Monitor the infrastructure status based on the UAV inspection path and conduct a functional integrity score to obtain the infrastructure damage assessment report; Evaluate the damage status of building facilities in the earthquake area based on the building appearance damage feature data, the building structure damage assessment data, the indoor damage status assessment result, and the infrastructure damage assessment report to obtain the building damage status data.

8. The rapid visualization method of earthquake emergency information according to claim 1, characterized in that In step S5, the evaluation of the disaster level of the target area based on the preliminary earthquake damage level assessment result and the building damage status data includes: Conduct cluster analysis and level division on the building damage status data to obtain the building damage level classification result; Perform spatial annotation and attribute association on the earthquake area map based on the building damage level classification result to obtain the building damage spatial distribution map; Perform spatial matching on the building damage spatial distribution map based on the preliminary earthquake damage level assessment result and conduct a correlation analysis to obtain the earthquake damage assessment verification report; Evaluate the secondary disaster risk based on the building damage status data and the earthquake damage assessment verification report, and conduct a spatio-temporal evolution simulation on the evaluation result to obtain the earthquake damage diffusion trend prediction data; Conduct a comprehensive disaster level assessment of the target area based on the preliminary earthquake damage level assessment result, the building damage status data, and the earthquake damage diffusion trend prediction model to obtain the preliminary disaster level assessment result; Conduct an analysis of the population impact on the preliminary disaster level assessment result to obtain the casualty prediction data; calculate the economic value loss of buildings and infrastructure based on the preliminary disaster level assessment result to obtain the economic loss assessment report; Calibrate and adjust the preliminary disaster level assessment result based on the casualty prediction data and the economic loss assessment report to obtain the final disaster level assessment result; Conduct high-precision mapping and spatial visualization processing on the earthquake area based on the final disaster level assessment result to obtain an accurate earthquake damage distribution sketch; Optimize the layer organization and symbol identification of the accurate earthquake damage distribution sketch to obtain the accurate earthquake damage distribution map.

9. The rapid visualization method of earthquake emergency information according to claim 1, characterized in that Step S6 includes the following steps: Step S61: Convert the format of the wave propagation time series data, the preliminary earthquake damage level assessment result, and the accurate earthquake damage distribution map, and conduct standardization processing to obtain the visual standard data set; Step S62: Design a multi-dimensional expression of the disaster situation information based on the visual standard data set, and conduct interactive logic design and time series dynamic processing on the design result to obtain the dynamic visual expression scheme; Step S63: Conduct an information demand survey and behavior analysis of each user role based on the dynamic visual expression scheme to obtain the user role characteristics; Step S64: Differentially design the information presentation method and key content based on the user role characteristics to obtain the role-based information demand matrix; Step S65: Classify visualization layers based on the dynamic visualization expression scheme and the role-based information requirement matrix, and perform priority sorting on the classification results to obtain a layer combination configuration library; Step S66: Calculate the optimal matching of user roles for the layer combination configuration library to generate a role-based display scheme.

10. An earthquake emergency information rapid visualization system, characterized in that, For implementing the rapid visualization method of earthquake emergency information as described in claim 1, the rapid visualization system of earthquake emergency information includes: An earthquake data monitoring module, configured to obtain real-time earthquake monitoring data; based on the real-time earthquake monitoring data, grid-divide the population density distribution and key facility locations in the earthquake area to generate an earthquake area sensitivity matrix; based on the real-time earthquake monitoring data and the earthquake area sensitivity matrix, dynamically simulate the earthquake wave propagation process to generate wave propagation timing data; An earthquake terrain detection module, configured to identify the epicenter terrain and geomorphic vector data of the real-time earthquake monitoring data, and respectively evaluate the terrain undulation and geological stability to obtain earthquake terrain undulation index data and earthquake geological stability scoring data; perform spatial overlay analysis on the earthquake terrain undulation index data and the earthquake geological stability scoring data to generate a high-risk earthquake damage area distribution map; An earthquake event set construction module, configured to retrieve earthquake cases in a preset historical earthquake database based on the real-time earthquake monitoring data to construct a similar earthquake event set; An earthquake damage level evaluation module, configured to predict the earthquake damage distribution probability in the target area based on the earthquake area sensitivity matrix, the high-risk earthquake damage area distribution map, and the similar earthquake event set to generate a preliminary earthquake damage level evaluation result; An earthquake damage distribution map division module, configured to collect building damage status data at the epicenter based on the real-time earthquake monitoring data; evaluate the disaster level in the target area based on the preliminary earthquake damage level evaluation result and the building damage status data to generate an accurate earthquake damage distribution map; An emergency visualization module, configured to perform disaster situation information visualization processing on the wave propagation timing data, the preliminary earthquake damage level evaluation result, and the accurate earthquake damage distribution map, and perform layer combination matching for each user role based on the disaster situation information visualization processing result to obtain a role-based display scheme.

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