A method, device and medium for simulating and predicting indoor vibration of a subway adjacent building

Abstract

The present application relates to the technical field of vibration simulation prediction, a subway adjacent building indoor vibration simulation prediction method, equipment and medium, comprising: constructing a building model, arranging vibration measuring points for the target track and the subway adjacent building, obtaining a subway vibration measuring point and a building vibration measuring point set, obtaining a subway vibration signal and a building vibration signal, constructing a geological model according to the geological parameters, dividing the grid for the geological model and the building model, obtaining the grid geological model and the grid building model, assembling the grid geological model and the grid building model, obtaining the initial coupling model, obtaining the suboptimal coupling model, obtaining the current subway operation parameters, the current geological parameters and the current subway adjacent building vibration data, obtaining the best coupling model, and completing the subway adjacent building indoor vibration simulation prediction based on the best coupling model. The present application can improve the efficiency and accuracy of vibration prediction.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of vibration simulation prediction, and particularly relates to a subway adjacent building indoor vibration simulation prediction method, device and medium. BACKGROUND

[0002] The subway adjacent building refers to the building located within a certain range along the subway track. The indoor vibration simulation prediction refers to predicting the vibration intensity, frequency distribution and spatial decay law of the vibration transmitted to the building interior through numerical models and measured data, so as to provide a basis for vibration reduction design or comfort evaluation.

[0003] At present, the research on indoor vibration of subway adjacent buildings has achieved certain results. The traditional vibration prediction method mainly relies on empirical formula and field monitoring data, which can reflect the vibration propagation law to a certain extent, but lacks in-depth mining and prediction function of vibration data. In addition, the traditional vibration prediction method cannot accurately predict the future vibration trend, and it is difficult to take effective vibration reduction measures in advance. Therefore, how to improve the efficiency and accuracy of vibration prediction is a technical problem to be solved. SUMMARY

[0004] The present application provides a subway adjacent building indoor vibration simulation prediction method and computer readable storage medium, which aims 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 purpose, the present application provides a subway adjacent building indoor vibration simulation prediction method, which comprises:

[0006] Confirming the target track and the subway adjacent building, receiving the building indoor vibration simulation instruction, and constructing the building model according to the building indoor vibration simulation instruction and the subway adjacent building;

[0007] Arranging vibration measuring points for the target track and the subway adjacent building to obtain the subway vibration measuring point set and the building vibration measuring point set;

[0008] Monitoring the subway vibration measuring point set and the building vibration measuring point set by using the preset monitoring frequency to obtain the initial subway vibration signal and the initial building vibration signal, and recording the subway running parameters and the geological parameters in real time when monitoring the subway vibration measuring point, wherein the subway running parameters include train speed, train formation and train travel time;

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

[0010] The geological model is constructed according to the geological parameters, and the grid geological model and the grid building model are obtained by performing grid division on the geological model and the building model;

[0011] The initial coupling model is obtained by assembling the grid geological model and the grid building model by using the pre-constructed finite element software;

[0012] The subway load is determined according to the subway operation parameters, and the initial coupling model is simulated by using the subway load to obtain simulation data;

[0013] The vibration acceleration amplitude set is obtained based on the subway vibration signal, the building vibration signal and the simulation data, the spectral feature is calculated according to the vibration acceleration amplitude set, and the suboptimal coupling model is obtained according to the spectral feature;

[0014] The current subway operation parameters, the current geological parameters and the current subway adjacent building vibration data are obtained, and the optimal coupling model is obtained based on the suboptimal coupling model, the current subway operation parameters, the current geological parameters and the current subway adjacent building vibration data;

[0015] The indoor vibration simulation and prediction of the subway adjacent building are completed based on the optimal coupling model.

[0016] Optionally, the target track and the subway adjacent building are arranged with vibration measuring points to obtain a set of subway vibration measuring points and building vibration measuring points, including:

[0017] The target track area above the target track is determined according to the subway adjacent building, and the vibration measuring points are arranged on the target track area above the target track to obtain the subway vibration measuring points;

[0018] The subway adjacent building is divided into floors to obtain low floors, middle floors and high floors, wherein the low floors are 1-10 floors, the middle floors are 11-20 floors, and the high floors are 21-32 floors;

[0019] The adjacent subway surface of the subway adjacent building is identified, and vibration measuring points are arranged on the low floors, the middle floors and the high floors of the adjacent subway surface to obtain the set of building vibration measuring points.

[0020] Optionally, the subway vibration signal and the building vibration signal are obtained based on the initial subway vibration signal and the initial building vibration signal, including:

[0021] The initial subway vibration signal is segmented to obtain a vibration signal sequence, wherein the vibration signal sequence includes multiple vibration signals;

[0022] The vibration signals are extracted from the vibration signal sequence in sequence, and the extracted vibration signals are subjected to the following operations:

[0023] An embedding dimension and a similarity tolerance are set, the vibration signal is reshaped according to the embedding dimension to obtain an embedding dimension vector sequence, wherein the embedding dimension vector sequence comprises a plurality of embedding dimension vectors;

[0024] The plurality of embedding dimension vectors are combined in pairs to obtain a plurality of vector combination pairs, and the following operations are performed on each vector combination pair in the plurality of vector combination pairs:

[0025] The maximum difference value of the vector combination pair is calculated, and the maximum difference value is compared with the similarity tolerance;

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

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

[0028] The first vector combination pair and the second combination vector pair are summarized respectively to obtain a first vector combination pair group and a second combination vector pair group, and the number of first vector combination pairs and the number of second vector combination pairs in the first vector combination pair group and the second combination vector pair group are counted respectively;

[0029] The first vector combination pair and the second combination vector pair are summarized respectively to obtain a first vector combination pair group and a second combination vector pair group, and the number of first vector combination pairs and the number of second vector combination pairs in the first vector combination pair group and the second combination vector pair group are counted respectively;

[0030] The wavelet coefficients are obtained by wavelet decomposition of the vibration signal to be denoised, the coefficient absolute value of the wavelet coefficients is obtained, and the coefficient absolute value is compared with a preset coefficient threshold;

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

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

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

[0034] Optionally, the vibration signal is obtained based on the first vector pair number set and the second vector pair number set, comprising:

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

[0036]

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

[0038] If the sample entropy is greater than the preset noise sample entropy, the vibration signal corresponding to the sample entropy is taken as a vibration signal requiring denoising.

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

[0040] Optionally, the geological model and the building model are both subjected to grid division to obtain a grid geological model and a grid building model, comprising:

[0041] The geological model is subjected to initial grid division by using a preset initial grid density to obtain an initial grid geological model, a key region group in the initial grid geological model is confirmed, and a plurality of grid densities are set, 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] The grid densities are extracted from the plurality of grid densities in sequence, each key region in the key region group is re-divided according to the grid density to obtain a grid key region, the grid key region is subjected to trial calculation to obtain trial vibration response data and trial time, the trial vibration response data and the trial time are respectively summarized to obtain a trial vibration response data group and a trial time group.

[0043] The optimal trial vibration response data and the optimal trial time are obtained from the trial vibration response data group and the trial time group.

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

[0045] Optionally, the best coupling model is obtained based on the suboptimal coupling model, the current subway operation parameter, the current geological parameter, and the current subway adjacent building vibration data, comprising:

[0046] The current subway operation parameter and the current geological parameter are subjected to vibration simulation prediction by using the suboptimal coupling model to obtain building vibration response prediction data.

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

[0048] A curve similarity value of the vibration prediction curve and the building vibration curve is calculated, and the curve similarity value is compared with a preset similarity threshold value;

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

[0050] If the curve similarity value is not greater than the preset similarity threshold value, the suboptimal coupling model is modified to obtain a modified coupling model, the modified coupling model is taken as the suboptimal coupling model, and the step of performing vibration simulation prediction on the current subway operation parameter and the current geological parameter by using the suboptimal coupling model is returned until the curve similarity value is greater than the preset similarity threshold value, and the optimal coupling model is obtained.

[0051] Optionally, the calculation of the curve similarity value of the vibration prediction curve and the building vibration curve comprises:

[0052] An attenuation coefficient is obtained, fast Fourier transform is performed on the vibration prediction curve and the building vibration curve to obtain a vibration prediction frequency domain signal and a building vibration frequency domain signal, and vibration prediction power spectral density and building vibration power spectral density are calculated according to the vibration prediction frequency domain signal and the building vibration frequency domain signal;

[0053] The curve similarity value is calculated according to the attenuation coefficient, the vibration prediction power spectral density and the building vibration power spectral density, and a calculation formula of the curve similarity value is as follows:

[0054]

[0055] Wherein, 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 a time delay, ||*|| represents an L2 norm, t represents a time sequence, e represents a natural constant, γ represents an attenuation coefficient, PSD(V p ) represents the vibration prediction power spectral density, PSD(V m ) represents the building vibration power spectral density, ||*||2 represents an Euclidean distance, V m (t+τ) represents a new signal obtained by shifting the building vibration signal on a time axis by a time delay, max represents a maximum value, and <*,*> represents an inner product.

[0056] Optionally, the attenuation coefficient is obtained by:

[0057] The subway vibration measuring point is taken as a vibration starting point, each building vibration measuring point in the building vibration measuring point set is taken as a vibration ending point, the vibration ending points are collected, and a vibration ending point set is obtained;

[0058] A plurality of historical vibration detection time periods are acquired, a historical vibration detection time period is sequentially extracted from the plurality of historical vibration detection time periods, and the following operations are performed on the extracted historical vibration detection time period:

[0059] A vibration ending point is sequentially extracted from the vibration ending point set, historical vibration data of the vibration ending point is collected according to the historical vibration detection time period and the vibration starting point, and a vibration propagation distance is acquired according to the vibration starting point and the vibration ending point;

[0060] A vibration energy value is calculated according to the historical vibration data, a vibration data pair is acquired based on the vibration propagation distance and the vibration energy value, the vibration data pairs are collected, and a vibration data pair group corresponding to the vibration ending point set is obtained;

[0061] A vibration energy attenuation curve and an original vibration curve are drawn based on the vibration data pair group, and a mean square error of the vibration energy attenuation curve and the original vibration curve is calculated;

[0062] The mean square errors are collected, a mean square error set corresponding to the plurality of historical vibration detection time periods is obtained, an optimal mean square error is acquired from the mean square error set, a vibration energy attenuation curve corresponding to the optimal mean square error is taken as an optimal energy attenuation curve, and an attenuation coefficient is acquired according to the optimal energy attenuation curve.

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

[0064] An initial exponential attenuation model is confirmed, the initial exponential attenuation model is fitted by using the vibration data pair group, an initial vibration energy and a fitted attenuation coefficient are obtained, and the initial exponential attenuation model is expressed as:

[0065] E(d)=E0×e -γ×d

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

[0067] An exponential attenuation model is acquired based on the initial vibration energy, the fitted attenuation coefficient and the initial exponential attenuation model;

[0068] A vibration data pair is sequentially extracted from the vibration data pair group, a vibration propagation distance is extracted from the vibration data pair, a fitted vibration energy is calculated by using the vibration propagation distance and the exponential attenuation model, and a fitted vibration data pair is acquired based on the fitted vibration energy and the vibration propagation distance;

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

[0070] To achieve the above object, the application further provides a subway adjacent building indoor vibration simulation prediction system, comprising:

[0071] The vibration data acquisition module is used for confirming the target track and the subway adjacent building, receiving a building indoor vibration simulation instruction, constructing a building model according to the building indoor vibration simulation instruction and the subway adjacent building, arranging vibration measuring points for the target track and the subway adjacent building, obtaining a subway vibration measuring point set and a building vibration measuring point set, monitoring the subway vibration measuring point set and the building vibration measuring point set by using a preset monitoring frequency, obtaining initial subway vibration signals and initial building vibration signals, and recording subway running parameters and geological parameters in real time when the subway vibration measuring point is monitored, wherein the subway running parameters include train speed, train marshalling and train travel time, and the subway vibration signals and the building vibration signals are obtained based on the initial subway vibration signals and the initial building vibration signals.

[0072] The model construction module is used for constructing a geological model according to the geological parameters, dividing grids for the geological model and the building model to obtain a grid geological model and a grid building model, and assembling the grid geological model and the grid building model by using a pre-constructed finite element software to obtain an initial coupling model.

[0073] The coupling model optimization module is used for determining a subway load according to the subway running parameters, simulating the initial coupling model by using the subway load to obtain simulation data, obtaining a vibration acceleration amplitude set from the subway vibration signals, the building vibration signals and the simulation data, calculating a frequency spectrum feature according to the vibration acceleration amplitude set, obtaining a suboptimal coupling model according to the frequency spectrum feature, obtaining current subway running parameters, current geological parameters and current subway adjacent building vibration data, and obtaining a best coupling model based on the suboptimal coupling model, the current subway running parameters, the current geological parameters and the current subway adjacent building vibration data.

[0074] The vibration simulation prediction module is used for completing the subway adjacent building indoor vibration simulation prediction based on the best coupling model.

[0075] To solve the above problems, the application further provides an electronic device, which comprises:

[0076] The memory stores at least one instruction.

[0077] The processor executes the instruction stored in the memory to implement the above-mentioned subway adjacent building indoor vibration simulation prediction method.

[0078] To solve the above problems, the application further provides a computer readable storage medium, wherein at least one instruction is stored in the computer readable storage medium, and the at least one instruction is executed by a processor in an electronic device to implement the subway adjacent building indoor vibration simulation prediction method.

[0079] The present application is to solve the problems described in the background art, the present application confirms the target track and the subway adjacent building, receives the building indoor vibration simulation instruction, constructs the building model according to the building indoor vibration simulation instruction and the subway adjacent building, the present application can be customized according to the actual demand vibration simulation through receiving instruction and constructing model, improves the pertinence and practicality of simulation, carries out vibration measuring point arrangement to target track and subway adjacent building, obtains subway vibration measuring point and building vibration measuring point set, the present application can fully capture the vibration information of subway operation and building response through reasonable measuring point arrangement, provides rich data source for subsequent data analysis, utilizes preset monitoring frequency to monitor subway vibration measuring point and building vibration measuring point set, obtains initial subway vibration signal and initial building vibration signal, when monitoring subway vibration measuring point, real-time records subway operation parameter and geological parameter, wherein, the subway operation parameter includes: train speed, train marshalling and train travel time, the present application records subway operation parameter and geological parameter in real time, helps to analyze the relationship between vibration and subway operation and geological conditions, improves the accuracy and reliability of vibration prediction, obtains subway vibration signal and building vibration signal based on initial subway vibration signal and initial building vibration signal, the present application extracts effective vibration signal from initial signal, removes possible noise and interference, improves the quality and availability of signal, constructs geological model according to geological parameter, carries out grid division to geological model and building model, obtains grid geological model and grid building model, the present application geological model can accurately reflect the geological conditions under subway along the line and building foundation, provides important basis for vibration propagation analysis, grid division makes complex model can be processed by finite element software, improves the calculation precision and efficiency of model, utilizes pre-constructed finite element software to assemble grid geological model and grid building model, obtains initial coupling model, the present application coupling model can comprehensively consider the interaction of subway operation, geological conditions and building structure, provides the possibility for more comprehensive vibration analysis, determines subway load according to the subway operation parameter, utilizes subway load to simulate initial coupling model, obtains simulation data, the present application determines load according to actual subway operation parameter, makes simulation more close to actual situation, improves the credibility of simulation result, obtains vibration acceleration amplitude set based on subway vibration signal, building vibration signal and simulation data, calculates frequency spectrum characteristics according to vibration acceleration amplitude set, obtains suboptimal coupling model according to frequency spectrum characteristics, the present application optimizes coupling model according to frequency spectrum characteristics, can gradually improve the fitting ability of model to actual vibration condition, provides more accurate model basis for final vibration prediction, obtains current subway operation parameter, current geological parameter and current subway adjacent building vibration data, obtains best coupling model based on suboptimal coupling model, current subway operation parameter, current geological parameter and current subway adjacent building vibration data, the present application combines current operation and geological conditions and actual vibration data, can further calibrate and optimize coupling model,The optimal coupling model can more accurately reflect vibration propagation and building response caused by subway operation, and indoor vibration simulation prediction of buildings near the subway is completed based on the optimal coupling model. Therefore, the application can improve the efficiency and accuracy of vibration prediction, and provide strong technical support for urban subway construction and operation. BRIEF DESCRIPTION OF DRAWINGS

[0080] Figure 1 A flowchart of an indoor vibration simulation prediction method of buildings near the subway provided by an embodiment of the application is shown in the figure.

[0081] Figure 2 A function module diagram of an indoor vibration simulation prediction system of buildings near the subway provided by an embodiment of the application is shown in the figure.

[0082] Figure 3 A structural diagram of an electronic device for implementing the indoor vibration simulation prediction method of buildings near the subway provided by an embodiment of the application is shown in the figure.

[0083] REFERENCE SIGNS:

[0084] 1, electronic device; 10, processor; 11, memory; 12, bus.

[0085] The implementation, functional features and advantages of the application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

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

[0087] The embodiment of the application provides an indoor vibration simulation prediction method of buildings near the subway. The execution subject of the indoor vibration simulation prediction method of buildings near the subway includes but is not limited to at least one of the electronic devices which can be configured to execute the method provided by the embodiment of the application, such as a server, a terminal and the like. In other words, the indoor vibration simulation prediction method of buildings near the subway can be executed by software or hardware installed in 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 A flowchart of an indoor vibration simulation prediction method of buildings near the subway provided by an embodiment of the application is shown in the figure. In this embodiment, the indoor vibration simulation prediction method of buildings near the subway includes:

[0089] S1, confirming a target track and buildings near the subway, receiving a building indoor vibration simulation instruction, and constructing a building model according to the building indoor vibration simulation instruction and the buildings near the subway.

[0090] It should be explained that the target track refers to a specific track in the subway line, which is the main position of the vibration source, and the vibration generated by the subway train running on it will propagate to the nearby building structure. The subway adjacent building refers to the building located near the subway line. The building indoor vibration simulation instruction is an instruction for starting the building indoor vibration simulation. The step of constructing a building model according to the building indoor vibration simulation instruction and the subway adjacent building is: obtaining the building floor distribution and room layout from the design drawings of the subway adjacent building, and constructing a building model according to the finite element software, the building floor distribution and the room layout. The finite element software refers to a computer software tool for numerical simulation and analysis of engineering problems. For example, the finite element software is ABAQUS, ANSYS, etc. The building model refers to a model constructed by using the finite element software, which is used to simulate and analyze the behavior and response of the building under various loads, and it includes the geometric shape, structural layout, material properties, boundary conditions, etc. Information of the building.

[0091] S2, vibration measuring points are arranged on the target track and the subway adjacent building, and subway vibration measuring points and building vibration measuring point sets are obtained.

[0092] In detail, the vibration measuring points are arranged on the target track and the subway adjacent building, and the subway vibration measuring points and the building vibration measuring point sets are obtained, including:

[0093] According to the subway adjacent building, the target track area above is confirmed, the vibration measuring points are deployed on the target track area above, and the subway vibration measuring points are obtained.

[0094] The floors of the subway adjacent building are divided, and low floors, middle floors and high floors are obtained, wherein the low floors are 1-10 floors, the middle floors are 11-20 floors, and the high floors are 21-32 floors.

[0095] The adjacent subway surface of the subway adjacent building is identified, and vibration measuring points are arranged on the low floors, middle floors and high floors of the adjacent subway surface, and a building vibration measuring point set is obtained.

[0096] It should be explained that the area above the target track refers to the ground area directly above the subway track, which is the area where vibrations are transmitted from the subway track to the ground buildings. The subway vibration measuring point refers to the measuring point on the ground area above the target track, which can fully capture the vibrations generated by the subway operation. The measuring point refers to the position where the vibration sensor is installed. The subway adjacent surface refers to the nearest surface of the building adjacent to the subway track, which is more susceptible to vibrations generated by the subway operation. The vibration measuring point arrangement indicates that one measuring point is arranged every 1 floor for low floors, one measuring point is arranged every 2 floors for middle floors, and one measuring point is arranged every 3 floors for high floors, and each measuring point position is the center position of the living room rest area and the center position of the bedroom floor on the side closest to the target track. The purpose of arranging one measuring point every 1 floor for low floors is that the vibration response of low floors is larger relative to middle floors and high floors, and the vibration propagation path is short, so more intensive measuring points are needed to capture the vibration changes. Each measuring point position is the center position of the living room rest area and the center position of the bedroom floor on the side closest to the target track, which are typical areas of indoor vibration response and can represent the vibration situation of the floor.

[0097] S3, monitoring the subway vibration measuring point and the building vibration measuring point set using a preset monitoring frequency to obtain initial subway vibration signals and initial building vibration signals, and recording the subway operation parameters and geological parameters in real time when monitoring the subway vibration measuring point, wherein the subway operation parameters include train speed, train formation, and train travel time.

[0098] It should be explained that the monitoring frequency refers to the pre-set frequency. The monitoring of the subway vibration measuring point and the building vibration measuring point set using the preset monitoring frequency means that vibration sensors are installed at each building vibration measuring point in the subway vibration measuring point and the building vibration measuring point set for monitoring. The initial subway vibration signal refers to the vibration data measured directly at the subway vibration measuring point. The initial building vibration signal refers to the vibration data measured directly at the building vibration measuring point. The geological parameters refer to the physical and mechanical property parameters describing the geological conditions of the subway along the line and the ground below the building foundation, wherein the geological parameters include soil type, soil density, soil thickness, soil elastic modulus, and soil shear wave velocity. The train formation refers to the composition of the subway train, including the number, type, and arrangement order of the vehicles. The train formation determines the total length, total weight, and power distribution of the train, which directly affects the vibration characteristics generated during subway operation.

[0099] S4, obtaining subway vibration signals and building vibration signals based on the initial subway vibration signals and the initial building vibration signals.

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

[0101] segmenting the initial subway vibration signal to obtain a vibration signal sequence, wherein the vibration signal sequence comprises a plurality of vibration signals;

[0102] extracting vibration signals from the vibration signal sequence in sequence, and performing the following operations on each extracted vibration signal:

[0103] setting an embedding dimension and a similarity tolerance, and remodeling the vibration signals according to the embedding dimension to obtain an embedding dimension vector sequence, wherein the embedding dimension vector sequence comprises a plurality of embedding dimension vectors;

[0104] combining the plurality of embedding dimension vectors in pairs to obtain a plurality of vector combination pairs, and performing the following operations on each vector combination pair in the plurality of vector combination pairs:

[0105] calculating a maximum difference value of the vector combination pair, and comparing the maximum difference value with the similarity tolerance;

[0106] if the maximum difference value is less than the similarity tolerance, taking the vector combination pair corresponding to the maximum difference value as a first vector combination pair;

[0107] if the maximum difference value is greater than or equal to the similarity tolerance, taking the vector combination pair corresponding to the maximum difference value as a second vector combination pair;

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

[0109] obtaining 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] wavelet-decomposing the vibration signal to be denoised to obtain wavelet coefficients, obtaining the coefficient absolute value of the wavelet coefficients, and comparing the coefficient absolute value with a preset coefficient threshold value;

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

[0112] if the coefficient absolute value is not greater than the preset coefficient threshold value, adjusting the wavelet coefficients until the coefficient absolute value is greater than the preset coefficient threshold value to obtain effective wavelet coefficients, and denoising the vibration signal to be denoised using the effective wavelet coefficients to obtain a denoised vibration signal;

[0113] integrating the denoised vibration signal and the reference vibration signal to obtain a subway vibration signal, and obtaining a building vibration signal based on the initial building vibration signal.

[0114] It should be explained that the signal segmentation of the initial subway vibration signal refers to segmenting the initial subway vibration signal by using a signal processing software. For example, the signal processing software is MATLAB, Python, etc. The embedding dimension and the similarity tolerance are set by humans. The embedding dimension refers to the dimension of converting a one-dimensional time series into a multi-dimensional vector space. The similarity tolerance is a threshold for judging whether two vectors are similar. Remodeling refers to the operation of converting a one-dimensional time series into a multi-dimensional vector. The embedding dimension vector sequence refers to the vector sequence obtained by remodeling the vibration signal by using the embedding dimension. The maximum difference refers to the maximum absolute difference of corresponding elements in a vector combination pair. The binary combination refers to the operation of selecting two vectors from a plurality of embedding dimension vectors 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, and the similarity tolerance is μ = 1.5. The vibration signal is remodeled into an embedding dimension vector sequence as follows: {x m (1) = [1, 2], x m (2) = [2, 3], x m (3) = [3, 4], x m (4) = [4, 5]}. A plurality of vector combination pairs are obtained by performing binary combination on a plurality of embedding dimension vectors 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]}. Two vectors are extracted from a plurality of vector combination pairs as follows: x m (1) = [1, 2], x m (2) = [2, 3]}. The maximum difference of x m (1) = [1, 2], x m (2) = [2, 3] is calculated as follows: max(|1-2|,|2-3|) = 1. Two vectors are extracted from a plurality of vector combination pairs as follows: x m (1) = [1, 2], x m (3) = [3, 4]}. The maximum difference of x m (1) = [1, 2], xm (3) = [3, 4] with max(|1-3|, |2-4|) = 2, extract two vectors from multiple vector combination pairs as x m (1) = [1, 2], x m (4) = [4, 5] with max(|1-4|, |2-5|) = 3, extract two vectors from multiple vector combination pairs as x m (1) = [1, 2], x m (4) = [4, 5] with max(|1-4|, |2-5|) = 3, extract two vectors from multiple vector combination pairs as x m (2) = [2, 3], x m (3) = [3, 4] with max(|2-3|, |3-4|) = 1, extract two vectors from multiple vector combination pairs as x m (2) = [2, 3], x m (3) = [3, 4] with max(|2-3|, |3-4|) = 1, extract two vectors from multiple vector combination pairs as x m (2) = [2, 3], x m (4) = [4, 5] with max(|2-4|, |3-5|) = 2, extract two vectors from multiple vector combination pairs as x m (2) = [2, 3], x m (4) = [4, 5] with max(|2-4|, |3-5|) = 2, extract two vectors from multiple vector combination pairs as x m (3) = [3, 4], x m (4) = [4, 5] with max(|3-4|, |4-5|) = 1, extract two vectors from multiple vector combination pairs as x m (3) = [3, 4], x m (4) = [4, 5] with max(|3-4|, |4-5|) = 1.

[0116] Importantly, the first number of vector pairs refers to the number of vector pairs with the maximum difference less than the similarity tolerance. The second number of vector pairs refers to the number of vector pairs with the maximum difference greater than or equal to the similarity tolerance. The first number of vector pair set refers to a set consisting of all first numbers of vector pairs. The second number of vector pair set refers to a set consisting of all second numbers of vector pairs. Wavelet decomposition refers to the operation of decomposing a signal into wavelet coefficients at different scales. Wavelet coefficients refer to numerical values representing the characteristics of a signal at different scales and locations after wavelet decomposition. The denoised vibration signal refers to the vibration signal after wavelet denoising processing. 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 denoised vibration signal is greater than the pre-set coefficient threshold. The adjustment of the wavelet coefficient refers to the adjustment of the wavelet coefficient using a pre-set coefficient adjustment factor. The coefficient adjustment factor is a pre-set numerical value used to amplify the absolute value of the wavelet coefficient to meet the needs 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 pre-set coefficient threshold, it may be misjudged as noise and removed, resulting in signal distortion. By using the coefficient adjustment factor, these wavelet coefficients can be appropriately amplified to retain the characteristics of the signal in subsequent processing. The integration of the denoised vibration signal and the reference vibration signal to obtain the subway vibration signal refers to the connection of the denoised vibration signal and 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, which will not be described here.

[0117] In detail, the method for obtaining the denoised vibration signal or the reference vibration signal based on the first number of vector pair set and the second number of vector pair set comprises:

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

[0119]

[0120] Where 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 first number of vector pairs, represents the second number of vector pairs, and ln represents the natural logarithm.

[0121] If the sample entropy is greater than the pre-set noise sample entropy, the vibration signal corresponding to the sample entropy is taken as the denoised vibration signal.

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

[0123] It should be explained that the sample entropy reflects the volatility of the vibration signal corresponding to the vibration signal sequence. The smaller the sample entropy value is, the smaller the vibration signal fluctuation is. The larger the sample entropy value is, the more noise the vibration signal has. The signal length refers to the number of vibration data in the vibration signal. The reference vibration signal refers to the vibration signal whose sample entropy is not greater than the preset noise sample entropy. The denoising vibration signal refers to the vibration signal whose sample entropy is greater than the preset noise sample entropy. The noise sample entropy refers to a preset value.

[0124] S5, construct a geological model according to the geological parameters, and perform grid division on the geological model and the building model to obtain a grid geological model and a grid building model.

[0125] In detail, the grid division on the geological model and the building model to obtain the grid geological model and the grid building model includes:

[0126] The initial grid division is performed on the geological model by using the preset initial grid density to obtain an initial grid geological model, a key region group in the initial grid geological model is confirmed, and a plurality of grid densities are set, 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] The grid densities are sequentially extracted from the plurality of grid densities, each key region in the key region group is re-divided according to the grid density to obtain a grid key region, the grid key region is trial calculated to obtain trial vibration response data and trial time, the trial vibration response data and the trial time are respectively summarized to obtain a trial vibration response data group and a trial time group;

[0128] The optimal trial vibration response data and the optimal trial time are obtained from the trial vibration response data group and the trial time group;

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

[0130] It should be explained that the initial grid density refers to the density artificially set in advance. The initial grid geological model refers to the model obtained by initially dividing the geological model by using the grid density. The key area group refers to a set of areas that have a great influence on the vibration response in the initial grid geological model. For example, the key area is an area where underground pipelines are dense, an area near a fault, an area along a shield construction line, etc. The grid key area refers to a grid area obtained by re-dividing the key area by using the grid density. The trial calculation on the grid key area refers to numerical simulation calculation on the grid key area in the finite element software. The trial calculation vibration response data refers to the vibration-related physical quantity data recorded in the process of trial calculation on the grid key area. For example, the vibration-related physical quantity data is vibration acceleration, displacement, velocity, etc. The trial calculation time refers to the calculation time spent for one trial calculation on the grid key area. The trial calculation vibration response data group refers to a set composed of all trial calculation vibration response data. The trial calculation time group refers to a set composed of all trial calculation times. The optimal trial calculation vibration response data refers to data that meets the standard trial calculation vibration response data. The standard trial calculation vibration response data refers to the response data set in advance, which is the data with the highest calculation accuracy. The optimal trial calculation time refers to the time that meets the standard trial calculation time. The standard trial calculation time refers to the time set in advance. The method for obtaining the grid building model based on the building model is the same as the method for obtaining the grid geological model based on the geological model, and will not be described here.

[0131] S6, assemble the grid geological model and the grid building model by using the pre-constructed finite element software to obtain an initial coupling model.

[0132] It should be explained that the initial coupling model refers to a model that combines the geological model and the building model together, which is used to simulate the interaction between the geological conditions and the building structure in vibration propagation and response analysis.

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

[0134] It should be explained that the subway operation parameters refer to parameters describing the running state of the subway train. For example, the subway operation parameters are train speed, train load, train frequency, track type, etc. The subway load refers to the force generated by the subway operation on the surrounding environment (such as geology and building structure). The simulation of the initial coupling model by using the subway load to obtain simulation data refers to applying the load generated by the subway operation to the initial coupling model, and calculating the vibration response data of the model by using the numerical simulation method (such as finite element analysis), which is the simulation data.

[0135] S8, obtaining a vibration acceleration amplitude set based on the subway vibration signal, the building vibration signal and the simulation data, calculating a spectrum feature according to the vibration acceleration amplitude set, and obtaining a suboptimal coupling model according to the spectrum feature.

[0136] It should be explained that the vibration acceleration amplitude set is obtained based on the subway vibration signal, the building vibration signal and the simulation data, which means that the vibration acceleration amplitudes are extracted from the subway vibration signal, the building vibration signal and the simulation data respectively, and the vibration acceleration amplitudes are collected to obtain the vibration acceleration amplitude set. The vibration acceleration amplitude refers to the maximum value of acceleration in the vibration process. The spectrum feature is calculated according to the vibration acceleration mean value of the vibration acceleration amplitude set, and the vibration acceleration mean value is taken as the spectrum feature. The suboptimal coupling model is obtained according to the spectrum feature, which means that the suboptimal coupling model is constructed by using a machine learning algorithm and the spectrum feature. For example, the machine learning algorithm is a support vector machine, linear regression, etc.

[0137] S9, obtaining current subway operation parameters, current geological parameters and current subway adjacent building vibration data, obtaining an optimal coupling model based on the suboptimal coupling model, the current subway operation parameters, the current geological parameters and the current subway adjacent building vibration data, and completing the indoor vibration simulation prediction of the subway adjacent building based on the optimal coupling model.

[0138] In detail, the optimal coupling model is obtained based on the suboptimal coupling model, the current subway operation parameters, the current geological parameters and the current subway adjacent building vibration data, which includes:

[0139] The vibration simulation prediction is performed on the current subway operation parameters and the current geological parameters by using the suboptimal coupling model to obtain building vibration response prediction data;

[0140] The building vibration response prediction data and the current subway adjacent building vibration data are respectively plotted to obtain vibration prediction curves and building vibration curves;

[0141] The curve similarity values of the vibration prediction curves and the building vibration curves are calculated, and the curve similarity values are compared with a preset similarity threshold value;

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

[0143] If the curve similarity value is not greater than the preset similarity threshold value, the suboptimal coupling model is modified to obtain a modified coupling model, the modified coupling model is taken as the suboptimal coupling model, and the step of performing the vibration simulation prediction on the current subway operation parameters and the current geological parameters by using the suboptimal coupling model is returned until the curve similarity value is greater than the preset similarity threshold value, and the optimal coupling model is obtained.

[0144] It should be explained that the current metro operation parameter refers to the operation parameter of the metro in the current time period. The current geological parameter refers to the geological parameter corresponding to the current metro operation parameter. The current metro adjacent building vibration data refers to the vibration data monitored in the adjacent building in the current time period. The vibration simulation prediction of the current metro operation parameter and the current geological parameter by using the suboptimal coupling model refers to that the current metro operation parameter and the current geological parameter are input into the suboptimal coupling model, the suboptimal coupling model calculates according to the input parameters, and the vibration response of the building under the operation of the metro is predicted by the model, so as to be compared with the actual measurement data. The building vibration response prediction data refers to the prediction data output by the suboptimal coupling model. The curve drawing of the building vibration response prediction data and the current metro adjacent building vibration data respectively refers to that the visualization tool is used to draw the curve of the building vibration response prediction data and the curve of the current metro adjacent building vibration data respectively. For example, the visualization tool is MATLAB, Origin, etc. The vibration prediction curve refers to the curve drawn according to the building vibration response prediction data. The building vibration curve refers to the curve drawn according to the current metro adjacent building vibration data. The similarity threshold value refers to a threshold value for judging the similarity of the curves, which is set in advance. The optimal coupling model refers to the suboptimal coupling model corresponding to the similarity value greater than the preset similarity threshold value. The modification of the suboptimal coupling model refers to the modification of the suboptimal coupling model by adjusting the model parameters, improving the model structure and the like, so as to improve the prediction accuracy of the suboptimal coupling model and make it better match the current metro adjacent building vibration data. The modified coupling model refers to the suboptimal coupling model after modification.

[0145] In detail, the calculation of the curve similarity value of the vibration prediction curve and the building vibration curve comprises:

[0146] The attenuation coefficient is obtained, and fast Fourier transform is performed on the vibration prediction curve and the building vibration curve to obtain vibration prediction frequency domain signals and building vibration frequency domain signals. The vibration prediction power spectrum density and the building vibration power spectrum density are calculated according to the vibration prediction frequency domain signals and the building vibration frequency domain signals.

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

[0148]

[0149] Wherein, S represents the curve similarity value, V p (t) represents the building vibration response prediction data, V m (t) represents the current metro adjacent building vibration data, τ represents the time delay, ||*|| represents the L2 norm, t represents the time sequence, e represents the natural constant, γ represents the attenuation coefficient, PSD(Vp represents a vibration prediction power spectral density, PSD(V m represents a building vibration power spectral density, ||*||2 represents a Euclidean distance, V m represents a new signal obtained by shifting the building vibration signal on the time axis by a time delay, max represents a maximum value, and <*,*> represents an inner product.

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

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

[0152] wherein PSD(X) represents the vibration prediction power spectral density or the building vibration power spectral density, X(f) represents the vibration prediction frequency domain signal or the building vibration frequency domain signal, and |X| represents the modulus of the vibration prediction frequency domain signal or the modulus of the building vibration frequency domain signal. The time delay refers to a parameter for shifting the building vibration signal on the time axis. By shifting the signal, the vibration prediction frequency domain signal and the building vibration frequency domain signal can be better aligned, so that the similarity can be more accurately calculated. The inner product is an operation for describing the degree of similarity between two vectors.

[0153] In detail, the attenuation coefficient is obtained by:

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

[0155] A plurality of historical vibration detection time periods are obtained, and historical vibration detection time periods are sequentially extracted from the plurality of historical vibration detection time periods. The extracted historical vibration detection time periods are all subjected to the following operations:

[0156] The vibration ending points are sequentially extracted from the vibration ending point set, the historical vibration data of the vibration ending points are collected according to the historical vibration detection time periods and the vibration starting points, and the vibration propagation distances are obtained according to the vibration starting points and the vibration ending points.

[0157] The vibration energy value is calculated according to historical vibration data, a vibration data pair is obtained based on a vibration propagation distance and the vibration energy value, vibration data pairs are collected, and a vibration data pair group corresponding to a vibration end point set is obtained;

[0158] A vibration energy attenuation curve and an original vibration curve are drawn based on the vibration data pair group, and a mean square error of the vibration energy attenuation curve and the original vibration curve is calculated;

[0159] The mean square errors are collected to obtain a mean square error set corresponding to a plurality of historical vibration detection time periods, an optimal mean square error is obtained from the mean square error set, a vibration energy attenuation curve corresponding to the optimal mean square error is taken as an optimal energy attenuation curve, and an attenuation coefficient is obtained according to the optimal energy attenuation curve.

[0160] It should be explained that the vibration starting point refers to a position corresponding to a subway vibration measuring point. The vibration end point refers to a position corresponding to a building vibration measuring point. The vibration end point set refers to a set composed of all vibration end points. The historical vibration detection time period refers to a time period in which vibration detection is performed in the past. The historical vibration data refers to vibration data collected from the vibration end point measuring point in the historical vibration detection time period. The vibration distance refers to a distance between the vibration starting point and the vibration end point. The vibration energy value refers to an energy value at the vibration end point. The vibration data pair refers to data in which the vibration propagation distance and the corresponding vibration energy value are combined together. The vibration data pair group refers to a set composed of all vibration data pairs.

[0161] It should be explained that the calculation formula of the vibration energy value in the step of calculating the vibration energy value according to 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. The unit mass refers to a standard unit of mass in the system, for example, 1 kg. The original vibration curve refers to a curve drawn according to the historical vibration data. The mean square error is a value for measuring the difference between the vibration energy attenuation curve and the original vibration curve. The smaller the mean square error, the closer the two curves. The optimal mean square error refers to a mean square error that meets a preset standard mean square error. The standard mean square error refers to a preset error value. The optimal energy attenuation curve refers to a vibration energy attenuation curve corresponding to the optimal mean square error. The mean square error set refers to a set composed of all mean square errors. The attenuation coefficient is obtained according to the optimal energy attenuation curve, which refers to confirming the attenuation coefficient in the exponential attenuation model corresponding to the optimal energy attenuation curve.

[0164] In detail, the vibration energy attenuation curve and the original vibration curve are drawn based on the vibration data pair group, including:

[0165] An initial exponential decay model is confirmed, the initial exponential decay model is fitted by using the vibration data pairs, and initial vibration energy and a fitting decay coefficient are obtained, wherein the initial exponential decay model is expressed as:

[0166] E(d) = E0 x e -γ×d

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

[0168] An exponential decay model is obtained based on the initial vibration energy, the fitting decay coefficient, and the initial exponential decay model;

[0169] Vibration data pairs are sequentially extracted from the vibration data pairs, the vibration propagation distance is extracted from the vibration data pairs, the fitting vibration energy is calculated by using the vibration propagation distance and the exponential decay model, and the fitting vibration data pairs are obtained based on the fitting vibration energy and the vibration propagation distance;

[0170] The fitting vibration data pairs are summarized to obtain a fitting vibration data pair group, a vibration energy decay curve is drawn based on the fitting 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 vibration. The initial exponential decay model is fitted by using the vibration data pair group and the least square method. The fitting decay coefficient refers to the coefficient obtained after the initial exponential decay model is fitted by using the vibration data pair group. The exponential decay model is obtained by substituting the initial vibration energy and the fitting decay coefficient into the initial exponential decay model. The fitting vibration energy refers to the energy calculated by inputting the vibration propagation distance into the exponential decay model. The fitting vibration data pair refers to the data pair composed of the fitting vibration energy and the vibration propagation distance. The method of drawing the vibration energy decay curve based on the fitting 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 vibration prediction curve and the building vibration curve by respectively drawing curves of the building vibration response prediction data and the current subway adjacent building vibration data, and thus will not be described herein.

[0172] The present application is to solve the problems described in the background art, the present application confirms the target track and the subway adjacent building, receives the building indoor vibration simulation instruction, constructs the building model according to the building indoor vibration simulation instruction and the subway adjacent building, the present application can be customized according to the actual demand vibration simulation through receiving instruction and constructing model, improves the pertinence and practicality of simulation, carries out vibration measuring point arrangement to target track and subway adjacent building, obtains subway vibration measuring point and building vibration measuring point set, the present application can fully capture the vibration information of subway operation and building response through reasonable measuring point arrangement, provides rich data source for subsequent data analysis, utilizes preset monitoring frequency to monitor subway vibration measuring point and building vibration measuring point set, obtains initial subway vibration signal and initial building vibration signal, when monitoring subway vibration measuring point, real-time records subway operation parameter and geological parameter, wherein, the subway operation parameter includes: train speed, train marshalling and train travel time, the present application records subway operation parameter and geological parameter in real time, helps to analyze the relationship between vibration and subway operation and geological conditions, improves the accuracy and reliability of vibration prediction, obtains subway vibration signal and building vibration signal based on initial subway vibration signal and initial building vibration signal, the present application extracts effective vibration signal from initial signal, removes possible noise and interference, improves the quality and availability of signal, constructs geological model according to geological parameter, carries out grid division to geological model and building model, obtains grid geological model and grid building model, the present application geological model can accurately reflect the geological conditions under subway along the line and building foundation, provides important basis for vibration propagation analysis, grid division makes complex model can be processed by finite element software, improves the calculation precision and efficiency of model, utilizes pre-constructed finite element software to assemble grid geological model and grid building model, obtains initial coupling model, the present application coupling model can comprehensively consider the interaction of subway operation, geological conditions and building structure, provides the possibility for more comprehensive vibration analysis, determines subway load according to the subway operation parameter, utilizes subway load to simulate initial coupling model, obtains simulation data, the present application determines load according to actual subway operation parameter, makes simulation more close to actual situation, improves the credibility of simulation result, obtains vibration acceleration amplitude set based on subway vibration signal, building vibration signal and simulation data, calculates frequency spectrum characteristics according to vibration acceleration amplitude set, obtains suboptimal coupling model according to frequency spectrum characteristics, the present application optimizes coupling model according to frequency spectrum characteristics, can gradually improve the fitting ability of model to actual vibration condition, provides more accurate model basis for final vibration prediction, obtains current subway operation parameter, current geological parameter and current subway adjacent building vibration data, obtains best coupling model based on suboptimal coupling model, current subway operation parameter, current geological parameter and current subway adjacent building vibration data, the present application combines current operation and geological conditions and actual vibration data, can further calibrate and optimize coupling model,Make it closer to the actual situation, the optimal coupling model can more accurately reflect the vibration propagation and building response caused by subway operation, based on the optimal coupling model, the indoor vibration simulation prediction of the subway adjacent building is completed. Therefore, the present application can improve the efficiency and accuracy of vibration prediction, and provide strong technical support for urban subway construction and operation.

[0173] As Figure 2 The figure is a functional module diagram of the indoor vibration simulation prediction system of the subway adjacent building provided by an embodiment of the present application.

[0174] The indoor vibration simulation prediction system 100 of the subway adjacent building provided by the present application can be installed in an electronic device. According to the functions realized, the indoor vibration simulation prediction system 100 of the subway adjacent building can include a vibration data acquisition module 101, a model construction module 102, a coupling model optimization module 103 and a vibration simulation prediction module 104. The modules of the present application can also be referred to as units, which refer to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0175] The vibration data acquisition module 101 is used to confirm the target track and the subway adjacent building, receive the building indoor vibration simulation instruction, construct the building model according to the building indoor vibration simulation instruction and the subway adjacent building, arrange the vibration measuring points for the target track and the subway adjacent building, obtain the subway vibration measuring point set and the building vibration measuring point set, monitor the subway vibration measuring point set and the building vibration measuring point set by using the preset monitoring frequency, obtain the initial subway vibration signal and the initial building vibration signal, and record the subway operation parameters and the geological parameters in real time when monitoring the subway vibration measuring point, wherein the subway operation parameters include the train speed, the train formation and the train travel time, and the subway vibration signal and the building vibration signal are obtained 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 according to the geological parameters, divide the grid for the geological model and the building model, obtain the grid geological model and the grid building model, assemble the grid geological model and the grid building model by using the pre-constructed finite element software, and obtain the initial coupling model.

[0177] The coupling model optimization module 103 is configured to determine a subway load according to the subway operation parameter, simulate the initial coupling model by using the subway load, obtain simulation data, acquire a vibration acceleration amplitude set from the subway vibration signal, the building vibration signal and the simulation data, calculate a frequency spectrum feature according to the vibration acceleration amplitude set, acquire a suboptimal coupling model according to the frequency spectrum feature, acquire a current subway operation parameter, a current geological parameter and a current subway adjacent building vibration data, and acquire a best coupling model based on the suboptimal coupling model, the current subway operation parameter, the current geological parameter and the current subway adjacent building vibration data.

[0178] The vibration simulation prediction module 104 is configured to complete the indoor vibration simulation prediction of the subway adjacent building based on the best coupling model.

[0179] In detail, the modules in the subway adjacent building indoor vibration simulation prediction system 100 in the embodiment of the present application use the same technical means as the subway adjacent building indoor vibration simulation prediction method in the above Figure 1 , and can produce the same technical effects, which will not be described here.

[0180] As shown in Figure 3 , it is a structural schematic diagram of an electronic device for implementing the subway adjacent building indoor vibration simulation prediction method according to an embodiment of the present application.

[0181] The electronic device 1 can include a processor 10, a memory 11 and a bus 12, and can further include a computer program stored in the memory 11 and executable on the processor 10, such as a subway adjacent building indoor vibration simulation prediction method program.

[0182] The memory 11 includes at least one type of readable storage medium, such as a flash memory, a mobile hard disk, a multimedia card, a card-type memory (such as an SD or DX memory, etc.), a magnetic memory, a magnetic 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 media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the memory 11 includes an internal storage unit of the electronic device 1 and also includes an external storage device. The memory 11 can be used not only to store application software and various data installed in the electronic device 1, such as the code of the subway adjacent building indoor vibration simulation prediction method program, but also to temporarily store data that has been output or will be output.

[0183] The processor 10 can be composed of integrated circuits in some embodiments, for example, can be composed of a single packaged integrated circuit, or can be composed of multiple packaged integrated circuits with the same function or different functions, including one or more combinations of central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, which connects various components of the entire electronic device through various interfaces and lines, executes programs or modules stored in the memory 11 (such as the subway adjacent building indoor vibration simulation prediction method program), and calls data stored in the memory 11 to perform various functions and process data of the electronic device 1.

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

[0185] Figure 3 Only the electronic device with components is shown, and those skilled in the art can understand that, Figure 3 The structure shown does not constitute a limitation on the electronic device 1, and can include fewer or more components than shown, or combine certain components, or different component arrangements.

[0186] For example, although not shown, the electronic device 1 can also include a power supply (such as a battery) for powering various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management device, so as to realize functions such as charge management, discharge management, and power consumption management through the power management device. The power supply can also include one or more direct current or alternating current power supplies, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and any other components. The electronic device 1 can also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which are not described here.

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

[0188] Optionally, the electronic device 1 can also include a user interface, which can be a display, an input unit such as a keyboard, and optionally a standard wired interface, a wireless interface. Optionally, in some embodiments, the display can be an LED display, a liquid crystal display, a touch liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, etc. Among them, the display can also be appropriately called a display screen or a display unit, which is used to display the information processed in the electronic device 1 and to display the visualized user interface.

[0189] The subway adjacent building indoor vibration simulation prediction method program stored in the memory 11 in the electronic device 1 is a combination of multiple instructions, which can realize the following functions when running in the processor 10:

[0190] Confirm the target track and the subway adjacent building, receive the building indoor vibration simulation instruction, and construct the building model according to the building indoor vibration simulation instruction and the subway adjacent building;

[0191] Vibration measuring points are arranged for the target track and the subway adjacent building, and a set of subway vibration measuring points and building vibration measuring points are obtained;

[0192] The set of subway vibration measuring points and building vibration measuring points are monitored by using a preset monitoring frequency, and initial subway vibration signals and initial building vibration signals are obtained. When monitoring the subway vibration measuring points, the subway operation parameters and the geological parameters are recorded in real time, wherein the subway operation parameters include train speed, train formation and train travel time;

[0193] Based on the initial subway vibration signals and the initial building vibration signals, subway vibration signals and building vibration signals are obtained;

[0194] A geological model is constructed according to the geological parameters, and the geological model and the building model are both meshed to obtain a meshed geological model and a meshed building model;

[0195] The meshed geological model and the meshed building model are assembled by using a pre-constructed finite element software to obtain an initial coupling model;

[0196] The subway load is determined according to the subway operation parameters, and the initial coupling model is simulated by using the subway load to obtain simulation data;

[0197] The vibration acceleration amplitude set is obtained based on the subway vibration signals, the building vibration signals and the simulation data, the frequency spectrum characteristics are calculated according to the vibration acceleration amplitude set, and the suboptimal coupling model is obtained according to the frequency spectrum characteristics;

[0198] obtaining current subway operation parameters, current geological parameters and current subway adjacent building vibration data, and obtaining an optimal coupling model based on the suboptimal coupling model, the current subway operation parameters, the current geological parameters and the current subway adjacent building vibration data;

[0199] Based on the optimal coupling model, indoor vibration simulation prediction of the subway adjacent building is completed.

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

[0201] Further, the modules / units integrated in the electronic device 1, if realized in the form of software function units and sold or used as independent products, 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, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory).

[0202] The application also provides a computer readable storage medium, which stores a computer program, and the computer program can realize the following when executed by a processor of an electronic device:

[0203] Confirming the target track and the subway adjacent building, receiving a building indoor vibration simulation instruction, and constructing a building model according to the building indoor vibration simulation instruction and the subway adjacent building;

[0204] Vibration measuring points are arranged for the target track and the subway adjacent building, and a set of subway vibration measuring points and building vibration measuring points is obtained;

[0205] The set of subway vibration measuring points and building vibration measuring points are monitored by using a preset monitoring frequency, and initial subway vibration signals and initial building vibration signals are obtained, wherein the subway operation parameters include train speed, train marshalling and train travel time when the subway vibration measuring points are monitored;

[0206] Subway vibration signals and building vibration signals are obtained based on the initial subway vibration signals and the initial building vibration signals;

[0207] A geological model is constructed according to the geological parameters, and the geological model and the building model are both grid divided, and a grid geological model and a grid building model are obtained;

[0208] The pre-constructed finite element software is used to assemble the grid geological model and the grid building model to obtain an initial coupling model;

[0209] A subway load is determined according to the subway operation parameter, and the initial coupling model is simulated by using the subway load to obtain simulation data;

[0210] A vibration acceleration amplitude set is obtained based on the subway vibration signal, the building vibration signal and the simulation data, a frequency spectrum feature is calculated according to the vibration acceleration amplitude set, and a suboptimal coupling model is obtained according to the frequency spectrum feature;

[0211] The current subway operation parameter, the current geological parameter and the current subway adjacent building vibration data are obtained, and a best coupling model is obtained based on the suboptimal coupling model, the current subway operation parameter, the current geological parameter and the current subway adjacent building vibration data;

[0212] The indoor vibration simulation and prediction of the subway adjacent building are completed based on the best coupling model.

[0213] In several embodiments provided by the present application, it should be understood that the disclosed devices, systems and methods can be implemented in other manners. For example, the system embodiments described above are merely illustrative, and the actual implementation can have other division manners.

[0214] The modules illustrated as separate components can or can not be physically separated, and the components illustrated as modules can or can not be physical units, i.e., can be located in one place, or can be distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments.

[0215] In addition, each functional module in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of hardware plus software functional modules.

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

[0217] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting, and although the present application 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 application can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A method for predicting indoor vibration simulation of a subway in the vicinity of a building, characterized by, The method comprises: Confirming a target track and a subway adjacent building, receiving a building indoor vibration simulation instruction, and constructing a building model according to the building indoor vibration simulation instruction and the subway adjacent building; Vibration measuring point arrangement is performed on the target track and the subway adjacent building to obtain a subway vibration measuring point set and a building vibration measuring point set; Initial subway vibration signals and initial building vibration signals are obtained by monitoring the subway vibration measuring point set and the building vibration measuring point set by using a preset monitoring frequency, and subway running parameters and geological parameters are recorded in real time when the subway vibration measuring point set is monitored, wherein the subway running parameters include train speed, train formation and train travel time; Subway vibration signals and building vibration signals are obtained based on the initial subway vibration signals and the initial building vibration signals; A geological model is constructed according to the geological parameters, and grid division is performed on the geological model and the building model to obtain a grid geological model and a grid building model; The grid geological model and the grid building model are assembled by using a pre-constructed finite element software to obtain an initial coupling model; A subway load is determined according to the subway running parameters, and simulation data are obtained by simulating the initial coupling model by using the subway load; Vibration acceleration amplitude sets are obtained based on the subway vibration signals, the building vibration signals and the simulation data, frequency spectrum characteristics are calculated according to the vibration acceleration amplitude sets, and a suboptimal coupling model is obtained according to the frequency spectrum characteristics; A best coupling model is obtained based on the suboptimal coupling model, current subway running parameters, current geological parameters and current subway adjacent building vibration data; The indoor vibration simulation prediction of the subway adjacent building is completed based on the best coupling model.

2. The method for predicting the vibration of a subway adjacent building according to claim 1, wherein The vibration measuring point arrangement on the target track and the subway adjacent building to obtain the subway vibration measuring point set and the building vibration measuring point set comprises: The target track is confirmed above the region of the subway adjacent building, and vibration measuring points are arranged on the region above the target track to obtain subway vibration measuring points; The subway adjacent building is divided into floors to obtain low floors, middle floors and high floors, wherein the low floors are from the first floor to the tenth floor, the middle floors are from the eleventh floor to the twentieth floor, and the high floors are from the twenty-first floor to the thirty-second floor; The subway adjacent surface of the subway adjacent building is identified, and vibration measuring points are arranged on the low floors, the middle floors and the high floors of the subway adjacent surface to obtain a building vibration measuring point set.

3. The method for predicting the vibration of a subway adjacent building according to claim 2, wherein The subway vibration signals and the building vibration signals are obtained based on the initial subway vibration signals and the initial building vibration signals, and the method comprises: The initial subway vibration signals are segmented to obtain vibration signal sequences, wherein the vibration signal sequences comprise multiple vibration signals; Vibration signals are extracted from the vibration signal sequences in sequence, and the following operations are performed on the extracted vibration signals: An embedding dimension and a similarity tolerance are set, the vibration signals are reshaped according to the embedding dimension to obtain an embedding dimension vector sequence, wherein the embedding dimension vector sequence comprises multiple embedding dimension vectors; The multiple embedding dimension vectors are combined in pairs to obtain multiple vector combination pairs, and the following operations are performed on each vector combination pair in the multiple vector combination pairs: Calculate the maximum difference of the vector combination pair, 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 taken 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 taken as the second vector combination pair; Respectively, the first vector combination pair and the second combination vector pair are summarized to obtain the first vector combination pair group and the second combination vector pair group, and the number of the first vector combination pair and the number of the second vector combination pair are counted respectively. Based on the first vector combination pair number and the second vector combination pair number, the vibration signal to be denoised or the reference vibration signal is obtained. The wavelet coefficients are obtained by wavelet decomposition of the vibration signal to be denoised, the coefficient absolute value of the wavelet coefficients is obtained, and the coefficient absolute value is compared with the preset coefficient threshold. If the coefficient absolute value is greater than the preset coefficient threshold, the vibration signal to be denoised is denoised using the wavelet coefficients to obtain a denoised vibration signal. If the coefficient absolute value is not greater than the preset coefficient threshold, the wavelet coefficients are adjusted until the coefficient absolute value is greater than the preset coefficient threshold to obtain effective wavelet coefficients, and the vibration signal to be denoised is denoised using the effective wavelet coefficients to obtain a denoised vibration signal. The denoised vibration signal and the reference vibration signal are integrated to obtain a subway vibration signal, and the building vibration signal is obtained based on the initial building vibration signal.

4. The method for predicting the vibration of a subway adjacent building according to claim 3, wherein The vibration signal to be denoised or the reference vibration signal is obtained based on the first vector pair number set and the second vector pair number set, which includes: The signal length of the vibration signal is obtained, and the sample entropy of the embedding dimension vector is calculated according to the signal length, the first vector pair number set and the second vector pair number set, wherein the calculation formula of the sample entropy of the embedding dimension vector is as follows: where H(μ, M) represents a sample entropy of the embedding dimension vector, μ represents a similarity tolerance, M represents a signal length, and m represents an embedding dimension, represents a first number of vector pairs, represents a second number of vector pairs, and ln represents a natural logarithm; If the sample entropy is greater than the preset noise sample entropy, the vibration signal corresponding to the sample entropy is taken 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 taken as the reference vibration signal.

5. The method for predicting the vibration of a subway adjacent building according to claim 4, wherein The geological model and the building model are both meshed to obtain a mesh geological model and a mesh building model, which includes: The initial mesh geological model is obtained by using the preset initial mesh density to perform initial mesh division on the geological model, the key region group in the initial mesh geological model is confirmed, and multiple mesh densities are set, wherein the multiple mesh densities are set from large to small, and the multiple mesh densities are all greater than the initial mesh density; The mesh density is extracted from the multiple mesh densities in turn, each key region in the key region group is re-divided according to the mesh density to obtain a mesh key region, and the mesh key region is trial calculated to obtain trial vibration response data and trial time, and the trial vibration response data and the trial time are summarized to obtain a trial vibration response data group and a trial time group; The optimal trial vibration response data and the optimal trial time are obtained from the trial vibration response data group and the trial time group; The initial mesh geological model with the mesh density corresponding to the optimal trial vibration response data and the optimal trial time is taken as the mesh geological model, and the mesh building model is obtained based on the building model.

6. The method for predicting the vibration of a subway adjacent building according to claim 5, wherein The optimal coupling model is obtained based on the suboptimal coupling model, the current subway operation parameter, the current geological parameter and the current subway adjacent building vibration data, and the method comprises the steps of: The vibration simulation prediction is performed on the current subway operation parameter and the current geological parameter by using the suboptimal coupling model to obtain building vibration response prediction data; The building vibration response prediction data and the current subway adjacent building vibration data are respectively subjected to curve drawing to obtain vibration prediction curve and building vibration curve; The curve similarity value of the vibration prediction curve and the building vibration curve is calculated, and the curve similarity value is compared with a preset similarity threshold value; If the curve similarity value is greater than the preset similarity threshold value, the suboptimal coupling model is taken as the optimal coupling model; If the curve similarity value is not greater than the preset similarity threshold value, the suboptimal coupling model is modified to obtain a modified coupling model, the modified coupling model is taken as the suboptimal coupling model, and the step of performing the vibration simulation prediction on the current subway operation parameter and the current geological parameter by using the suboptimal coupling model is returned until the curve similarity value is greater than the preset similarity threshold value, and the optimal coupling model is obtained.

7. The method for predicting the vibration of a subway adjacent building according to claim 6, wherein The curve similarity value of the vibration prediction curve and the building vibration curve is calculated, and the curve similarity value is compared with a preset similarity threshold value; The attenuation coefficient is obtained, the vibration prediction curve and the building vibration curve are subjected to fast Fourier transform to obtain vibration prediction frequency domain signal and building vibration frequency domain signal, and vibration prediction power spectral density and building vibration power spectral density are calculated according to the vibration prediction frequency domain signal and the building vibration frequency domain signal; The curve similarity value is calculated according to the attenuation coefficient, the vibration prediction power spectral density and the building vibration power spectral density, wherein the calculation formula of the curve similarity value is as follows: where S denotes the curve similarity value, V p (t) denotes the building vibration response prediction data, V m (t) denotes the current metro adjacent building vibration data, τ denotes the time delay, ||*|| denotes the L2 norm, t denotes the time series, e denotes the natural constant, γ denotes the attenuation coefficient, PSD(V p ) denotes the vibration prediction power spectral density, PSD(V m ) denotes the building vibration power spectral density, ||*||2 denotes the Euclidean distance, V m (t+τ) denotes the new signal after the building vibration signal is shifted on the time axis by the time delay, max denotes the maximum value, <*,*> denotes the inner product.

8. The method for predicting the vibration of a subway adjacent building according to claim 7, wherein The attenuation coefficient is obtained, the vibration prediction curve and the building vibration curve are subjected to fast Fourier transform to obtain vibration prediction frequency domain signal and building vibration frequency domain signal, and vibration prediction power spectral density and building vibration power spectral density are calculated according to the vibration prediction frequency domain signal and the building vibration frequency domain signal; The subway vibration measuring point is taken as a vibration starting point, each building vibration measuring point in the building vibration measuring point set is taken as a vibration ending point, the vibration ending points are summarized to obtain a vibration ending point set; A plurality of historical vibration detection time periods are obtained, and historical vibration detection time periods are sequentially extracted from the plurality of historical vibration detection time periods, and the following operations are performed on the extracted historical vibration detection time periods: The vibration ending points are sequentially extracted from the vibration ending point set, the historical vibration data of the vibration ending points are collected according to the historical vibration detection time period and the vibration starting point, and the vibration propagation distance is obtained according to the vibration starting point and the vibration ending point; The vibration energy value is calculated according to the historical vibration data, the vibration data pair is obtained based on the vibration propagation distance and the vibration energy value, the vibration data pairs are summarized to obtain a vibration data pair group corresponding to the vibration ending point set; The vibration energy attenuation curve and the original vibration curve are drawn based on the vibration data pair group, and the mean square error of the vibration energy attenuation curve and the original vibration curve is calculated; The mean square errors are summarized to obtain a mean square error set corresponding to the plurality of historical vibration detection time 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 taken as an optimal energy attenuation curve, and the attenuation coefficient is obtained according to the optimal energy attenuation curve.

9. The method for predicting the vibration of a subway adjacent building according to claim 8, wherein The vibration energy attenuation curve and the original vibration curve are drawn based on the vibration data pair group, and the mean square error of the vibration energy attenuation curve and the original vibration curve is calculated; An initial exponential decay model is confirmed, and the initial exponential decay model is fitted by using vibration data to obtain an initial vibration energy and a fitting decay coefficient, wherein the initial exponential decay model is expressed as: E(d) = E0 x e -γ×d wherein E(d) represents the initial exponential decay model, E0 represents the initial vibration energy, and d represents a vibration propagation distance; An exponential decay model is obtained based on the initial vibration energy, the fitting decay coefficient, and the initial exponential decay model; Vibration data pairs are sequentially extracted from the vibration data pair set, a vibration propagation distance is extracted from the vibration data pair, a fitting vibration energy is calculated by using the vibration propagation distance and the exponential decay model, and a fitting vibration data pair is obtained based on the fitting vibration energy and the vibration propagation distance; The fitting vibration data pairs are summarized to obtain a fitting vibration data pair set, a vibration energy decay curve is drawn based on the fitting vibration data pair set, and an original vibration curve is drawn based on the vibration data pair set.

10. A system for simulating and predicting vibrations in a subway adjacent building indoor, characterized by, The system comprises: The vibration data acquisition module is configured to confirm a target track and a subway adjacent building, receive a building indoor vibration simulation instruction, construct a building model according to the building indoor vibration simulation instruction and the subway adjacent building, arrange vibration measuring points for the target track and the subway adjacent building, obtain a subway vibration measuring point set and a building vibration measuring point set, monitor the subway vibration measuring point set and the building vibration measuring point set by using a preset monitoring frequency, obtain an initial subway vibration signal and an initial building vibration signal, record subway operation parameters and geological parameters in real time when the subway vibration measuring point set is monitored, wherein the subway operation parameters include train speed, train formation, and train travel time, and obtain a subway vibration signal and a building vibration signal based on the initial subway vibration signal and the initial building vibration signal; The model construction module is configured to construct a geological model according to the geological parameters, divide the geological model and the building model into grids to obtain a grid geological model and a grid building model, and assemble the grid geological model and the grid building model by using a pre-constructed finite element software to obtain an initial coupling model; The coupling model optimization module is configured to determine a subway load according to the subway operation parameters, simulate the initial coupling model by using the subway load to obtain simulation data, obtain a vibration acceleration amplitude set from the subway vibration signal, the building vibration signal, and the simulation data, calculate a frequency spectrum feature according to the vibration acceleration amplitude set, obtain a suboptimal coupling model according to the frequency spectrum feature, obtain current subway operation parameters, current geological parameters, and current subway adjacent building vibration data, and obtain a best coupling model based on the suboptimal coupling model, the current subway operation parameters, the current geological parameters, and the current subway adjacent building vibration data; The vibration simulation prediction module is configured to complete indoor vibration simulation prediction of the subway adjacent building based on the best coupling model.

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