A method and system for rapid visualization of earthquake emergency information

Abstract

The present invention relates to the field of data visualization technology, and in particular to a method and system for rapid visualization of earthquake emergency information. The method comprises the following steps: acquiring real-time earthquake monitoring data; gridding the population density distribution and key facility locations in the earthquake zone based on the real-time earthquake monitoring data to generate a sensitivity matrix for the earthquake zone; dynamically simulating the seismic wave propagation process based on the real-time earthquake monitoring data and the seismic zone sensitivity matrix to generate wave propagation time series data; identifying epicenter topographic and geomorphological vector data from the real-time earthquake monitoring data, and separately evaluating the topographic relief and geological stability to obtain earthquake topographic relief index data and earthquake geological stability score data. The present invention utilizes data visualization technology to achieve rapid and accurate assessment and visualization of earthquake damage distribution, thereby improving the efficiency of earthquake emergency response and the scientific nature of disaster relief decision-making.

Description

Technical Field

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

[0002] Early approaches primarily used simple statistical analysis methods, rule bases, classical accidental error processing models, and data clustering to extract and verify partial data. However, these methods were mostly tailored to the specific characteristics of specific application domains, and research on data extraction techniques specifically for earthquake emergency information is limited. Currently, earthquake emergency information visualization is developing towards platform-based and intelligent development. For example, earthquake product and information visualization platforms utilize big data analysis and various data visualization analysis methods, leveraging flexible portal technologies to achieve multidimensional business analysis views. Their visualization modules utilize technologies such as 2D GIS and HTML5 to categorize and visualize various earthquake information. However, previous seismic wave propagation simulations have not adequately incorporated factors such as the actual geography and population of the earthquake zone. Seismic wave propagation is influenced by multiple factors, including topography and geological conditions. Traditional simulations rely solely on simple theoretical models, failing to dynamically account for these complex real-world conditions. This results in discrepancies between simulation results and actual seismic wave propagation. 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 purpose, a method for rapid visualization of earthquake emergency information includes the following steps:

[0005] Step S1: Acquire real-time earthquake monitoring data; divide the population density distribution and key facility locations of the earthquake zone into grids based on the real-time earthquake monitoring data to generate an earthquake zone sensitivity matrix; dynamically simulate the earthquake wave propagation process based on the real-time earthquake monitoring data and the earthquake zone sensitivity matrix to generate wave propagation time series data;

[0006] Step S2: Identify the epicenter topographic vector data of the real-time earthquake monitoring data, and evaluate the topographic relief and geological stability respectively to obtain earthquake topographic relief index data and earthquake geological stability score data; perform spatial overlay analysis on the earthquake topographic relief index data and earthquake geological stability score data to generate a distribution map of high-risk earthquake damage areas;

[0007] Step S3: Retrieving earthquake cases in a preset historical earthquake database based on real-time earthquake monitoring data to construct a set of similar earthquake events;

[0008] Step S4: Based on the earthquake zone sensitivity matrix, the distribution map of high-risk earthquake damage areas, and the set of similar earthquake events, the earthquake damage distribution probability of the target area is predicted to generate a preliminary earthquake damage level assessment result;

[0009] Step S5: Collecting damage status data of buildings at the epicenter based on real-time earthquake monitoring data; assessing the disaster level of the target area based on the preliminary earthquake damage level assessment results and the building damage status data, and generating an accurate earthquake damage distribution map;

[0010] Step S6: Visualize the wave propagation time series data, preliminary earthquake damage level assessment results, and accurate earthquake damage distribution map to perform disaster information visualization, and perform layer combination matching for each user role based on the disaster information visualization results to obtain a role-based display solution.

[0011] In step S1, the present invention grids the population density distribution and key facility locations of the earthquake zone based on real-time earthquake monitoring data to generate a sensitivity matrix for the earthquake zone. Simultaneously, the seismic wave propagation process is dynamically simulated based on the real-time earthquake monitoring data and the seismic wave sensitivity matrix to generate wave propagation time series data. This process accurately depicts the sensitivity of the earthquake zone and precisely simulates the dynamics of seismic wave propagation. The seismic zone sensitivity matrix clearly reflects the population density and key facility distribution in different areas of the earthquake zone, providing critical regional sensitivity data for subsequent earthquake damage assessment. The wave propagation time series data records in detail the propagation characteristics of seismic waves at different time and spatial locations, including information such as amplitude and frequency. This synergistic effect enables the dynamic characteristics of seismic wave propagation and the differences in sensitivity between earthquake zones to be fully considered in subsequent earthquake damage prediction and assessment, thereby improving the accuracy and reliability of earthquake damage prediction. In step S2, the epicenter topographic and geomorphological vector data from the real-time earthquake monitoring data is identified, and the topographic relief and geological stability are evaluated separately to obtain earthquake topographic relief index data and earthquake geological stability score data. Spatial overlay analysis of earthquake terrain relief index data and earthquake geological stability score data generates a distribution map of high-risk earthquake damage areas. This process accurately locates high-risk earthquake damage areas by combining a detailed analysis of the epicenter's topography and geomorphology with two key factors: topographic relief and geological stability. The earthquake terrain relief index data reflects the complexity of the terrain, such as the impact of steep slopes and valleys on seismic wave propagation and earthquake hazards. The seismic geological stability score data assesses 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 hazards. Spatial overlay analysis comprehensively considers these two data sets to generate a distribution map of high-risk earthquake damage areas. This map visually illustrates which areas are most likely to suffer severe damage in the event of an earthquake, providing clear guidance for earthquake emergency rescue and disaster prevention, facilitating the early deployment of rescue forces and the implementation of targeted protective measures. Step S3 retrieves earthquake examples from a preset historical earthquake database based on real-time earthquake monitoring data to construct a set of similar earthquake events. This process searches the historical earthquake database to identify historical earthquake examples similar to the current earthquake in terms of magnitude, focal depth, and seismic wave propagation characteristics, thereby constructing a set of similar earthquake events. The construction of a set of similar earthquake events provides rich historical data support for earthquake damage prediction. By analyzing similar earthquake events, we can understand the distribution patterns and disaster levels of earthquake damage in different regions under similar earthquake conditions. These historical data serve as a reference, making the process of predicting the probability of earthquake damage distribution in the target area based on the earthquake zone sensitivity matrix, the distribution map of high-risk areas for earthquake damage, and the set of similar earthquake events more scientific and reasonable. The preliminary earthquake damage level assessment results generated in step S4 have been significantly improved in accuracy and credibility 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, damage data on buildings at the epicenter is collected based on real-time earthquake monitoring data. The preliminary damage assessment results generated in step S4 are combined with the building damage data to assess the disaster level in the target area and generate a precise damage distribution map. This process corrects and refines the preliminary damage assessment results by collecting actual damage data on buildings at the epicenter, such as crack width and degree of collapse. The preliminary damage assessment results are based on model predictions and historical data, while the building damage data are based on actual on-site damage observations. Combining these two allows for a more accurate assessment of the disaster level in the target area. The precise damage distribution map clearly displays the extent of damage in different areas, including detailed information such as building damage and casualties. This accurate disaster assessment helps to rationally allocate rescue resources during earthquake emergency rescue, prioritize rescue efforts in the most severely affected areas, improve rescue efficiency, and reduce casualties and property damage. Step S6 visualizes the wave propagation time series data, the preliminary damage assessment results, and the precise damage distribution map. Based on the visualization results, user role layers are combined and matched to create a role-based display solution. Disaster information visualization can present complex earthquake monitoring data and damage assessment results in intuitive graphics, images, and charts, enabling different user roles to quickly understand and grasp disaster information. Role-based display solutions customize the visualization of information based on the needs of different user roles, such as earthquake rescue workers and disaster-affected residents. For example, rescue workers can see detailed information on the distribution of affected areas and rescue routes; they can also see an overall overview of the disaster and resource allocation needs; and disaster-affected residents can see information such as the location of shelters and rescue progress. This efficient information transmission method ensures that each user role can obtain the most useful information for them in a timely manner during the earthquake emergency response, improving the overall efficiency and coordination of the earthquake emergency response.

[0012] Preferably, the present invention further provides an earthquake emergency information rapid visualization system for executing the above-mentioned earthquake emergency information rapid visualization method, wherein the earthquake emergency information rapid visualization system comprises:

[0013] The earthquake data monitoring module 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 of the earthquake zone are divided into grids to generate the earthquake zone sensitivity matrix; based on the real-time earthquake monitoring data and the earthquake zone sensitivity matrix, the earthquake wave propagation process is dynamically simulated to generate wave propagation time series data;

[0014] The earthquake terrain detection module is used to identify the epicenter topographic vector data of real-time earthquake monitoring data, and evaluate the terrain relief and geological stability respectively to obtain earthquake terrain relief index data and earthquake geological stability score data; the earthquake terrain relief index data and earthquake geological stability score data are spatially superimposed and analyzed to generate a distribution map of high-risk earthquake damage areas;

[0015] An earthquake event set construction module is used to retrieve earthquake cases in a preset historical earthquake database based on real-time earthquake monitoring data and construct a set of similar earthquake events;

[0016] The earthquake damage level assessment module is used to predict the probability of earthquake damage distribution in the target area based on the earthquake zone sensitivity matrix, the distribution map of high-risk earthquake damage areas, and a set of similar earthquake events, and generate preliminary earthquake damage level assessment results;

[0017] The earthquake damage distribution map division module is used to collect building damage status data at the epicenter based on real-time earthquake monitoring data; based on the preliminary earthquake damage level assessment results and building damage status data, the disaster level of the target area is assessed and an accurate earthquake damage distribution map is generated;

[0018] The emergency visualization module is used to visualize the disaster information of wave propagation time series data, preliminary earthquake damage level assessment results and accurate earthquake damage distribution map, and to match the user role layers based on the disaster information visualization results to obtain a role-based display solution.

[0019] The system of the present invention obtains real-time data through the earthquake data monitoring module and generates the earthquake zone sensitivity matrix and wave propagation time series data. The earthquake terrain detection module accurately locates the high-risk areas of earthquake damage. The earthquake event set construction module provides historical data support. The earthquake damage level assessment module and the earthquake damage distribution map division module perform earthquake damage prediction and accurate assessment in turn. The emergency visualization module realizes the visualization and role-based display of disaster information. The whole process works together to achieve a rapid and efficient transformation from earthquake monitoring to accurate disaster assessment and visualization, providing comprehensive, accurate and timely information support for earthquake emergency rescue and decision-making, and significantly improving the scientific nature and effectiveness of earthquake emergency response. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0021] Figure 1 A schematic flow chart of the steps of a method for rapid visualization of earthquake emergency information according to the present invention;

[0022] Figure 2 for Figure 1 Detailed step flow diagram of step S1; DETAILED DESCRIPTION

[0023] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0024] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0025] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0026] To achieve this, please refer to Figures 1 to 2 The present invention provides a method for rapid visualization of earthquake emergency information, the method comprising the following steps:

[0027] Step S1: Acquire real-time earthquake monitoring data; divide the population density distribution and key facility locations of the earthquake zone into grids based on the real-time earthquake monitoring data to generate an earthquake zone sensitivity matrix; dynamically simulate the earthquake wave propagation process based on the real-time earthquake monitoring data and the earthquake zone sensitivity matrix to generate wave propagation time series data;

[0028] Step S2: Identify the epicenter topographic vector data of the real-time earthquake monitoring data, and evaluate the topographic relief and geological stability respectively to obtain earthquake topographic relief index data and earthquake geological stability score data; perform spatial overlay analysis on the earthquake topographic relief index data and earthquake geological stability score data to generate a distribution map of high-risk earthquake damage areas;

[0029] Step S3: Retrieving earthquake cases in a preset historical earthquake database based on real-time earthquake monitoring data to construct a set of similar earthquake events;

[0030] Step S4: Based on the earthquake zone sensitivity matrix, the distribution map of high-risk earthquake damage areas, and the set of similar earthquake events, the earthquake damage distribution probability of the target area is predicted to generate a preliminary earthquake damage level assessment result;

[0031] Step S5: Collecting damage status data of buildings at the epicenter based on real-time earthquake monitoring data; assessing the disaster level of the target area based on the preliminary earthquake damage level assessment results and the building damage status data, and generating an accurate earthquake damage distribution map;

[0032] Step S6: Visualize the wave propagation time series data, preliminary earthquake damage level assessment results, and accurate earthquake damage distribution map to perform disaster information visualization, and perform layer combination matching for each user role based on the disaster information visualization results to obtain a role-based display solution.

[0033] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a flow chart showing the steps of a method for rapid visualization of earthquake emergency information according to the present invention. In this example, the method for rapid visualization of earthquake emergency information includes the following steps:

[0034] Step S1: Acquire real-time earthquake monitoring data; divide the population density distribution and key facility locations of the earthquake zone into grids based on the real-time earthquake monitoring data to generate an earthquake zone sensitivity matrix; dynamically simulate the earthquake wave propagation process based on the real-time earthquake monitoring data and the earthquake zone sensitivity matrix to generate wave propagation time series data;

[0035] In this embodiment of the present invention, real-time earthquake monitoring data is first acquired through a high-precision earthquake monitoring network. This network consists of multiple geographically distributed earthquake monitoring stations, each equipped with a three-component seismometer capable of real-time acquisition of seismic wave vibration information, including amplitude, frequency, arrival time, and other parameters, for longitudinal (P) waves, shear (S) waves, and surface waves. Seismic data collected by each monitoring station is sampled at preset intervals (e.g., every 0.1 seconds) based on timestamps, forming a real-time earthquake monitoring dataset containing a time series. Each data point in the dataset contains specific seismic wave vibration parameters and the corresponding monitoring station location information (longitude, latitude, and altitude). Subsequently, based on the real-time earthquake monitoring data, a grid is generated to identify the population density distribution and key facility locations in the earthquake zone. With the epicenter as the origin, the earthquake zone is divided into multiple regular grid cells using a preset grid size (e.g., 1 km x 1 km). Using geographic information system (GIS) technology, combined with demographic data from the earthquake zone and the geographic coordinates of key facilities, the population and location information of key facilities are mapped to each grid cell. For population density distribution, the population density value is calculated based on the number of people within each grid cell. For the location of key facilities, the grid cell number and the specific location coordinates within each grid cell are recorded. This information is integrated to generate a seismic sensitivity matrix, with rows and columns corresponding to the grid cell numbers in the seismic zone. Each element in the matrix is ​​the sum of the population density value of the grid cell and the weight of the key facility (a weight coefficient is pre-set based on the importance of the facility). This quantifies the sensitivity of each grid cell. Next, a dynamic simulation of seismic wave propagation is performed based on real-time seismic monitoring data and the seismic sensitivity matrix. The seismic wave propagation equation is numerically solved using the finite difference method. Using the initial vibration parameters of the seismic waves from the real-time seismic monitoring data as the initial conditions for the simulation, the seismic zone is divided into discrete computational grids consistent with the grid division. Based on the propagation velocity of seismic waves in different media (geological medium parameters such as rock density and elastic modulus are obtained from geological exploration data in different regions of the seismic zone, and the propagation velocity of seismic waves in each medium is calculated), and the sensitivity of each grid cell in the seismic sensitivity matrix, corresponding boundary conditions and damping coefficients are set. During the simulation process, the propagation state of seismic waves in each calculation grid is calculated step by step according to the preset time step (for example, 0.01 seconds), including the amplitude, phase and other information of the wave field, and finally the wave propagation time series data is generated. This data records in detail the propagation of seismic waves at different locations and times in the earthquake zone, providing basic data support for the subsequent visualization of earthquake emergency information.

[0036] Step S2: Identify the epicenter topographic vector data of the real-time earthquake monitoring data, and evaluate the topographic relief and geological stability respectively to obtain earthquake topographic relief index data and earthquake geological stability score data; perform spatial overlay analysis on the earthquake topographic relief index data and earthquake geological stability score data to generate a distribution map of high-risk earthquake damage areas;

[0037] In one embodiment of the present invention, topographic vector data of the epicenter region is extracted from real-time earthquake monitoring data. Using a digital elevation model (DEM) data source and combined with the location information of earthquake monitoring stations, the topographic area within a certain range around the epicenter is determined. Using the vectorization function of geographic information system (GIS) software, the topographic data of this area is converted into a vector format, including vector elements such as contour lines, slope, and aspect. The contour lines are spaced 5 meters apart, and the slope and aspect are calculated with an accuracy of 0.1 degrees. This generates a vector dataset of the epicenter topography, containing detailed information such as the spatial coordinates, elevation, slope, and aspect values ​​of each vector element. Next, the terrain relief and geological stability are assessed separately. For the terrain relief assessment, the terrain relief formula is used: the terrain relief is calculated as the elevation difference between the highest and lowest points in the area divided by the area's planar surface area. Using a 1 square kilometer x 1 square kilometer grid as the evaluation unit, the terrain relief is calculated for each grid cell to generate earthquake terrain relief index data. Each grid cell in the data corresponds to a terrain relief value, ranging from 0 to 1000 meters per square kilometer. For the geological stability assessment, based on geological survey data, including parameters such as rock and soil shear strength, lithology classification, and groundwater level, a geological stability evaluation model (based on pre-defined geological parameter weights and evaluation criteria) was used to score each grid cell on a scale of 0 to 100, with higher scores indicating poorer geological stability. This yielded a seismic geological stability score, with each grid cell corresponding to a single geological stability score. The seismic terrain relief index data and the seismic geological stability score data were then spatially overlaid for analysis. Using GIS software, the two data sets were spatially registered according to the same geographic coordinate system, with the grid cell serving as the basic analysis unit, and a weighted summation method was used for the overlay analysis. The weight of the terrain relief index was set to 0.4, and the weight of the geological stability score was set to 0.6. A comprehensive risk value was calculated for each grid cell, ranging from 0 to 100. According to the size of the comprehensive risk value, the earthquake zone is divided into different risk level areas. For example, a comprehensive risk value greater than 80 is a high-risk area, 60 to 80 is a medium-risk area, and less than 60 is a low-risk area. A distribution map of high-risk areas for earthquake damage is generated. The distribution map uses different colors or legends to identify areas of different risk levels, intuitively showing the spatial distribution of high-risk areas for earthquake damage.

[0038] Step S3: Retrieving earthquake cases in a preset historical earthquake database based on real-time earthquake monitoring data to construct a set of similar earthquake events;

[0039] In an embodiment of the present invention, key characteristic parameters are extracted from real-time earthquake monitoring data, including the magnitude, focal depth, time of occurrence, epicenter location (longitude, latitude) of the earthquake, and waveform characteristics of the main seismic waves (such as the time difference between the first arrival of P waves and S waves, amplitude ratio, etc.). These parameters are collected by seismographs in the earthquake monitoring network and obtained through preliminary processing, wherein the accuracy of the magnitude is 0.1, the accuracy of the focal depth is 1 kilometer, the accuracy of the time of occurrence is at the second level, the accuracy of the epicenter location is 0.01 degrees, and the extraction accuracy of the waveform characteristic parameters is 1%. Then, database retrieval technology is used to search for similar earthquake cases in a preset historical earthquake database based on these key characteristic parameters. The historical earthquake database adopts a relational database structure, which contains multiple fields, which respectively store information such as the magnitude, focal depth, time of occurrence, epicenter location, and waveform characteristics of historical earthquakes. During the search, the matching thresholds for magnitude were set to ±0.5, the matching threshold for focal depth to ±5 kilometers, the matching range for earthquake onset time to within one hour, the matching range for epicenter location to within a latitude and longitude difference of less than 0.1 degrees, and the matching thresholds for waveform characteristic parameters to within a relative error of less than 10% for both first arrival time difference and amplitude ratio. Using these search parameters, historical earthquake events that met the matching criteria were screened from the historical earthquake database. The relevant data for these events was extracted, including basic earthquake information (such as magnitude, focal depth, onset time, and epicenter location), earthquake waveform data, and corresponding disaster impact data (such as affected area, number of casualties, and economic losses). This data was then integrated into a set of similar earthquake events, with each event represented as an independent record within the set. This record contained complete earthquake characteristic parameters and disaster impact data for subsequent analysis and application.

[0040] Step S4: Based on the earthquake zone sensitivity matrix, the distribution map of high-risk earthquake damage areas, and the set of similar earthquake events, the earthquake damage distribution probability of the target area is predicted to generate a preliminary earthquake damage level assessment result;

[0041] In this embodiment of the present invention, the data from the earthquake zone sensitivity matrix, the high-risk earthquake damage area distribution map, and the similar earthquake event set are first integrated. The earthquake zone sensitivity matrix contains the sensitivity values ​​of each grid cell in the earthquake zone. The high-risk earthquake damage area distribution map identifies areas of varying risk levels. The similar earthquake event set provides historical earthquake damage data. Using geographic information system (GIS) technology, the earthquake zone sensitivity matrix and the high-risk earthquake damage area distribution map are spatially registered according to a unified geographic coordinate system to ensure spatial consistency between the two. Simultaneously, the historical earthquake events from the similar earthquake event set that most closely match the target region's characteristic parameters, such as magnitude and focal depth, are extracted and their damage distribution data is recorded. Next, a probability prediction of earthquake damage distribution is performed for the target region based on spatial analysis techniques. In the GIS software, the sensitivity values ​​from the earthquake zone sensitivity matrix are used as weighting factors to weight the high-risk areas in the high-risk earthquake damage area distribution map. For each grid cell, the frequency of occurrence of the corresponding historical earthquake damage level in the similar earthquake event set is calculated as the grid cell's probability of damage distribution. The specific calculation method is to count the number of occurrences of the corresponding damage level (e.g., minor, moderate, severe) for each grid cell in a set of similar earthquake events and divide this by the total number of similar earthquake events to obtain the damage distribution probability value for that grid cell. For example, if a grid cell experiences minor damage three times, moderate damage four times, and severe damage three times out of 10 similar earthquake events, the distribution probabilities of minor, moderate, and severe damage are 0.3, 0.4, and 0.3, respectively. Based on the calculated damage distribution probability and combined with pre-set damage level classification criteria (e.g., a minor damage probability greater than 0.6, a moderate damage probability greater than 0.6, and a severe damage probability greater than 0.6), a damage level assessment is performed for each grid cell in the target area. If the damage distribution probability for a grid cell does not meet the criteria for a single damage level, a comprehensive assessment is made based on the probability values ​​to generate a preliminary damage level assessment result. Finally, the preliminary earthquake damage level assessment results will be visualized in the form of a map, and areas with different earthquake damage levels will be distinguished by different colors or legends to intuitively present the distribution of earthquake damage in the target area.

[0042] Step S5: Collecting damage status data of buildings at the epicenter based on real-time earthquake monitoring data; assessing the disaster level of the target area based on the preliminary earthquake damage level assessment results and the building damage status data, and generating an accurate earthquake damage distribution map;

[0043] In this embodiment of the present invention, drone remote sensing technology and a ground sensor network are used to collect damage status data for buildings at the epicenter. The drone, equipped with a high-resolution optical camera and lidar equipment, flies at low altitude along a pre-set flight path (covering the epicenter and surrounding area), at a speed of 1 meter per second and an altitude of 100 meters. It captures images of the building's exterior with an image resolution of no less than 0.1 meter per pixel. Simultaneously, accelerometers and displacement sensors in the ground sensor network are installed at key locations of the building (such as the roof and wall joints) to collect vibration acceleration and displacement data in real time. The sampling frequency is 100 Hz, and the data is recorded for 10 minutes after the earthquake. The drone image data and the vibration data collected by the ground sensors are integrated to form a building damage status dataset. The dataset contains information such as the building's image number, shooting location coordinates, peak vibration acceleration, and maximum displacement. Next, the disaster level of the target area is assessed based on the preliminary damage level assessment results and the building damage status data. The building damage status data is spatially matched with the preliminary damage level assessment results, using the building's geographic coordinates as the matching basis. The building damage status data is mapped to the grid cells of the preliminary damage level assessment results. For each building within each grid cell, the damage severity is assessed based on the peak vibration acceleration and maximum displacement values ​​in the damage status data, combined with the building's structural type (e.g., reinforced concrete, brick-concrete, etc.). Pre-defined building damage assessment criteria (for example, a peak vibration acceleration greater than 0.3g or a maximum displacement exceeding 10cm for reinforced concrete structures is considered severe damage) are used to determine each building's damage level (minor, moderate, or severe). This damage level is then combined with the preliminary damage level assessment results for a comprehensive analysis. For each grid cell, the damage level distribution of buildings within that cell is statistically analyzed, and the percentage of buildings with minor, moderate, and severe damage is calculated. If the percentage of severely damaged buildings in a grid cell exceeds 50%, the damage level for that grid cell is raised by one level; if the percentage of moderately damaged buildings exceeds 70%, the initial damage level is maintained; and if the percentage of slightly damaged buildings exceeds 80%, the damage level for that grid cell is lowered by one level. This comprehensive analysis generates a precise damage distribution map, with areas of different damage levels indicated by different colors or legends, accurately reflecting the damage distribution within the target area.

[0044] Step S6: Visualize the wave propagation time series data, preliminary earthquake damage level assessment results, and accurate earthquake damage distribution map to perform disaster information visualization, and perform layer combination matching for each user role based on the disaster information visualization results to obtain a role-based display solution.

[0045] In one embodiment of the present invention, wave propagation time series data is visualized. Using the time series visualization tool within geographic information system (GIS) software, information such as seismic wave amplitude, frequency, and propagation time in the wave propagation time series data is dynamically displayed in chronological order and spatial location. The time step is set to 0.1 seconds, and the amplitude distribution of the seismic waves is represented by contour lines with a spacing of 0.1 unit amplitude. The frequency distribution is represented by a color cloud map, ranging from blue (low frequency) to red (high frequency). The propagation path and temporal changes of the seismic waves are displayed in an animated form, with the animation playing at 1 / 100 of the real-time seismic wave propagation speed. This generates a visualization layer for the wave propagation time series data. Next, the preliminary damage assessment results are visualized. Within the GIS software, the grid cells in the preliminary damage assessment results are categorized according to damage level, with different colors used to indicate areas with minor, moderate, and severe damage levels. Areas with minor damage are indicated in light yellow, areas with moderate damage in orange, and areas with severe damage in red. The boundaries of each grid cell are clearly visible, and the grid cell size is 1 km x 1 km. A visualization layer of the preliminary earthquake damage assessment results is generated. Then, the precise earthquake damage distribution map is visualized. The precise earthquake damage distribution map already contains detailed information on the damage status of buildings. 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 assessment results. At the same time, detailed building annotations are added, including the building number, damage level, and specific location coordinates. The annotation font size is 8 and the color is black, generating a visualization layer of the precise earthquake damage distribution map. Finally, based on the results of the disaster information visualization processing, the layer combination of each user role is matched. According to the preset user roles (such as earthquake emergency commander, rescue team, disaster-stricken people, etc.), different layer combinations are configured for each role. For earthquake emergency response personnel, a combination of visualization layers for wave propagation time series data, preliminary damage assessment results, and precise damage distribution maps highlights high-risk areas and key facility locations. For rescue teams, a combination of visualization layers for precise damage distribution maps and building damage status annotation layers highlights the location and damage levels of damaged buildings. For the affected population, a combination of visualization layers for preliminary damage assessment results and safe zone identification layers highlights safe areas and evacuation routes. Through the GIS software's layer management function, layer combinations for different user roles are saved and corresponding role-based display schemes are generated. Each scheme includes detailed parameters such as layer name, display order, transparency settings, and annotation information to meet the emergency information needs of different user roles.

[0046] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1Detailed step flow diagram of step S1 in the embodiment of the present invention, step S1 includes the following steps:

[0047] Step S11: collecting raw seismic waveform data in real time through a distributed seismic monitoring sensor network, and performing filter calibration and standardization processing to generate real-time seismic monitoring data;

[0048] Step S12: Adaptively dividing the earthquake zone into grids based on the real-time earthquake monitoring data to obtain grid unit data of the earthquake zone;

[0049] Step S13: performing spatial interpolation analysis of population density based on the earthquake zone grid cell data to obtain gridded population density distribution data;

[0050] Step S14: performing spatial query of geographic information of key facilities based on the grid unit data of the earthquake zone to obtain gridded location data of key facilities;

[0051] Step S15: Calculating sensitivity weights for earthquake zone grid cell data based on the gridded population density distribution data and the gridded key facility location data to generate an earthquake zone sensitivity matrix;

[0052] Step S16: numerically simulate the seismic wave propagation mechanism based on the real-time earthquake monitoring data and the earthquake zone sensitivity matrix to generate wave propagation time data.

[0053] In this embodiment of the present invention, the collected raw seismic waveform data undergoes filtering, calibration, and standardization. Digital filtering technology is employed to filter the data using a bandpass filter with a frequency range of 0.1 Hz to 10 Hz to remove high-frequency noise and low-frequency interference. The filtered data is then calibrated using a calibration algorithm. Calibration parameters include sensor gain, phase delay, and zero drift. The calibrated data is further standardized to normalize the amplitude data to a range of 0 to 1, generating real-time seismic monitoring data. Real-time seismic monitoring data is timestamped, with each data point containing specific seismic wave vibration parameters and the corresponding monitoring station location information (longitude, latitude, and altitude). Based on the real-time seismic monitoring data, adaptive meshing technology is used to create a grid for the earthquake zone. First, the earthquake zone is defined as a circular area with a radius of 100 kilometers centered on the epicenter. The grid size and density are adaptively adjusted based on parameters such as magnitude, focal depth, and seismic wave propagation velocity in the real-time seismic monitoring data. Near the epicenter, the grid size was set to 1 km x 1 km. As the distance from the epicenter increased, the grid size gradually increased to 5 km x 5 km. Using geographic information system (GIS) software, the earthquake zone was divided into multiple regular grid cells. Each grid cell had a unique number and spatial location information (longitude, latitude, and area). This generated grid cell data for the earthquake zone. Spatial interpolation analysis of population density was performed using this grid cell data. First, demographic data for the earthquake zone was obtained, including the population and geographic boundaries of each administrative division. This demographic data was imported into the GIS software and, combined with the grid cell data for the earthquake zone, spatial interpolation analysis of population density was performed using the Kriging interpolation method. The Kriging interpolation parameters were set to a spherical semivariogram model, a search radius of 10 km, and an interpolation accuracy of 0.01 people per square kilometer. Through interpolation analysis, the population density value within each grid cell was calculated, generating gridded population density distribution data. This data contains the population density value and spatial location information for each grid cell. Spatial queries of key facility geographic information were performed using the grid cell data for the earthquake zone. First, geographic information data on key facilities in the earthquake zone was collected, including the location coordinates and attribute information of facilities such as hospitals, schools, hydropower stations, and bridges. This data was imported into GIS software and spatially matched with the grid cell data in the earthquake zone. Using the spatial query function, the grid cell number of each key facility was determined, and its specific location coordinates within the grid cell were recorded. Gridded key facility location data was generated, which included the number, type, and specific location coordinates of key facilities in each grid cell. Based on the gridded population density distribution data and the gridded key facility location data, a sensitivity weight calculation was performed on the grid cell data in the earthquake zone. First, the weight coefficient for population density was set to 0.6, and the weight coefficient for key facilities was set to 0.4.For each grid cell, a sensitivity weight is calculated using the following formula: Sensitivity Weight = Population Density × Population Density Weight Coefficient + Number of Key Facilities × Key Facilities Weight Coefficient. For example, if the population density of a grid cell is 100 people / square kilometer and the number of key facilities is 2, the sensitivity weight for that grid cell is (100 × 0.6) + (2 × 0.4) = 60.8. The calculated sensitivity weight is assigned to each grid cell to generate a seismic zone sensitivity matrix. The rows and columns of the matrix correspond to the grid cell numbers in the seismic zone, and each element in the matrix represents the sensitivity weight for that grid cell. Based on real-time earthquake monitoring data and the seismic zone sensitivity matrix, numerical simulation techniques are used to simulate the seismic wave propagation mechanism. The finite difference method is used to numerically solve the seismic wave propagation equation, dividing the seismic zone into discrete computational grids consistent with the grid divisions. Using the initial vibration parameters of seismic waves from real-time seismic monitoring data as the initial conditions for the simulation, the propagation speeds of seismic waves in different media are set. (Geological medium parameters such as rock density and elastic modulus are obtained from geological exploration data for different regions of the earthquake zone, and the propagation speeds of seismic waves in each medium are then calculated.) Simultaneously, a damping coefficient is set based on the sensitivity weight of each grid cell in the earthquake zone sensitivity matrix. The damping coefficient ranges from 0.1 to 1.0, with higher sensitivity weights indicating lower damping coefficients. During the simulation, the propagation state of seismic waves in each computational grid is calculated stepwise according to a preset time step (0.01 seconds), including information such as the amplitude and phase of the wave field. This ultimately generates wave propagation time series data, which details the propagation of seismic waves at different locations and times within the earthquake zone.

[0054] Preferably, in step S2, identifying epicenter topographic and geomorphic vector data of real-time earthquake monitoring data and respectively evaluating topographic relief and geological stability include:

[0055] Retrieve multi-source remote sensing images based on real-time earthquake monitoring data, and perform geometric correction and orthorectification to obtain topographic image data of the earthquake zone;

[0056] Extract the terrain boundary line based on the earthquake zone terrain image data, build a triangulated network model of the earthquake zone, and obtain the digital elevation model of the earthquake zone;

[0057] Calculate the slope, aspect and curvature of the digital elevation model of the earthquake zone, and enhance the calculation results to obtain enhanced terrain feature data;

[0058] Based on the digital elevation model of the earthquake zone, the local elevation changes are statistically analyzed, and then standardized and graded to obtain earthquake terrain relief index data;

[0059] Based on the spatial matching of enhanced terrain feature data with the preset geological structure database, a geological structure distribution map of the earthquake zone is obtained; historical earthquake response analysis is performed on the geological structure distribution map of the earthquake zone to obtain a geological historical stability score;

[0060] The fault activity is evaluated based on real-time earthquake monitoring data and geological historical stability scores to obtain earthquake geological stability score data.

[0061] In this embodiment of the present invention, high-resolution optical and radar images covering the earthquake zone are retrieved from a multi-source remote sensing image database using epicenter location and impact range information from real-time earthquake monitoring data. The resolution of the optical images must be no less than 0.5 meters per pixel, and the resolution of the radar images must be no less than 1 meter per pixel. Geometric correction is then performed on the retrieved remote sensing images using geographic information system (GIS) and image processing software. A polynomial transformation model is used, and the accuracy of control points is set to 0.1 pixels. The geometric accuracy error of the corrected images is controlled to within 1 pixel. Orthorectification is then performed based on digital elevation model (DEM) data, with the projection set to UTM and the ellipsoid model to WGS-84. The horizontal accuracy of the corrected images reaches 1 meter, resulting in topographic image data of the earthquake zone. This data includes image pixel values, spatial resolution, projection information, and geographic coordinate information. Using this topographic image data, terrain boundaries are extracted using an edge detection algorithm. The Canny edge detection algorithm is used with a low threshold of 50 and a high threshold of 150 to extract clear terrain boundaries. Then, based on the extracted terrain boundaries and elevation information from the terrain image data, a triangulated network model of the earthquake zone was constructed using the Delaunay triangulation method. The minimum angle of the triangulated network was set to no less than 20 degrees, and the maximum side length was set to no more than 50 meters to ensure model accuracy and stability. A digital elevation model (DEM) of the earthquake zone was generated through interpolation calculations. The DEM grid spacing was set to 1 meter by 1 meter, with an elevation accuracy of 0.1 meter. The model contains the elevation value and geographic coordinate information of each grid point. Based on the DEM of the earthquake zone, the slope, aspect, and curvature were calculated using terrain analysis tools in GIS software. The slope was calculated using the Horn algorithm with a 3×3 neighborhood, with an accuracy of 0.1 degree; the aspect was calculated using the 8-azimuth encoding method, with an accuracy of 1 degree; and the curvature was calculated using a quadratic polynomial fitting method, with an accuracy of 0.01. The calculated slope, aspect, and curvature data were enhanced using histogram equalization to enhance contrast and readability. This yielded enhanced terrain feature data, which includes slope, aspect, and curvature values ​​for each grid point, along with their enhanced pixel values. Local elevation changes were statistically analyzed within the earthquake-affected digital elevation model using a moving window method with a 3×3 grid size. The elevation standard deviation within each window was calculated. The calculated elevation standard deviation data were normalized using the Z-score method to convert the data to a mean of 0 and a standard deviation of 1. The earthquake-affected area was then classified into three levels based on the standardized elevation standard deviation: mild (-1 to 1), moderate (-2 to -1 and 1 to 2), and severe (less than -2 and greater than 2). This yielded earthquake terrain relief index data, which includes the terrain relief level and its corresponding standardized elevation standard deviation for each grid point.Enhanced terrain feature data was used to match the data with a pre-set geological structure database using spatial matching technology. The database contains geological structure information for the earthquake zone and surrounding areas, such as the location, type, and properties of faults and folds. Spatial analysis tools were used to match the slope, aspect, and curvature values ​​in the terrain feature data. Matching thresholds were set to slope differences less than 5 degrees, aspect differences less than 10 degrees, and curvature differences less than 0.1. This spatial query and matching was performed against the geological structure information in the database, resulting in a geological structure distribution map for the earthquake zone. Different types of geological structures and their spatial locations were identified using different colors or legends. The geological structure distribution map for the earthquake zone was analyzed for historical earthquake responses. Historical earthquake data from the earthquake zone and surrounding areas was collected, including information such as magnitude, focal depth, onset time, and epicenter location. Spatial analysis tools within GIS software were used to analyze the response of geological structures to historical earthquakes, such as the activity of faults and the degree of deformation of folds. Geological structures are scored based on their response to historical earthquakes using a pre-defined scoring criteria, ranging from 0 to 100. Higher scores indicate lower historical stability. This yields a historical stability score, which includes the stability score and spatial location information for each tectonic unit. Fault activity in the earthquake zone is assessed by combining seismic waveform characteristics (such as the time difference between the first arrival of P and S waves and the amplitude ratio) from real-time earthquake monitoring data with the historical stability score. Active faults within the earthquake zone are identified using a pre-defined fault activity identification algorithm using seismic waveform characteristic parameters. Then, combined with the historical stability score, a weighted comprehensive assessment method is used, with a weight of 0.6 for the seismic waveform characteristic parameters and a weight of 0.4 for the historical stability score. A seismic stability score for each fault unit is calculated, ranging from 0 to 100, with higher scores indicating lower stability. This yields the historical stability score data, which includes the stability score and spatial location information for each fault unit.

[0062] Preferably, performing spatial superposition analysis on the earthquake topography index data and the earthquake geological stability score data in step S2 includes:

[0063] Based on the earthquake terrain relief index data and the earthquake geological stability score data, the spatial data superposition operation of the earthquake zone is carried out to obtain the comprehensive risk score matrix;

[0064] Perform threshold segmentation and cluster analysis on the comprehensive risk score matrix to obtain risk level partition data;

[0065] Based on the risk level zoning data, high-risk areas in the earthquake zone are extracted and their boundaries are identified to obtain a distribution map of high-risk areas for earthquake damage.

[0066] In this embodiment of the present invention, seismic terrain relief index data and seismic geological stability score data are imported into geographic information system (GIS) software. The spatial resolution of the two data sets is ensured to be consistent, with both being 1 meter x 1 meter grid data. Spatial data overlay calculations are performed on the two data sets, using a weighted summation method. The weight of the seismic terrain relief index data is set to 0.5, and the weight of the seismic geological stability score data is set to 0.5. The calculation formula is: Comprehensive Risk Score = (Seismic Terrain Relief Index Data × 0.5) + (Seismic Geological Stability Score Data × 0.5). This formula is used to calculate the comprehensive risk score for each grid point, generating a comprehensive risk score matrix. Each element in the matrix represents the comprehensive risk score for the corresponding grid point, ranging from 0 to 100. The comprehensive risk score matrix is ​​then thresholded, with three thresholds set at 33, 66, and 100. The comprehensive risk scores are then categorized 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, a cluster analysis was performed on the grid points within each risk level using the K-means clustering algorithm, with a set number of cluster centers of 4 and 100 iterations. The convergence criterion was that the change in cluster centers was less than 0.01. Through cluster analysis, grid points with similar risk characteristics were grouped into the same cluster area, generating risk level zoning data. This data contains the boundary information, risk level, and number of grid points for each cluster area. Based on the risk level zoning data, high-risk areas (including high-risk and extremely high-risk areas) were extracted. Spatial analysis tools in GIS software were used to identify the boundaries of these high-risk areas. A morphological boundary extraction method was used, with a 3×3 square as the structuring element. Dilation and erosion operations were performed to extract the precise boundaries of the high-risk areas. The extracted high-risk area boundaries were saved as a vector layer and overlaid with the risk level zoning data. A distribution map of high-risk areas was generated, with different risk levels indicated by different colors: high-risk areas are represented in red, and extremely high-risk areas are represented in dark red. The boundaries are clearly visible, visually demonstrating the distribution of high-risk areas in the earthquake zone.

[0067] Preferably, step S3 includes the following steps:

[0068] Step S31: querying a high-performance historical earthquake data server based on real-time earthquake monitoring data parameters, and performing screening and sorting to obtain candidate similar earthquake event data sets;

[0069] Step S32: performing similarity calculation on the candidate similar earthquake event data set, and performing weight assignment and threshold screening of the candidate events based on the similarity calculation results to obtain a preliminary screening result of similar earthquake events;

[0070] Step S33: extracting historical earthquake damage based on the initial screening results of similar earthquake events, and performing structured integration and metadata annotation on the extracted results to obtain a set of similar earthquake events.

[0071] In this embodiment of the present invention, key parameters are first extracted from real-time earthquake monitoring data, including magnitude (M), focal depth (D), epicenter location (longitude Lon, latitude Late), and earthquake occurrence time (T). The magnitude is accurate to 0.1, the focal depth is accurate to 1 kilometer, the epicenter location is accurate to 0.01 degrees, and the earthquake occurrence time is accurate to the second level. These parameters are used to construct a query statement, which is then executed through the parameterized query interface of a high-performance historical earthquake data server. The query conditions are set as follows: magnitude range M ± 0.5, focal depth range D ± 10 kilometers, epicenter location range latitude and longitude difference less than 0.1 degrees, and earthquake occurrence time range within 1 hour before and after T. The server returns historical earthquake event data that meets these conditions. The data fields include event number, magnitude, focal depth, epicenter location, earthquake occurrence time, and corresponding earthquake damage data. Next, the historical earthquake event data obtained from the query is filtered and sorted. The filtering condition is to exclude events with a magnitude less than 3.0 or a focal depth greater than 300 kilometers. The ranking is based on the combined similarity of magnitude and focal depth, calculated using the formula: 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 by similarity to obtain a candidate similar earthquake event dataset, which contains the event number, magnitude, focal depth, epicenter location, time of occurrence, similarity value, and corresponding damage data. For each event in the candidate similar earthquake event dataset, its similarity with the real-time earthquake monitoring data is further calculated. In addition to magnitude and focal depth, the similarity of epicenter location and time of occurrence is also considered. The formula for calculating the similarity of epicenter location is: Similarity = 1-(ΔLon²+ΔLat²)^(1 / 2), where ΔLon and ΔLat are the differences in longitude and latitude between the candidate event and the epicenter location in the real-time data, respectively. The formula for calculating earthquake occurrence time similarity is: Similarity = 1 - |ΔT| / 3600, where Σ comprehensive similarity is the sum of the comprehensive similarities of all events in the candidate event set. Then, a similarity threshold of 0.7 was set to screen out events with a comprehensive similarity greater than or equal to 0.7, resulting in a preliminary screening of similar earthquake events. The dataset contains event number, magnitude, focal depth, epicenter location, occurrence time, comprehensive similarity, weight, and corresponding earthquake damage data. Historical earthquake damage data was extracted from this preliminary screening of similar earthquake events, including information such as the affected area, casualties, damaged buildings, and economic losses. This extracted historical earthquake damage data was structured and integrated, organized in a unified data format. The data table contains fields such as event number, magnitude, focal depth, epicenter location, occurrence time, comprehensive similarity, weight, affected area, casualties, damaged buildings, and economic losses. The integrated data was then annotated with metadata. The metadata includes information such as the data source, data collection time, data processing method, and data quality assessment.The data source is labeled "High-Performance Historical Earthquake Data Server," the data acquisition time is labeled the exact time of the query, the data processing method is labeled "Similarity Calculation and Weight Assignment," and the data quality assessment is labeled "High," indicating that the data has been rigorously screened and processed and has a high degree of credibility. The resulting dataset of similar earthquake events contains complete event information, damage data, and metadata annotations, providing a foundation for subsequent analysis and application.

[0072] Preferably, step S4 includes the following steps:

[0073] Step S41: performing data format unification processing on the earthquake zone sensitivity matrix, the earthquake damage high risk area distribution map and the similar earthquake event set to obtain standardized input data for earthquake damage assessment;

[0074] Step S42: matching the geographical features of the current earthquake zone with a set of similar earthquake events based on the standardized input data of earthquake damage assessment, and performing optimization calibration to obtain regional correspondence data;

[0075] Step S43: performing pattern recognition and feature extraction of historical earthquake damage on a set of similar earthquake events to obtain a historical earthquake damage pattern feature library;

[0076] Step S44: Adaptively reconstructing the historical earthquake damage pattern feature library based on the regional correspondence data to obtain the earthquake damage pattern of the current earthquake zone;

[0077] Step S45: performing a quantitative analysis of the impact of terrain undulation and geological stability factors based on the distribution map of high-risk earthquake damage areas to obtain a terrain geological impact coefficient;

[0078] Step S46: Calculate the earthquake damage probability of the target area based on the current earthquake damage pattern, the earthquake sensitivity matrix, and the topographic and geological influence coefficient to obtain a preliminary assessment result of the earthquake damage distribution probability;

[0079] Step S47: performing threshold segmentation and regional clustering on the preliminary evaluation results of earthquake damage distribution probability to obtain an earthquake damage level regional division scheme;

[0080] Step S48: Based on the earthquake damage level zoning scheme, target area level marking and boundary optimization are performed to obtain preliminary earthquake damage level assessment results.

[0081] In an embodiment of the present invention, the data formats of the earthquake zone sensitivity matrix and the distribution map of high-risk earthquake damage areas are unified to ensure that their spatial references are consistent, using the WGS-84 coordinate system and UTM projection. The coordinates of the geographic information fields (such as the epicenter location) in the similar earthquake event set are converted to the WGS-84 coordinate system. The earthquake damage data in the similar earthquake event set are gridded, divided into a 1 km × 1 km grid with the epicenter as the center, and a gridded earthquake damage data layer is generated in GeoTIFF format. Finally, standardized input data for earthquake damage assessment is obtained, including the earthquake zone sensitivity matrix, the distribution map of high-risk earthquake damage areas, and the gridded earthquake damage data layer. Spatial analysis tools in GIS software are used to match geographic features of the standardized input data for earthquake damage assessment. The geographic features of the current earthquake zone are extracted, including terrain boundaries, major rivers, transportation arteries, etc. The corresponding geographic features of each event in the similar earthquake event set are extracted. A spatial matching algorithm was used to calculate the geographic feature similarity between the current earthquake zone and each event in the similar earthquake event set, using topographic boundaries, major rivers, and transportation arteries as matching criteria. The similarity calculation formula is: Similarity = 1 - (Δ boundary length + Δ river length + Δ transportation artery length) / (boundary length + river length + transportation artery length), where Δ represents the difference in length between the geographic features in the current earthquake zone and the similar earthquake event set. The matching results were optimized and calibrated using an iterative optimization algorithm with 10 iterations, adjusting the matching parameters each time to optimize the matching results. Regional correspondence data was ultimately generated, containing the geographic feature similarity values, correspondence identifiers, and optimized matching parameters between the current earthquake zone and each event in the similar earthquake event set. Pattern recognition and feature extraction were performed on historical earthquake damage data from the similar earthquake event set. Principal component analysis (PCA) was used to extract the main features of the damage data. The cumulative variance contribution rate for principal component extraction was set at 95%. The main components extracted included the affected area, number of casualties, number of damaged buildings, and economic losses. For each similar earthquake event, its eigenvector in the principal component space is calculated, and the dimension of the eigenvector is consistent with the number of principal components. The extracted eigenvectors are clustered using the cluster analysis method. The K-means clustering algorithm is used, the number of cluster centers is set to 5, the number of iterations is set to 100, and the convergence condition is that the change of the cluster center is less than 0.01. The events in the set of similar earthquake events are divided into 5 types of earthquake damage patterns, and each pattern corresponds to a cluster center. Finally, a historical earthquake damage pattern feature library is obtained, which contains the cluster center feature vector of each earthquake damage pattern, the corresponding event number set, and the description information of the earthquake damage pattern. According to the regional correspondence data, the historical earthquake damage pattern feature library is adaptively reconstructed. For the correspondence between the current earthquake zone and each event in the set of similar earthquake events, the corresponding earthquake damage pattern feature vector is extracted.Calculate the comprehensive feature vector for the current earthquake zone using a weighted average method, with the weights being the similarity values ​​in the regional correspondence data. The formula for calculating the comprehensive feature vector is: Comprehensive feature vector = Σ(similarity value × feature vector of the corresponding event) / Σsimilarity value. Normalize the comprehensive feature vector, normalizing the values ​​of each principal component to a range of 0 to 1. Determine the damage pattern for the current earthquake zone based on the normalized comprehensive feature vector. Compare the damage pattern for the current earthquake zone with patterns in the historical 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). The pattern with the highest similarity is selected as the damage pattern for the current earthquake zone. The resulting damage pattern for the current earthquake zone includes the pattern feature vector, corresponding damage pattern description, and similarity value. Using the distribution map of high-risk earthquake damage areas, quantitatively analyze the impact of terrain relief and geological stability factors. Extract terrain relief index data and seismic geological stability score data from high-risk areas. For each high-risk area, the average terrain relief index and the average seismic geological stability score were calculated. The formula for quantifying the impact of terrain relief is: Terrain relief impact = 1 - (average terrain relief index / maximum terrain relief index). The formula for quantifying the impact of geological stability is: Geological stability impact = 1 - (average geological stability score / maximum geological stability score). The terrain relief impact and geological stability impact are combined to obtain the terrain geological impact coefficient. The combined formula is: Terrain geological impact coefficient = 0.6 × Terrain relief impact + 0.4 × Geological stability impact. The terrain geological impact coefficient is calculated for each high-risk area, resulting in a terrain geological impact coefficient dataset containing each high-risk area's number, terrain geological impact coefficient value, and corresponding geographic coordinates. Based on the current earthquake damage pattern, the earthquake sensitivity matrix, and the terrain geological impact coefficient, the earthquake damage probability of the target area is calculated. First, the characteristic vector of the current earthquake damage pattern is extracted, which contains the principal component values ​​of the affected area, number of casualties, number of damaged buildings, and economic losses. The sensitivity values ​​in the earthquake sensitivity matrix are used as weighting factors to weight the damage probability of each grid point. The calculation formula is: earthquake damage probability = sensitivity value × (damage pattern eigenvector × topographic and geological influence coefficient). The earthquake damage probability is calculated for each grid point, yielding a preliminary assessment of the earthquake damage distribution probability. The resulting dataset contains the earthquake damage probability value, geographic coordinates, and the corresponding earthquake damage pattern eigenvector and topographic and geological influence coefficient for each grid point. The earthquake damage probability value ranges from 0 to 1, indicating the likelihood of earthquake damage occurring at that grid point. The preliminary assessment of the earthquake damage distribution probability is then thresholded, with three thresholds set at 0.3, 0.6, and 0.9, respectively.The probability of earthquake damage distribution was 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). A regional cluster analysis was performed on the grid points within each risk level using the DBSCAN clustering algorithm, with a neighborhood radius of 10 meters and a minimum number of neighborhood points of 5. Through cluster analysis, grid points with similar earthquake damage probabilities were grouped into the same cluster area, resulting in a earthquake damage level zoning scheme. This scheme includes the boundary information, earthquake damage level, and number of grid points for each cluster area. Based on the earthquake damage level zoning scheme, the target areas were graded. Spatial analysis tools in GIS software were used to optimize the boundaries of each cluster area. A morphological boundary optimization method was used, with the structuring element set to a 3×3 square. Dilation and erosion operations were performed to extract the optimized regional boundaries. The optimized regional boundaries were saved as a vector layer and displayed overlaid with the earthquake damage level zoning scheme.

[0082] Preferably, collecting the building damage status data at the epicenter based on the real-time earthquake monitoring data in step S5 includes:

[0083] Plan drone inspection routes based on real-time earthquake monitoring data, and scan buildings in the epicenter area based on the planning results to obtain building appearance image data and 3D point cloud data;

[0084] Perform image enhancement on building appearance image data, identify cracks, tilt and structural deformation, and obtain building appearance damage feature data;

[0085] De-noising the 3D point cloud data, constructing a 3D building model, and performing structural integrity assessment and stress analysis on the 3D building model to obtain building structural damage assessment data;

[0086] Scan the interior of a building with a thermal imaging sensor to generate indoor structural abnormal signal data, extract features, and generate indoor damage status assessment results;

[0087] Monitor infrastructure status based on drone inspection paths, perform functional integrity scoring, and generate infrastructure damage assessment reports;

[0088] Based on the building appearance damage feature data, building structure damage assessment data, indoor damage status assessment results and infrastructure damage assessment report, the damage status of buildings and facilities in the earthquake zone is assessed to obtain building damage status data.

[0089] In this embodiment of the present invention, the epicenter location and impact radius are first extracted from real-time earthquake monitoring data to determine the geographic coordinates of the epicenter area. Using Geographic Information System (GIS) software, combined with high-resolution terrain data and building distribution maps, a drone inspection route is planned. This route planning utilizes a grid coverage method, dividing the epicenter area into a 100-meter x 100-meter grid. The drone then flies in a zigzag pattern, covering all grid cells. The drone's flight altitude is set at 100 meters and its speed at 5 meters per second to ensure the accuracy of image and point cloud data acquisition. The drone, equipped with a high-resolution optical camera and lidar sensor, scans buildings in the epicenter area along the planned route. The optical camera has a resolution of 4000 x 3000 pixels and a capture interval of 1 second, generating building exterior image data. The lidar scans at a frequency of 100 Hz and a point cloud density of 100 points per square meter, generating three-dimensional point cloud data. After data acquisition is complete, the building exterior image data and 3D point cloud data are transmitted to a ground control center for processing. The building exterior image data is then imported into image processing software for image enhancement. Histogram equalization is used to enhance image contrast and clarify building details. The grayscale range for histogram equalization is set from 0 to 255. The enhanced image better highlights damage features such as cracks, tilt, and structural deformation. Image recognition algorithms are used to identify cracks, tilt, and structural deformation in the enhanced image. Crack identification uses an edge detection algorithm with a Canny operator low threshold of 50 and a high threshold of 150 to detect the location and length of cracks in the image. Tilt identification uses a Hough transform algorithm to detect the building's tilt angle, with a Hough transform threshold of 100 to determine the direction and angle of tilt. Structural deformation identification uses a template matching algorithm with a matching accuracy of 0.9 to detect areas of structural deformation. The identification results are structured and integrated to generate building exterior damage feature data. This data includes crack location, length, tilt angle, direction, and coordinates of structural deformation areas, providing basic data for subsequent damage assessment. 3D point cloud data is denoised using statistical filtering to remove outliers. Filter parameters were set to a neighborhood radius of 0.5 meters and a minimum number of neighborhood points of 10. Point cloud data that did not conform to statistical patterns was removed to ensure the accuracy and integrity of the point cloud data. A 3D building model was constructed using the point cloud data. Multi-view geometry reconstruction was used to convert the point cloud data into a triangular mesh model. The mesh resolution was set to match the point cloud density to ensure model detail and accuracy. The resulting 3D model visually displayed the building's appearance and structural features. Structural integrity assessment and stress analysis were performed on the 3D building model. Finite element analysis was used to apply seismic loads to the model to calculate the stress distribution and deformation of the building under earthquake loads.The seismic load is set to the seismic waveform from real-time seismic monitoring data. The stress concentration areas and deformation levels in the model are analyzed to assess the structural integrity of the building. The assessment results are structured and integrated to generate building structural damage assessment data. This data includes the location of stress concentration areas, stress values, and quantitative indicators of deformation, providing a scientific basis for the structural safety assessment of the building. A drone equipped with a thermal imaging sensor scans the interior of the building. The thermal imaging sensor has a resolution of at least 640×480 pixels and covers the walls, floors, and key structural parts of the building. The resulting thermal images reflect the temperature distribution within the building. Abnormal temperature fluctuations may indicate structural damage. Feature extraction is performed on the thermal images, and a region segmentation algorithm is used to identify areas with abnormal temperatures. A temperature threshold is set at 1.2 times the room temperature. The area, location, and shape characteristics of the abnormal temperature areas are extracted. By comparing the abnormal temperature distribution with the normal temperature distribution, the damage status of the indoor structure is assessed. The feature extraction results are structured and integrated to generate indoor damage assessment results. The data includes the location, area, and shape characteristics of the abnormal temperature areas, as well as the corresponding damage types (such as cracks and leaks), providing a reference for rapid assessment of internal damage in the building. Along drone inspection routes, infrastructure (such as roads, bridges, and hydropower facilities) is monitored. Optical cameras and lidar sensors are used to capture exterior images and 3D point cloud data of the infrastructure. Image recognition algorithms are used to identify infrastructure damage, such as cracks in roads, deformation in bridges, and damage to hydropower facilities. The functional integrity of the infrastructure is scored using a pre-set scoring criteria, quantitatively assessing the extent of damage and the scope of impact. Scores range from 0 to 100, with lower scores indicating poorer functional integrity. An infrastructure damage assessment report is generated, including the infrastructure type, location, damage status, and functional integrity score, providing decision support for emergency repairs. A comprehensive analysis is conducted on building exterior damage feature data, structural damage assessment data, interior damage status assessment results, and infrastructure damage assessment reports. A weighted summation method is used to comprehensively assess different types of damage data. Weighting parameters are set as follows: exterior damage weighted at 0.3, structural damage weighted at 0.4, interior damage weighted at 0.2, and infrastructure damage weighted at 0.1. A comprehensive damage score is calculated for each building, ranging from 0 to 100, with higher scores indicating more severe damage. The comprehensive damage score is compared with pre-set damage grade standards to categorize the damage level (e.g., minor, moderate, or severe). Building damage status data is generated, including each building's number, location, comprehensive damage score, damage level, and detailed damage characteristics. This provides comprehensive building and facility damage information for emergency rescue and post-disaster reconstruction in the earthquake zone.

[0090] Preferably, in step S5, evaluating the disaster level of the target area based on the preliminary earthquake damage level evaluation result and the building damage status data includes:

[0091] Cluster analysis and classification of building damage status data are performed to obtain building damage level classification results;

[0092] Based on the building damage grade classification results, spatial annotation and attribute association are performed on the earthquake zone map to obtain the spatial distribution map of building damage;

[0093] Based on the preliminary earthquake damage level assessment results, spatial matching is performed on the building damage spatial distribution map, and correlation analysis is performed to obtain an earthquake damage assessment verification report;

[0094] Assess secondary disaster risks based on building damage status data and earthquake damage assessment verification reports, and simulate the spatiotemporal evolution of the assessment results to obtain earthquake damage spread trend prediction data;

[0095] Based on the preliminary earthquake damage level assessment results, building damage status data and earthquake damage spread trend prediction model, a comprehensive disaster level assessment of the target area is conducted to obtain a preliminary disaster level assessment result;

[0096] Conduct a population impact analysis based on the preliminary disaster level assessment results to obtain casualty forecast data; calculate the economic value losses of buildings and infrastructure based on the preliminary disaster level assessment results to obtain an economic loss assessment report;

[0097] Calibrate and adjust the preliminary disaster level assessment results based on casualty prediction data and economic loss assessment reports to obtain the final disaster level assessment results;

[0098] Based on the final disaster level assessment results, high-precision mapping and spatial visualization of the earthquake zone are carried out to obtain an accurate sketch of earthquake damage distribution;

[0099] The precise earthquake damage distribution sketch is organized into layers and its symbol identification is optimized to obtain a precise earthquake damage distribution map.

[0100] In this embodiment of the present invention, building damage status data is imported into data analysis software. Data fields include building number, location coordinates, comprehensive damage score, and damage characteristic description. A K-means clustering algorithm is used to perform cluster analysis on the building damage status data. The number of cluster centers is set to 3, corresponding to the three damage levels of minor damage, moderate damage, and severe damage. The number of iterations is set to 100, and the convergence criterion is that the change in cluster center is less than 0.01. The Euclidean distance between each building and the cluster center is calculated, and the building is assigned to the damage level corresponding to the nearest cluster center. Ultimately, a building damage level classification result is obtained, which includes the building number, location coordinates, damage level, and the corresponding cluster center distance. Using Geographic Information System (GIS) software, the building damage level classification result is spatially aligned with the earthquake zone map. The earthquake zone map contains the building's geographic location information and basic geographic information. In the GIS software, the damage level classification result is annotated on the earthquake zone map based on the building's location coordinates. Buildings with different damage levels are indicated by different colors or symbols: minor damage is indicated by yellow, moderate damage by orange, and severe damage by red. At the same time, the damage characteristic description of the building is associated with the corresponding building location on the map as attribute information. A spatial distribution map of building damage is generated, which intuitively shows the damage level and location distribution of the building. The preliminary earthquake damage level assessment results are spatially matched with the building damage spatial distribution map. The preliminary earthquake damage level assessment results are represented in grid form, and each grid unit has earthquake damage level information. In the GIS software, the building damage spatial distribution map and the earthquake damage level grid map are superimposed to ensure that the spatial references of the two are consistent. Spatial analysis tools are used to calculate the correlation between the earthquake damage level of each grid unit where the building is located and the building damage level. The Pearson correlation coefficient is used for correlation analysis. The secondary disaster risk is assessed by combining the building damage status data and the earthquake damage assessment verification report. Secondary disaster risks include fire, landslide, building collapse, etc. For each building, a secondary disaster risk index is calculated based on the damage level and damage characteristics. The risk index is calculated as follows: Risk Index = Damage Level Weight × Damage Characteristic Weight, where the damage level weight is set based on the damage level (0.3 for minor damage, 0.6 for moderate damage, and 1.0 for severe damage), and the damage characteristic weight is set based on characteristics such as crack width and tilt angle (a crack width greater than 10 mm has a weight of 1.0, less than 10 mm has a weight of 0.5; a tilt angle greater than 5 degrees has a weight of 1.0, less than 5 degrees has a weight of 0.5). A spatiotemporal evolution model is used to simulate secondary disaster risk. The model considers factors such as seismic wave propagation velocity and building structural stability, with a simulation time step of 1 hour and a duration of 24 hours. This generates earthquake damage spread trend forecast data, which includes the distribution and spread trend of secondary disaster risk within each time step.A comprehensive disaster level assessment was conducted for the target area, combining preliminary earthquake damage assessment results, building damage status data, and damage spread trend prediction data. A weighted summation method was used to calculate the comprehensive disaster level score. Weights were set as follows: preliminary earthquake damage level, 0.4; building damage status, 0.3; and damage spread trend, 0.3. The comprehensive disaster level score formula is: Comprehensive Disaster Level Score = 0.4 × Preliminary Damage Level Score + 0.3 × Building Damage Status Score + 0.3 × Damage Spread Trend Score. Based on the comprehensive disaster level score, the target area was categorized into low (0 to 33 points), medium (34 to 66 points), and high (67 to 100 points). A preliminary disaster level assessment was generated, containing the target area's comprehensive disaster level score, disaster level, and corresponding geographic area. A demographic impact analysis was conducted on the preliminary disaster level assessment results. Using population distribution data within the earthquake zone and a disaster level distribution map, the permanent population within each disaster level area was calculated. The casualty ratio was estimated based on the disaster level and building damage status. The casualty ratio in areas with a minor disaster level is 0.1%, in areas with a moderate disaster level it is 1%, and in areas with a high disaster level it is 5%. The calculation formula is: Number of casualties = Number of permanent residents × Casualty ratio. Casualty forecast data is generated, including the number of casualties and the casualty ratio for each disaster level area. Based on the preliminary disaster level assessment results, the economic value of buildings and infrastructure losses is calculated. For buildings and infrastructure in each disaster level area, economic losses are estimated based on the damage level and repair costs. The repair cost for minor damage is 10% of the building value, for moderate damage it is 30%, and for severe damage it is 60%. The preliminary disaster level assessment results are calibrated and adjusted based on the casualty forecast data and the economic loss assessment report. A comprehensive assessment method is used, using casualties and economic losses as calibration indicators. The weight for casualties is set at 0.6, and the weight for economic losses is set at 0.4. The comprehensive calibration formula is: Comprehensive calibration score = 0.6 × number of casualties + 0.4 × economic losses. The disaster level is adjusted based on the comprehensive calibration score. If the comprehensive calibration score exceeds the current disaster level threshold, the disaster level is raised; if it falls below the threshold, the current level remains unchanged. The final disaster level assessment results were obtained, including the adjusted disaster level, the comprehensive calibration score, and the corresponding geographic range. Using GIS software, high-precision mapping and spatial visualization of the earthquake zone were performed based on the final disaster level assessment results. Using high-resolution terrain data and satellite imagery as a basemap, the final disaster level assessment results were overlaid onto the earthquake zone map as a layer. Different colors and symbols were used to indicate areas of different disaster levels: light yellow indicates a minor disaster level, orange indicates a moderate disaster level, and red indicates a high disaster level.At the same time, casualty and economic loss data were annotated to generate a precise earthquake damage distribution sketch, which visually displays the disaster level distribution, casualties, and economic losses in the earthquake zone. Layer organization and symbology optimization were performed on the precise earthquake damage distribution sketch. Within the GIS software, layers of different disaster levels were categorized and organized to ensure logical and readable layers. Symbols were optimized, using gradient colors and transparency adjustments to enhance visual distinction between layers. Legends, scale bars, and geographic coordinate grids were added to improve map readability and usability.

[0101] Preferably, step S6 includes the following steps:

[0102] Step S61: converting the format of the wave propagation time series data, the preliminary earthquake damage level assessment results, and the precise earthquake damage distribution map, and performing standardization processing to obtain a visualization standard data set;

[0103] Step S62: Design a multi-dimensional representation of disaster information based on a visualization standard data set, and perform interactive logic design and time-series dynamic processing on the design results to obtain a dynamic visualization expression solution;

[0104] Step S63: Conduct information demand research and behavior analysis for each user role based on the dynamic visual expression solution to obtain user role characteristics;

[0105] Step S64: Differentiate the information presentation method and key content based on the user role characteristics to obtain a role-based information demand matrix;

[0106] Step S65: Classify the visualization layers based on the dynamic visualization expression scheme and the role-based information requirement matrix, and prioritize the classification results to obtain a layer combination configuration library;

[0107] Step S66: Calculate the optimal match of the user role for the layer combination configuration library and generate a role-based display solution.

[0108] In this embodiment of the present invention, wave propagation time series data, preliminary damage level assessment results, and precise damage distribution maps are imported into data processing software. The wave propagation time series data is in CSV format, containing information such as timestamps, waveform amplitude, and frequency. The preliminary damage level assessment results and precise damage distribution maps are in GeoTIFF format, containing information such as geographic coordinates and damage level. The wave propagation time series data is formatted, its timestamps are standardized to UTC time format, and the amplitude and frequency data are normalized to a range of 0 to 1. The preliminary damage level assessment results and precise damage distribution maps are standardized, converting the damage levels to standardized values ​​(minor damage is 0.3, moderate damage is 0.6, and severe damage is 1.0), and the geographic coordinate system is unified to the WGS-84 coordinate system. Finally, a standard visualization dataset is generated, containing the standardized wave propagation time series data, damage level assessment results, and damage distribution maps. The data format is unified to facilitate subsequent visualization. Multi-dimensional representation design of the standard visualization dataset is performed using geographic information system (GIS) software and visualization design tools. Design disaster information presentation methods, including map, time series, and 3D views. In the map view, different colors are used to identify areas with different damage levels. In the time series view, dynamic changes in wave propagation time series data are displayed. In the 3D view, terrain data is combined to present a three-dimensional representation of damage distribution. Interactive logic is designed for the design results, allowing users to control dynamic changes in the time series using a time slider and zoom and drag the map to view detailed information for different areas. Time-series processing of the wave propagation time series data is performed, with the animation playback speed set to 1 / 100 of the real-time earthquake wave propagation speed. A dynamic visualization scheme is generated, incorporating multi-dimensional presentation methods, interactive logic, and time-series dynamic parameters. Based on the dynamic visualization scheme, an information needs survey is conducted for different user roles (such as earthquake emergency command personnel, rescue teams, and disaster-affected residents). Questionnaires and interviews are used to gather information on users' priorities, usage frequency, and operational habits regarding disaster information. Earthquake emergency command personnel focus on the overall damage distribution and the status of key facilities; rescue teams focus on the affected areas and rescue routes; and disaster-affected residents focus on safe areas and evacuation routes. Conduct behavioral analysis on the collected data to identify user role behavior patterns, such as commanders frequently using the time slider to view historical data, and rescue teams frequently zooming in and out to view detailed information. Ultimately, we identify user role profiles, which encompass their information needs, focus areas, and behavior patterns. Based on these user role profiles, we then differentiate the information presentation and key content.For earthquake emergency response personnel, the damage level distribution map and key facility status are highlighted using large screens, with emphasis on high-risk areas and facility damage. For rescue teams, the affected areas and rescue routes are highlighted using mobile devices, with emphasis on the location of rescue targets and route planning. For affected residents, safe areas and evacuation routes are highlighted using mobile apps, with emphasis on the location of the nearest safe area and evacuation routes. The design results are organized into a role-based information needs matrix, which includes user roles, information presentation methods, key content, and display device types. Visualization layers are categorized based on the dynamic visualization expression scheme and the role-based information needs matrix. Layers are divided into basic geographic information layers (such as terrain and roads), damage distribution layers (such as damage level and building damage), dynamic data layers (such as wave propagation time series data), and thematic information layers (such as rescue routes and safe areas). The categorized results are prioritized based on user role needs and operational habits. For example, for earthquake emergency response personnel, the damage distribution layer and key facility status layer have the highest priority; for rescue teams, the affected area and rescue route layers have the highest priority. Generate a layer combination configuration library containing layer combinations, priority orders, and display parameters for different user roles. Use the layer combination configuration library to calculate the optimal match for each user role. Use an algorithm to analyze the user role's operational behavior patterns and information needs, assigning the most appropriate layer combination and display parameters to each user role. Set the matching algorithm parameters to the user role's priority weight and operation frequency to calculate the optimal match. Generate a role-based display scheme containing the layer combination, display parameters, and interaction logic for each user role. This role-based display scheme automatically adjusts information presentation and key content based on the user role, providing personalized visualization services.

[0109] Preferably, the present invention further provides an earthquake emergency information rapid visualization system for executing the above-mentioned earthquake emergency information rapid visualization method, wherein the earthquake emergency information rapid visualization system comprises:

[0110] The earthquake data monitoring module 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 of the earthquake zone are divided into grids to generate the earthquake zone sensitivity matrix; based on the real-time earthquake monitoring data and the earthquake zone sensitivity matrix, the earthquake wave propagation process is dynamically simulated to generate wave propagation time series data;

[0111] The earthquake terrain detection module is used to identify the epicenter topographic vector data of real-time earthquake monitoring data, and evaluate the terrain relief and geological stability respectively to obtain earthquake terrain relief index data and earthquake geological stability score data; the earthquake terrain relief index data and earthquake geological stability score data are spatially superimposed and analyzed to generate a distribution map of high-risk earthquake damage areas;

[0112] An earthquake event set construction module is used to retrieve earthquake cases in a preset historical earthquake database based on real-time earthquake monitoring data and construct a set of similar earthquake events;

[0113] The earthquake damage level assessment module is used to predict the probability of earthquake damage distribution in the target area based on the earthquake zone sensitivity matrix, the distribution map of high-risk earthquake damage areas, and a set of similar earthquake events, and generate preliminary earthquake damage level assessment results;

[0114] The earthquake damage distribution map division module is used to collect building damage status data at the epicenter based on real-time earthquake monitoring data; based on the preliminary earthquake damage level assessment results and building damage status data, the disaster level of the target area is assessed and an accurate earthquake damage distribution map is generated;

[0115] The emergency visualization module is used to visualize the disaster information of wave propagation time series data, preliminary earthquake damage level assessment results and accurate earthquake damage distribution map, and to match the user role layers based on the disaster information visualization results to obtain a role-based display solution.

[0116] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is not limited by the above description. Therefore, it is intended that all changes that fall within the meaning and scope of the equivalent elements of the application documents are included in the present invention.

[0117] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for rapid visualization of earthquake emergency information, characterized in that: The following steps are involved: Step S1: Acquire real-time earthquake monitoring data; divide the population density distribution and key facility locations of the earthquake zone into grids based on the real-time earthquake monitoring data to generate an earthquake zone sensitivity matrix; dynamically simulate the earthquake wave propagation process based on the real-time earthquake monitoring data and the earthquake zone sensitivity matrix to generate wave propagation time series data; Step S2: Identify the epicenter topographic vector data of the real-time earthquake monitoring data, and evaluate the topographic relief and geological stability respectively to obtain earthquake topographic relief index data and earthquake geological stability score data; perform spatial overlay analysis on the earthquake topographic relief index data and earthquake geological stability score data to generate a distribution map of high-risk earthquake damage areas; Step S3: Retrieving earthquake cases in a preset historical earthquake database based on real-time earthquake monitoring data to construct a set of similar earthquake events; Step S4: Based on the earthquake zone sensitivity matrix, the distribution map of high-risk earthquake damage areas, and the set of similar earthquake events, the earthquake damage distribution probability of the target area is predicted to generate a preliminary earthquake damage level assessment result; Step S5: Collecting damage status data of buildings at the epicenter based on real-time earthquake monitoring data; assessing the disaster level of the target area based on the preliminary earthquake damage level assessment results and the building damage status data, and generating an accurate earthquake damage distribution map; Step S6: Visualize the wave propagation time series data, preliminary earthquake damage level assessment results, and accurate earthquake damage distribution map to perform disaster information visualization, and perform layer combination matching for each user role based on the disaster information visualization results to obtain a role-based display solution.

2. The earthquake emergency information rapid visualization method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: collecting raw seismic waveform data in real time through a distributed seismic monitoring sensor network, and performing filter calibration and standardization processing to generate real-time seismic monitoring data; Step S12: Adaptively dividing the earthquake zone into grids based on the real-time earthquake monitoring data to obtain grid unit data of the earthquake zone; Step S13: performing spatial interpolation analysis of population density based on the earthquake zone grid cell data to obtain gridded population density distribution data; Step S14: performing a spatial query of geographic information of key facilities based on the grid unit data of the earthquake zone to obtain gridded location data of key facilities; Step S15: Calculating sensitivity weights for earthquake zone grid cell data based on the gridded population density distribution data and the gridded key facility location data to generate an earthquake zone sensitivity matrix; Step S16: numerically simulate the seismic wave propagation mechanism based on the real-time earthquake monitoring data and the earthquake zone sensitivity matrix to generate wave propagation time series data.

3. The earthquake emergency information rapid visualization method according to claim 1, characterized in that: Step S2 identifies epicenter topographic vector data from real-time earthquake monitoring data and evaluates topographic relief and geological stability, including: Retrieve multi-source remote sensing images based on real-time earthquake monitoring data, and perform geometric correction and orthorectification to obtain topographic image data of the earthquake zone; Extract the terrain boundary line based on the earthquake zone terrain image data, build a triangulated network model of the earthquake zone, and obtain the digital elevation model of the earthquake zone; Calculate the slope, aspect and curvature of the digital elevation model of the earthquake zone, and enhance the calculation results to obtain enhanced terrain feature data; Based on the digital elevation model of the earthquake zone, the local elevation changes are statistically analyzed, and then standardized and graded to obtain earthquake terrain relief index data; Based on the spatial matching of enhanced terrain feature data with the preset geological structure database, a geological structure distribution map of the earthquake zone is obtained; historical earthquake response analysis is performed on the geological structure distribution map of the earthquake zone to obtain a geological historical stability score; The fault activity is evaluated based on real-time earthquake monitoring data and geological historical stability scores to obtain earthquake geological stability score data.

4. The method for rapid visualization of earthquake emergency information according to claim 1, characterized in that: In step S2, spatial superposition analysis of earthquake topography index data and earthquake geological stability score data includes: Based on the earthquake terrain relief index data and the earthquake geological stability score data, the spatial data superposition operation of the earthquake zone is carried out to obtain the comprehensive risk score matrix; Perform threshold segmentation and cluster analysis on the comprehensive risk score matrix to obtain risk level partition data; Based on the risk level zoning data, high-risk areas in the earthquake zone are extracted and their boundaries are identified to obtain a distribution map of high-risk areas for earthquake damage.

5. The method for rapid visualization of earthquake emergency information according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: querying a high-performance historical earthquake data server based on real-time earthquake monitoring data parameters, and performing screening and sorting to obtain candidate similar earthquake event data sets; Step S32: performing similarity calculation on the candidate similar earthquake event data set, and performing weight assignment and threshold screening of the candidate events based on the similarity calculation results to obtain a preliminary screening result of similar earthquake events; Step S33: extracting historical earthquake damage based on the initial screening results of similar earthquake events, and performing structured integration and metadata annotation on the extracted results to obtain a set of similar earthquake events.

6. The method for rapid visualization of earthquake emergency information according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing data format unification processing on the earthquake zone sensitivity matrix, the earthquake damage high risk area distribution map and the similar earthquake event set to obtain standardized input data for earthquake damage assessment; Step S42: matching the geographical features of the current earthquake zone with a set of similar earthquake events based on the standardized input data of earthquake damage assessment, and performing optimization calibration to obtain regional correspondence data; Step S43: performing pattern recognition and feature extraction of historical earthquake damage on a set of similar earthquake events to obtain a historical earthquake damage pattern feature library; Step S44: Adaptively reconstructing the historical earthquake damage pattern feature library based on the regional correspondence data to obtain the earthquake damage pattern of the current earthquake zone; Step S45: performing a quantitative analysis of the impact of terrain undulation and geological stability factors based on the distribution map of high-risk earthquake damage areas to obtain a terrain geological impact coefficient; Step S46: Calculate the earthquake damage probability of the target area based on the current earthquake damage pattern, the earthquake sensitivity matrix, and the topographic and geological influence coefficient to obtain a preliminary assessment result of the earthquake damage distribution probability; Step S47: performing threshold segmentation and regional clustering on the preliminary evaluation results of earthquake damage distribution probability to obtain an earthquake damage level regional division scheme; Step S48: Based on the earthquake damage level zoning scheme, target area level marking and boundary optimization are performed to obtain preliminary earthquake damage level assessment results.

7. The method for rapid visualization of earthquake emergency information according to claim 1, characterized in that: In step S5, collecting the damage status data of the building at the epicenter based on the real-time earthquake monitoring data includes: Plan drone inspection routes based on real-time earthquake monitoring data, and scan buildings in the epicenter area based on the planning results to obtain building appearance image data and 3D point cloud data; Perform image enhancement on building appearance image data, identify cracks, tilt and structural deformation, and obtain building appearance damage feature data; De-noising the 3D point cloud data, constructing a 3D building model, and performing structural integrity assessment and stress analysis on the 3D building model to obtain building structural damage assessment data; Scan the interior of a building with a thermal imaging sensor to generate indoor structural abnormal signal data, extract features, and generate indoor damage status assessment results; Monitor infrastructure status based on drone inspection paths, perform functional integrity scoring, and generate infrastructure damage assessment reports; Based on the building appearance damage feature data, building structure damage assessment data, indoor damage status assessment results and infrastructure damage assessment report, the damage status of buildings and facilities in the earthquake zone is assessed to obtain building damage status data.

8. The method for rapid visualization of earthquake emergency information according to claim 1, characterized in that: In step S5, the disaster level of the target area is assessed based on the preliminary earthquake damage level assessment results and the building damage status data, including: Cluster analysis and classification of building damage status data are performed to obtain building damage level classification results; Based on the building damage grade classification results, spatial annotation and attribute association are performed on the earthquake zone map to obtain the spatial distribution map of building damage; Based on the preliminary earthquake damage level assessment results, spatial matching is performed on the building damage spatial distribution map, and correlation analysis is performed to obtain an earthquake damage assessment verification report; Assess secondary disaster risks based on building damage status data and earthquake damage assessment verification reports, and simulate the spatiotemporal evolution of the assessment results to obtain earthquake damage spread trend prediction data; Based on the preliminary earthquake damage level assessment results, building damage status data and earthquake damage spread trend prediction model, a comprehensive disaster level assessment of the target area is conducted to obtain a preliminary disaster level assessment result; Conduct a population impact analysis based on the preliminary disaster level assessment results to obtain casualty forecast data; calculate the economic value losses of buildings and infrastructure based on the preliminary disaster level assessment results to obtain an economic loss assessment report; Calibrate and adjust the preliminary disaster level assessment results based on casualty prediction data and economic loss assessment reports to obtain the final disaster level assessment results; Based on the final disaster level assessment results, high-precision mapping and spatial visualization of the earthquake zone are carried out to obtain an accurate sketch of earthquake damage distribution; The precise earthquake damage distribution sketch is organized into layers and its symbol identification is optimized to obtain a precise earthquake damage distribution map.

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

10. A rapid visualization system for earthquake emergency information, characterized in that: For executing the earthquake emergency information rapid visualization method according to claim 1, the earthquake emergency information rapid visualization system comprises: The earthquake data monitoring module 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 of the earthquake zone are divided into grids to generate the earthquake zone sensitivity matrix; based on the real-time earthquake monitoring data and the earthquake zone sensitivity matrix, the earthquake wave propagation process is dynamically simulated to generate wave propagation time series data; The earthquake terrain detection module is used to identify the epicenter topographic vector data of real-time earthquake monitoring data, and evaluate the terrain relief and geological stability respectively to obtain earthquake terrain relief index data and earthquake geological stability score data; the earthquake terrain relief index data and earthquake geological stability score data are spatially superimposed and analyzed to generate a distribution map of high-risk earthquake damage areas; An earthquake event set construction module is used to retrieve earthquake cases in a preset historical earthquake database based on real-time earthquake monitoring data and construct a set of similar earthquake events; The earthquake damage level assessment module is used to predict the probability of earthquake damage distribution in the target area based on the earthquake zone sensitivity matrix, the distribution map of high-risk earthquake damage areas, and a set of similar earthquake events, and generate preliminary earthquake damage level assessment results; The earthquake damage distribution map division module is used to collect building damage status data at the epicenter based on real-time earthquake monitoring data; based on the preliminary earthquake damage level assessment results and building damage status data, the disaster level of the target area is assessed and an accurate earthquake damage distribution map is generated; The emergency visualization module is used to visualize the disaster information of wave propagation time series data, preliminary earthquake damage level assessment results and accurate earthquake damage distribution map, and to match the user role layers based on the disaster information visualization results to obtain a role-based display solution.

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