Indoor vibration simulation and prediction method and equipment for building close to subway and medium

Abstract

The invention relates to the technical field of vibration simulation and prediction, in particular to an indoor vibration simulation and prediction method, equipment and medium for a building close to a subway, and the method comprises the steps: building a building model, carrying out the vibration measurement point arrangement of a target track and the building close to the subway, obtaining a subway vibration measurement point and building vibration measurement point set, and obtaining a subway vibration signal and a building vibration signal; constructing a geologic model according to the geologic parameters, performing grid division on the geologic model and the building model to obtain a grid geologic model and a grid building model, assembling the grid geologic model and the grid building model to obtain an initial coupling model, and obtaining a suboptimal coupling model; obtaining current subway operation parameters, current geological parameters and current subway adjacent building vibration data, obtaining an optimal coupling model, and completing subway adjacent building indoor vibration simulation prediction based on the optimal coupling model. According to the invention, the efficiency and accuracy of vibration prediction can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vibration simulation prediction, and in particular to a method, equipment and medium for simulating and predicting indoor vibration of a building adjacent to a subway. Background Art

[0002] Subway-adjacent buildings are those located within a certain radius of the subway tracks. Indoor vibration simulation and prediction uses numerical models and measured data to predict the intensity, frequency distribution, and spatial attenuation of subway vibrations transmitted to the interior of a building, providing a basis for vibration reduction design or comfort assessment.

[0003] Research on indoor vibration in buildings near subways has yielded some results. Traditional vibration prediction methods rely primarily on empirical formulas and field monitoring data. While these methods can provide a certain understanding of vibration propagation patterns, they lack the ability to deeply analyze and predict vibration data. Furthermore, these methods are unable to accurately predict future vibration trends, making it difficult to proactively implement effective vibration reduction measures. Therefore, improving the efficiency and accuracy of vibration prediction is an urgent technical challenge. Summary of the Invention

[0004] The present invention provides a method for simulating and predicting indoor vibration of buildings adjacent to subways and a computer-readable storage medium, the main purpose of which is to improve the efficiency and accuracy of vibration prediction and provide strong technical support for urban subway construction and operation.

[0005] To achieve the above-mentioned purpose, the present invention provides a method for simulating and predicting indoor vibration of buildings adjacent to subways, comprising:

[0006] Identify the target track and the adjacent buildings of the subway, receive the building indoor vibration simulation instruction, and construct the building model based on the building indoor vibration simulation instruction and the adjacent buildings of the subway;

[0007] Vibration measurement points are arranged on the target track and nearby buildings of the subway to obtain subway vibration measurement point sets and building vibration measurement point sets;

[0008] The subway vibration measurement points and the building vibration measurement point set are monitored using a preset monitoring frequency to obtain initial subway vibration signals and initial building vibration signals. When monitoring the subway vibration measurement points, subway operation parameters and geological parameters are recorded in real time. The subway operation parameters include: train speed, train formation and train travel time;

[0009] Acquire a subway vibration signal and a building vibration signal based on the initial subway vibration signal and the initial building vibration signal;

[0010] Constructing a geological model based on geological parameters, and meshing the geological model and the building model to obtain a mesh geological model and a mesh building model;

[0011] The grid geological model and the grid building model are assembled using pre-built finite element software to obtain an initial coupled model;

[0012] determining a subway load according to the subway operation parameters, and simulating an initial coupling model using the subway load to obtain simulation data;

[0013] Based on the subway vibration signal, building vibration signal and simulation data, a vibration acceleration amplitude set is obtained, the spectrum characteristics are calculated according to the vibration acceleration amplitude set, and the suboptimal coupling model is obtained according to the spectrum characteristics;

[0014] Obtain the current subway operation parameters, current geological parameters, and vibration data of buildings adjacent to the subway, and obtain the optimal coupling model based on the suboptimal coupling model, the current subway operation parameters, current geological parameters, and vibration data of buildings adjacent to the subway;

[0015] Complete indoor vibration simulation and prediction of buildings near the subway based on the optimal coupling model.

[0016] Optionally, arranging vibration measurement points on the target track and the buildings adjacent to the subway to obtain a set of subway vibration measurement points and building vibration measurement points includes:

[0017] The area above the target track is identified based on the adjacent subway buildings, and vibration measurement points are deployed in the area above the target track to obtain the subway vibration measurement points;

[0018] The floors of buildings near the subway are divided into low floors, middle floors and high floors, among which the low floors are 1 to 10 floors, the middle floors are 11 to 20 floors, and the high floors are 21 to 32 floors;

[0019] Identify the subway surfaces adjacent to the buildings near the subway, arrange vibration measurement points on the low, middle and high floors adjacent to the subway surface, and obtain the building vibration measurement point set.

[0020] Optionally, the acquiring of the subway vibration signal and the building vibration signal based on the initial subway vibration signal and the initial building vibration signal includes:

[0021] Segmenting the initial subway vibration signal to obtain a vibration signal sequence, wherein the vibration signal sequence includes multiple vibration signal segments;

[0022] Extract vibration signals from the vibration signal sequence in sequence, and perform the following operations on the extracted vibration signals:

[0023] Setting an embedding dimension and a similarity tolerance, reshaping the vibration signal according to the embedding dimension, and obtaining an embedding dimension vector sequence, wherein the embedding dimension vector sequence includes a plurality of embedding dimension vectors;

[0024] Perform binary combinations on multiple embedding dimension vectors to obtain multiple vector combination pairs, and perform the following operations on each of the multiple vector combination pairs:

[0025] Calculate the maximum difference between the vector combinations and compare the maximum difference with the similarity tolerance;

[0026] If the maximum difference is less than the similarity tolerance, the vector combination pair corresponding to the maximum difference is used as the first vector combination pair;

[0027] If the maximum difference is greater than or equal to the similarity tolerance, the vector combination pair corresponding to the maximum difference is used as the second vector combination pair;

[0028] Summarizing the first vector combination pairs and the second vector combination pairs to obtain first vector combination pair groups and second vector combination pair groups, and counting the number of first vector combination pairs and the number of second vector combination pairs in the first vector combination pair groups and the second vector combination pair groups respectively;

[0029] Acquire a vibration signal to be denoised or a reference vibration signal based on the number of first vector combination pairs and the number of second vector combination pairs;

[0030] Perform wavelet decomposition on the vibration signal to be denoised to obtain wavelet coefficients, obtain the absolute value of the wavelet coefficients, and compare the absolute value of the coefficients with a preset coefficient threshold;

[0031] If the absolute value of the coefficient is greater than a preset coefficient threshold, the vibration signal to be denoised is denoised using the wavelet coefficient to obtain a denoised vibration signal;

[0032] If the absolute value of the coefficient is not greater than the preset coefficient threshold, the wavelet coefficient is adjusted until the absolute value of the coefficient is greater than the preset coefficient threshold, thereby obtaining a valid wavelet coefficient, and the vibration signal to be denoised is denoised using the valid wavelet coefficient to obtain a denoised vibration signal;

[0033] The denoised vibration signal and the reference vibration signal are integrated to obtain the subway vibration signal, and the building vibration signal is obtained based on the initial building vibration signal.

[0034] Optionally, obtaining the vibration signal to be denoised or the reference vibration signal based on the first vector logarithm set and the second vector logarithm set includes:

[0035] The signal length of the vibration signal is obtained, and the sample entropy of the embedded dimension vector is calculated according to the signal length, the first vector logarithm set, and the second vector logarithm set. The calculation formula of the sample entropy of the embedded dimension vector is as follows:

[0036]

[0037] Among them, H(μ,M) represents the sample entropy of the embedding dimension vector, μ represents the similarity tolerance, M represents the signal length, and m represents the embedding dimension. represents the number of first vector pairs, represents the logarithm of the second vector, ln represents the natural logarithm;

[0038] If the sample entropy is greater than the preset noise sample entropy, the vibration signal corresponding to the sample entropy is used as the vibration signal to be denoised;

[0039] If the sample entropy is not greater than the preset noise sample entropy, the vibration signal corresponding to the sample entropy is used as the reference vibration signal.

[0040] Optionally, the step of gridding both the geological model and the architectural model to obtain a grid geological model and a grid architectural model includes:

[0041] Performing initial grid division on the geological model using a preset initial grid density to obtain an initial grid geological model, identifying a key area group in the initial grid geological model, and setting a plurality of grid densities, wherein the plurality of grid densities are set from large to small, and the plurality of grid densities are all greater than the initial grid density;

[0042] extracting mesh densities from the plurality of mesh densities in sequence, re-dividing each key area in the key area group according to the mesh density to obtain mesh key areas, performing trial calculations on the mesh key areas to obtain trial calculation vibration response data and trial calculation time, and summarizing the trial calculation vibration response data and trial calculation time to obtain a trial calculation vibration response data group and a trial calculation time group;

[0043] Obtaining optimal trial calculation vibration response data and optimal trial calculation time from the trial calculation vibration response data group and the trial calculation time group;

[0044] An initial grid geological model of a grid density corresponding to the optimal trial calculation vibration response data and the optimal trial calculation time is used as a grid geological model, and a grid building model is obtained based on the building model.

[0045] Optionally, obtaining the optimal coupling model based on the suboptimal coupling model, current subway operation parameters, current geological parameters, and vibration data of buildings adjacent to the current subway includes:

[0046] Use the suboptimal coupling model to simulate and predict the vibration of the current subway operation parameters and current geological parameters to obtain the predicted data of the building vibration response;

[0047] The building vibration response prediction data and the vibration data of the current subway adjacent buildings are plotted to obtain the vibration prediction curve and the building vibration curve;

[0048] Calculating a curve similarity value between the vibration prediction curve and the building vibration curve, and comparing the curve similarity value with a preset similarity threshold;

[0049] If the curve similarity value is greater than a preset similarity threshold, the suboptimal coupling model is used as the optimal coupling model;

[0050] If the curve similarity value is not greater than a preset similarity threshold, the suboptimal coupling model is corrected to obtain a corrected coupling model. The corrected coupling model is used as the suboptimal coupling model, and the process returns to the step of using the suboptimal coupling model to perform vibration simulation prediction on the current subway operating parameters and the current geological parameters until the curve similarity value is greater than the preset similarity threshold, thereby obtaining the optimal coupling model.

[0051] Optionally, calculating the curve similarity value between the vibration prediction curve and the building vibration curve includes:

[0052] Obtain the attenuation coefficient, perform fast Fourier transform on the vibration prediction curve and the building vibration curve, obtain the vibration prediction frequency domain signal and the building vibration frequency domain signal, and calculate the vibration prediction power spectrum density and the building vibration power spectrum density based on the vibration prediction frequency domain signal and the building vibration frequency domain signal;

[0053] The curve similarity value is calculated based on the attenuation coefficient, vibration prediction power spectrum density, and building vibration power spectrum density. The calculation formula of the curve similarity value is as follows:

[0054]

[0055] Among them, S represents the curve similarity value, V p (t) represents the building vibration response prediction data, V m (t) represents the current subway adjacent building vibration data, τ represents time delay, ||*|| represents L2 norm, t represents time series, e represents natural constant, γ represents attenuation coefficient, PSD(V p ) represents the vibration prediction power spectrum density, PSD(V m ) represents the building vibration power spectrum density, ||*||2 represents the Euclidean distance, V m (t+τ) represents the new signal after the building vibration signal is shifted on the time axis by the time delay, max represents the maximum value, and <*,*> represents the inner product.

[0056] Optionally, obtaining the attenuation coefficient includes:

[0057] The subway vibration measurement point is taken as the vibration starting point, each building vibration measurement point in the building vibration measurement point set is taken as the vibration end point, and the vibration end points are summarized to obtain a vibration end point set;

[0058] Acquire multiple historical vibration detection periods, extract historical vibration detection periods from the multiple historical vibration detection periods in sequence, and perform the following operations on each of the extracted historical vibration detection periods:

[0059] Extracting vibration endpoints from the vibration endpoint set in sequence, collecting historical vibration data of the vibration endpoints according to the historical vibration detection period and the vibration starting point, and obtaining the vibration propagation distance according to the vibration starting point and the vibration endpoint;

[0060] Calculate the vibration energy value based on the historical vibration data, obtain vibration data pairs based on the vibration propagation distance and the vibration energy value, summarize the vibration data pairs, and obtain a vibration data pair group corresponding to the vibration endpoint set;

[0061] Drawing a vibration energy attenuation curve and an original vibration curve based on the vibration data pair, and calculating the mean square error between the vibration energy attenuation curve and the original vibration curve;

[0062] The mean square errors are summarized to obtain a mean square error set corresponding to multiple historical vibration detection periods. The optimal mean square error is obtained from the mean square error set. The vibration energy attenuation curve corresponding to the optimal mean square error is used as the optimal energy attenuation curve, and the attenuation coefficient is obtained according to the optimal energy attenuation curve.

[0063] Optionally, drawing the vibration energy attenuation curve and the original vibration curve based on the vibration data pair group includes:

[0064] The initial exponential decay model is confirmed and fitted using the vibration data to obtain the initial vibration energy and the fitting attenuation coefficient. The initial exponential decay model is expressed as:

[0065] E(d)=E0×e -γ×d

[0066] Where E(d) represents the initial exponential decay model, E0 represents the initial vibration energy, and d represents the vibration propagation distance;

[0067] Obtaining an exponential decay model based on the initial vibration energy, the fitted attenuation coefficient, and the initial exponential decay model;

[0068] extracting vibration data pairs from the vibration data pair group in sequence, extracting vibration propagation distances from the vibration data pairs, calculating fitting vibration energy using the vibration propagation distance and an exponential decay model, and obtaining fitting vibration data pairs based on the fitting vibration energy and the vibration propagation distance;

[0069] The fitted vibration data pairs are summarized to obtain a fitted vibration data pair group, a vibration energy attenuation curve is drawn based on the fitted vibration data pair group, and an original vibration curve is drawn based on the vibration data pair group.

[0070] To achieve the above-mentioned object, the present invention further provides a system for simulating and predicting indoor vibration of buildings adjacent to subways, comprising:

[0071] A vibration data acquisition module is used to identify the target track and adjacent subway buildings, receive building indoor vibration simulation instructions, construct a building model based on the building indoor vibration simulation instructions and the subway adjacent buildings, arrange vibration measurement points for the target track and adjacent subway buildings, obtain subway vibration measurement points and building vibration measurement point sets, monitor the subway vibration measurement points and building vibration measurement point sets using a preset monitoring frequency, obtain initial subway vibration signals and initial building vibration signals, and while monitoring the subway vibration measurement points, record subway operating parameters and geological parameters in real time, where subway operating parameters include train speed, train formation, and train travel time. The subway vibration signal and building vibration signal are obtained based on the initial subway vibration signal and initial building vibration signal.

[0072] A model construction module is used to construct a geological model based on geological parameters, mesh the geological model and the building model to obtain a mesh geological model and a mesh building model, and assemble the mesh geological model and the mesh building model using pre-built finite element software to obtain an initial coupled model;

[0073] a coupling model optimization module, configured to determine a subway load based on the subway operating parameters, simulate an initial coupling model using the subway load to obtain simulation data, obtain a vibration acceleration amplitude set based on the subway vibration signal, the building vibration signal, and the simulation data, calculate a frequency spectrum characteristic based on the vibration acceleration amplitude set, obtain a suboptimal coupling model based on the frequency spectrum characteristic, obtain current subway operating parameters, current geological parameters, and vibration data of buildings adjacent to the subway, and obtain an optimal coupling model based on the suboptimal coupling model, the current subway operating parameters, the current geological parameters, and vibration data of buildings adjacent to the subway;

[0074] The vibration simulation prediction module is used to complete the indoor vibration simulation prediction of buildings adjacent to the subway based on the optimal coupling model.

[0075] In order to solve the above problem, the present invention further provides an electronic device, comprising:

[0076] a memory storing at least one instruction;

[0077] The processor executes the instructions stored in the memory to implement the above-mentioned method for simulating and predicting indoor vibration of buildings adjacent to subways.

[0078] In order to solve the above problems, the present invention also provides a computer-readable storage medium, which stores at least one instruction. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned method for simulating and predicting indoor vibration of buildings adjacent to subways.

[0079] The present invention solves the problems described in the background technology. The present invention confirms the target track and the adjacent buildings of the subway, receives the indoor vibration simulation instruction of the building, and constructs a building model according to the indoor vibration simulation instruction of the building and the adjacent buildings of the subway. The present invention can customize the vibration simulation according to actual needs by receiving the instruction and constructing the model, thereby improving the pertinence and practicality of the simulation. Vibration measurement points are arranged for the target track and the adjacent buildings of the subway to obtain the subway vibration measurement points and the building vibration measurement point set. The present invention can fully capture the vibration information of the subway operation and the building response through reasonable measurement point arrangement, and provides a rich data source for subsequent data analysis. The subway vibration measurement points and the building vibration measurement point set are monitored using a preset monitoring frequency to obtain the initial Iron vibration signals and initial building vibration signals. When monitoring subway vibration measuring points, subway operation parameters and geological parameters are recorded in real time. Subway operation parameters include train speed, train formation and train travel time. The present invention records subway operation parameters and geological parameters in real time, which helps to analyze the relationship between vibration and subway operation and geological conditions, and improves the accuracy and reliability of vibration prediction. Subway vibration signals and building vibration signals are obtained based on initial subway vibration signals and initial building vibration signals. The present invention extracts effective vibration signals from the initial signals, removes possible noise and interference, and improves signal quality and availability. A geological model is constructed according to geological parameters, and both the geological model and the building model are gridded to obtain a grid geological model. The geological model of the present invention can accurately reflect the geological conditions along the subway and under the building foundation, providing an important basis for vibration propagation analysis. The grid division enables the complex model to be processed by the finite element software, improving the calculation accuracy and efficiency of the model. The grid geological model and the grid building model are assembled using the pre-built finite element software to obtain the initial coupling model. The coupling model of the present invention can comprehensively consider the interaction between subway operation, geological conditions and building structure, providing the possibility for more comprehensive vibration analysis. The subway load is determined according to the subway operation parameters, and the initial coupling model is simulated using the subway load to obtain simulation data. The present invention determines the load according to the actual subway operation parameters, making the simulation closer to the actual situation. The reliability of the simulation results is improved. A vibration acceleration amplitude set is obtained based on the subway vibration signal, the building vibration signal and the simulation data. The spectrum characteristics are calculated according to the vibration acceleration amplitude set. The suboptimal coupling model is obtained according to the spectrum characteristics. The present invention optimizes the coupling model according to the spectrum characteristics, which can gradually improve the model's fitting ability to the actual vibration situation and provide a more accurate model basis for the final vibration prediction. The current subway operation parameters, current geological parameters and the vibration data of the current subway adjacent buildings are obtained. The optimal coupling model is obtained based on the suboptimal coupling model, the current subway operation parameters, the current geological parameters and the vibration data of the current subway adjacent buildings. The present invention combines the current operation and geological conditions and the actual vibration data to further calibrate and optimize the coupling model.This makes it closer to reality. The optimal coupling model can more accurately reflect the vibration propagation and building response caused by subway operation. Based on the optimal coupling model, indoor vibration simulation prediction of buildings adjacent to the subway can be completed. Therefore, this invention can improve the efficiency and accuracy of vibration prediction and provide strong technical support for urban subway construction and operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 A schematic flow chart of a method for simulating and predicting indoor vibration of buildings adjacent to subways provided in one embodiment of the present invention;

[0081] Figure 2 This is a functional module diagram of a system for simulating and predicting indoor vibration of buildings adjacent to subways provided by one embodiment of the present invention;

[0082] Figure 3 A schematic structural diagram of an electronic device for implementing the method for simulating and predicting indoor vibration of buildings adjacent to subways provided in one embodiment of the present invention.

[0083] Description of reference numerals:

[0084] 1. Electronic device; 10. Processor; 11. Memory; 12. Bus.

[0085] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0086] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0087] The embodiments of the present application provide a method for simulating and predicting indoor vibrations of buildings adjacent to subways. The execution subject of the method includes, but is not limited to, at least one of electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiments of the present application. In other words, the method for simulating and predicting indoor vibrations of buildings adjacent to subways can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0088] Reference Figure 1 FIG. 1 is a flow chart of a method for simulating and predicting indoor vibration of a building adjacent to a subway provided by an embodiment of the present invention. In this embodiment, the method for simulating and predicting indoor vibration of a building adjacent to a subway includes:

[0089] S1. Identify the target track and the adjacent buildings of the subway, receive the building indoor vibration simulation instruction, and construct a building model according to the building indoor vibration simulation instruction and the adjacent buildings of the subway.

[0090] It should be explained that the target track refers to a specific section of track from the subway line. This section of track is the main location of the vibration source, and the vibration generated by the subway trains running on it will be transmitted to the nearby building structures. The subway-adjacent buildings refer to buildings located near the subway line. The building indoor vibration simulation instruction is an instruction for starting the building indoor vibration simulation. The steps of constructing a building model according to the building indoor vibration simulation instruction and the subway-adjacent buildings are: obtaining the building floor distribution and room layout from the design drawings of the subway-adjacent buildings, and constructing a building model according to the finite element software, the building floor distribution and the room layout. Finite element software refers to a computer software tool used for numerical simulation and analysis of engineering problems. For example, finite element software is ABAQUS, ANSYS, etc. The building model refers to a model constructed using finite element software, which is used to simulate and analyze the behavior and response of the building under various loads. It includes information such as the building's geometry, structural layout, material properties, boundary conditions, etc.

[0091] S2. Arrange vibration measurement points on the target track and buildings adjacent to the subway to obtain a set of subway vibration measurement points and building vibration measurement points.

[0092] Specifically, the vibration measurement points are arranged on the target track and the buildings adjacent to the subway to obtain the subway vibration measurement point set and the building vibration measurement point set, including:

[0093] The area above the target track is identified based on the adjacent subway buildings, and vibration measurement points are deployed in the area above the target track to obtain the subway vibration measurement points;

[0094] The floors of buildings near the subway are divided into low floors, middle floors and high floors, among which the low floors are 1 to 10 floors, the middle floors are 11 to 20 floors, and the high floors are 21 to 32 floors;

[0095] Identify the subway surfaces adjacent to the buildings near the subway, arrange vibration measurement points on the low, middle and high floors adjacent to the subway surface, and obtain the building vibration measurement point set.

[0096] It should be explained that the area above the target track refers to the surface area directly above the subway track. This area is where vibrations propagate from the subway track to the ground buildings. Subway vibration measurement points refer to measurement points located on the surface above the target track, which are capable of fully capturing the vibrations generated by subway operation. Measurement points are locations where vibration sensors are installed. The surface adjacent to the subway refers to the surface of the subway-adjacent building closest to the subway track. This area is more susceptible to vibrations generated by subway operation. The vibration measurement point layout means placing one measurement point every other floor on lower floors, one every two floors on middle floors, and one every three floors on higher floors. Each measurement point is located at the center of the living room seating area and the center of the bedroom floor closest to the target track. The purpose of placing one measurement point every other floor on lower floors is that the vibration response of lower floors is greater than that of middle and upper floors, and the vibration propagation path is shorter, therefore requiring more dense measurement points to capture vibration changes. Each measuring point is located at the center of the living room rest area and the center of the bedroom floor closest to the target track. These locations are typical areas of indoor vibration response and can represent the vibration conditions of the floor.

[0097] S3. Monitor the subway vibration measuring points and the building vibration measuring point set using a preset monitoring frequency to obtain initial subway vibration signals and initial building vibration signals. When monitoring the subway vibration measuring points, record subway operating parameters and geological parameters in real time. The subway operating parameters include train speed, train formation, and train travel time.

[0098] It should be explained that the monitoring frequency refers to a pre-set frequency. The use of the preset monitoring frequency to monitor both the subway vibration measuring points and the building vibration measuring point set means that vibration sensors are installed at each building vibration measuring point in the subway vibration measuring points and the building vibration measuring point set for monitoring. The initial subway vibration signal refers to the vibration data directly measured at the subway vibration measuring point. The initial building vibration signal refers to the vibration data directly measured at the building vibration measuring point. Geological parameters refer to the physical and mechanical properties that describe the geological conditions along the subway and under the building foundation, wherein the geological parameters include: soil layer type, soil layer density, soil layer thickness, soil layer elastic modulus and soil layer shear wave velocity. Train formation refers to the composition of the subway train, including the number and type of vehicles and their arrangement order. The train formation determines the total length, total weight and power distribution of the train, and these factors directly affect the vibration characteristics generated during the operation of the subway.

[0099] S4. Acquire a subway vibration signal and a building vibration signal based on the initial subway vibration signal and the initial building vibration signal.

[0100] In detail, the obtaining of the subway vibration signal and the building vibration signal based on the initial subway vibration signal and the initial building vibration signal includes:

[0101] Segmenting the initial subway vibration signal to obtain a vibration signal sequence, wherein the vibration signal sequence includes multiple vibration signal segments;

[0102] Extract vibration signals from the vibration signal sequence in sequence, and perform the following operations on the extracted vibration signals:

[0103] Setting an embedding dimension and a similarity tolerance, reshaping the vibration signal according to the embedding dimension, and obtaining an embedding dimension vector sequence, wherein the embedding dimension vector sequence includes a plurality of embedding dimension vectors;

[0104] Perform binary combinations on multiple embedding dimension vectors to obtain multiple vector combination pairs, and perform the following operations on each of the multiple vector combination pairs:

[0105] Calculate the maximum difference between the vector combinations and compare the maximum difference with the similarity tolerance;

[0106] If the maximum difference is less than the similarity tolerance, the vector combination pair corresponding to the maximum difference is used as the first vector combination pair;

[0107] If the maximum difference is greater than or equal to the similarity tolerance, the vector combination pair corresponding to the maximum difference is used as the second vector combination pair;

[0108] Summarizing the first vector combination pairs and the second vector combination pairs to obtain first vector combination pair groups and second vector combination pair groups, and counting the number of first vector combination pairs and the number of second vector combination pairs in the first vector combination pair groups and the second vector combination pair groups respectively;

[0109] Acquire a vibration signal to be denoised or a reference vibration signal based on the number of first vector combination pairs and the number of second vector combination pairs;

[0110] Perform wavelet decomposition on the vibration signal to be denoised to obtain wavelet coefficients, obtain the absolute value of the wavelet coefficients, and compare the absolute value of the coefficients with a preset coefficient threshold;

[0111] If the absolute value of the coefficient is greater than a preset coefficient threshold, the vibration signal to be denoised is denoised using the wavelet coefficient to obtain a denoised vibration signal;

[0112] If the absolute value of the coefficient is not greater than the preset coefficient threshold, the wavelet coefficient is adjusted until the absolute value of the coefficient is greater than the preset coefficient threshold, thereby obtaining a valid wavelet coefficient, and the vibration signal to be denoised is denoised using the valid wavelet coefficient to obtain a denoised vibration signal;

[0113] The denoised vibration signal and the reference vibration signal are integrated to obtain the subway vibration signal, and the building vibration signal is obtained based on the initial building vibration signal.

[0114] It should be explained that the signal segmentation of the initial subway vibration signal refers to the segmentation of the initial subway vibration signal using signal processing software. For example, the signal processing software is MATLAB, Python, etc. The setting of the embedding dimension and the similarity tolerance is set manually. The embedding dimension refers to the dimension of converting a one-dimensional time series into a multidimensional vector space. The similarity tolerance is the threshold for judging whether two vectors are similar. Reshaping refers to the operation of converting a one-dimensional time series into a multidimensional vector. The embedding dimension vector sequence refers to the vector sequence obtained after reshaping the vibration signal using the embedding dimension. The maximum difference refers to the maximum absolute difference between the corresponding elements in the vector combination pair. Binary combination refers to the operation of selecting two vectors from multiple embedding dimension vectors to combine to form a vector combination pair.

[0115] For example, the vibration signal is: X = [1, 2, 3, 4, 5], the embedding dimension is m = 2, the similarity tolerance is μ = 1.5, and the vibration signal is reshaped into an embedding dimension vector sequence: {x m (1)=[1,2],x m (2)=[2,3],x m (3)=[3,4],x m (4)=[4,5]}, perform binary combination on multiple embedding dimension vectors, and obtain multiple vector combination pairs as follows: {x m (1)=[1,2],x m (2)=[2,3]},{x m (1)=[1,2],x m (3)=[3,4]},{x m (1)=[1,2],x m (4)=[4,5]},{x m (2)=[2,3],x m (3)=[3,4]},{x m (2)=[2,3],x m (4)=[4,5]},{x m (3)=[3,4],x m (4) = [4,5]}. Extract two vectors from multiple vector combinations as x m (1)=[1,2],x m (2) = [2, 3], calculate x m (1)=[1,2],x m The maximum difference between (2) = [2, 3] is: max(|1-2|,|2-3|) = 1, and two vectors are extracted from multiple vector combinations as x. m (1)=[1,2],x m (3) = [3, 4], calculate x m (1)=[1,2],xm The maximum difference between (3) = [3, 4] is: max(|1-3|,|2-4|) = 2, and two vectors are extracted from multiple vector combinations as x. m (1)=[1,2],x m (4) = [4, 5], calculate x m (1)=[1,2],x m The maximum difference between (4) = [4, 5] is: max(|1-4|,|2-5|) = 3, and two vectors are extracted from multiple vector combinations as x. m (2)=[2,3],x m (3) = [3, 4], calculate x m (2)=[2,3],x m (3)=[3,4]The maximum difference is: max(|2-3|,|3-4|)=1, extracting two vectors from multiple vector combinations is x m (2)=[2,3],x m (4) = [4, 5], calculate x m (2)=[2,3],x m The maximum difference between (4) = [4, 5] is: max(|2-4|,|3-5|) = 2, and two vectors are extracted from multiple vector combinations as x. m (3)=[3,4],x m (4) = [4, 5], calculate x m (3)=[3,4],x m The maximum difference between (4)=[4,5] is: max(|3-4|,|4-5|)=1.

[0116] Importantly, the first number of vector pairs refers to the number of vector pairs whose maximum difference is less than the similarity tolerance. The second number of vector pairs refers to the number of vector pairs whose maximum difference is greater than or equal to the similarity tolerance. The first vector pair set refers to the set consisting of all first vector pair sets. The second vector pair set refers to the set consisting of all second vector pair sets. Wavelet decomposition refers to the operation of decomposing a signal into wavelet coefficients of different scales. Wavelet coefficients refer to the numerical values ​​obtained after wavelet decomposition that represent the characteristics of the signal at different scales and positions. Denoised vibration signal refers to the vibration signal after wavelet denoising. The coefficient threshold refers to a pre-set threshold. The effective wavelet coefficient refers to the coefficient obtained after the absolute value of the coefficient corresponding to the vibration signal to be denoised is greater than the preset coefficient threshold. The said adjustment of the wavelet coefficient refers to adjusting the wavelet coefficient using a preset coefficient adjustment factor. The coefficient adjustment factor refers to a pre-set numerical value used to amplify the absolute value of the wavelet coefficient so that it meets the requirements of denoising or signal processing. In the wavelet denoising process, the value of the wavelet coefficient directly affects the denoising effect. If the absolute value of the wavelet coefficient is not greater than the preset coefficient threshold, it may be mistakenly judged as noise and removed, resulting in signal distortion. By using the coefficient adjustment factor, these wavelet coefficients can be appropriately amplified so that the characteristics of the signal can be retained in subsequent processing. The integration of the denoised vibration signal and the reference vibration signal to obtain the subway vibration signal refers to connecting the denoised vibration signal with the reference vibration signal to obtain the subway vibration signal. The method for obtaining the building vibration signal based on the initial building vibration signal is the same as the method for obtaining the subway vibration signal based on the initial subway vibration signal, and will not be repeated here.

[0117] In detail, the step of obtaining the vibration signal to be denoised or the reference vibration signal based on the first vector logarithm set and the second vector logarithm set includes:

[0118] The signal length of the vibration signal is obtained, and the sample entropy of the embedded dimension vector is calculated according to the signal length, the first vector logarithm set, and the second vector logarithm set. The calculation formula of the sample entropy of the embedded dimension vector is as follows:

[0119]

[0120] Among them, H(μ,M) represents the sample entropy of the embedding dimension vector, μ represents the similarity tolerance, M represents the signal length, and m represents the embedding dimension. represents the number of first vector pairs, represents the logarithm of the second vector, ln represents the natural logarithm;

[0121] If the sample entropy is greater than the preset noise sample entropy, the vibration signal corresponding to the sample entropy is used as the vibration signal to be denoised;

[0122] If the sample entropy is not greater than the preset noise sample entropy, the vibration signal corresponding to the sample entropy is used as the reference vibration signal.

[0123] It should be explained that sample entropy reflects the volatility of the vibration signal corresponding to the vibration signal sequence. A smaller sample entropy value indicates less vibration signal volatility, while a larger sample entropy value indicates more noise in the vibration signal. Signal length refers to the amount of vibration data in the vibration signal. A baseline vibration signal is one whose sample entropy is no greater than the preset noise sample entropy. A vibration signal to be denoised is one whose sample entropy is greater than the preset noise sample entropy. The noise sample entropy is a pre-set value.

[0124] S5. Construct a geological model based on geological parameters, and divide the geological model and the building model into grids to obtain a grid geological model and a grid building model.

[0125] In detail, the said geologic model and the architectural model are both gridded to obtain a grid geologic model and a grid architectural model, including:

[0126] Performing initial grid division on the geological model using a preset initial grid density to obtain an initial grid geological model, identifying a key area group in the initial grid geological model, and setting a plurality of grid densities, wherein the plurality of grid densities are set from large to small, and the plurality of grid densities are all greater than the initial grid density;

[0127] extracting mesh densities from the plurality of mesh densities in sequence, re-dividing each key area in the key area group according to the mesh density to obtain mesh key areas, performing trial calculations on the mesh key areas to obtain trial calculation vibration response data and trial calculation time, and summarizing the trial calculation vibration response data and trial calculation time to obtain a trial calculation vibration response data group and a trial calculation time group;

[0128] Obtaining optimal trial calculation vibration response data and optimal trial calculation time from the trial calculation vibration response data group and the trial calculation time group;

[0129] An initial grid geological model of a grid density corresponding to the optimal trial calculation vibration response data and the optimal trial calculation time is used as a grid geological model, and a grid building model is obtained based on the building model.

[0130] It should be explained that the initial grid density refers to a density that is pre-set manually. The initial grid geological model refers to the model obtained by performing an initial grid division on the geological model using the grid density. The key area group refers to a collection of areas in the initial grid geological model that have a significant impact on the vibration response. For example, key areas include areas with dense underground pipelines, areas near faults, areas along shield construction lines, etc. The grid key area refers to the grid area obtained by re-dividing the key area using the grid density. The trial calculation of the grid key area refers to performing numerical simulation calculations on the grid key area in finite element software. The trial calculation vibration response data refers to the physical quantity data related to vibration recorded during the trial calculation of the grid key area. For example, the physical quantity data related to vibration include vibration acceleration, displacement, velocity, etc. The trial calculation time refers to the calculation time spent on performing a trial calculation on the grid key area. The trial calculation vibration response data group refers to the collection of all trial calculation vibration response data. The trial calculation time group refers to the collection of all trial calculation times. The optimal trial calculation vibration response data refers to data that meets the preset standard trial calculation vibration response data. Standard trial calculation vibration response data refers to pre-set response data, which provides the highest calculation accuracy. Optimal trial calculation time refers to a time that meets a pre-set standard trial calculation time. Standard trial calculation time refers to a pre-set time. The method for obtaining a grid building model based on a building model is the same as the method for obtaining a grid geological model based on a geological model, and will not be further described here.

[0131] S6. Assemble the grid geological model and the grid building model using pre-built finite element software to obtain an initial coupled model.

[0132] It should be explained that the initial coupled model refers to a model that combines the geological model and the building model to simulate the interaction between geological conditions and building structures in vibration propagation and response analysis.

[0133] S7. Determine the subway load according to the subway operation parameters, and simulate the initial coupling model using the subway load to obtain simulation data.

[0134] It should be explained that subway operating parameters refer to parameters that describe the operating status of subway trains. For example, subway operating parameters include train speed, train load, train frequency, track type, etc. Subway load refers to the forces exerted on the surrounding environment (such as geology and building structures) during subway operation. Using subway loads to simulate the initial coupling model to obtain simulation data refers to applying the loads generated by subway operation to the initial coupling model and calculating the vibration response data of the model using numerical simulation methods (such as finite element analysis). This vibration response data is simulation data.

[0135] S8. Obtain a vibration acceleration amplitude set based on the subway vibration signal, the building vibration signal and the simulation data, calculate the spectrum characteristics according to the vibration acceleration amplitude set, and obtain a suboptimal coupling model according to the spectrum characteristics.

[0136] It should be explained that the acquisition of a vibration acceleration amplitude set based on subway vibration signals, building vibration signals, and simulation data refers to extracting vibration acceleration amplitudes from the subway vibration signals, building vibration signals, and simulation data, respectively, summarizing the vibration acceleration amplitudes, and obtaining a vibration acceleration amplitude set. The vibration acceleration amplitude refers to the maximum value of acceleration during the vibration process. The calculation of spectral features based on the vibration acceleration amplitude set refers to obtaining the vibration acceleration mean of the vibration acceleration amplitude set and using the vibration acceleration mean as the spectral feature. The acquisition of a suboptimal coupling model based on spectral features refers to constructing a suboptimal coupling model using a machine learning algorithm and spectral features. For example, the machine learning algorithm is a support vector machine, linear regression, etc.

[0137] S9. Obtain the current subway operation parameters, current geological parameters, and vibration data of buildings adjacent to the subway. Based on the suboptimal coupling model, the current subway operation parameters, current geological parameters, and vibration data of buildings adjacent to the subway, obtain the optimal coupling model. Complete the indoor vibration simulation prediction of buildings adjacent to the subway based on the optimal coupling model.

[0138] In detail, the method of obtaining the optimal coupling model based on the suboptimal coupling model, current subway operation parameters, current geological parameters, and vibration data of buildings adjacent to the subway includes:

[0139] Use the suboptimal coupling model to simulate and predict the vibration of the current subway operation parameters and current geological parameters to obtain the predicted data of the building vibration response;

[0140] The building vibration response prediction data and the vibration data of the current subway adjacent buildings are plotted to obtain the vibration prediction curve and the building vibration curve;

[0141] Calculating a curve similarity value between the vibration prediction curve and the building vibration curve, and comparing the curve similarity value with a preset similarity threshold;

[0142] If the curve similarity value is greater than a preset similarity threshold, the suboptimal coupling model is used as the optimal coupling model;

[0143] If the curve similarity value is not greater than a preset similarity threshold, the suboptimal coupling model is corrected to obtain a corrected coupling model. The corrected coupling model is used as the suboptimal coupling model, and the process returns to the step of using the suboptimal coupling model to perform vibration simulation prediction on the current subway operating parameters and the current geological parameters until the curve similarity value is greater than the preset similarity threshold, thereby obtaining the optimal coupling model.

[0144] It should be explained that the current subway operating parameters refer to the subway operating parameters during the current time period. The current geological parameters refer to the geological parameters corresponding to the current subway operating parameters. The current vibration data of adjacent buildings in the subway refer to the vibration data monitored within adjacent buildings during the current time period. Using a suboptimal coupling model to simulate and predict vibrations based on the current subway operating parameters and current geological parameters refers to inputting the current subway operating parameters and current geological parameters into the suboptimal coupling model. The suboptimal coupling model then performs calculations based on these input parameters, predicting the vibration response of the building under subway operation for comparison with actual measured data. The predicted building vibration response data refers to the predicted data output by the suboptimal coupling model. Plotting the predicted building vibration response data and the current vibration data of adjacent buildings in the subway refers to plotting the predicted building vibration response data and the current vibration data of adjacent buildings in the subway using a visualization tool. For example, the visualization tool may be MATLAB or Origin. The vibration prediction curve refers to a curve drawn based on the predicted building vibration response data. The building vibration curve refers to a curve drawn based on the vibration data of adjacent buildings in the subway. The similarity threshold refers to a pre-set threshold for determining curve similarity. The optimal coupling model refers to the suboptimal coupling model corresponding to a curve similarity value greater than a preset similarity threshold. Modifying the suboptimal coupling model refers to modifying the suboptimal coupling model by adjusting model parameters, improving the model structure, and other methods to improve the prediction accuracy of the suboptimal coupling model and better match the current subway adjacent building vibration data. The modified coupling model refers to the modified suboptimal coupling model.

[0145] Specifically, the calculation of the curve similarity value between the vibration prediction curve and the building vibration curve includes:

[0146] Obtain the attenuation coefficient, perform fast Fourier transform on the vibration prediction curve and the building vibration curve, obtain the vibration prediction frequency domain signal and the building vibration frequency domain signal, and calculate the vibration prediction power spectrum density and the building vibration power spectrum density based on the vibration prediction frequency domain signal and the building vibration frequency domain signal;

[0147] The curve similarity value is calculated based on the attenuation coefficient, vibration prediction power spectrum density, and building vibration power spectrum density. The calculation formula of the curve similarity value is as follows:

[0148]

[0149] Among them, S represents the curve similarity value, V p (t) represents the building vibration response prediction data, V m (t) represents the current subway adjacent building vibration data, τ represents time delay, ||*|| represents L2 norm, t represents time series, e represents natural constant, γ represents attenuation coefficient, PSD(Vp ) represents the vibration prediction power spectrum density, PSD(V m ) represents the building vibration power spectrum density, ||*||2 represents the Euclidean distance, V m (t+τ) represents the new signal after the building vibration signal is shifted on the time axis by the time delay, max represents the maximum value, and <*,*> represents the inner product.

[0150] It should be explained that the attenuation coefficient is a coefficient used to control the degree of influence of frequency domain differences on similarity. The larger the attenuation coefficient, the greater the degree of influence on similarity. Fast Fourier transform refers to an operation used to convert a time domain signal into a frequency domain signal. The vibration prediction frequency domain signal refers to the vibration prediction data obtained by converting the vibration prediction curve through fast Fourier transform. The building vibration frequency domain signal refers to the vibration data obtained by converting the building vibration curve through fast Fourier transform. The step of calculating the vibration prediction power spectrum density and the building vibration power spectrum density based on the vibration prediction frequency domain signal and the building vibration frequency domain signal is: using the following formula to calculate the vibration prediction power spectrum density and the building vibration power spectrum density:

[0151] PSD(X)=|X| 2

[0152] Here, PSD(X) represents the vibration prediction power spectral density or building vibration power spectral density, X(f) represents the vibration prediction frequency domain signal or building vibration frequency domain signal, and |X| represents the modulus of the vibration prediction frequency domain signal or building vibration frequency domain signal. Time delay is a parameter used to shift the building vibration signal on the time axis. By shifting the signals, the vibration prediction frequency domain signal and the building vibration frequency domain signal can be better aligned, thereby more accurately calculating the similarity. The inner product is an operation used to describe the degree of similarity between two vectors.

[0153] In detail, obtaining the attenuation coefficient includes:

[0154] The subway vibration measurement point is taken as the vibration starting point, each building vibration measurement point in the building vibration measurement point set is taken as the vibration end point, and the vibration end points are summarized to obtain a vibration end point set;

[0155] Acquire multiple historical vibration detection periods, extract historical vibration detection periods from the multiple historical vibration detection periods in sequence, and perform the following operations on each of the extracted historical vibration detection periods:

[0156] Extracting vibration endpoints from the vibration endpoint set in sequence, collecting historical vibration data of the vibration endpoints according to the historical vibration detection period and the vibration starting point, and obtaining the vibration propagation distance according to the vibration starting point and the vibration endpoint;

[0157] Calculate the vibration energy value based on the historical vibration data, obtain vibration data pairs based on the vibration propagation distance and the vibration energy value, summarize the vibration data pairs, and obtain a vibration data pair group corresponding to the vibration endpoint set;

[0158] Drawing a vibration energy attenuation curve and an original vibration curve based on the vibration data pair, and calculating the mean square error between the vibration energy attenuation curve and the original vibration curve;

[0159] The mean square errors are summarized to obtain a mean square error set corresponding to multiple historical vibration detection periods. The optimal mean square error is obtained from the mean square error set. The vibration energy attenuation curve corresponding to the optimal mean square error is used as the optimal energy attenuation curve, and the attenuation coefficient is obtained according to the optimal energy attenuation curve.

[0160] It should be explained that the vibration starting point refers to the location corresponding to the subway vibration measurement point. The vibration endpoint refers to the location corresponding to the building vibration measurement point. The vibration endpoint set refers to the set consisting of all vibration endpoints. The historical vibration detection period refers to the time period during which vibration detection was conducted in the past. Historical vibration data refers to the vibration data collected from the vibration endpoint measurement points during the historical vibration detection period. The vibration distance refers to the distance from the vibration starting point to the vibration endpoint. The vibration energy value refers to the energy value at the vibration endpoint. A vibration data pair refers to data that combines the vibration propagation distance and the corresponding vibration energy value. A vibration data pair group refers to the set consisting of all vibration data pairs.

[0161] It should be explained that the calculation formula for calculating the vibration energy value in the step of calculating the vibration energy value based on the historical vibration data is as follows:

[0162]

[0163] Wherein, E represents the vibration energy value, m represents the unit mass, ω represents the angular frequency, and A represents the amplitude. Unit mass refers to the mass of a standard unit in the system, such as 1 kilogram. The original vibration curve refers to the curve drawn based on historical vibration data. The mean square error is a numerical value that measures the difference between the vibration energy attenuation curve and the original vibration curve. The smaller the mean square error, the closer the two curves are. The optimal mean square error refers to the mean square error that meets the preset standard mean square error. The standard mean square error refers to a pre-set error value. The optimal energy attenuation curve refers to the vibration energy attenuation curve corresponding to the optimal mean square error. The mean square error set refers to the set of all mean square error combinations. The said obtaining the attenuation coefficient according to the optimal energy attenuation curve refers to confirming the attenuation coefficient in the exponential attenuation model corresponding to the optimal energy attenuation curve.

[0164] In detail, the drawing of the vibration energy attenuation curve and the original vibration curve based on the vibration data pair group includes:

[0165] The initial exponential decay model is confirmed and fitted using the vibration data to obtain the initial vibration energy and the fitting attenuation coefficient. The initial exponential decay model is expressed as:

[0166] E(d)=E0×e -γ×d

[0167] Where E(d) represents the initial exponential decay model, E0 represents the initial vibration energy, and d represents the vibration propagation distance;

[0168] Obtaining an exponential decay model based on the initial vibration energy, the fitted attenuation coefficient, and the initial exponential decay model;

[0169] extracting vibration data pairs from the vibration data pair group in sequence, extracting vibration propagation distances from the vibration data pairs, calculating fitting vibration energy using the vibration propagation distance and an exponential decay model, and obtaining fitting vibration data pairs based on the fitting vibration energy and the vibration propagation distance;

[0170] The fitted vibration data pairs are summarized to obtain a fitted vibration data pair group, a vibration energy attenuation curve is drawn based on the fitted vibration data pair group, and an original vibration curve is drawn based on the vibration data pair group.

[0171] It should be explained that the initial vibration energy refers to the initial vibration energy at the starting point of the vibration. The method of fitting the initial exponential decay model using the vibration data pair group refers to fitting the initial exponential decay model using the vibration data pair group and the least squares method. The fitted attenuation coefficient refers to the coefficient obtained after fitting the initial exponential decay model using the vibration data pair group. The method of obtaining the exponential decay model based on the initial vibration energy, the fitted attenuation coefficient and the initial exponential decay model refers to substituting the initial vibration energy and the fitted attenuation coefficient into the initial exponential decay model to obtain the exponential decay model. The fitted vibration energy refers to the energy calculated by inputting the vibration propagation distance into the exponential decay model. The fitted vibration data pair refers to a data pair composed of the fitted vibration energy and the vibration propagation distance. The method of drawing the vibration energy attenuation curve based on the fitted vibration data pair group and the method of drawing the original vibration curve based on the vibration data pair group are the same as the method of drawing the curves of the building vibration response prediction data and the vibration data of the current subway adjacent buildings to obtain the vibration prediction curve and the building vibration curve, and will not be repeated here.

[0172] The present invention solves the problems described in the background technology. The present invention confirms the target track and the adjacent buildings of the subway, receives the indoor vibration simulation instruction of the building, and constructs a building model according to the indoor vibration simulation instruction of the building and the adjacent buildings of the subway. The present invention can customize the vibration simulation according to actual needs by receiving the instruction and constructing the model, thereby improving the pertinence and practicality of the simulation. Vibration measurement points are arranged for the target track and the adjacent buildings of the subway to obtain the subway vibration measurement points and the building vibration measurement point set. The present invention can fully capture the vibration information of the subway operation and the building response through reasonable measurement point arrangement, and provides a rich data source for subsequent data analysis. The subway vibration measurement points and the building vibration measurement point set are monitored using a preset monitoring frequency to obtain the initial Iron vibration signals and initial building vibration signals. When monitoring subway vibration measuring points, subway operation parameters and geological parameters are recorded in real time. Subway operation parameters include train speed, train formation and train travel time. The present invention records subway operation parameters and geological parameters in real time, which helps to analyze the relationship between vibration and subway operation and geological conditions, and improves the accuracy and reliability of vibration prediction. Subway vibration signals and building vibration signals are obtained based on initial subway vibration signals and initial building vibration signals. The present invention extracts effective vibration signals from the initial signals, removes possible noise and interference, and improves signal quality and availability. A geological model is constructed according to geological parameters, and both the geological model and the building model are gridded to obtain a grid geological model. The geological model of the present invention can accurately reflect the geological conditions along the subway and under the building foundation, providing an important basis for vibration propagation analysis. The grid division enables the complex model to be processed by the finite element software, improving the calculation accuracy and efficiency of the model. The grid geological model and the grid building model are assembled using the pre-built finite element software to obtain the initial coupling model. The coupling model of the present invention can comprehensively consider the interaction between subway operation, geological conditions and building structure, providing the possibility for more comprehensive vibration analysis. The subway load is determined according to the subway operation parameters, and the initial coupling model is simulated using the subway load to obtain simulation data. The present invention determines the load according to the actual subway operation parameters, making the simulation closer to the actual situation. The reliability of the simulation results is improved. A vibration acceleration amplitude set is obtained based on the subway vibration signal, the building vibration signal and the simulation data. The spectrum characteristics are calculated according to the vibration acceleration amplitude set. The suboptimal coupling model is obtained according to the spectrum characteristics. The present invention optimizes the coupling model according to the spectrum characteristics, which can gradually improve the model's fitting ability to the actual vibration situation and provide a more accurate model basis for the final vibration prediction. The current subway operation parameters, current geological parameters and the vibration data of the current subway adjacent buildings are obtained. The optimal coupling model is obtained based on the suboptimal coupling model, the current subway operation parameters, the current geological parameters and the vibration data of the current subway adjacent buildings. The present invention combines the current operation and geological conditions and the actual vibration data to further calibrate and optimize the coupling model.This makes it closer to reality. The optimal coupling model can more accurately reflect the vibration propagation and building response caused by subway operation. Based on the optimal coupling model, indoor vibration simulation prediction of buildings adjacent to the subway can be completed. Therefore, this invention can improve the efficiency and accuracy of vibration prediction and provide strong technical support for urban subway construction and operation.

[0173] like Figure 2 FIG. 1 is a functional module diagram of a system for simulating and predicting indoor vibration of a building adjacent to a subway provided by an embodiment of the present invention.

[0174] The subway-adjacent building indoor vibration simulation and prediction system 100 of the present invention can be installed in an electronic device. Depending on the functionality to be implemented, the subway-adjacent building indoor vibration simulation and prediction system 100 can include a vibration data acquisition module 101, a model building module 102, a coupling model optimization module 103, and a vibration simulation and prediction module 104. The modules of the present invention, also referred to as units, are a series of computer program segments that can be executed by an electronic device processor and perform fixed functions, and are stored in the memory of the electronic device.

[0175] The vibration data acquisition module 101 is used to identify the target track and the adjacent buildings of the subway, receive the building indoor vibration simulation instruction, construct a building model according to the building indoor vibration simulation instruction and the adjacent buildings of the subway, arrange vibration measurement points for the target track and the adjacent buildings of the subway, obtain a subway vibration measurement point set and a building vibration measurement point set, monitor the subway vibration measurement points and the building vibration measurement point set using a preset monitoring frequency, obtain an initial subway vibration signal and an initial building vibration signal, and when monitoring the subway vibration measurement points, record subway operation parameters and geological parameters in real time, wherein the subway operation parameters include: train speed, train formation and train travel time, and obtain the subway vibration signal and the building vibration signal based on the initial subway vibration signal and the initial building vibration signal;

[0176] The model construction module 102 is used to construct a geological model based on geological parameters, grid the geological model and the building model to obtain a grid geological model and a grid building model, and assemble the grid geological model and the grid building model using pre-built finite element software to obtain an initial coupled model;

[0177] The coupling model optimization module 103 is configured to determine a subway load based on the subway operating parameters, simulate an initial coupling model using the subway load to obtain simulation data, obtain a vibration acceleration amplitude set based on the subway vibration signal, the building vibration signal, and the simulation data, calculate a frequency spectrum characteristic based on the vibration acceleration amplitude set, obtain a suboptimal coupling model based on the frequency spectrum characteristic, obtain current subway operating parameters, current geological parameters, and vibration data of buildings adjacent to the subway, and obtain an optimal coupling model based on the suboptimal coupling model, the current subway operating parameters, the current geological parameters, and vibration data of buildings adjacent to the subway;

[0178] The vibration simulation prediction module 104 is used to perform indoor vibration simulation prediction of buildings adjacent to the subway based on the optimal coupling model.

[0179] In detail, the modules in the subway adjacent building indoor vibration simulation prediction system 100 in the embodiment of the present invention are used in the same manner as above. Figure 1 The same technical means as the indoor vibration simulation prediction method of subway adjacent buildings described in the previous section can produce the same technical effects, so they will not be repeated here.

[0180] like Figure 3 FIG. 1 is a schematic diagram of the structure of an electronic device for implementing a method for simulating and predicting indoor vibration of buildings adjacent to a subway, provided by an embodiment of the present invention.

[0181] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a method program for simulating and predicting indoor vibration of a building adjacent to a subway.

[0182] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 can also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Furthermore, the memory 11 also includes an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the indoor vibration simulation prediction method program for the adjacent building of the subway, etc., but can also be used to temporarily store data that has been output or is to be output.

[0183] In some embodiments, the processor 10 may be comprised of an integrated circuit, such as a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and a combination of various control chips. The processor 10 is the control core (Control Unit) of the electronic device, connecting the various components of the entire electronic device using various interfaces and circuits. It executes or runs programs or modules stored in the memory 11 (e.g., a program for indoor vibration simulation prediction of buildings adjacent to subways), and calls data stored in the memory 11 to perform various functions of the electronic device 1 and process data.

[0184] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0185] Figure 3 Only the electronic device with components is shown, and it can be understood by those skilled in the art that Figure 3 The structure shown does not constitute a limitation on the electronic device 1 , and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0186] For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for powering the various components. Preferably, the power source may be logically connected to the at least one processor 10 via a power management device, thereby implementing functions such as charging management, discharging management, and power consumption management through the power management device. The power source may further include any components such as one or more DC or AC power sources, a recharging device, a power failure detection circuit, a power converter or inverter, a power status indicator, etc. The electronic device 1 may further include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0187] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0188] Optionally, the electronic device 1 may further include a user interface, which may be a display or an input unit (such as a keyboard). Optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0189] The program of the method for simulating and predicting indoor vibration of a subway adjacent to a building stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When executed in the processor 10, the following can be achieved:

[0190] Identify the target track and the adjacent buildings of the subway, receive the building indoor vibration simulation instruction, and construct the building model based on the building indoor vibration simulation instruction and the adjacent buildings of the subway;

[0191] Vibration measurement points are arranged on the target track and nearby buildings of the subway to obtain subway vibration measurement point sets and building vibration measurement point sets;

[0192] The subway vibration measurement points and the building vibration measurement point set are monitored using a preset monitoring frequency to obtain initial subway vibration signals and initial building vibration signals. When monitoring the subway vibration measurement points, subway operation parameters and geological parameters are recorded in real time. The subway operation parameters include: train speed, train formation and train travel time;

[0193] Acquire a subway vibration signal and a building vibration signal based on the initial subway vibration signal and the initial building vibration signal;

[0194] Constructing a geological model based on geological parameters, and meshing the geological model and the building model to obtain a mesh geological model and a mesh building model;

[0195] The grid geological model and the grid building model are assembled using pre-built finite element software to obtain an initial coupled model;

[0196] determining a subway load according to the subway operation parameters, and simulating an initial coupling model using the subway load to obtain simulation data;

[0197] Based on the subway vibration signal, building vibration signal and simulation data, a vibration acceleration amplitude set is obtained, the spectrum characteristics are calculated according to the vibration acceleration amplitude set, and the suboptimal coupling model is obtained according to the spectrum characteristics;

[0198] Obtain the current subway operation parameters, current geological parameters, and vibration data of buildings adjacent to the subway, and obtain the optimal coupling model based on the suboptimal coupling model, the current subway operation parameters, current geological parameters, and vibration data of buildings adjacent to the subway;

[0199] Complete indoor vibration simulation and prediction of buildings near the subway based on the optimal coupling model.

[0200] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0201] Furthermore, if the modules / units integrated in the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0202] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program. When the computer program is executed by a processor of an electronic device, the computer program can implement:

[0203] Identify the target track and the adjacent buildings of the subway, receive the building indoor vibration simulation instruction, and construct the building model based on the building indoor vibration simulation instruction and the adjacent buildings of the subway;

[0204] Vibration measurement points are arranged on the target track and nearby buildings of the subway to obtain subway vibration measurement point sets and building vibration measurement point sets;

[0205] The subway vibration measurement points and the building vibration measurement point set are monitored using a preset monitoring frequency to obtain initial subway vibration signals and initial building vibration signals. When monitoring the subway vibration measurement points, subway operation parameters and geological parameters are recorded in real time. The subway operation parameters include: train speed, train formation and train travel time;

[0206] Acquire a subway vibration signal and a building vibration signal based on the initial subway vibration signal and the initial building vibration signal;

[0207] Constructing a geological model based on geological parameters, and meshing the geological model and the building model to obtain a mesh geological model and a mesh building model;

[0208] The grid geological model and the grid building model are assembled using pre-built finite element software to obtain an initial coupled model;

[0209] determining a subway load according to the subway operation parameters, and simulating an initial coupling model using the subway load to obtain simulation data;

[0210] Based on the subway vibration signal, building vibration signal and simulation data, a vibration acceleration amplitude set is obtained, the spectrum characteristics are calculated according to the vibration acceleration amplitude set, and the suboptimal coupling model is obtained according to the spectrum characteristics;

[0211] Obtain the current subway operation parameters, current geological parameters, and vibration data of buildings adjacent to the subway, and obtain the optimal coupling model based on the suboptimal coupling model, the current subway operation parameters, current geological parameters, and vibration data of buildings adjacent to the subway;

[0212] Complete indoor vibration simulation and prediction of buildings near the subway based on the optimal coupling model.

[0213] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only exemplary, and actual implementations may have other division methods.

[0214] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules may be selected to achieve the purpose of the solution of this embodiment according to actual needs.

[0215] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional modules.

[0216] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0217] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for simulating and predicting indoor vibration of buildings adjacent to subways, characterized in that: The method comprises: Identify the target track and the adjacent buildings of the subway, receive the building indoor vibration simulation instruction, and construct the building model based on the building indoor vibration simulation instruction and the adjacent buildings of the subway; Vibration measurement points are arranged on the target track and nearby buildings of the subway to obtain subway vibration measurement point sets and building vibration measurement point sets; The subway vibration measurement points and the building vibration measurement point set are monitored using a preset monitoring frequency to obtain initial subway vibration signals and initial building vibration signals. When monitoring the subway vibration measurement points, subway operation parameters and geological parameters are recorded in real time. The subway operation parameters include: train speed, train formation and train travel time; Acquire a subway vibration signal and a building vibration signal based on the initial subway vibration signal and the initial building vibration signal; Constructing a geological model based on geological parameters, and meshing the geological model and the building model to obtain a mesh geological model and a mesh building model; The grid geological model and the grid building model are assembled using pre-built finite element software to obtain an initial coupled model; determining a subway load according to the subway operation parameters, and simulating an initial coupling model using the subway load to obtain simulation data; Based on the subway vibration signal, building vibration signal and simulation data, a vibration acceleration amplitude set is obtained, the spectrum characteristics are calculated according to the vibration acceleration amplitude set, and the suboptimal coupling model is obtained according to the spectrum characteristics; Obtain the current subway operation parameters, current geological parameters, and vibration data of buildings adjacent to the subway, and obtain the optimal coupling model based on the suboptimal coupling model, the current subway operation parameters, current geological parameters, and vibration data of buildings adjacent to the subway; Complete indoor vibration simulation and prediction of buildings near the subway based on the optimal coupling model.

2. The method for simulating and predicting indoor vibration of a building adjacent to a subway according to claim 1, wherein: The vibration measurement points are arranged on the target track and the buildings adjacent to the subway to obtain the subway vibration measurement point set and the building vibration measurement point set, including: The area above the target track is identified based on the adjacent subway buildings, and vibration measurement points are deployed in the area above the target track to obtain the subway vibration measurement points; The floors of buildings near the subway are divided into low floors, middle floors and high floors, among which the low floors are 1 to 10 floors, the middle floors are 11 to 20 floors, and the high floors are 21 to 32 floors; Identify the subway surfaces adjacent to the buildings near the subway, arrange vibration measurement points on the low, middle and high floors adjacent to the subway surface, and obtain the building vibration measurement point set.

3. The method for simulating and predicting indoor vibration of a building adjacent to a subway according to claim 2, wherein: The obtaining of the subway vibration signal and the building vibration signal based on the initial subway vibration signal and the initial building vibration signal includes: Segmenting the initial subway vibration signal to obtain a vibration signal sequence, wherein the vibration signal sequence includes multiple vibration signal segments; Extract vibration signals from the vibration signal sequence in sequence, and perform the following operations on the extracted vibration signals: Setting an embedding dimension and a similarity tolerance, reshaping the vibration signal according to the embedding dimension, and obtaining an embedding dimension vector sequence, wherein the embedding dimension vector sequence includes a plurality of embedding dimension vectors; Perform binary combinations on multiple embedding dimension vectors to obtain multiple vector combination pairs, and perform the following operations on each of the multiple vector combination pairs: Calculate the maximum difference between the vector combinations and compare the maximum difference with the similarity tolerance; If the maximum difference is less than the similarity tolerance, the vector combination pair corresponding to the maximum difference is used as the first vector combination pair; If the maximum difference is greater than or equal to the similarity tolerance, the vector combination pair corresponding to the maximum difference is used as the second vector combination pair; Summarizing the first vector combination pairs and the second vector combination pairs to obtain first vector combination pair groups and second vector combination pair groups, and counting the number of first vector combination pairs and the number of second vector combination pairs in the first vector combination pair groups and the second vector combination pair groups respectively; Acquire a vibration signal to be denoised or a reference vibration signal based on the number of first vector combination pairs and the number of second vector combination pairs; Perform wavelet decomposition on the vibration signal to be denoised to obtain wavelet coefficients, obtain the absolute value of the wavelet coefficients, and compare the absolute value of the coefficients with a preset coefficient threshold; If the absolute value of the coefficient is greater than a preset coefficient threshold, the vibration signal to be denoised is denoised using the wavelet coefficient to obtain a denoised vibration signal; If the absolute value of the coefficient is not greater than the preset coefficient threshold, the wavelet coefficient is adjusted until the absolute value of the coefficient is greater than the preset coefficient threshold, thereby obtaining a valid wavelet coefficient, and the vibration signal to be denoised is denoised using the valid wavelet coefficient to obtain a denoised vibration signal; The denoised vibration signal and the reference vibration signal are integrated to obtain the subway vibration signal, and the building vibration signal is obtained based on the initial building vibration signal.

4. The method for simulating and predicting indoor vibration of a building adjacent to a subway according to claim 3, wherein: The step of obtaining the vibration signal to be denoised or the reference vibration signal based on the first vector logarithm set and the second vector logarithm set includes: The signal length of the vibration signal is obtained, and the sample entropy of the embedded dimension vector is calculated according to the signal length, the first vector logarithm set, and the second vector logarithm set. The calculation formula of the sample entropy of the embedded dimension vector is as follows: Among them, H(μ,M) represents the sample entropy of the embedding dimension vector, μ represents the similarity tolerance, M represents the signal length, and m represents the embedding dimension. represents the number of first vector pairs, represents the logarithm of the second vector, ln represents the natural logarithm; If the sample entropy is greater than the preset noise sample entropy, the vibration signal corresponding to the sample entropy is used as the vibration signal to be denoised; If the sample entropy is not greater than the preset noise sample entropy, the vibration signal corresponding to the sample entropy is used as the reference vibration signal.

5. The method for simulating and predicting indoor vibration of a building adjacent to a subway according to claim 4, wherein: The geological model and the building model are both gridded to obtain a grid geological model and a grid building model, including: Performing initial grid division on the geological model using a preset initial grid density to obtain an initial grid geological model, identifying a key area group in the initial grid geological model, and setting a plurality of grid densities, wherein the plurality of grid densities are set from large to small, and the plurality of grid densities are all greater than the initial grid density; extracting mesh densities from the plurality of mesh densities in sequence, re-dividing each key area in the key area group according to the mesh density to obtain mesh key areas, performing trial calculations on the mesh key areas to obtain trial calculation vibration response data and trial calculation time, and summarizing the trial calculation vibration response data and trial calculation time to obtain a trial calculation vibration response data group and a trial calculation time group; Obtaining optimal trial calculation vibration response data and optimal trial calculation time from the trial calculation vibration response data group and the trial calculation time group; An initial grid geological model of a grid density corresponding to the optimal trial calculation vibration response data and the optimal trial calculation time is used as a grid geological model, and a grid building model is obtained based on the building model.

6. The method for simulating and predicting indoor vibration of buildings adjacent to subways according to claim 5, characterized in that: The obtaining of the optimal coupling model based on the suboptimal coupling model, current subway operation parameters, current geological parameters, and vibration data of buildings adjacent to the subway includes: Use the suboptimal coupling model to simulate and predict the vibration of the current subway operation parameters and current geological parameters to obtain the predicted data of the building vibration response; The building vibration response prediction data and the vibration data of the current subway adjacent buildings are plotted to obtain the vibration prediction curve and the building vibration curve; Calculating a curve similarity value between the vibration prediction curve and the building vibration curve, and comparing the curve similarity value with a preset similarity threshold; If the curve similarity value is greater than a preset similarity threshold, the suboptimal coupling model is used as the optimal coupling model; If the curve similarity value is not greater than a preset similarity threshold, the suboptimal coupling model is corrected to obtain a corrected coupling model. The corrected coupling model is used as the suboptimal coupling model, and the process returns to the step of using the suboptimal coupling model to perform vibration simulation prediction on the current subway operating parameters and the current geological parameters until the curve similarity value is greater than the preset similarity threshold, thereby obtaining the optimal coupling model.

7. The method for simulating and predicting indoor vibration of buildings adjacent to subways according to claim 6, characterized in that: The calculating of the curve similarity value of the vibration prediction curve and the building vibration curve includes: Obtain the attenuation coefficient, perform fast Fourier transform on the vibration prediction curve and the building vibration curve, obtain the vibration prediction frequency domain signal and the building vibration frequency domain signal, and calculate the vibration prediction power spectrum density and the building vibration power spectrum density based on the vibration prediction frequency domain signal and the building vibration frequency domain signal; The curve similarity value is calculated based on the attenuation coefficient, vibration prediction power spectrum density, and building vibration power spectrum density. The calculation formula of the curve similarity value is as follows: Among them, S represents the curve similarity value, V p (t) represents the building vibration response prediction data, V m (t) represents the current subway adjacent building vibration data, τ represents time delay, ||*|| represents L2 norm, t represents time series, e represents natural constant, γ represents attenuation coefficient, PSD(V p ) represents the vibration prediction power spectrum density, PSD(V m ) represents the building vibration power spectrum density, ||*||2 represents the Euclidean distance, V m (t+τ) represents the new signal after the building vibration signal is shifted on the time axis by the time delay, max represents the maximum value, and <*,*> represents the inner product.

8. The method for simulating and predicting indoor vibration of buildings adjacent to subways according to claim 7, characterized in that: The obtaining of the attenuation coefficient includes: The subway vibration measurement point is taken as the vibration starting point, each building vibration measurement point in the building vibration measurement point set is taken as the vibration end point, and the vibration end points are summarized to obtain a vibration end point set; Acquire multiple historical vibration detection periods, extract historical vibration detection periods from the multiple historical vibration detection periods in sequence, and perform the following operations on each of the extracted historical vibration detection periods: Extracting vibration endpoints from the vibration endpoint set in sequence, collecting historical vibration data of the vibration endpoints according to the historical vibration detection period and the vibration starting point, and obtaining the vibration propagation distance according to the vibration starting point and the vibration endpoint; Calculate the vibration energy value based on the historical vibration data, obtain vibration data pairs based on the vibration propagation distance and the vibration energy value, summarize the vibration data pairs, and obtain a vibration data pair group corresponding to the vibration endpoint set; Drawing a vibration energy attenuation curve and an original vibration curve based on the vibration data pair, and calculating the mean square error between the vibration energy attenuation curve and the original vibration curve; The mean square errors are summarized to obtain a mean square error set corresponding to multiple historical vibration detection periods. The optimal mean square error is obtained from the mean square error set. The vibration energy attenuation curve corresponding to the optimal mean square error is used as the optimal energy attenuation curve, and the attenuation coefficient is obtained according to the optimal energy attenuation curve.

9. The method for simulating and predicting indoor vibration of buildings adjacent to subways according to claim 8, characterized in that: The step of drawing a vibration energy attenuation curve and an original vibration curve based on the vibration data pair includes: The initial exponential decay model is confirmed and fitted using the vibration data to obtain the initial vibration energy and the fitting attenuation coefficient. The initial exponential decay model is expressed as: E(d)=E0×e -γ×d Where E(d) represents the initial exponential decay model, E0 represents the initial vibration energy, and d represents the vibration propagation distance; Obtaining an exponential decay model based on the initial vibration energy, the fitted attenuation coefficient, and the initial exponential decay model; extracting vibration data pairs from the vibration data pair group in sequence, extracting vibration propagation distances from the vibration data pairs, calculating fitting vibration energy using the vibration propagation distance and an exponential decay model, and obtaining fitting vibration data pairs based on the fitting vibration energy and the vibration propagation distance; The fitted vibration data pairs are summarized to obtain a fitted vibration data pair group, a vibration energy attenuation curve is drawn based on the fitted vibration data pair group, and an original vibration curve is drawn based on the vibration data pair group.

10. A system for simulating and predicting indoor vibration of buildings near subways, characterized in that: The system comprises: A vibration data acquisition module is used to identify the target track and adjacent subway buildings, receive building indoor vibration simulation instructions, construct a building model based on the building indoor vibration simulation instructions and the subway adjacent buildings, arrange vibration measurement points for the target track and adjacent subway buildings, obtain subway vibration measurement points and building vibration measurement point sets, monitor the subway vibration measurement points and building vibration measurement point sets using a preset monitoring frequency, obtain initial subway vibration signals and initial building vibration signals, and while monitoring the subway vibration measurement points, record subway operating parameters and geological parameters in real time, where subway operating parameters include train speed, train formation, and train travel time. The subway vibration signal and building vibration signal are obtained based on the initial subway vibration signal and initial building vibration signal. A model construction module is used to construct a geological model based on geological parameters, mesh the geological model and the building model to obtain a mesh geological model and a mesh building model, and assemble the mesh geological model and the mesh building model using pre-built finite element software to obtain an initial coupled model; a coupling model optimization module, configured to determine a subway load based on the subway operating parameters, simulate an initial coupling model using the subway load to obtain simulation data, obtain a vibration acceleration amplitude set based on the subway vibration signal, the building vibration signal, and the simulation data, calculate a frequency spectrum characteristic based on the vibration acceleration amplitude set, obtain a suboptimal coupling model based on the frequency spectrum characteristic, obtain current subway operating parameters, current geological parameters, and vibration data of buildings adjacent to the subway, and obtain an optimal coupling model based on the suboptimal coupling model, the current subway operating parameters, the current geological parameters, and vibration data of buildings adjacent to the subway; The vibration simulation prediction module is used to complete the indoor vibration simulation prediction of buildings adjacent to the subway based on the optimal coupling model.

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