Intelligent urban and rural earthquake disaster risk assessment method and system

Abstract

The invention discloses an intelligent urban and rural earthquake disaster risk assessment method and system. The method comprises the steps of data construction, weight determination, factor association degree measurement and calculation, construction of an urban and rural earthquake disaster risk assessment model and urban and rural earthquake disaster risk assessment. The invention belongs to the field of data processing, and particularly relates to an intelligent urban and rural earthquake disaster risk assessment method and system. According to the scheme, an environmental adaptability factor is introduced to adjust the regional difference of indexes; a variable coefficient is adopted to balance data fluctuation; by introducing a nonlinear attenuation factor of the building age limit, it is ensured that the influence of the building age limit on risk assessment is not underestimated; introducing an urban and rural loss weight to balance urban and rural sample distribution; the sensitivity of the model to extreme abnormal values is suppressed through multiple extreme loss design, so that the stable evaluation capability can still be kept on historical extra-large seismic data; and the urban and rural earthquake disaster risk assessment effect is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an intelligent urban and rural earthquake disaster risk assessment method and system. Background Art

[0002] The urban and rural earthquake disaster risk assessment method is a method system used to assess the degree of impact of urban and rural areas in earthquake disasters. This method predicts the disaster risk of different regions so that governments, disaster management agencies and urban planners can formulate more effective disaster reduction measures. However, the general urban and rural earthquake disaster risk assessment method is affected by extreme values, resulting in unreasonable weight distribution, which affects the final disaster risk assessment, ignores the nonlinear influence of time factors, and leads to poor accuracy in risk assessment of old buildings; the general urban and rural earthquake disaster risk assessment method is not balanced enough, lacks the ability to dynamically adjust time, and cannot adapt to the trend of earthquake risk changing over time in real time, which leads to poor results in urban and rural earthquake disaster risk assessment. Summary of the invention

[0003] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides an intelligent urban and rural earthquake disaster risk assessment method and system. In view of the fact that the general urban and rural earthquake disaster risk assessment method is affected by extreme values, resulting in unreasonable weight distribution, thus affecting the final disaster risk assessment, ignoring the nonlinear influence of time factors, resulting in poor accuracy of risk assessment for old buildings, this solution introduces environmental adaptability factors to adjust regional differences in indicators, thereby making weight distribution more stable and reasonable; the coefficient of variation is used to balance data fluctuations and avoid the instability of weight distribution; by introducing the nonlinear attenuation factor of the building age, the impact of the building age on the risk is better quantified, ensuring that the impact of the building age on risk assessment is not underestimated; thereby improving urban and rural Accuracy of earthquake disaster risk assessment; In view of the lack of balance in general urban and rural earthquake disaster risk assessment methods, lack of time dynamic adjustment capabilities, and inability to adapt to the trend of earthquake risk changes over time in real time, which leads to poor results in urban and rural earthquake disaster risk assessment, this solution introduces urban and rural loss weights to balance the distribution of urban and rural samples. The density weight factor reduces the bias of the model for urban data by increasing the weight of low-density rural areas. The dynamic weight factor takes into account the characteristics of earthquake risk changes over time, so that the model can adapt to different risk situations in different periods and improve timeliness; and through the design of multiple extreme losses, the model's sensitivity to extreme outliers can be suppressed, so that it can still maintain a stable assessment capability on historical major earthquake data; thereby improving the results of urban and rural earthquake disaster risk assessment.

[0004] The technical solution adopted by the present invention is as follows: The present invention provides an intelligent urban and rural earthquake disaster risk assessment method, which includes the following steps:

[0005] Step S1: data construction;

[0006] Step S2: weight determination;

[0007] Step S3: factor correlation calculation;

[0008] Step S4: constructing an urban and rural earthquake disaster risk assessment model;

[0009] Step S5: Urban and rural earthquake disaster risk assessment.

[0010] Furthermore, in step S1, the data construction is to obtain historical urban and rural earthquake disaster assessment data and disaster risk levels; the historical urban and rural earthquake disaster assessment data include geological exploration data, socio-economic data, building strength data and environmental characteristic data; the disaster risk level is used as a data label; and missing value processing and feature standardization are performed.

[0011] Furthermore, in step S2, the weight determination is to calculate the environmental information entropy of each indicator of the historical urban and rural earthquake disaster assessment data, and introduce the environmental adaptability factor , the environmental information entropy is expressed as: ; ;in, is the environmental information entropy of the ith indicator; n is the total number of samples; m is the sample index; is the sample value of the ith indicator at the mth sample; is the sensitivity adjustment parameter; and are the maximum and minimum values ​​of the i-th indicator in all samples respectively; is the sample mean of the i-th indicator; using the stabilization formula, the coefficient of variation is introduced , the weight is expressed as: ; ;in is the weight of the i-th indicator; L is the total number of indicators; u is the indicator index; and are the entropy values ​​of the i-th indicator and the u-th indicator of the m-th sample respectively; G is the average information entropy of all evaluation indicators; and are the standard deviation and mean of the ith indicator respectively.

[0012] Furthermore, in step S3, the factor correlation degree measurement is to quantify the contribution of each risk factor to the overall risk; adding the time decay factor , the correlation coefficient is expressed as: ; ; The factor correlation is expressed as: ; ;in, is the correlation coefficient of the ith indicator in the mth sample; is the reference sequence value; is the attenuation coefficient; t is the building age of the sample; is the minimum absolute difference between all factors and the reference sequence; is the maximum absolute difference between all factors and the reference sequence; is the resolution coefficient; set the correlation threshold, and select the indicators whose average correlation coefficient of all samples is higher than the correlation threshold as the final selected features, so as to construct the urban and rural earthquake disaster data set.

[0013] Furthermore, in step S4, the construction of the urban and rural earthquake disaster risk assessment model specifically includes the following steps:

[0014] Step S41: Architecture design; an S-layer fully connected neural network is used as the basic architecture; the input layer directly inputs the feature vector constructed based on the urban and rural earthquake disaster data set; the hidden layer designs D fully connected layers, the number of nodes in each layer gradually decreases to capture the nonlinear relationship within the data, and the ReLU activation function is used; Dropout is added after some hidden layers; the output layer uses the Sigmoid activation function to output the predicted disaster risk level;

[0015] Step S42: Design urban and rural losses; increase density weight factor Ws and introduce dynamic weight factor , the urban and rural loss FC is expressed as: ; ; ; Among them, W and are the current weight and the initial weight respectively; P is the classification parameter; and A are the sample prediction labels and true labels respectively; , and They are the current time, the last evaluation time and the evaluation time interval; is the density adjustment parameter; is the sample density of the region to which the sample belongs; is the maximum sample density;

[0016] Step S43: multiple extreme loss design; multiple extreme loss FJ is expressed as: ;in, and is the bandwidth parameter;

[0017] Step S44: a phased strategy; FC is used in the 0th to Uth rounds of training; and FJ is used in the Uth to Eth rounds of training.

[0018] Furthermore, in step S5, the urban and rural earthquake disaster risk assessment collects urban and rural earthquake disaster assessment data in real time, inputs it into the urban and rural earthquake disaster risk assessment model after feature engineering processing, and uses the disaster risk level output by the model as the urban and rural earthquake disaster risk assessment result.

[0019] The present invention provides an intelligent urban and rural earthquake disaster risk assessment system, which includes a data construction module, a weight determination module, a factor correlation degree measurement module, an urban and rural earthquake disaster risk assessment model construction module and an urban and rural earthquake disaster risk assessment module;

[0020] The data construction module obtains historical urban and rural earthquake disaster assessment data and disaster risk levels, and uses the disaster risk level as a data label;

[0021] The weight determination module determines the weight of each indicator in the historical urban and rural earthquake disaster assessment data by introducing environmental adaptability factors and coefficients of variation; the indicator is a specific dimension of the historical urban and rural earthquake disaster assessment data;

[0022] The factor correlation calculation module adds a time decay factor to calculate the factor correlation; quantifies the contribution of each indicator to the overall risk;

[0023] The urban and rural earthquake disaster risk assessment model construction module constructs a fully connected neural network model, designs an urban and rural loss function and a multiple extreme loss function; takes the urban and rural earthquake disaster assessment data processed by the data construction module and the weight determination module as input, and then constructs an urban and rural earthquake disaster risk assessment model;

[0024] The urban and rural earthquake disaster risk assessment module performs an urban and rural earthquake disaster risk assessment on the urban and rural earthquake disaster assessment data collected in real time based on the urban and rural earthquake disaster risk assessment model.

[0025] The beneficial effects achieved by the present invention using the above scheme are as follows:

[0026] (1) In view of the fact that the general urban and rural earthquake disaster risk assessment methods are affected by extreme values, resulting in unreasonable weight distribution, which in turn affects the final disaster risk assessment, and ignores the nonlinear influence of time factors, resulting in poor accuracy in risk assessment of old buildings, this scheme introduces the environmental adaptability factor to adjust the regional differences in indicators, thereby making the weight distribution more stable and reasonable; the coefficient of variation is used to balance data fluctuations and avoid the instability of weight distribution; by introducing the nonlinear attenuation factor of the building age, the impact of the building age on the risk is better quantified, ensuring that the impact of the building age on the risk assessment is not underestimated; thereby improving the accuracy of urban and rural earthquake disaster risk assessment.

[0027] (2) In view of the fact that general urban and rural earthquake disaster risk assessment methods are not balanced enough and lack the ability to dynamically adjust over time, they are unable to adapt in real time to the trend of earthquake risk changing over time, which leads to poor results in urban and rural earthquake disaster risk assessment. This scheme introduces urban and rural loss weights to balance the distribution of urban and rural samples. The density weight factor reduces the bias of the model towards urban data by increasing the weight of low-density rural areas. The dynamic weight factor takes into account the characteristics of earthquake risk changing over time, so that the model can adapt to different risk situations in different periods and improve timeliness. It also suppresses the sensitivity of the model to extreme outliers through multiple extreme loss designs, so that it can still maintain a stable assessment capability on historical major earthquake data, thereby improving the results of urban and rural earthquake disaster risk assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 A schematic diagram of a flow chart of an intelligent urban and rural earthquake disaster risk assessment method provided by the present invention;

[0029] Figure 2 A schematic diagram of an intelligent urban and rural earthquake disaster risk assessment system provided by the present invention;

[0030] Figure 3 It is a schematic diagram of the process of step S4.

[0031] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0032] The technical solutions 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, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0033] In the description of the present invention, it is necessary to understand that terms such as “upper”, “lower”, “front”, “back”, “left”, “right”, “top”, “bottom”, “inside” and “outside” indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the referred system or element must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as a limitation on the present invention.

[0034] Example 1, see Figure 1 The present invention provides an intelligent urban and rural earthquake disaster risk assessment method, which includes the following steps:

[0035] Step S1: Data construction: Obtain historical urban and rural earthquake disaster assessment data and disaster risk levels, and use the disaster risk level as a data label;

[0036] Step S2: weight determination; by introducing environmental adaptability factors and coefficients of variation, the weights of various indicators in the historical urban and rural earthquake disaster assessment data are determined; the indicators are specific dimensions of the historical urban and rural earthquake disaster assessment data;

[0037] Step S3: Calculate the correlation of factors; add the time decay factor to calculate the correlation of factors; quantify the contribution of each indicator to the overall risk;

[0038] Step S4: constructing an urban and rural earthquake disaster risk assessment model; constructing a fully connected neural network model, designing an urban and rural loss function and a multiple extreme loss function; taking the urban and rural earthquake disaster assessment data processed by steps S2 and S3 as input, and then constructing an urban and rural earthquake disaster risk assessment model;

[0039] Step S5: Urban and rural earthquake disaster risk assessment: Based on the urban and rural earthquake disaster risk assessment model, the urban and rural earthquake disaster risk assessment is performed on the urban and rural earthquake disaster assessment data collected in real time.

[0040] Example 2, see Figure 1 , this embodiment is based on the above embodiment. In step S1, data construction is to obtain historical urban and rural earthquake disaster assessment data and disaster risk level; the historical urban and rural earthquake disaster assessment data include geological exploration data, socio-economic data, building strength data and environmental characteristic data; the disaster risk level is used as a data label; the geological exploration data include earthquake intensity, geological structure, soil liquefaction index and soil type; the socio-economic data include population density, urbanization level, economic development level, education level and distribution of social service facilities; the building strength data include building type, building age, building height, seismic design standard and building seismic performance; the environmental characteristic data include topography, hydrological conditions and vegetation coverage; the disaster risk level includes high risk and low risk; and missing value processing and feature standardization are performed; the continuous variables of earthquake intensity and population density are filled based on the weighted average of neighboring samples using K nearest neighbor interpolation, and Z-score standardization is performed; the discrete variables of soil type and building type are filled with mode, and the categories are converted into numerical form based on the target code.

[0041] Example 3, see Figure 1This embodiment is based on the above embodiment. In step S2, the weight determination is that earthquake assessment involves geological exploration data, socio-economic data and building strength data, which are often of different scales and sources and have high uncertainty; by objectively measuring the amount of information of each indicator and avoiding weight abnormality due to high entropy values; making more stable and reasonable weight allocation for indicators with extremely high or low information entropy of building seismic performance and population density; calculating environmental information entropy for each indicator of historical urban and rural earthquake disaster assessment data. When the indicator fluctuates greatly in a region, it means that the indicator is significantly affected by regional characteristics, so its influence should be appropriately smoothed in the information entropy calculation, so the environmental adaptability factor is introduced. , the environmental information entropy is expressed as: ; ;in, is the environmental information entropy of the ith indicator; n is the total number of samples; m is the sample index; is the sample value of the ith indicator at the mth sample; is the sensitivity adjustment parameter; and are the maximum and minimum values ​​of the i-th indicator in all samples respectively; is the sample mean of the i-th indicator; a stabilization formula is used to avoid weight distortion due to high entropy values. In urban and rural earthquake disaster assessment, the information entropy of indicators such as building seismic performance and population density may be extremely high or low, and weight distortion may occur due to extreme values. Therefore, the coefficient of variation is introduced. , making the weight distribution more stable; the weight is expressed as: ; ;in is the weight of the i-th indicator; L is the total number of indicators; u is the indicator index; and are the entropy values ​​of the i-th indicator and the u-th indicator of the m-th sample respectively; G is the average information entropy of all evaluation indicators; and are the standard deviation and mean of the ith indicator respectively.

[0042] Example 4, see Figure 1 This embodiment is based on the above embodiment. In step S3, the factor correlation degree measurement needs to quantify the contribution of each risk factor to the overall risk; considering that the impact of the building age on the sample changes nonlinearly over time, a time decay factor is added , the correlation coefficient is expressed as: ; ; The factor correlation is expressed as: ; ;in, is the correlation coefficient of the ith indicator in the mth sample; is the reference sequence value; is the attenuation coefficient; t is the building age of the sample; is the minimum absolute difference between all factors and the reference sequence; is the maximum absolute difference between all factors and the reference sequence; is the resolution coefficient; set the correlation threshold, and select the indicators whose average correlation coefficient of all samples is higher than the correlation threshold as the final selected features, so as to construct the urban and rural earthquake disaster data set.

[0043] By performing the above operations, the general urban and rural earthquake disaster risk assessment method is affected by extreme values, resulting in unreasonable weight distribution, which affects the final disaster risk assessment, and ignores the nonlinear influence of time factors, resulting in poor accuracy of risk assessment for old buildings. This scheme introduces the environmental adaptability factor to adjust the regional differences in indicators, so that the weight distribution is more stable and reasonable; the coefficient of variation is used to balance data fluctuations and avoid the instability of weight distribution; by introducing the nonlinear attenuation factor of the building age, the impact of the building age on the risk is better quantified, ensuring that the impact of the building age on the risk assessment is not underestimated; thereby improving the accuracy of urban and rural earthquake disaster risk assessment.

[0044] Example 5, see Figure 1 and Figure 3 This embodiment is based on the above embodiment. In step S4, the urban and rural earthquake disaster risk assessment model is constructed to process the complex data in the urban and rural earthquake disaster risk assessment. A multi-layer fully connected neural network is used, and the number of nodes in each layer gradually decreases, aiming to capture the nonlinear relationship of the data; an urban and rural earthquake disaster risk assessment model is constructed based on the urban and rural earthquake disaster data set; specifically, the following steps are included:

[0045] Step S41: Architecture design; an S-layer fully connected neural network is used as the basic architecture; the input layer directly inputs the feature vector constructed based on the urban and rural earthquake disaster data set; the hidden layer designs D fully connected layers, the number of nodes in each layer gradually decreases to capture the nonlinear relationship within the data, and the ReLU activation function is used; Dropout is added after some hidden layers; the output layer uses the Sigmoid activation function to output the predicted disaster risk level;

[0046] Step S42: Design urban and rural losses; increase the density weight factor Ws to account for the difference in the number of urban and rural samples, and give higher weights to rural low-density areas to avoid the model being biased towards urban data; consider that earthquake risk assessments in different regions may change over time, and introduce a dynamic weight factor , the urban and rural loss FC is expressed as: ; ; ; Among them, W and are the current weight and the initial weight respectively; P is the classification parameter; and A are the sample prediction labels and true labels respectively; , and They are the current time, the last evaluation time and the evaluation time interval; is the density adjustment parameter; is the sample density of the region to which the sample belongs; is the maximum sample density;

[0047] Step S43: Multiple extreme loss design to enhance the adaptability to extreme outliers of historical major earthquakes; multiple extreme loss FJ is expressed as: ;in, and is the bandwidth parameter;

[0048] Step S44: phased strategy; FC is used in the 0th to Uth rounds of training to mainly solve the problem of sample imbalance; FJ is used in the Uth to Eth rounds of training to improve adaptability to outliers.

[0049] By performing the above operations, in view of the fact that general urban and rural earthquake disaster risk assessment methods are not balanced enough, lack the ability to dynamically adjust in time, and cannot adapt to the trend of earthquake risk changing over time in real time, which leads to poor urban and rural earthquake disaster risk assessment results, this scheme introduces urban and rural loss weights to balance the distribution of urban and rural samples. The density weight factor reduces the bias of the model for urban data by increasing the weight of low-density rural areas. The dynamic weight factor considers the characteristics of earthquake risk changing over time, so that the model can adapt to different risk situations in different periods and improve timeliness. It also suppresses the sensitivity of the model to extreme outliers through multiple extreme loss design, so that it can still maintain a stable assessment capability on historical major earthquake data, thereby improving the urban and rural earthquake disaster risk assessment results.

[0050] Example 6, see Figure 1 This embodiment is based on the above embodiment. In step S5, the urban and rural earthquake disaster risk assessment collects urban and rural earthquake disaster assessment data in real time, and inputs it into the urban and rural earthquake disaster risk assessment model after feature engineering processing, and uses the disaster risk level output by the model as the urban and rural earthquake disaster risk assessment result; when the output disaster risk level is high risk, the management personnel are warned.

[0051] Embodiment 7, see Figure 2 , this embodiment is based on the above embodiment, and the present invention provides an intelligent urban and rural earthquake disaster risk assessment system, including a data construction module, a weight determination module, a factor correlation degree measurement module, an urban and rural earthquake disaster risk assessment model construction module and an urban and rural earthquake disaster risk assessment module;

[0052] The data construction module obtains historical urban and rural earthquake disaster assessment data and disaster risk levels, and uses the disaster risk level as a data label;

[0053] The weight determination module determines the weight of each indicator by introducing environmental adaptability factors and coefficients of variation; the indicators are specific dimensions of historical urban and rural earthquake disaster assessment data;

[0054] The factor correlation calculation module adds a time decay factor to calculate the factor correlation; quantifies the contribution of each indicator to the overall risk;

[0055] The urban and rural earthquake disaster risk assessment model construction module constructs a fully connected neural network model, designs an urban and rural loss function and a multiple extreme loss function; takes the urban and rural earthquake disaster assessment data processed by the weight determination module and the factor correlation degree measurement module as input, and then constructs an urban and rural earthquake disaster risk assessment model;

[0056] The urban and rural earthquake disaster risk assessment module performs an urban and rural earthquake disaster risk assessment on the urban and rural earthquake disaster assessment data collected in real time based on the urban and rural earthquake disaster risk assessment model.

[0057] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0058] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

[0059] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. An intelligent urban and rural earthquake disaster risk assessment method, characterized by: The method comprises the following steps: Step S1: Data construction: Obtain historical urban and rural earthquake disaster assessment data and disaster risk levels, and use the disaster risk level as a data label; Step S2: weight determination; by introducing environmental adaptability factors and coefficients of variation, the weights of various indicators in the historical urban and rural earthquake disaster assessment data are determined; the indicators are specific dimensions of the historical urban and rural earthquake disaster assessment data; Step S3: Calculate the correlation of factors; add the time decay factor to calculate the correlation of factors; quantify the contribution of each indicator to the overall risk; Step S4: constructing an urban and rural earthquake disaster risk assessment model; constructing a fully connected neural network model, designing an urban and rural loss function and a multiple extreme loss function; taking the urban and rural earthquake disaster assessment data processed by steps S2 and S3 as input, and then constructing an urban and rural earthquake disaster risk assessment model; Step S5: urban and rural earthquake disaster risk assessment: Based on the urban and rural earthquake disaster risk assessment model, the urban and rural earthquake disaster risk assessment is performed on the urban and rural earthquake disaster assessment data collected in real time; Step S2 includes calculating the environmental information entropy of each indicator of historical urban and rural earthquake disaster assessment data and introducing the environmental adaptability factor , the environmental information entropy is expressed as: ; ;in, is the environmental information entropy of the ith indicator; n is the total number of samples; m is the sample index; is the sample value of the ith indicator at the mth sample; is the sensitivity adjustment parameter; and are the maximum and minimum values ​​of the i-th indicator in all samples respectively; is the sample mean of the ith indicator.

2. The intelligent urban and rural earthquake disaster risk assessment method according to claim 1 is characterized by: In step S2, the weight determination also includes adopting a stabilization formula to introduce a coefficient of variation , the weight is expressed as: ; ;in is the weight of the ith indicator; L is the total number of indicators; u is the indicator index; and are the entropy values ​​of the i-th indicator and the u-th indicator of the m-th sample respectively; G is the average information entropy of all evaluation indicators; and are the standard deviation and mean of the ith indicator respectively.

3. The intelligent urban and rural earthquake disaster risk assessment method according to claim 2 is characterized by: In step S3, the factor correlation degree measurement is to quantify the contribution of each risk factor to the overall risk; adding the time decay factor , the correlation coefficient is expressed as: ; ; The factor correlation is expressed as: ; ;in, is the correlation coefficient of the ith indicator in the mth sample; is the reference sequence value; is the attenuation coefficient; t is the building age of the sample; is the minimum absolute difference between all factors and the reference sequence; is the maximum absolute difference between all factors and the reference sequence; is the resolution coefficient; set the correlation threshold, and select the indicators whose average correlation coefficient of all samples is higher than the correlation threshold as the final selected features, so as to construct the urban and rural earthquake disaster data set.

4. The intelligent urban and rural earthquake disaster risk assessment method according to claim 3 is characterized by: In step S4, the construction of the urban and rural earthquake disaster risk assessment model specifically includes the following steps: Step S41: Architecture design; an S-layer fully connected neural network is used as the basic architecture; the input layer directly inputs the feature vector constructed based on the urban and rural earthquake disaster data set; the hidden layer designs D fully connected layers, the number of nodes in each layer gradually decreases to capture the nonlinear relationship within the data, and the ReLU activation function is used; Dropout is added after some hidden layers; the output layer uses the Sigmoid activation function to output the predicted disaster risk level; Step S42: Design urban and rural losses; increase density weight factor Ws and introduce dynamic weight factor , the urban and rural loss FC is expressed as: ; ; ; Among them, W and are the current weight and the initial weight respectively; P is the classification parameter; and A are the sample prediction labels and true labels respectively; , and They are the current time, the last evaluation time, and the evaluation time interval; is the density adjustment parameter; is the sample density of the region to which the sample belongs; is the maximum sample density; Step S43: multiple extreme loss design; multiple extreme loss FJ is expressed as: ;in, and is the bandwidth parameter; Step S44: a phased strategy; FC is used in the 0th to Uth rounds of training; and FJ is used in the Uth to Eth rounds of training.

5. The intelligent urban and rural earthquake disaster risk assessment method according to claim 4 is characterized by: In step S1, the data construction is to obtain historical urban and rural earthquake disaster assessment data and disaster risk levels; the historical urban and rural earthquake disaster assessment data include geological exploration data, socio-economic data, building strength data and environmental characteristic data; the disaster risk level is used as a data label; and missing value processing and feature standardization are performed.

6. The intelligent urban and rural earthquake disaster risk assessment method according to claim 5 is characterized by: In step S5, the urban and rural earthquake disaster risk assessment collects urban and rural earthquake disaster assessment data in real time, inputs it into the urban and rural earthquake disaster risk assessment model after feature engineering processing, and uses the disaster risk level output by the model as the urban and rural earthquake disaster risk assessment result.

7. An intelligent urban and rural earthquake disaster risk assessment system, used to implement an intelligent urban and rural earthquake disaster risk assessment method as described in any one of claims 1 to 6, characterized in that: It includes a data construction module, a weight determination module, a factor correlation calculation module, an urban and rural earthquake disaster risk assessment model construction module and an urban and rural earthquake disaster risk assessment module; The data construction module obtains historical urban and rural earthquake disaster assessment data and disaster risk levels, and uses the disaster risk level as a data label; The weight determination module determines the weight of each indicator in the historical urban and rural earthquake disaster assessment data by introducing environmental adaptability factors and coefficients of variation; the indicator is a specific dimension of the historical urban and rural earthquake disaster assessment data; The factor correlation calculation module adds a time decay factor to calculate the factor correlation; quantifies the contribution of each indicator to the overall risk; The urban and rural earthquake disaster risk assessment model construction module constructs a fully connected neural network model, designs an urban and rural loss function and a multiple extreme loss function; takes the urban and rural earthquake disaster assessment data processed by the data construction module and the weight determination module as input, and then constructs an urban and rural earthquake disaster risk assessment model; The urban and rural earthquake disaster risk assessment module performs an urban and rural earthquake disaster risk assessment on the urban and rural earthquake disaster assessment data collected in real time based on the urban and rural earthquake disaster risk assessment model.

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