An earthquake intensity prediction method and device based on earthquake destructive force

Abstract

The present invention relates to the technical field of earthquake intensity prediction. Specifically, it relates to an earthquake intensity prediction method and device based on earthquake destructive force. The method includes the following steps: obtaining earthquake destructive force data of a target area; inputting the above earthquake destructive force data into a pre-established earthquake intensity prediction model to obtain the earthquake intensity of a target station, so as to accurately predict the earthquake intensity according to the building damage situation. Among them, the earthquake destructive force data is the ratio between various damage states of each type of building disaster-bearing body and the ratio between various damage states of the overall building disaster-bearing body. The earthquake intensity prediction model is obtained by training a machine learning algorithm based on historical earthquake intensity data and historical earthquake destructive force. The target area is the area adjacent to the target station.

Description

Technical Field

[0001] The present invention relates to the technical field of earthquake intensity prediction, and in particular, to an earthquake intensity prediction method and device based on earthquake destructive force. Background Art

[0002] Earthquake activities are considered to be one of the most destructive natural disasters globally. Earthquake intensity represents the degree of influence of an earthquake on the ground surface and engineering buildings (or can be interpreted as the degree of earthquake influence and damage). It is a macroscopic scale determined by people's feelings during an earthquake, the reaction degree of utensils after the earthquake occurs, the damage or destruction degree of engineering buildings, and the change condition of the ground surface in the absence of instrument records. However, existing methods generally use earthquake parameters to predict earthquake intensity, and the influence of building damage on earthquake intensity is not considered in the actual process of predicting earthquake intensity.

[0003] Currently, when an earthquake occurs, buildings in the target area are often damaged. Since existing methods do not consider the influence of building damage on earthquake intensity, the earthquake intensity cannot be accurately predicted based on the building damage situation. Summary of the Invention

[0004] The purpose of the present invention is to provide an earthquake intensity prediction method and device based on earthquake destructive force to solve the problem that the earthquake intensity cannot be accurately predicted based on the building damage situation.

[0005] An embodiment of the present invention provides an earthquake intensity prediction method based on earthquake destructive force. The method includes the following steps: obtaining earthquake destructive force data of a target area, where the earthquake destructive force data is the ratio between various damage states of each type of building disaster body and the ratio between various damage states of the overall building disaster body; inputting the earthquake destructive force data into a pre-established earthquake intensity prediction model to obtain the earthquake intensity of a target station, where the earthquake intensity prediction model is obtained by training a machine learning algorithm based on historical earthquake intensity data and historical earthquake destructive force; where the target area is an area adjacent to the target station.

[0006] Optionally, the obtaining of the earthquake destructive force data of the target area includes: obtaining the measured ground motion data of the target station; inputting the measured ground motion data into the pre-established building earthquake damage analysis model of the target area to obtain the measured earthquake response data of the target area; obtaining the damage state of each building disaster body according to the measured earthquake response data and the earthquake damage state judgment criterion; and calculating the earthquake destructive force data of the target area according to the damage state of each building disaster body.

[0007] Optionally, the method further includes: obtaining historical ground motion data of the target station and building attribute data of the building disaster-bearing body model of the target area; constructing the building earthquake damage analysis model based on the historical ground motion data, the building attribute data, and the urban seismic elastoplastic analysis method.

[0008] Optionally, the method further includes: constructing the building disaster-bearing body model according to the building statistical rule data of the target area; wherein the building statistical rule data includes statistical rules of building structure types, construction years, and building floors.

[0009] Optionally, the method further includes: obtaining historical earthquake intensity data and historical earthquake destructive force data of the target area; converting the historical earthquake destructive force data into a first feature matrix as the input of the earthquake intensity prediction model, converting the historical earthquake intensity data into a one-dimensional vector as the output of the earthquake intensity prediction model, and constructing the earthquake intensity prediction model by training with historical data using the K-nearest neighbor algorithm; wherein the historical data includes the historical earthquake intensity data and the historical earthquake destructive force data of the target area, the first feature matrix is a matrix of i×j×k, i is the type of damage state, j is the number of types of building structure types of the building disaster-bearing body plus one, and k is the number of groups of the historical data, and the one-dimensional vector is a vector of 1×k.

[0010] Optionally, the step of inputting the earthquake destructive force data into a pre-established earthquake intensity prediction model to obtain the earthquake intensity of the target station includes: converting the earthquake destructive force data into a second feature matrix and inputting it into the pre-established earthquake intensity prediction model to obtain the earthquake intensity of the target station; wherein the second feature matrix is a matrix of i×j, and the earthquake intensity of the target station is a vector of 1×1.

[0011] Optionally, the structure types of the building disaster-bearing body include frame structure, frame-shear wall structure, unfortified masonry structure, fortified masonry, and civil structure; the damage states include intact, slightly damaged, moderately damaged, severely damaged, and collapsed.

[0012] Optionally, the earthquake response data includes inter-story drift ratio, and the earthquake damage state judgment criterion is to divide the damage states of each building disaster-bearing body according to the inter-story drift ratio.

[0013] Compared with the prior art, the beneficial effects of the earthquake intensity prediction method based on earthquake destructive force provided by the present invention are as follows:

[0014] The earthquake intensity prediction method based on earthquake destructive force provided by the embodiments of the present invention obtains the earthquake destructive force data of the target area; inputs the earthquake destructive force data into a pre-established earthquake intensity prediction model to obtain the earthquake intensity of the target station, so as to accurately predict the earthquake intensity according to the building damage situation. Among them, the earthquake destructive force data is the ratio between the various damage states of each type of building disaster-bearing body and the ratio between the various damage states of the overall building disaster-bearing body. The earthquake intensity prediction model is obtained by training a machine learning algorithm based on historical earthquake intensity data and historical earthquake destructive force. The target area is the area adjacent to the target station.

[0015] The embodiments of the present invention further provide an earthquake intensity prediction device based on earthquake destructive force for implementing the earthquake intensity prediction method based on earthquake destructive force. The device includes: an acquisition module for acquiring the earthquake destructive force data of the target area, where the earthquake destructive force data is the ratio between the various damage states of each type of building disaster-bearing body and the ratio between the various damage states of the overall building disaster-bearing body; a prediction module for inputting the earthquake destructive force data into a pre-established earthquake intensity prediction model to obtain the earthquake intensity of the target station, where the earthquake intensity prediction model is obtained by training a machine learning algorithm based on historical earthquake intensity data and historical earthquake destructive force; among them, the target area is the area adjacent to the target station.

[0016] The beneficial effect of the earthquake intensity prediction device based on earthquake destructive force provided by the present invention is that it can achieve the same technical effect as the above earthquake intensity prediction method based on earthquake destructive force. To avoid repetition, it will not be elaborated here.

[0017] The embodiments of the present invention further provide a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is read and run by a processor, it implements the earthquake intensity prediction method based on earthquake destructive force.

[0018] The beneficial effect of the computer-readable storage medium provided by the present invention is that it can achieve the same technical effect as the above earthquake intensity prediction method based on earthquake destructive force. To avoid repetition, it will not be elaborated here. Description of the Drawings

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to the provided drawings.

[0020] Figure 1Schematic flowchart of a seismic intensity prediction method based on seismic destructive force provided by an embodiment of the present invention;

[0021] Figure 2 Schematic diagram of the principle of the K-nearest neighbor algorithm model in an embodiment of the present invention;

[0022] Figure 3 Schematic flowchart of a seismic intensity prediction method based on the K-nearest neighbor algorithm and seismic destructive force provided by an embodiment of the present invention;

[0023] Figure 4 Schematic diagram of the confusion matrix corresponding to the prediction result of the seismic intensity prediction model in an embodiment of the present invention;

[0024] Figure 5 Schematic diagram of the comparison of the prediction accuracy of the seismic intensity in an embodiment of the present invention;

[0025] Figure 6 Schematic diagram of the structure of a seismic intensity prediction device based on seismic destructive force provided by an embodiment of the present invention. Detailed implementation manners

[0026] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following detailed description of the specific embodiments of the present invention is provided in conjunction with the accompanying drawings.

[0027] An embodiment of the present invention provides a seismic intensity prediction method based on seismic destructive force. Refer to Figure 1 the schematic flowchart of a seismic intensity prediction method based on seismic destructive force shown below. The method includes the following steps:

[0028] S110, obtain the seismic destructive force data of the target area.

[0029] Among them, the above seismic destructive force data is the ratio between the various damage states of each type of building disaster-bearing body and the ratio between the various damage states of the overall building disaster-bearing body.

[0030] Optionally, the above step S110 includes: obtaining the measured ground motion data of the target station; inputting the above ground motion data into the pre-established building seismic damage analysis model of the target area to obtain the seismic response data of the target area; according to the above measured seismic response data and the seismic damage state judgment criterion, obtaining the damage state of each building disaster-bearing body; and calculating the seismic destructive force data of the target area according to the damage state of each above building disaster-bearing body.

[0031] S120, input the above seismic destructive force data into the pre-established seismic intensity prediction model to obtain the seismic intensity of the target station.

[0032] Among them, the above-mentioned earthquake intensity prediction model is obtained by training a machine learning algorithm based on historical earthquake intensity data and historical earthquake destructive power. The above-mentioned target area is an area adjacent to the above-mentioned target station. It should be noted that the above-mentioned historical earthquake intensity data can be obtained from an earthquake intensity map.

[0033] Optionally, the above-mentioned step S120 includes: converting the earthquake destructive power data into a second feature matrix and inputting it into a pre-established earthquake intensity prediction model to obtain the earthquake intensity of the target station.

[0034] Among them, the above-mentioned second feature matrix is an i×j matrix, where i is the type of damage state and j is the number of types of building disaster-bearing body structure types plus one; the earthquake intensity of the above-mentioned target station is a 1×1 vector, which is equivalent to a number.

[0035] The earthquake intensity prediction method based on earthquake destructive power provided by the embodiments of the present invention realizes accurate prediction of earthquake intensity according to building damage conditions by obtaining earthquake destructive power data of a target area; inputting the above-mentioned earthquake destructive power data into a pre-established earthquake intensity prediction model to obtain the earthquake intensity of the target station. Among them, the above-mentioned earthquake destructive power data is the ratio between various damage states of each type of building disaster-bearing body and the ratio between various damage states of the overall building disaster-bearing body. The above-mentioned earthquake intensity prediction model is obtained by training a machine learning algorithm based on historical earthquake intensity data and historical earthquake destructive power. The above-mentioned target area is an area adjacent to the above-mentioned target station.

[0036] Optionally, the above-mentioned method further includes: obtaining historical ground motion data of the target station and building attribute data of the building disaster-bearing body model of the target area; constructing a building earthquake damage analysis model of the above-mentioned target area based on the above-mentioned historical ground motion data, the above-mentioned building attribute data, and the urban seismic elastoplastic analysis method.

[0037] Optionally, the above-mentioned method further includes: constructing a building disaster-bearing body model according to the building statistical rule data of the above-mentioned target area.

[0038] Among them, the above-mentioned building statistical rule data includes statistical rules of building structure types, construction years, and building floors.

[0039] Optionally, the above-mentioned method further includes: obtaining historical earthquake intensity data and historical earthquake destructive power data of the target area; converting the above-mentioned historical earthquake destructive power data into a first feature matrix as the input of the earthquake intensity prediction model, converting the above-mentioned historical earthquake intensity data into a one-dimensional vector as the output of the earthquake intensity prediction model, and using the K-nearest neighbor algorithm to train and construct the above-mentioned earthquake intensity prediction model.

[0040] Among them, the above historical data includes historical earthquake intensity data and historical earthquake damage force data of the target area. The above historical earthquake intensity data can be obtained from publicly released earthquake intensity maps. The above first feature matrix is a matrix of i×j×k, where i is the type of the above damage state, j is the number of types of building disaster-bearing body structure types plus one, k is the number of groups of historical data, and the above one-dimensional vector is a vector of 1×k.

[0041] Exemplarily, the historical earthquake damage force data corresponding to each group of historical data is written into the corresponding classification elements to form the first feature matrix, and the above first feature matrix is used as the input of the earthquake intensity prediction model. The above input is a variable selected according to the Permutation Importance method; the above one-dimensional vector is a vector of 1×k. The historical earthquake intensity data corresponding to each group of historical data is written into the corresponding classification elements to form a one-dimensional vector, and the above one-dimensional vector is used as the output of the earthquake intensity prediction model. In addition, the above first feature matrix and the above one-dimensional vector can be transposed as needed.

[0042] It should be noted that the above K-nearest neighbor algorithm is selected according to the principle of the highest prediction accuracy after comparison with algorithms such as random forest, support vector machine, and logistic regression. See Figure 2 the schematic diagram of the principle of the K-nearest neighbor algorithm model shown. The principle of the K-nearest neighbor algorithm model is that in the feature space, if most of the k nearest neighbors (i.e., the nearest in the feature space) of a sample belong to a certain category, then the sample is also determined to belong to this category, that is Figure 2 in which the circle is the feature space of the sample. Since the number of black dots is the largest, it is determined that the sample belongs to the category of black dots; Exemplarily, in the embodiment of the present invention, the type of damage state can be used as the abscissa, and the number of types of building structure types plus one can be used as the ordinate to construct the feature space corresponding to the K-nearest neighbor algorithm model. In addition, since one earthquake contains multiple ground motions, and each historical ground motion can determine a group of historical data, multiple training sample points can be determined in the feature space during one earthquake to realize the training of the earthquake intensity prediction model; during the earthquake intensity prediction process, if most of the k nearest neighbor training sample points near the measured sample point belong to a certain earthquake intensity category, then it is determined that the measured sample point belongs to this earthquake intensity category.

[0043] Optionally, the above building disaster-bearing body structure types include frame structure, frame-shear wall structure, unfortified masonry structure, fortified masonry and civil structure; the above damage states include intact, slightly damaged, moderately damaged, severely damaged and collapsed.

[0044] Optionally, the above earthquake response data includes inter-story drift ratio, and the above earthquake damage state judgment criterion is to divide the damage states of each building disaster-bearing body according to the inter-story drift ratio.

[0045] It should be noted that the earthquake response also includes floor acceleration and velocity. Based on the above floor acceleration and velocity, and then according to the pre - constructed damage state determination criterion, the damage state of each building can be obtained.

[0046] The embodiment of the present invention also provides an earthquake intensity prediction method based on the K - nearest neighbor algorithm and earthquake destructive force. Refer to Figure 3 the schematic flow chart of an earthquake intensity prediction method based on the K - nearest neighbor algorithm and earthquake destructive force shown in the figure. The method mainly includes the following steps:

[0047] Step S302: Obtain the strong motion records of the station, construct a building disaster - bearing body model that conforms to the statistical law of buildings in the target area, and use urban seismic elastoplastic analysis and calculation to determine the earthquake destructive force of the strong motion here.

[0048] It should be noted that the above - mentioned strong motion records of the station are the completed strong motion time histories, including the earthquake ground motion acceleration time - history data in the east - west and north - south horizontal directions.

[0049] Specifically, the above - mentioned construction of a building disaster - bearing body model that conforms to the statistical law of buildings in the target area is to generate a virtual building disaster - bearing body model that conforms to the statistical law of target buildings according to the statistical laws of building structure types, construction years, and building floors in the target area; the above - mentioned earthquake destructive force refers to the proportions of five damage states of each structural type of building and the proportion of five damage states of the overall building calculated under the given strong motion records; the five structural type building damage states of the above - mentioned building disaster - bearing body are specifically divided into: intact, slightly damaged, moderately damaged, severely damaged, and collapsed damaged; the proportion of the overall damage state of the above - mentioned building disaster - bearing body is the proportion of each damage state in the overall building disaster - bearing body.

[0050] Step S304: Input the earthquake destructive force into the earthquake intensity prediction model to obtain the earthquake intensity at the station output by the earthquake intensity prediction model.

[0051] Specifically, extract the earthquake destructive force and earthquake intensity characteristics, form a set of feature matrices with the proportions of each structural type of building and the overall five damage states under each specific ground motion and input them into the earthquake intensity prediction model, and use the station earthquake intensity to form a one - dimensional vector as the output of the earthquake intensity prediction model; the above - mentioned earthquake intensity prediction model is trained using the K - nearest neighbor algorithm based on the sample data of historical earthquake destructive force and historical earthquake intensity; the above - mentioned historical earthquake destructive force and historical earthquake intensity sample data sets are divided into data sets, with a part of the data as the training set and the rest as the test set.

[0052] The earthquake intensity prediction model is trained using the above training set, and the prediction performance of the trained earthquake intensity prediction model is tested using the above test set to obtain a test result. The test result is a comprehensive evaluation index of the earthquake intensity prediction model based on the K-nearest neighbor algorithm and earthquake destructive force. If the obtained comprehensive evaluation index meets the expectation, the prediction result can accurately predict the earthquake intensity within the allowable error range.

[0053] Finally, based on the earthquake intensity prediction model of the K-nearest neighbor algorithm and earthquake destructive force, the earthquake intensity of the target station is predicted.

[0054] It should be noted that the comprehensive evaluation index of the above earthquake intensity prediction model includes: Precision (accuracy rate), Recall (recall rate), F1-score (F1 score), MacroAvg (macro average), and WeightedAvg (weighted average). The above comprehensive evaluation index is used to evaluate the prediction ability of the earthquake intensity prediction model.

[0055] Specifically, the calculation formula of the above comprehensive evaluation index is:

[0056] Precision (accuracy rate) = TP / (TP + FP) (1)

[0057] Recall (recall rate) = TP / (TP + FN) (2)

[0058] F1-score (F1 score) = 2 * (Precision * Recall) / (Precision + Recall) (3)

[0059]

[0060] Among them, TP represents the number of samples that belong to this category and are correctly predicted as this category, FP represents the number of samples that do not belong to this category but are wrongly predicted as this category, FN represents the number of samples that belong to this category but are wrongly predicted as other categories, and Precision i represents the accuracy rate of category i, and N i represents the number of samples of category i.

[0061] Exemplarily, Table 1 is a case data set in the embodiment of the present invention. The above earthquake intensity prediction method based on earthquake destructive force provided by the embodiment of the present invention is applicable to but not limited to the earthquake cases in Table 1.

[0062] Table 1

[0063]

[0064] It should be noted that earthquake data are measured by multiple stations for each earthquake, and ground motion data for a certain area can be measured for each earthquake.

[0065] Exemplarily, Table 2 shows the results of comprehensive evaluation indicators of the earthquake intensity prediction model in a case of an embodiment of the present invention.

[0066] Table 2

[0067]

[0068]

[0069] Exemplarily, referring to Figure 4 the schematic diagram of the confusion matrix corresponding to the prediction results of the earthquake intensity prediction model shown, it can be seen that only 6 out of 32 samples have incorrect prediction results, indicating that the prediction results obtained by the earthquake intensity prediction method based on earthquake destructive force provided in the embodiment of the present invention are reliable.

[0070] Exemplarily, referring to Figure 5 the schematic diagram of the comparison of earthquake intensity prediction accuracies shown, wherein the accuracy of the earthquake intensity prediction method based on earthquake destructive force provided in the embodiment of the present invention is higher than that of the traditional method of directly predicting earthquake intensity using ground motion parameters.

[0071] The earthquake intensity prediction method based on the K-nearest neighbor algorithm and earthquake destructive force provided in the embodiment of the present invention can construct a building disaster-bearing body model that conforms to the building statistical law of the target area based on the building types in the target area, determine the earthquake destructive force of strong ground motion here through urban seismic elastoplastic analysis and calculation, establish a mapping relationship between earthquake ground motion destructive force and earthquake intensity through the K-nearest neighbor algorithm, and predict the earthquake intensity of the target station. Compared with the existing methods for predicting earthquake intensity, the present invention takes into account the influence of building damage on earthquake intensity, can effectively improve the prediction accuracy of earthquake intensity at the target station, and reflects the influence of building damage on earthquake intensity.

[0072] The embodiment of the present invention also provides an earthquake intensity prediction device based on earthquake destructive force for implementing the above earthquake intensity prediction method based on earthquake destructive force. Referring to Figure 6 the structural schematic diagram of an earthquake intensity prediction device based on earthquake destructive force shown, the device includes:

[0073] An acquisition module 602, configured to acquire earthquake destructive force data of the target area.

[0074] Wherein, the above earthquake destructive force data are the ratios between the various damage states of each type of building disaster-bearing body and the ratios between the various damage states of the overall building disaster-bearing body.

[0075] A prediction module 604, configured to input the above earthquake destructive force data into a pre-established earthquake intensity prediction model to obtain the earthquake intensity of the target station.

[0076] Wherein, the above earthquake intensity prediction model is obtained by training a machine learning algorithm based on historical earthquake intensity data and historical earthquake destructive forces, and the above target area is an area adjacent to the target station.

[0077] The beneficial effect of the earthquake intensity prediction device based on earthquake destructive force provided by the present invention is as follows: it can achieve the same technical effect as the above earthquake intensity prediction method based on earthquake destructive force. To avoid repetition, it will not be elaborated here.

[0078] An embodiment of the present invention further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program. When the computer program is read and run by a processor, the earthquake intensity prediction method based on earthquake destructive force is implemented.

[0079] The beneficial effect of the computer-readable storage medium provided by the present invention is as follows: it can achieve the same technical effect as the above earthquake intensity prediction method based on earthquake destructive force. To avoid repetition, it will not be elaborated here.

[0080] Of course, those skilled in the art can understand that all or part of the processes of implementing the above method embodiments can be completed by instructing a control device through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above method embodiments. The storage medium can be a memory, a disk, an optical disc, etc.

[0081] Finally, it should also be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or device including the element.

[0082] Each embodiment in this specification is described in a progressive manner. The key points of each embodiment are the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other.

[0083] Although the present invention is disclosed as above, the present invention is not limited thereto. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the scope defined by the claims.

Claims

1. A method for predicting earthquake intensity based on earthquake destructive force, characterized in that, The method includes the following steps: Obtain the seismic destructive force data of the target area, where the seismic destructive force data is the ratio between various damage states of each type of building disaster-bearing body and the ratio between various damage states of the overall building disaster-bearing body; Obtain the historical seismic intensity data and the historical seismic destructive force data of the target area; Convert the historical earthquake destructive force data into a first feature matrix as the input of the earthquake intensity prediction model, convert the historical earthquake intensity data into a one-dimensional vector as the output of the earthquake intensity prediction model, and construct the earthquake intensity prediction model through training on historical data using the K-nearest neighbor algorithm; wherein, the historical data includes the historical earthquake intensity data and the historical earthquake destructive force data of the target area, and the first feature matrix is i × j × k matrix, i is the type of the damage state, j is the number of types of the structural types of the building disaster-bearing bodies plus one, k is the number of groups of the historical data, and the one-dimensional vector is a 1× k vector; Input the earthquake destructive force data into a pre-established earthquake intensity prediction model to obtain the earthquake intensity of the target station. The earthquake intensity prediction model is trained based on historical earthquake intensity data and historical earthquake destructive forces for a machine learning algorithm; this step includes: converting the earthquake destructive force data into a second feature matrix and inputting it into the pre-established earthquake intensity prediction model to obtain the earthquake intensity of the target station; wherein, the second feature matrix is i × j matrix, and the earthquake intensity of the target station is a 1×1 vector; the target area is the area adjacent to the target station.

2. The earthquake intensity prediction method based on earthquake destructive force according to claim 1, wherein The obtaining of the seismic destructive force data of the target area includes: Obtain the measured ground motion data of the target station; Input the measured ground motion data into the pre-established building seismic damage analysis model of the target area to obtain the measured seismic response data of the target area; According to the measured seismic response data and the seismic damage state judgment criterion, obtain the damage states of each building disaster-bearing body; Calculate the seismic destructive force data of the target area based on the damage states of each building disaster-bearing body.

3. The earthquake intensity prediction method based on earthquake destructive force according to claim 2, wherein, The method further includes: Obtain the historical ground motion data of the target station and the building attribute data of the building disaster-bearing body model of the target area; Based on the historical ground motion data, the building attribute data and the urban seismic elastoplastic analysis method, construct the building seismic damage analysis model.

4. The earthquake intensity prediction method based on earthquake destructive force according to claim 3, wherein The method further includes: Construct the building disaster-bearing body model according to the building statistical law data of the target area; Among them, the building statistical law data includes the statistical laws of building structure types, construction years and building floors.

5. The earthquake intensity prediction method based on earthquake destructive force according to claim 1, characterized in that The structural types of the building disaster-bearing body include frame structure, frame-shear wall structure, unfortified masonry structure, fortified masonry and civil structure; The damage states include intact, slightly damaged, moderately damaged, severely damaged and collapsed.

6. The earthquake intensity prediction method based on earthquake destructive force according to claim 2, wherein The seismic response data includes the inter-story drift angle, and the seismic damage state judgment criterion is to divide the damage states of each building disaster-bearing body according to the inter-story drift angle.

7. An earthquake intensity prediction device based on earthquake destructive force, characterized in that, For implementing the seismic intensity prediction method based on seismic destructive force according to any one of claims 1-6, the device includes: An obtaining module, configured to obtain the seismic destructive force data of the target area, where the seismic destructive force data is the ratio between various damage states of each type of building disaster-bearing body and the ratio between various damage states of the overall building disaster-bearing body; A prediction module, configured to input the seismic destructive force data into a pre-established seismic intensity prediction model to obtain the seismic intensity of the target station, where the seismic intensity prediction model is obtained by training a machine learning algorithm based on historical seismic intensity data and historical seismic destructive force; Among them, the target area is an area adjacent to the target station.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and when the computer program is read and run by a processor, the method according to any one of claims 1-6 is implemented.

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