Earthquake disaster risk assessment method, device, equipment and medium

Abstract

The invention discloses an earthquake disaster risk assessment method, device and equipment and a medium, and the method comprises the steps: determining an earthquake risk assessment parameter corresponding to a target region based on seismic waveform data and earthquake disaster associated data corresponding to a to-be-assessed target region; inputting the earthquake risk assessment parameters into a pre-trained earthquake disaster risk assessment model for earthquake disaster risk assessment, and determining earthquake risk early warning information corresponding to the target area; and determining an earthquake risk level, an earthquake risk point location, earthquake risk time and earthquake risk decision information corresponding to the target area based on the earthquake risk early warning information, and generating an earthquake disaster risk assessment report based on the earthquake risk level, the earthquake risk point location, the earthquake risk time and the earthquake risk decision information. According to the technical scheme of the embodiment of the invention, automatic assessment of the earthquake disaster risk is realized, and early warning can be carried out on the earthquake disaster hidden danger of the target area more accurately and efficiently.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to an earthquake disaster risk assessment method, device, equipment and medium. Background Art

[0002] With the acceleration of urbanization, the impact of human activities on the natural environment is increasing, and the risk of earthquake disasters is also increasing. Especially in earthquake-prone areas, risk assessment of earthquake disasters is particularly important.

[0003] At present, traditional earthquake disaster risk assessment methods are mainly based on simple statistical methods and empirical assessments. However, traditional earthquake disaster risk assessment methods often rely on limited historical data and experience, have high labor costs, and lack sufficient samples and data support, resulting in inaccurate assessment results and low assessment efficiency, which cannot provide strong support for disaster risk management and disaster reduction work. Summary of the invention

[0004] The present invention provides an earthquake disaster risk assessment method, device, equipment and medium to realize automatic assessment of earthquake disaster risk, reduce labor costs, and more accurately and efficiently warn of earthquake disaster hazards in target areas, providing more powerful support for disaster risk management and mitigation work.

[0005] In a first aspect, an embodiment of the present invention provides a method for earthquake disaster risk assessment, comprising:

[0006] Based on the seismic waveform data and seismic disaster associated data corresponding to the target area to be evaluated, determining the seismic risk assessment parameters corresponding to the target area, wherein the seismic risk assessment parameters include: first average shear wave velocity, second average shear wave velocity, standard equivalent shear wave velocity, site type, dominant period and stratigraphic information;

[0007] Inputting the earthquake risk assessment parameters into a pre-trained earthquake disaster risk assessment model to perform earthquake disaster risk assessment, and determining earthquake risk warning information corresponding to the target area, wherein the earthquake disaster risk assessment model is obtained through training based on a deep learning model;

[0008] Based on the earthquake risk warning information, the earthquake risk level, earthquake risk point, earthquake risk time and earthquake risk decision information corresponding to the target area are determined, and based on the earthquake risk level, earthquake risk point, earthquake risk time and earthquake risk decision information, an earthquake disaster risk assessment report is generated.

[0009] In a second aspect, an embodiment of the present invention further provides an earthquake disaster risk assessment device, comprising:

[0010] An assessment parameter determination module is used to determine the earthquake risk assessment parameters corresponding to the target area to be assessed based on the earthquake waveform data and earthquake disaster associated data corresponding to the target area to be assessed, wherein the earthquake risk assessment parameters include: first average shear wave velocity, second average shear wave velocity, standard equivalent shear wave velocity, site type, dominant period and stratigraphic information;

[0011] A disaster risk assessment module is used to input the earthquake risk assessment parameters into a pre-trained earthquake disaster risk assessment model to perform earthquake disaster risk assessment and determine earthquake risk warning information corresponding to the target area, wherein the earthquake disaster risk assessment model is obtained through training based on a deep learning model;

[0012] An assessment report generation module is used to determine the earthquake risk level, earthquake risk point, earthquake risk time and earthquake risk decision information corresponding to the target area based on the earthquake risk warning information, and generate an earthquake disaster risk assessment report based on the earthquake risk level, earthquake risk point, earthquake risk time and earthquake risk decision information.

[0013] In a third aspect, an embodiment of the present invention further provides an electronic device, characterized in that the electronic device comprises: at least one processor; and

[0014] a memory communicatively connected to the at least one processor; wherein,

[0015] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the earthquake disaster risk assessment method provided by any embodiment of the present invention.

[0016] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to execute the earthquake disaster risk assessment method provided by any embodiment of the present invention when executing the computer instructions.

[0017] The technical solution of the embodiment of the present invention determines the earthquake risk assessment parameters corresponding to the target area based on the earthquake waveform data and earthquake disaster associated data corresponding to the target area to be evaluated, wherein the earthquake risk assessment parameters include: the first average shear wave velocity, the second average shear wave velocity, the standard equivalent shear wave velocity, the site type, the dominant period and the stratigraphic information, so as to accurately reflect the geological characteristics and seismic response characteristics of the target area, and provide a reliable basis for subsequent risk assessment. The earthquake risk assessment parameters are input into a pre-trained earthquake disaster risk assessment model for earthquake disaster risk assessment, and the earthquake risk warning information corresponding to the target area is determined, wherein the earthquake disaster risk assessment model is obtained by training based on a deep learning model, and the deep learning model can capture the nonlinear relationship in the data, which can improve the accuracy of risk assessment. Based on the earthquake risk warning information, the earthquake risk level, earthquake risk point, earthquake risk time and earthquake risk decision information corresponding to the target area are determined, and based on the earthquake risk level, earthquake risk point, earthquake risk time and earthquake risk decision information, an earthquake disaster risk assessment report is generated, thereby providing more powerful support for disaster risk management and disaster reduction work. By processing seismic data to obtain earthquake risk assessment parameters, the geological characteristics and seismic response characteristics of the target area can be accurately reflected, and then accurate earthquake risk warning information can be obtained based on the earthquake disaster risk assessment model, and then a clear and intuitive earthquake disaster risk assessment report can be obtained, which can more accurately and efficiently warn of earthquake disaster hazards in the target area and provide stronger support for disaster risk management and mitigation work.

[0018] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 is a flow chart of an earthquake disaster risk assessment method provided according to Embodiment 1 of the present invention;

[0021] Figure 2 is a flow chart of an earthquake disaster risk assessment method provided according to Embodiment 2 of the present invention;

[0022] Figure 3is a structural schematic diagram of an earthquake disaster risk assessment device provided according to Embodiment 3 of the present invention;

[0023] Figure 4 It is a schematic diagram of the structure of an electronic device for implementing the earthquake disaster risk assessment method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "target", "current", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] Embodiment 1

[0027] Figure 1 A flowchart of an earthquake disaster risk assessment method is provided for the first embodiment of the present invention. This embodiment is applicable to the situation of assessing earthquake disaster risks. Figure 1 As shown, the method can be performed by an earthquake disaster risk assessment device, which can be implemented in the form of hardware and / or software, and can be configured in an electronic device. Figure 1 As shown, the method specifically comprises the following steps:

[0028] S110. Based on the seismic waveform data and earthquake disaster-related data corresponding to the target area to be evaluated, determine the earthquake risk assessment parameters corresponding to the target area, wherein the earthquake risk assessment parameters include: first average shear wave velocity, second average shear wave velocity, standard equivalent shear wave velocity, site type, dominant period and stratigraphic information.

[0029] Among them, the target area may refer to an area where earthquake disaster risk assessment is required. Seismic waveform data may refer to data recorded by seismographs when seismic waves propagate in the earth's crust when an earthquake occurs. For example, seismic waveform data may refer to data collected by a seismic monitoring network, including seismic stations, strong vibration observation arrays, etc. Seismic disaster-related data may refer to multivariate data related to earthquake disasters other than seismic waveform data. For example, earthquake disaster-related data may be data such as geological structure, topography, and seismic performance of buildings. Seismic risk assessment parameters may refer to key indicators used to quantify earthquake disaster risks. The first average shear wave velocity may refer to the average shear wave velocity of soil or rock within a depth range of 20 meters below the surface. The second average shear wave velocity may refer to the average shear wave velocity of soil or rock within a depth range of 30 meters below the surface. The standard equivalent shear wave velocity may refer to an equivalent value that can represent the overall dynamic characteristics of the site, taking into account the influence of multiple soil layers on shear wave propagation within a certain depth range (such as 20 meters or the thickness of the covering layer), and can be used to reflect the stiffness of the soil layer. Site type can refer to the classification of sites into different categories based on the soil characteristics of the site (such as soil lithology, cover thickness, shear wave velocity, etc.) to reflect the different effects of the site on the propagation and amplification of seismic waves. The dominant period can refer to the vibration period that is most easily amplified by the site under the action of seismic motion. Stratigraphic information can refer to the spatial distribution and physical properties of underground rock and soil layers obtained through drilling and geological exploration. For example, stratigraphic information can be information such as the geological structure, soil layer distribution, lithology characteristics, and groundwater level of the site.

[0030] Specifically, the seismic waveform data and earthquake disaster-related data of the target area can be obtained from seismic stations and other channels. The seismic waveform data is pre-processed by filtering and denoising, and the earthquake disaster-related data is cleaned and integrated to extract geological, topographic, and other information directly related to earthquake disasters. Based on the processed data, the first average shear wave velocity (Vs20, i.e., the average shear wave velocity within a depth of 20 meters below the surface), the second average shear wave velocity (Vs30, i.e., the average shear wave velocity within a depth of 30 meters below the surface), the standard equivalent shear wave velocity (standard Vs20, representing the equivalent shear wave velocity of the overall dynamic characteristics of the site), the site type, the dominant period, and the stratigraphic information are calculated. By processing the seismic data and calculating the seismic risk assessment parameters, the geological characteristics and seismic response characteristics of the target area can be accurately reflected, providing a reliable basis for subsequent risk assessment.

[0031] Exemplarily, S110 may include: performing data fusion on the seismic waveform data and earthquake disaster-related data corresponding to the target area to be evaluated, and performing data cleaning on the fused data, wherein the data cleaning includes at least one of noise removal and outlier removal; performing feature extraction on the cleaned seismic data, and determining the earthquake risk assessment parameters corresponding to the target area based on the extracted seismic features.

[0032] Among them, earthquake characteristics can refer to information extracted from earthquake waveform data and earthquake disaster related data that can reflect the characteristics and laws of earthquakes.

[0033] Specifically, the seismic waveform data and earthquake disaster-related data corresponding to the target area to be evaluated are integrated to form a unified data set. In the process of data fusion, the consistency, completeness and accuracy of the data need to be considered to ensure that the fused data can fully reflect the earthquake disaster risk situation in the target area and improve the comprehensiveness and accuracy of the data. Data cleaning, noise removal and outlier removal of the fused data can improve the accuracy and reliability of the data and reduce the evaluation error. Extract seismic features closely related to earthquake disasters from the cleaned seismic data, and determine the earthquake risk assessment parameters corresponding to the target area based on the extracted seismic features and combined with the earthquake disaster-related data, such as the first average shear wave velocity, the second average shear wave velocity, the standard equivalent shear wave velocity, the site type, the dominant period and the stratigraphic information, etc., so as to provide a data basis for the subsequent earthquake disaster risk assessment.

[0034] Exemplarily, the earthquake waveform data include: strong vibration observation station data; the earthquake disaster related data include: urban housing and construction data, rural housing and construction data, geological data, river data, drilling data, strong vibration observation station data, macro site data and at least one of the five generations of map data.

[0035] Among them, strong vibration observation station data may refer to data obtained by strong vibration observation stations for measuring and recording earthquake phenomena and effects. Urban housing and construction data may refer to data related to urban housing construction, which may include information such as building structure, building materials, seismic fortification level, and house distribution. Rural housing and construction data may refer to data related to rural housing construction, which may include information such as the structural type, building materials, seismic performance of rural houses, and house distribution in rural areas. Geological data may refer to a collection of numbers, letters, and symbols representing geological information, which are used to describe geological features such as geological structure, stratigraphic distribution, and rock type. River data may refer to data related to rivers, which may include information such as the direction, flow, flow rate, and riverbed morphology of the river. Borehole data may refer to data of control points such as stratigraphic boundary points and sampling points distributed along the borehole trajectory in the vertical direction. Macro site data may refer to information such as site conditions, topography, and soil type. Five-generation map data may refer to earthquake zoning map data, which divides the country into different regions according to earthquake risk, and specifies different earthquake fortification parameters for different regions.

[0036] Exemplarily, "determining the first average shear wave velocity corresponding to the target area based on the seismic waveform data and seismic disaster-related data corresponding to the target area to be evaluated" in S110 may include: in response to the bottom depth corresponding to the last soil layer in the target area being greater than or equal to the preset bottom depth, summing the ratios of the soil layer thicknesses corresponding to each soil layer within the preset bottom depth range to the shear wave velocity, and determining the ratio of the preset bottom depth to the summed result as the first average shear wave velocity corresponding to the target area; in response to the bottom depth corresponding to the last soil layer in the target area being less than the preset bottom depth range, summing the ratios of the soil layer thicknesses corresponding to each soil layer within the bottom depth range corresponding to the last soil layer to the shear wave velocity, and determining the ratio of the bottom depth corresponding to the last soil layer to the summed result as the first average shear wave velocity corresponding to the target area.

[0037] The layer bottom depth may refer to the vertical distance between the bottom surface of the soil layer and the ground. The preset layer bottom depth may refer to the layer bottom depth pre-set based on earthquake engineering experience and local geological conditions.

[0038] Specifically, the soil layers of the target area can be divided according to a preset stratification method to determine the bottom depth of the last soil layer. The bottom depth of the last layer is compared with the preset bottom depth. If the bottom depth of the last soil layer is greater than or equal to the preset bottom depth, the ratio of the soil layer thickness to the shear wave velocity (i.e., the thickness of each soil layer divided by the shear wave velocity of the layer) is calculated for all soil layers within the preset bottom depth range (less than the preset bottom depth), and these ratios are summed, and then the preset bottom depth is divided by the summed result to obtain the first average shear wave velocity. If the bottom depth of the last soil layer is less than the preset bottom depth, the ratio of the soil layer thickness to the shear wave velocity is calculated for the last soil layer and all the soil layers above it, and these ratios are summed, and then the bottom depth of the last soil layer is divided by the summed result to obtain the first average shear wave velocity. By considering the actual soil layer distribution and depth of the target area, the first average shear wave velocity can be calculated more accurately, thereby more accurately assessing the earthquake disaster risk.

[0039] Exemplarily, when the bottom depth of the last soil layer D>20m (preset bottom depth), the first average shear wave velocity (Vs20) = 20m / T1, where T1 = the sum of the thickness of each soil layer d / corresponding shear wave velocity within 20m. When the bottom depth of the last layer D<20m, Vs20 = D / T2, where T2 = the sum of the thickness of each soil layer d / corresponding shear wave velocity within D.

[0040] Exemplarily, when calculating the second average shear wave velocity, the preset layer bottom depth can be 30 meters. When the layer bottom depth D of the last soil layer>30m, the second average shear wave velocity (Vs30)=30m / T3, where T3=the sum of the thickness d of each soil layer within 30m / the corresponding shear wave velocity. When the layer bottom depth D of the last layer<30m, Vs30=D / T4, where T4=the sum of the thickness d of each soil layer within D / the corresponding shear wave velocity.

[0041] Exemplarily, "determining the standard equivalent shear wave velocity corresponding to the target area based on the seismic waveform data and seismic disaster-related data corresponding to the target area to be evaluated" in S110 may include: in response to the cover layer thickness of the target area being greater than or equal to the preset layer bottom depth, summing the ratios of the soil layer thickness corresponding to each soil layer within the preset layer bottom depth range to the shear wave velocity, and determining the ratio of the preset layer bottom depth to the summed result as the standard equivalent shear wave velocity corresponding to the target area; in response to the cover layer thickness of the target area being less than the preset layer bottom depth, summing the ratios of the soil layer thickness corresponding to each soil layer within the cover layer thickness range to the shear wave velocity, and determining the ratio of the cover layer thickness to the summed result as the standard equivalent shear wave velocity corresponding to the target area.

[0042] The thickness of the cover layer can refer to the distance from the ground surface to the underground bedrock surface, that is, the vertical distance from the ground surface to the harder rock layer (usually a hard soil layer or rock layer with a shear wave velocity greater than 500m / s). The thickness of the soil layer can refer to the thickness of a specific soil layer in the soil profile.

[0043] Specifically, according to the collected soil layer information, the thickness of the cover layer in the target area is determined, that is, the vertical distance from the surface to the lowest hard rock layer (or the designated reference layer). The cover layer thickness of the target area is compared with the preset layer bottom depth. If the cover layer thickness is greater than or equal to the preset layer bottom depth, then within the preset layer bottom depth range (less than the preset layer bottom depth), for each soil layer, the ratio of its soil layer thickness to shear wave velocity (i.e., thickness / shear wave velocity) is calculated, these ratios are summed, and the preset layer bottom depth is divided by the summed result to obtain the standard equivalent shear wave velocity. If the cover layer thickness is less than the preset layer bottom depth, then within the cover layer thickness range (less than the cover layer thickness), the ratio of its soil layer thickness to shear wave velocity is calculated for each soil layer, these ratios are summed, and the cover layer thickness is divided by the summed result to obtain the standard equivalent shear wave velocity. By considering the comparison between the cover layer thickness and the preset layer bottom depth, the stratigraphic structure and seismic wave propagation characteristics of the target area can be more accurately reflected, which helps to improve the accuracy of earthquake disaster risk assessment.

[0044] For example, H is the thickness of the cover layer. When H>20m, the standard equivalent shear wave velocity (standard Vs20) = 20m / T, T = the sum of the thickness of each soil layer d / corresponding shear wave velocity within 20m. When H<20m, standard Vs20 = H / T, T = the sum of the thickness of each soil layer d / corresponding shear wave velocity within H.

[0045] Exemplarily, "determining the site type corresponding to the target area based on the seismic waveform data and seismic disaster-related data corresponding to the target area to be evaluated" in S110 may include: in response to the standard equivalent shear wave velocity of the target area being within the first preset velocity range and the covering layer thickness being zero, determining that the site type corresponding to the target area is a Class I site; in response to the standard equivalent shear wave velocity of the target area being within the second preset velocity range and the covering layer thickness being within the first preset thickness range, determining that the site type corresponding to the target area is a Class II site; in response to the standard equivalent shear wave velocity of the target area being within the third preset velocity range and the covering layer thickness being within the second preset thickness range, determining that the site type corresponding to the target area is a Class III site; in response to the standard equivalent shear wave velocity of the target area being within the fourth preset velocity range and the covering layer thickness being within the third preset thickness range, determining that the site type corresponding to the target area is a Class IV site.

[0046] Among them, the first preset speed range may refer to a range where the pre-set shear wave speed is relatively high (may be greater than a certain specific value, such as 500m / s or higher). The second preset speed range may refer to a value where the pre-set shear wave speed is relatively high but lower than the first preset speed range (such as between 250m / s and 500m / s). The third preset speed range may refer to a range where the pre-set shear wave speed is medium (such as between 150m / s and 250m / s). The fourth preset speed range may refer to a range where the pre-set shear wave speed is relatively low (such as less than 150m / s). The first preset thickness range may refer to a range where the pre-set cover layer thickness is relatively small (may be less than a certain specific value, such as 5 meters or 5 meters to 50 meters). The second preset thickness range may refer to a range where the pre-set cover layer thickness is relatively large (may be greater than a certain specific value, such as 5 meters). The third preset thickness range may refer to a range where the pre-set cover layer thickness is relatively large (may be greater than a certain specific value, such as 3 meters). A type of site may refer to a site with a high shear wave speed and a small cover layer thickness. Category II sites may refer to sites with high shear wave velocity and moderate cover thickness. Category III sites may refer to sites with moderate shear wave velocity and large cover thickness. Category IV sites may refer to sites with low shear wave velocity and large cover thickness.

[0047] Specifically, the calculated standard equivalent shear wave velocity is compared with the first preset velocity range, the second preset velocity range, the third preset velocity range and the fourth preset velocity range, and at the same time, the cover layer thickness is compared with the first preset thickness range, the second preset thickness range and the third preset thickness range. According to the comparison result, if the standard equivalent shear wave velocity is in the first preset velocity range and the cover layer thickness is zero (directly bedrock or hard soil layer), the target area is determined to be a Class I site. If the standard equivalent shear wave velocity is in the second preset velocity range and the cover layer thickness is in the first preset thickness range, the target area is determined to be a Class II site. If the standard equivalent shear wave velocity is in the third preset velocity range and the cover layer thickness is in the second preset thickness range, the target area is determined to be a Class III site. If the standard equivalent shear wave velocity is in the fourth preset velocity range and the cover layer thickness is in the third preset thickness range, the target area is determined to be a Class IV site. By comprehensively considering the two parameters of the standard equivalent shear wave velocity and the cover layer thickness, the seismic response characteristics and potential earthquake disaster risks of the target area can be more accurately reflected, thereby improving the accuracy of the assessment.

[0048] It should be noted that the first type of site is a hard site with the smallest seismic motion amplification effect; the fourth type of site is a soft and thick overburden land with a significant seismic motion amplification effect and a long characteristic period.

[0049] S120. Input earthquake risk assessment parameters into a pre-trained earthquake disaster risk assessment model to perform earthquake disaster risk assessment and determine earthquake risk warning information corresponding to the target area, wherein the earthquake disaster risk assessment model is trained based on a deep learning model.

[0050] Among them, the earthquake disaster risk assessment model may refer to a deep learning model used to predict the probability and impact range of earthquake disasters that may occur or have occurred in a certain period of time in the future. Earthquake risk warning information may refer to the scientific prediction of earthquake events that have not yet occurred and the warning information released, including information such as the time, location and magnitude of the earthquake. The deep learning model may refer to a machine learning model based on a neural network that can automatically extract and abstract the features of input data.

[0051] Specifically, an earthquake disaster risk assessment model based on deep learning training can be selected. This model has powerful data processing and recognition capabilities and can accurately assess earthquake disaster risks. The extracted earthquake risk assessment parameters are input into the pre-trained earthquake disaster risk assessment model. The model calculates based on the input earthquake risk assessment parameters and outputs earthquake risk warning information. Through the earthquake disaster risk assessment model, a multi-dimensional assessment of earthquake disaster risks can be achieved, which can improve the accuracy and efficiency of earthquake disaster risk assessment and provide a geological basis for emergency evacuation route planning and rescue resource allocation.

[0052] S130. Based on the earthquake risk warning information, determine the earthquake risk level, earthquake risk point, earthquake risk time and earthquake risk decision information corresponding to the target area, and generate an earthquake disaster risk assessment report based on the earthquake risk level, earthquake risk point, earthquake risk time and earthquake risk decision information.

[0053] Among them, the earthquake risk level may refer to a classification method of earthquake risk according to the degree of damage and impact range that an earthquake may cause. The earthquake risk point may refer to a specific geographical location or area where an earthquake may occur. The earthquake risk time may refer to a time period when an earthquake may occur. Earthquake risk decision information may refer to a response strategy for possible earthquake disasters to achieve disaster prevention and mitigation. The earthquake disaster risk assessment report may refer to a report that records and displays the results of the earthquake disaster risk assessment.

[0054] Specifically, according to the earthquake risk warning information, the target area can be divided into different earthquake risk levels (such as low, medium, high, and extremely high). Potential earthquake risk points are identified in the target area. These points may be areas with frequent earthquake activities or fragile geological conditions, and the earthquake risk time corresponding to the earthquake risk points is determined. According to the earthquake risk level, earthquake risk point and earthquake risk time, targeted earthquake risk decision information is formulated, including disaster prevention and mitigation measures, emergency response plans, etc. Detailed risk warning information helps to formulate and implement effective disaster prevention and mitigation measures in advance. The determined earthquake risk warning information is filled into the report, including earthquake risk level, earthquake risk point, earthquake risk time and earthquake risk decision information, etc., and an earthquake disaster risk assessment report is generated based on the filled data. By displaying the earthquake disaster risk assessment results in the form of intuitive reports, it is possible to more accurately and efficiently warn of earthquake disaster hazards in the target area, and provide stronger support for disaster risk management and mitigation work.

[0055] The technical solution of the embodiment of the present invention determines the earthquake risk assessment parameters corresponding to the target area based on the earthquake waveform data and earthquake disaster associated data corresponding to the target area to be evaluated, wherein the earthquake risk assessment parameters include: the first average shear wave velocity, the second average shear wave velocity, the standard equivalent shear wave velocity, the site type, the dominant period and the stratigraphic information, so as to accurately reflect the geological characteristics and seismic response characteristics of the target area, and provide a reliable basis for subsequent risk assessment. The earthquake risk assessment parameters are input into a pre-trained earthquake disaster risk assessment model for earthquake disaster risk assessment, and the earthquake risk warning information corresponding to the target area is determined, wherein the earthquake disaster risk assessment model is obtained by training based on a deep learning model, and the deep learning model can capture the nonlinear relationship in the data, which can improve the accuracy of risk assessment. Based on the earthquake risk warning information, the earthquake risk level, earthquake risk point, earthquake risk time and earthquake risk decision information corresponding to the target area are determined, and based on the earthquake risk level, earthquake risk point, earthquake risk time and earthquake risk decision information, an earthquake disaster risk assessment report is generated, thereby providing more powerful support for disaster risk management and disaster reduction work. By processing seismic data to obtain earthquake risk assessment parameters, the geological characteristics and seismic response characteristics of the target area can be accurately reflected, and then accurate earthquake risk warning information can be obtained based on the earthquake disaster risk assessment model, and then a clear and intuitive earthquake disaster risk assessment report can be obtained, which can more accurately and efficiently warn of earthquake disaster hazards in the target area and provide stronger support for disaster risk management and mitigation work.

[0056] Embodiment 2

[0057] Figure 2This is a flow chart of an earthquake disaster risk assessment method provided in Embodiment 2 of the present invention. Based on the above embodiments, this embodiment adds a new step after the step of "generating an earthquake disaster risk assessment report". The explanations of the terms that are the same or corresponding to the above embodiments are not repeated here.

[0058] See also Figure 2 Another earthquake disaster risk assessment method provided in this embodiment specifically includes the following steps:

[0059] S210. Based on the seismic waveform data and earthquake disaster-related data corresponding to the target area to be evaluated, determine the earthquake risk assessment parameters corresponding to the target area, wherein the earthquake risk assessment parameters include: first average shear wave velocity, second average shear wave velocity, standard equivalent shear wave velocity, site type, dominant period and stratigraphic information.

[0060] S220. Input earthquake risk assessment parameters into a pre-trained earthquake disaster risk assessment model to perform earthquake disaster risk assessment and determine earthquake risk warning information corresponding to the target area, wherein the earthquake disaster risk assessment model is trained based on a deep learning model.

[0061] S230. Based on the earthquake risk warning information, determine the earthquake risk level, earthquake risk point, earthquake risk time and earthquake risk decision information corresponding to the target area, and generate an earthquake disaster risk assessment report based on the earthquake risk level, earthquake risk point, earthquake risk time and earthquake risk decision information.

[0062] S240, using the earthquake waveform data, earthquake disaster related data and earthquake disaster risk assessment report corresponding to the target area as new historical earthquake data, optimizing the earthquake disaster risk assessment model, and obtaining an optimized earthquake disaster risk assessment model.

[0063] Among them, historical earthquake data can refer to the relevant information and data recorded about earthquake events that occurred or were predicted in the past.

[0064] Specifically, the earthquake waveform data, earthquake disaster-related data and earthquake disaster risk assessment report corresponding to the target area are used as new historical earthquake data to form a complete data set. The earthquake waveform data in the data set is cleaned to remove noise and outliers, and the earthquake disaster-related data is standardized to ensure the consistency and comparability of the data. The data set is divided into a training set and a validation set for model training and validation. The earthquake disaster risk assessment model is trained using the training set data to adjust the model parameters. The prediction performance of the model, such as accuracy and stability, is evaluated using the validation set data. According to the evaluation results, the model is optimized, such as adjusting the model structure, adding feature variables, etc., to obtain an optimized earthquake disaster risk assessment model. By continuously introducing new historical earthquake data, the model can learn more earthquake characteristics and laws, thereby improving the accuracy of earthquake disaster prediction.

[0065] The technical solution of the embodiment of the present invention optimizes the earthquake disaster risk assessment model by taking the earthquake waveform data, earthquake disaster related data and earthquake disaster risk assessment report corresponding to the target area as new historical earthquake data to obtain an optimized earthquake disaster risk assessment model. By continuously introducing new historical earthquake data, the model can learn more earthquake characteristics and laws, thereby improving the accuracy of the prediction and making it better meet the needs of actual earthquake disaster risk assessment.

[0066] Embodiment 3

[0067] Figure 3 This is a schematic diagram of the structure of an earthquake disaster risk assessment device provided by Embodiment 3 of the present invention. Figure 3 As shown, the device includes: an assessment parameter determination module 310, a disaster risk assessment module 320 and an assessment report generation module 330.

[0068] The assessment parameter determination module 310 is used to determine the earthquake risk assessment parameters corresponding to the target area to be assessed based on the earthquake waveform data and earthquake disaster associated data corresponding to the target area to be assessed, wherein the earthquake risk assessment parameters include: the first average shear wave velocity, the second average shear wave velocity, the standard equivalent shear wave velocity, the site type, the dominant period and the stratigraphic information;

[0069] A disaster risk assessment module 320 is used to input the earthquake risk assessment parameters into a pre-trained earthquake disaster risk assessment model to perform earthquake disaster risk assessment and determine earthquake risk warning information corresponding to the target area, wherein the earthquake disaster risk assessment model is obtained through training based on a deep learning model;

[0070] The assessment report generation module 330 is used to determine the earthquake risk level, earthquake risk point, earthquake risk time and earthquake risk decision information corresponding to the target area based on the earthquake risk warning information, and generate an earthquake disaster risk assessment report based on the earthquake risk level, earthquake risk point, earthquake risk time and earthquake risk decision information.

[0071] The technical solution of this embodiment is to determine the earthquake risk assessment parameters corresponding to the target area based on the earthquake waveform data and earthquake disaster related data corresponding to the target area to be evaluated, wherein the earthquake risk assessment parameters include: the first average shear wave velocity, the second average shear wave velocity, the standard equivalent shear wave velocity, the site type, the dominant period and the stratigraphic information, so as to accurately reflect the geological characteristics and seismic response characteristics of the target area, and provide a reliable basis for subsequent risk assessment. The earthquake risk assessment parameters are input into the pre-trained earthquake disaster risk assessment model for earthquake disaster risk assessment, and the earthquake risk warning information corresponding to the target area is determined, wherein the earthquake disaster risk assessment model is obtained by training based on the deep learning model, and the deep learning model can capture the nonlinear relationship in the data, which can improve the accuracy of risk assessment. Based on the earthquake risk warning information, the earthquake risk level, earthquake risk point, earthquake risk time and earthquake risk decision information corresponding to the target area are determined, and based on the earthquake risk level, earthquake risk point, earthquake risk time and earthquake risk decision information, an earthquake disaster risk assessment report is generated, thereby providing more powerful support for disaster risk management and disaster reduction work. By processing seismic data to obtain earthquake risk assessment parameters, the geological characteristics and seismic response characteristics of the target area can be accurately reflected, and then accurate earthquake risk warning information can be obtained based on the earthquake disaster risk assessment model, and then a clear and intuitive earthquake disaster risk assessment report can be obtained, which can more accurately and efficiently warn of earthquake disaster hazards in the target area and provide stronger support for disaster risk management and mitigation work.

[0072] Optionally, the evaluation parameter determination module 310 is specifically used to: perform data fusion on the seismic waveform data and the earthquake disaster related data corresponding to the target area to be evaluated, and perform data cleaning processing on the fused data, wherein the data cleaning includes at least one of noise removal and outlier removal;

[0073] Feature extraction is performed on the cleaned seismic data, and seismic risk assessment parameters corresponding to the target area are determined based on the extracted seismic features.

[0074] Optionally, the seismic waveform data includes: strong vibration observation station data; the earthquake disaster related data includes: urban housing and construction data, rural housing and construction data, geological data, river data, drilling data, strong vibration observation station data, macro site data and at least one of the fifth generation map data.

[0075] Optionally, the evaluation parameter determination module 310 includes:

[0076] The first wave velocity determination unit is used to, in response to the bottom depth corresponding to the last soil layer in the target area being greater than or equal to the preset bottom depth, sum the ratios of the soil layer thickness corresponding to each soil layer within the preset bottom depth range to the shear wave velocity, and determine the ratio of the preset bottom depth to the summed result as the first average shear wave velocity corresponding to the target area; in response to the bottom depth corresponding to the last soil layer in the target area being less than the preset bottom depth range, sum the ratios of the soil layer thickness corresponding to each soil layer within the bottom depth range corresponding to the last soil layer to the shear wave velocity, and determine the ratio of the bottom depth corresponding to the last soil layer to the summed result as the first average shear wave velocity corresponding to the target area.

[0077] Optionally, the evaluation parameter determination module 310 includes:

[0078] A standard wave velocity determination unit is used to sum the ratios of soil layer thickness corresponding to each soil layer within the preset layer bottom depth range to the shear wave velocity in response to the cover layer thickness of the target area being greater than or equal to the preset layer bottom depth, and determine the ratio of the preset layer bottom depth to the summed result as the standard equivalent shear wave velocity corresponding to the target area; in response to the cover layer thickness of the target area being less than the preset layer bottom depth, sum the ratios of soil layer thickness corresponding to each soil layer within the cover layer thickness range to the shear wave velocity, and determine the ratio of the cover layer thickness to the summed result as the standard equivalent shear wave velocity corresponding to the target area.

[0079] Optionally, the evaluation parameter determination module 310 includes:

[0080] A site type determination unit is used to determine that the site type corresponding to the target area is a Class I site in response to the standard equivalent shear wave velocity of the target area being within a first preset velocity range and the covering layer thickness being zero; determine that the site type corresponding to the target area is a Class II site in response to the standard equivalent shear wave velocity of the target area being within a second preset velocity range and the covering layer thickness being within a first preset thickness range; determine that the site type corresponding to the target area is a Class III site in response to the standard equivalent shear wave velocity of the target area being within a third preset velocity range and the covering layer thickness being within a second preset thickness range; determine that the site type corresponding to the target area is a Class IV site in response to the standard equivalent shear wave velocity of the target area being within a fourth preset velocity range and the covering layer thickness being within a third preset thickness range.

[0081] Optionally, the device further comprises: a model optimization module;

[0082] The model optimization module is specifically used to: use the earthquake waveform data, earthquake disaster related data and the earthquake disaster risk assessment report corresponding to the target area as new historical earthquake data, optimize the earthquake disaster risk assessment model, and obtain the optimized earthquake disaster risk assessment model.

[0083] The earthquake disaster risk assessment device provided in the embodiment of the present invention can execute the earthquake disaster risk assessment method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0084] Figure 4 A schematic diagram of an electronic device 12 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as desktop computers, workstations, servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0085] like Figure 4 As shown, the electronic device 12 is in the form of a general purpose computing device. The components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that connects various system components (including the system memory 28 and the processing unit 16).

[0086] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. By way of example, these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0087] The electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 12, including volatile and non-volatile media, removable and non-removable media.

[0088] The system memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 4 not shown, usually called a "hard drive"). Although Figure 4 Not shown in the figure, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The system memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of various embodiments of the present invention.

[0089] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. Program modules 42 generally perform the functions and / or methods of the embodiments described herein.

[0090] The electronic device 12 may also communicate with one or more external devices 14 (e.g., keyboards, pointing devices, displays 24, etc.), may communicate with one or more devices that enable a user to interact with the electronic device 12, and / or may communicate with any device that enables the electronic device 12 to communicate with one or more other computing devices (e.g., network cards, modems, etc.). This communication may be performed via an input / output (I / O) interface 22. In addition, the electronic device 12 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 20. As shown, the network adapter 20 communicates with other modules of the electronic device 12 via a bus 18. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0091] The processing unit 16 executes various functional applications and data processing by running the program stored in the system memory 28, for example, implementing a method for earthquake disaster risk assessment provided by the embodiment of the present invention, the method comprising:

[0092] Based on the seismic waveform data and seismic disaster associated data corresponding to the target area to be evaluated, determining the seismic risk assessment parameters corresponding to the target area, wherein the seismic risk assessment parameters include: first average shear wave velocity, second average shear wave velocity, standard equivalent shear wave velocity, site type, dominant period and stratigraphic information;

[0093] Inputting the earthquake risk assessment parameters into a pre-trained earthquake disaster risk assessment model to perform earthquake disaster risk assessment, and determining earthquake risk warning information corresponding to the target area, wherein the earthquake disaster risk assessment model is obtained through training based on a deep learning model;

[0094] Based on the earthquake risk warning information, the earthquake risk level, earthquake risk point, earthquake risk time and earthquake risk decision information corresponding to the target area are determined, and based on the earthquake risk level, earthquake risk point, earthquake risk time and earthquake risk decision information, an earthquake disaster risk assessment report is generated.

[0095] Of course, those skilled in the art will appreciate that the processor may also implement the technical solution of the earthquake disaster risk assessment method provided by any embodiment of the present invention.

[0096] This embodiment provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the steps of the earthquake disaster risk assessment method provided in any embodiment of the present invention are implemented. The method includes:

[0097] Based on the seismic waveform data and seismic disaster associated data corresponding to the target area to be evaluated, determining the seismic risk assessment parameters corresponding to the target area, wherein the seismic risk assessment parameters include: first average shear wave velocity, second average shear wave velocity, standard equivalent shear wave velocity, site type, dominant period and stratigraphic information;

[0098] Inputting the earthquake risk assessment parameters into a pre-trained earthquake disaster risk assessment model to perform earthquake disaster risk assessment, and determining earthquake risk warning information corresponding to the target area, wherein the earthquake disaster risk assessment model is obtained through training based on a deep learning model;

[0099] Based on the earthquake risk warning information, the earthquake risk level, earthquake risk point, earthquake risk time and earthquake risk decision information corresponding to the target area are determined, and based on the earthquake risk level, earthquake risk point, earthquake risk time and earthquake risk decision information, an earthquake disaster risk assessment report is generated.

[0100] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0101] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0102] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0103] Computer program code for performing the operation of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0104] It should be understood by those skilled in the art that the modules or steps of the present invention described above can be implemented by a general-purpose computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, optionally, they can be implemented by a program code executable by a computer device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0105] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for earthquake disaster risk assessment, characterized in that: include: Based on the seismic waveform data and seismic disaster associated data corresponding to the target area to be evaluated, determining the seismic risk assessment parameters corresponding to the target area, wherein the seismic risk assessment parameters include: first average shear wave velocity, second average shear wave velocity, standard equivalent shear wave velocity, site type, dominant period and stratigraphic information; Inputting the earthquake risk assessment parameters into a pre-trained earthquake disaster risk assessment model to perform earthquake disaster risk assessment, and determining earthquake risk warning information corresponding to the target area, wherein the earthquake disaster risk assessment model is obtained through training based on a deep learning model; Based on the earthquake risk warning information, the earthquake risk level, earthquake risk point, earthquake risk time and earthquake risk decision information corresponding to the target area are determined, and based on the earthquake risk level, the earthquake risk point, the earthquake risk time and the earthquake risk decision information, an earthquake disaster risk assessment report is generated.

2. The method according to claim 1, characterized in that The determining of earthquake risk assessment parameters corresponding to the target area to be assessed based on earthquake waveform data and earthquake disaster associated data corresponding to the target area to be assessed includes: Performing data fusion on the seismic waveform data and earthquake disaster-related data corresponding to the target area to be evaluated, and performing data cleaning processing on the fused data, wherein the data cleaning includes at least one of noise removal and outlier removal; Feature extraction is performed on the cleaned seismic data, and seismic risk assessment parameters corresponding to the target area are determined based on the extracted seismic features.

3. The method according to claim 1, characterized in that The seismic waveform data includes: strong vibration observation station data; the earthquake disaster related data includes: urban housing and construction data, rural housing and construction data, geological data, river data, drilling data, strong vibration observation station data, macro site data and at least one of the five generations of map data.

4. The method according to claim 1, characterized in that: The determining, based on the seismic waveform data and the seismic disaster associated data corresponding to the target area to be evaluated, a first average shear wave velocity corresponding to the target area includes: In response to the bottom depth corresponding to the last soil layer in the target area being greater than or equal to the preset bottom depth, summing the ratios of soil layer thicknesses corresponding to each soil layer within the preset bottom depth range to the shear wave velocity, and determining the ratio of the preset bottom depth to the summation result as the first average shear wave velocity corresponding to the target area; In response to the bottom depth corresponding to the last soil layer in the target area being less than a preset bottom depth range, the ratios of the soil layer thicknesses corresponding to each soil layer within the bottom depth range corresponding to the last soil layer to the shear wave velocity are summed, and the ratio of the bottom depth corresponding to the last soil layer to the summed result is determined as the first average shear wave velocity corresponding to the target area.

5. The method according to claim 1, characterized in that The determining of the standard equivalent shear wave velocity corresponding to the target area to be evaluated based on the seismic waveform data and the seismic disaster associated data corresponding to the target area to be evaluated includes: In response to the thickness of the cover layer in the target area being greater than or equal to the preset layer bottom depth, the ratio of the soil layer thickness corresponding to each soil layer within the preset layer bottom depth range to the shear wave velocity is summed, and the ratio of the preset layer bottom depth to the sum is determined as the standard equivalent shear wave velocity corresponding to the target area; In response to the cover layer thickness of the target area being less than a preset layer bottom depth, the ratios of the soil layer thicknesses corresponding to each soil layer within the cover layer thickness range to the shear wave velocity are summed, and the ratio of the cover layer thickness to the summation result is determined as the standard equivalent shear wave velocity corresponding to the target area.

6. The method according to claim 1, characterized in that The determining of the site type corresponding to the target area to be evaluated based on the seismic waveform data and the seismic disaster associated data corresponding to the target area to be evaluated includes: In response to the standard equivalent shear wave velocity of the target area being within a first preset velocity range and the thickness of the cover layer being zero, determining that the site type corresponding to the target area is a Class I site; In response to the standard equivalent shear wave velocity of the target area being within the second preset velocity range and the cover layer thickness being within the first preset thickness range, determining that the site type corresponding to the target area is a Class II site; In response to the standard equivalent shear wave velocity of the target area being within a third preset velocity range and the cover layer thickness being within a second preset thickness range, determining that the site type corresponding to the target area is a Class III site; In response to the standard equivalent shear wave velocity of the target area being within a fourth preset velocity range and the cover layer thickness being within a third preset thickness range, it is determined that the site type corresponding to the target area is a fourth type site.

7. The method according to claim 1, characterized in that After generating the earthquake disaster risk assessment report, it also includes: The earthquake waveform data, earthquake disaster related data and the earthquake disaster risk assessment report corresponding to the target area are used as new historical earthquake data to optimize the earthquake disaster risk assessment model to obtain the optimized earthquake disaster risk assessment model.

8. An earthquake disaster risk assessment device, characterized in that: include: An assessment parameter determination module is used to determine the earthquake risk assessment parameters corresponding to the target area to be assessed based on the earthquake waveform data and earthquake disaster associated data corresponding to the target area to be assessed, wherein the earthquake risk assessment parameters include: first average shear wave velocity, second average shear wave velocity, standard equivalent shear wave velocity, site type, dominant period and stratigraphic information; A disaster risk assessment module is used to input the earthquake risk assessment parameters into a pre-trained earthquake disaster risk assessment model to perform earthquake disaster risk assessment and determine earthquake risk warning information corresponding to the target area, wherein the earthquake disaster risk assessment model is obtained through training based on a deep learning model; An assessment report generation module is used to determine the earthquake risk level, earthquake risk point, earthquake risk time and earthquake risk decision information corresponding to the target area based on the earthquake risk warning information, and generate an earthquake disaster risk assessment report based on the earthquake risk level, the earthquake risk point, the earthquake risk time and the earthquake risk decision information.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the earthquake disaster risk assessment method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the earthquake disaster risk assessment method according to any one of claims 1 to 7 when executed.

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