Automatic modal parameter identification method and device based on SSI algorithm

Abstract

The invention relates to the technical field of earthquake resistance of concrete dams, in particular to an automatic modal parameter identification method and device based on an SSI algorithm, electronic equipment and a storage medium, and the method comprises the steps: carrying out the filtering and trend elimination processing of current dam data, and obtaining the preprocessing data; based on the pre-processed data, utilizing an SSI algorithm to draw and obtain a stability diagram; calculating a distance matrix of modal points in the stability diagram by using a DBSCAN algorithm to obtain a data clustering result; and calculating an average value of each cluster in the data clustering result to obtain an identification modal parameter. According to the method, DBSCAN clustering and an SSI algorithm are combined, a Hankel matrix is constructed by using the SSI algorithm, a stability diagram is drawn through singular value decomposition, the stability diagram is automatically explained by using the DBSCAN algorithm, clustering results are averaged to obtain an identification result of the group of data, the influence of abnormal points on the identification result is avoided, and automatic identification of modal parameters of the high arch dam is realized.

Description

Technical Field

[0001] The present application relates to the technical field of concrete dam seismic resistance, and in particular to a method, device, electronic device, computer-readable storage medium and computer program product for automatic identification of modal parameters based on an SSI algorithm. Background Art

[0002] Southwest my country is rich in hydropower resources, and many high-dam hydropower station projects are planned, constructed and operated, including Xiaowan, Nuozadu, Jinghong and other high-dam projects with a height of more than 100m. At the same time, the southwest region is also a high-incidence earthquake area, and the seismic safety of high dams faces severe challenges. Due to the huge reservoir capacity, once the high dam collapses, the reservoir water will flow out of control, which will cause huge casualties and property losses downstream, and the consequences will be disastrous. On the other hand, the high concrete dam has a complex structure, and there are many uncertain factors in natural environmental conditions and loads. The actual working state is often quite different from the design stage. During the long-term operation of the high arch dam, due to the evolution of the dam body and foundation materials, the load on the dam changes, and the working state evolves. By identifying the modal parameters of the arch dam, the real working state evolution law of the dam can be reflected. During the long-term operation of the high arch dam, due to the evolution of the dam body and foundation materials, the load on the dam changes, and the working state evolves. By identifying the modal parameters of the arch dam, the real working state evolution law of the dam can be reflected. However, the existing dam modal parameter identification results are affected by many factors and have large errors, and abnormal points have a greater impact on the identification results.

[0003] In summary, how to design an efficient and accurate method for automatic identification of modal parameters is an urgent problem to be solved. Summary of the invention

[0004] The present application aims to solve one of the technical problems in the related art at least to some extent.

[0005] To this end, the first purpose of this application is to propose an automatic modal parameter identification method based on the SSI algorithm to solve the problems of the existing technical means that the dam modal parameter identification results are affected by various factors and have large errors, and abnormal points have a great impact on the identification results.

[0006] The second object of the present application is to provide a device.

[0007] The third objective of the present application is to provide an electronic device.

[0008] A fourth objective of the present application is to provide a computer-readable storage medium.

[0009] The fifth objective of this application is to provide a computer program product.

[0010] To achieve the above-mentioned purpose, the first embodiment of the present application proposes a method for automatic identification of modal parameters based on the SSI algorithm, comprising:

[0011] Filter and eliminate trend processing of current dam data to obtain preprocessed data;

[0012] Based on the pre-processed data, a stability map is obtained by using an SSI algorithm;

[0013] The DBSCAN algorithm is used to calculate the distance matrix of the modal points in the stability graph to obtain a data clustering result;

[0014] The average value of each cluster in the data clustering result is calculated to obtain the identified modal parameters.

[0015] Preferably, the step of obtaining a stability map by using an SSI algorithm based on the preprocessed data comprises:

[0016] Based on the preprocessed data, construct a Hankel matrix;

[0017] Based on the Hankel matrix, calculating the covariance matrix;

[0018] Based on the covariance matrix, a system matrix is ​​obtained using an SVD method;

[0019] The system matrix is ​​used to obtain modal parameters and construct a stability diagram.

[0020] Preferably, the covariance matrix expression is:

[0021]

[0022] Among them, E[] is the expectation operator, T is the transposition operator, and y k is the measured structural response vector at discrete time k, A d is the state matrix, C d is the output matrix.

[0023] Preferably, the obtaining modal parameters by using the system matrix and constructing a stability diagram comprises:

[0024] The identification results are arranged and summarized to generate a stability diagram, and the stability axis of the stability diagram is used to determine the real modal parameters of the structure, wherein the preset value of the stable modal point of the stability diagram is:

[0025]

[0026] Where H is the Hermitian transpose operator, MAC is the modal determination criterion, Δf lim is the receiving threshold of the frequency, ξ lim is the receiving threshold of the damping ratio, is the acceptance threshold of the vibration mode.

[0027] Preferably, the using of the DBSCAN algorithm to calculate the distance matrix of the modal points in the stability graph to obtain the data clustering result comprises:

[0028] Using the modal points in the stability diagram as input to the DBSCAN algorithm, adjusting the preset parameter values, and calculating the distance between every two modal points;

[0029] Determine the modal point and confirm the type of the modal point. If the modal point is a core modal point, construct a new cluster using all the modal points reachable from the core modal point.

[0030] Deleting the modal points in the new cluster, repeatedly determining the modal points and confirming the modal point types, and marking the remaining modal points as noise points;

[0031] A clustering diagram is drawn based on the noise points, and a clustering result is obtained.

[0032] Preferably, the distance calculation formula between every two modal points is:

[0033]

[0034] Among them, MAC is the mode determination criterion, and q,r are the mode points.

[0035] To achieve the above-mentioned purpose, the second embodiment of the present application proposes a modal parameter automatic identification device based on the SSI algorithm, comprising:

[0036] The preprocessing module filters and eliminates the trend of the current dam data to obtain preprocessed data;

[0037] A stability map drawing module, based on the pre-processed data, draws a stability map using an SSI algorithm;

[0038] A clustering module, using a DBSCAN algorithm to calculate a distance matrix of modal points in the stability graph to obtain a data clustering result;

[0039] The modal parameter acquisition module calculates the average value of each cluster in the data clustering result to obtain the identified modal parameters.

[0040] To achieve the above-mentioned purpose, the third aspect of the present application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0041] The memory stores computer-executable instructions;

[0042] The processor executes the computer-executable instructions stored in the memory to implement any of the above methods.

[0043] To achieve the above-mentioned purpose, the fourth aspect embodiment of the present application proposes a computer-readable storage medium, including computer-executable instructions stored in the computer-readable storage medium, and the computer-executable instructions are used to implement any of the methods described above when executed by a processor.

[0044] To achieve the above objectives, the fifth aspect of the present application proposes a computer program product, including a computer program / instruction, which implements any of the methods described above when executed by a processor.

[0045] The present application provides an automatic modal parameter identification method based on the SSI algorithm, which combines DBSCAN clustering and the SSI algorithm, takes the current data as the analysis object, performs relevant preprocessing, uses the SSI algorithm to construct the Hankel matrix, and uses the singular value decomposition to draw the stability diagram, and uses the DBSCAN algorithm to automatically interpret the stability diagram and average each clustering result to obtain the identification result of the group of data, thereby avoiding the influence of abnormal points on the identification result. Compared with the k-means clustering and hierarchical clustering algorithms, it does not need to pre-specify the number of clusters, cluster centers or cluster truncation thresholds, and is robust to user-defined parameters, thereby realizing automatic identification of modal parameters of high arch dams.

[0046] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0048] Figure 1 A flowchart of a first specific embodiment of a method for automatic identification of modal parameters based on an SSI algorithm provided by the present invention;

[0049] Figure 2 It is the flow chart of the algorithm for automatic identification of modal parameters;

[0050] Figure 3 This is a conceptual analysis diagram of the DBSCAN clustering algorithm;

[0051] Figure 4 Schematic diagram of the six-degree-of-freedom model for verifying automatic identification;

[0052] Figure 5 is the acceleration time history curve;

[0053] Figure 6 Clustering graph generated by DBSCAN algorithm;

[0054] Figure 7 A structural block diagram of a modal parameter automatic identification device based on the SSI algorithm provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The core of the present invention is to provide a method, device, electronic device and storage medium for automatic identification of modal parameters based on the SSI algorithm, which combines DBSCAN clustering with the SSI algorithm to avoid the influence of abnormal points on the identification results and realize the automatic identification of modal parameters of high arch dams.

[0056] In order to enable those skilled in the art to better understand the scheme of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0057] Please refer to Figure 1 , Figure 1 This is a flowchart of a first specific embodiment of a method for automatic identification of modal parameters based on an SSI algorithm provided by the present invention; the specific operation steps are as follows:

[0058] Step S101: filtering and eliminating trend processing of current dam data to obtain preprocessed data;

[0059] Step S102: Based on the pre-processed data, a stability map is obtained by using an SSI algorithm;

[0060] Based on the preprocessed data, construct a Hankel matrix;

[0061] Based on the Hankel matrix, the covariance matrix is ​​calculated. The covariance matrix expression is:

[0062]

[0063] Among them, E[] is the expectation operator, T is the transposition operator, and y k is the measured structural response vector at discrete time k, A d is the state matrix, C d is the output matrix.

[0064] Based on the covariance matrix, the system matrix is ​​obtained using the SVD method, including:

[0065] The identification results are arranged and summarized to generate a stability diagram, and the stability axis of the stability diagram is used to determine the real modal parameters of the structure, wherein the preset value of the stable modal point of the stability diagram is:

[0066]

[0067] Where H is the Hermitian transpose operator, MAC is the modal determination criterion, Δf lim is the receiving threshold of the frequency, ξ lim is the receiving threshold of the damping ratio, is the acceptance threshold of the vibration mode.

[0068] The system matrix is ​​used to obtain modal parameters and construct a stability diagram.

[0069] Step S103: using the DBSCAN algorithm to calculate the distance matrix of the modal points in the stability graph to obtain a data clustering result;

[0070] The modal points in the stability diagram are used as the input of the DBSCAN algorithm, the preset parameter values ​​are adjusted, and the distance between every two modal points is calculated. The calculation formula for calculating the distance between every two modal points is:

[0071]

[0072] Among them, MAC is the mode determination criterion, and q,r are the mode points.

[0073] Determine the modal point and confirm the type of the modal point. If the modal point is a core modal point, construct a new cluster using all the modal points reachable from the core modal point.

[0074] Deleting the modal points in the new cluster, repeatedly determining the modal points and confirming the modal point types, and marking the remaining modal points as noise points;

[0075] A clustering diagram is drawn based on the noise points, and a clustering result is obtained.

[0076] Step S104: Calculate the average value of each cluster in the data clustering result to obtain identification modal parameters.

[0077] This embodiment provides a method for automatic identification of modal parameters based on the SSI algorithm, takes the current data as the analysis object, performs relevant preprocessing, uses the SSI algorithm to construct the Hankel matrix, and uses singular value decomposition to draw the stability diagram. The DBSCAN algorithm is used to automatically interpret the stability diagram and average each clustering result to obtain the identification result of the group of data, thereby avoiding the influence of abnormal points on the identification result. The DBSCAN clustering and SSI algorithms are combined. Compared with the k-means clustering and hierarchical clustering algorithms, there is no need to pre-specify the number of clusters, cluster centers or cluster truncation thresholds, and the method is robust to user-defined parameters, thereby realizing automatic identification of modal parameters of high arch dams.

[0078] Based on the above embodiment, this embodiment describes the automatic identification method of modal parameters based on the SSI algorithm, such as Figure 2 As shown, the details are as follows:

[0079] Taking the current data as the analysis object, relevant preprocessing such as filtering and eliminating trend items is performed;

[0080] Use the SSI algorithm to draw a stable map;

[0081] Using measured data k Construct the following Hankel matrix:

[0082]

[0083] in, and are the past output matrix and the future output matrix respectively; the user-defined parameters NRH(i) and NCH(j) are the Hankel matrices NRP(g) is the number of rows and columns of the matrix Yp.

[0084] Covariance matrix of the output signal It can be expressed as:

[0085]

[0086] Among them, E[·] is the expectation operator, T is the transpose operator;

[0087] In order to obtain the matrices Ad and Cd, define the matrices

[0088] Θ=Y f Y p T

[0089]

[0090] Among them, the observable matrix and controllable matrix They are:

[0091]

[0092] Γ g =[A d g-1 G … A d GG]

[0093] According to the singular value decomposition theory, the matrix Θ can be rewritten as:

[0094]

[0095] Among them, P and Q are orthogonal left and right singular matrices respectively, P1 and P2 are sub-matrices of P, Q1 and Q2 are sub-matrices of Q, S is a singular value matrix, S1 and S2 are sub-matrices of S. In particular, S1 contains the first 2n singular values, and S2 contains the remaining singular values. Theoretically, the singular values ​​in S1 have an impact on the recognition results, and the singular values ​​in S2 are very small and can be regarded as noise, and they do not affect the recognition results.

[0096] O i-g and Γ g It can be expressed as:

[0097]

[0098] State Matrix A d And the output matrix C d It can be obtained by the following formula:

[0099]

[0100] Among them, O i-g Indicates the lack of O in the last line i-g , Indicates the lack of O in the first row i-g . Indicates O i-g The first line of represents the pseudo-inverse.

[0101] State Matrix A d It can be further expressed as:

[0102] A d =ΨΛΨ -1

[0103] Among them, Ψ∈R 2n×2n is the singular vector matrix, Λ∈R 2n×2n is the eigenvalue μ i The diagonal matrix of .

[0104] Based on the eigenvalue μ i , the complex eigenvalue λ of the structure i and λ i * It can be expressed as:

[0105]

[0106] Among them, ω i and i are the i-th order frequency and damping ratio respectively.

[0107] Based on the above formula, the modal parameters of the structure can be obtained:

[0108]

[0109] Φ=C d Ψ

[0110] in, is a complex vibration vector, and the superscripts I and R represent the imaginary part and the real part respectively.

[0111] The model order n plays an important role in modal parameter identification. However, for actual structures, this order is unknown. Therefore, it is necessary to select the model order n within a certain range (n∈[N min , N max ]) to calculate, arrange and summarize the identification results to generate a stability diagram, and judge the real modal parameters of the structure by observing the stable vertical axis of the stability diagram. The stable modal points of the stability diagram need to meet the following conditions:

[0112]

[0113] Where H is the Hermitian transpose operator, MAC is the modal determination criterion, Δf lim is the receiving threshold of the frequency, ξ lim is the receiving threshold of the damping ratio, is the receiving threshold of the vibration mode. In this embodiment, the thresholds are set to Δf lim = 0.01, Δξlim = 0.05 and Only modal points that satisfy all stability conditions are shown on the stability diagram.

[0114] According to the theoretical derivation of the aforementioned SSI algorithm, the following user-defined parameters such as model order (n), NRH (i), NCH (j) and NRP (g) need to be defined by the user to obtain better recognition results. The user-defined parameters to be studied in this embodiment are summarized as follows:

[0115] The upper limit Nmax of the model order n used to generate the stability diagram;

[0116] The number of rows of the Hankel matrix NRH(i);

[0117] The number of rows in the past output matrix NRP(g);

[0118] The number of columns of the Hankel matrix NCH(j);

[0119] In order to establish the relationship between the above user-defined parameters and the sampling frequency and fundamental frequency, the parameter β is defined as:

[0120] β=f s / f1

[0121] Among them, f sis the sampling frequency, f1 is the dam base frequency. The selection suggestions for user-defined parameters are shown in Table 1;

[0122] Table 1 Recommendations for selecting user-defined parameters (Nmax, i, g, and j)

[0123]

[0124] Automatic interpretation of stability maps using the DBSCAN algorithm;

[0125] DBSCAN clustering is a density-based clustering algorithm. The user-defined parameters (ε, MinPts) are used to describe the compactness of the modal points in the stability graph. ε is the maximum neighborhood of the modal point; MinPts is the minimum number of modal points in a cluster. In simple terms, ε is the maximum radius of a circle drawn around a modal point, and MinPts is the minimum number of modal points required to form a cluster. In order to intuitively describe the DBSCAN algorithm, Figure 3 As shown in the figure, under this working condition, it is assumed that MinPts = 4 and ε is the radius of the circle in the figure, then the modal points in the figure can be divided into the following three categories:

[0126] (1) Core modal point: There are at least MinPts modal points (including itself) in the circle with the modal point as the center and ε as the radius. For example, a1, a2, a3, a4, and a5 are all core modal points because their ε neighborhood contains at least four modal points.

[0127] (2) Reachable modal point (non-core modal point): a modal point that can be reached from a specific core modal point through a related path (all modal points on the path must be core modal points, and the distance between adjacent modal points on the path must be less than ε). For example, b1 is a reachable modal point of a1 in the path a1→a5→b1, where a1 and a5 are core modal points, and the distances between a1-a5 and a5-b1 are both less than ε. Similarly, b2 is also a reachable modal point of a1.

[0128] (3) Noise point (abnormal point): a modal point that is neither a core modal point nor a reachable modal point, such as modal point c.

[0129] The core modal points and all their reachable modal points are clustered into one class, for example, the core modal point a1 and its reachable modal points a2, a3, a4, a5, b1, and b2 are clustered into class Cnew. Note that the reachable non-core modal points are located at the “edge” of the cluster because they cannot be used to reach other modal points.

[0130] The modal points (frequency f, damping ratio ξ and vibration mode φ) in the stability diagram generated by the SSI algorithm are used as the input of the DBSCAN algorithm, and appropriate parameters ε and MinPts are selected (these two parameters need to be adjusted to obtain a better clustering effect)

[0131] Calculate the distance between every two modal points. The distance between modal points q and r is calculated by the following formula:

[0132]

[0133] Arbitrarily select a modal point and determine its type. If it is a core modal point, determine all its reachable modal points and then create a new cluster C n .

[0134] Cluster C n The modal points within are deleted, and the above steps are repeated for the remaining modal points. Finally, the remaining points are marked as noise points.

[0135] Draw a cluster diagram and obtain the clustering results.

[0136] The average of each clustering result is used to obtain the recognition result of the group of data. Repeat the above steps for the next group of data to achieve automatic recognition.

[0137] An automatic modal parameter identification method based on the SSI algorithm provided in an embodiment of the present invention takes current data as the analysis object, performs relevant preprocessing, uses the SSI algorithm to construct a Hankel matrix, and uses singular value decomposition to draw a stability diagram. The DBSCAN algorithm is used to automatically interpret the stability diagram and average each clustering result to obtain the identification result of the group of data, thereby avoiding the influence of abnormal points on the identification result. The DBSCAN clustering and SSI algorithms are combined. Compared with the k-means clustering and hierarchical clustering algorithms, there is no need to pre-specify the number of clusters, cluster centers or cluster cutoff thresholds, and the method is robust to user-defined parameters, thereby realizing automatic identification of modal parameters of high arch dams.

[0138] Based on the above embodiment, this embodiment uses specific data to illustrate the automatic identification method of modal parameters based on the SSI algorithm, as follows:

[0139] like Figure 4 As shown, the calculation model of the six-degree-of-freedom system, wherein m1 = m2 = m3 = m4 = m5 = m6 = 0.1, k1 = 8, k2 = 5, k3 = 3, k4 = 9, k5 = 5, k6 = 7. The damping adopts Rayleigh damping, and the damping ratio is 5%.

[0140] The system is excited by uncorrelated white noise with a sampling frequency of 100 Hz and a duration of 50 s. Figure 5As shown in the figure, the acceleration time history curve of measuring point 4 (10% white noise is added to simulate the actual environmental vibration test) and its Fourier spectrum are given. It can be seen from the figure that the signal contains frequency components of 0.87Hz, 2.87Hz, 4.00Hz, 6.30Hz and 7.70Hz.

[0141] The stability graph generated by the SSI algorithm is shown in Figure 6 As shown in the figure, there are six stable vertical axes, which means that the sixth-order mode can be identified. The modal points (frequency, damping ratio and vibration mode) in the stability diagram are used as the input of the DBSCAN algorithm (ε=0.25, MinPts=10) and the clustering process is performed. The modal points are clustered in six clusters and drawn in different colors, representing the identified sixth-order modes. In addition, the DBSCAN clustering algorithm can eliminate noise modes. If noise modes participate in clustering, they will bring certain errors. Compared with the theoretical values, the identification errors of the frequencies are 0.23%, 0.60%, 0.65%, 1.93%, 1.60% and 2.39% respectively. The errors of the damping ratios are 12%, 6%, 12%, 8%, 2% and 8% respectively. Considering the complexity and difficulty of damping ratio identification, it is believed that the automatic identification accuracy based on the SSI algorithm and the DBSCAN algorithm is acceptable.

[0142] Clustering diagram of the k-means clustering algorithm with different k values. The choice of k value will affect the clustering results. Only when k=6 can the ideal clustering effect be obtained; when k=5, the fourth-order and fifth-order modes are clustered into one category, which means that dense modes cannot be distinguished; when k=7, the first-order mode is divided into two modes, and modal splitting occurs, which means that the use of the k-means clustering algorithm requires pre-knowledge of other modal orders in advance. Generally speaking, individual modes cannot be identified during continuous monitoring, so a fixed k value is not suitable for online automatic identification of modal parameters.

[0143] For the k-means algorithm and the hierarchical clustering algorithm, when appropriate user-defined parameters (k = 6 or dlim = 5) are selected, the recognition results are basically consistent with those based on the DBSCAN algorithm (since there are fewer noise modal points in this case, the impact on the recognition results is not significant). However, inappropriate user-defined parameters will result in modal loss (for example, k = 5 or dlim = 10) and false modal (k = 7 or dlim = 0.7) phenomena. These results show that the k value of the k-means algorithm and the cutoff threshold of the hierarchical clustering algorithm have a greater impact on the recognition results.

[0144] Like other clustering algorithms, the DBSCAN clustering algorithm also requires two user-defined parameters (ε, MinPts) to be given. The influence of these two parameters on the recognition results will be discussed below. When ε varies from 0.1 to 0.6, and MinPts varies from 10 to 60, within this range, no matter how the parameters are selected, a complete sixth-order mode can always be recognized, and the recognized frequency remains unchanged. This indicates that the DBSCAN clustering algorithm has strong robustness to the user-defined parameters (ε, MinPts).

[0145] According to the modal point distance, when ε = 1, dense modes (controlled by distance) can be distinguished. Due to computational errors and the influence of noise in engineering measurements, when using this algorithm, ε is usually set to a value less than 1 (e.g., 0.6), which can ensure the successful recognition of each mode (including dense modes). Considering that DBSCAN clustering is a density-based clustering algorithm with properties such as internal connectivity and transitivity, a smaller ε value will not affect the clustering results. It should be noted that if the ε value is too small, it may lead to modal splitting, for example, ε < 0.1. Overall, ε has a wide range of selection. The research of this project recommends that ε be selected within the range of [0.1, 0.6] for better universality.

[0146] For the stability diagram generated with a given Nmax, the number N of a certain core modal point and its reachable modal points can be determined according to ε. Once the condition MinPts < N is satisfied, a new cluster will be formed. Therefore, there are many choices of MinPts that satisfy this condition, and neither the number of clusters nor the final recognition results will be affected. For a high-quality stability diagram, N is usually large and basically equal to ΔNm (ΔNm is the difference in the model order of the stability diagram, and in this example, ΔNm = 90), which further increases the selection range of MinPts. The DBSCAN algorithm has strong robustness in the range of MinPts = 10 - 60. Therefore, this section recommends MinPts = (0.1 - 0.6)ΔNm. For actual engineering structures, the two parameters should be appropriately adjusted within the ranges of ε = 0.1 - 0.6 and MinPts = (0.1 - 0.6)ΔNm to obtain ideal recognition results.

[0147] In addition to having strong robustness to user-defined parameters, the DBSCAN clustering algorithm also has the advantage of eliminating abnormal modal points (noise modal points in the stability diagram). It can automatically exclude abnormal modal points for clustering. However, when using the k-means or hierarchical clustering algorithms, these abnormal modal points will participate in the final clustering, thus affecting the recognition accuracy. Compared with the k-means algorithm and the hierarchical clustering algorithm, the DBSCAN clustering algorithm has better performance in removing noise modal points. These characteristics make the DBSCAN clustering algorithm have better effects in the automatic recognition of modal parameters than other clustering algorithms.

[0148] Please refer to Figure 7 , Figure 7 A structural block diagram of a modal parameter automatic identification device based on the SSI algorithm provided in an embodiment of the present invention; the specific device may include:

[0149] The preprocessing module 100 filters and eliminates the trend of the current dam data to obtain preprocessed data;

[0150] A stability map drawing module 200, based on the pre-processed data, draws a stability map using an SSI algorithm;

[0151] The clustering module 300 calculates the distance matrix of the modal points in the stability graph by using the DBSCAN algorithm to obtain a data clustering result;

[0152] The modal parameter acquisition module 400 calculates the average value of each cluster in the data clustering result to obtain the identified modal parameters.

[0153] An automatic modal parameter identification device based on the SSI algorithm of this embodiment is used to implement the aforementioned automatic modal parameter identification method based on the SSI algorithm. Therefore, the specific implementation method of an automatic modal parameter identification device based on the SSI algorithm can be seen in the embodiment part of the automatic modal parameter identification method based on the SSI algorithm in the previous text. For example, the preprocessing module 100, the stability diagram drawing module 200, the clustering module 300, and the modal parameter acquisition module 400 are respectively used to implement steps S101, S102, S103, and S104 in the above-mentioned automatic modal parameter identification method based on the SSI algorithm. Therefore, its specific implementation method can refer to the description of the corresponding embodiments of each part, which will not be repeated here.

[0154] In order to implement the above embodiments, the present application also proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided by the above embodiments.

[0155] In order to implement the above embodiments, the present application also proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the methods provided by the above embodiments.

[0156] In order to implement the above embodiments, the present application also proposes a computer program product, including a computer program, which implements the methods provided by the above embodiments when executed by a processor.

[0157] The collection, storage, use, processing, transmission, provision and disclosure of user personal information involved in this application are in compliance with relevant laws and regulations and do not violate public order and good morals.

[0158] It should be noted that personal information from users should be collected for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. In addition, such collection / sharing should be carried out after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign the agreement / authorization including authorization of relevant user information before the user uses the function. In addition, any necessary steps should be taken to protect and safeguard access to such personal information data and ensure that others who have access to personal information data comply with its privacy policy and procedures.

[0159] The present application is expected to provide an implementation scheme for users to selectively block the use or access of personal information data. That is, the present disclosure is expected to provide hardware and / or software to prevent or block access to such personal information data. Once the personal information data is no longer needed, the risk can be minimized by limiting data collection and deleting the data. In addition, when applicable, such personal information is de-identified to protect the privacy of the user.

[0160] In the description of the aforementioned embodiments, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0161] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0162] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0163] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0164] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0165] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0166] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0167] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for automatic identification of modal parameters based on SSI algorithm, characterized in that: include: Filter and eliminate trend processing of current dam data to obtain preprocessed data; Based on the pre-processed data, a stability map is obtained by using an SSI algorithm; The DBSCAN algorithm is used to calculate the distance matrix of the modal points in the stability graph to obtain a data clustering result; The average value of each cluster in the data clustering result is calculated to obtain the identified modal parameters.

2. The method for automatic identification of modal parameters based on the SSI algorithm according to claim 1, characterized in that: The method of obtaining a stability map by using an SSI algorithm based on the preprocessed data includes: Based on the preprocessed data, construct a Hankel matrix; Based on the Hankel matrix, calculating the covariance matrix; Based on the covariance matrix, a system matrix is ​​obtained using an SVD method; The system matrix is ​​used to obtain modal parameters and construct a stability diagram.

3. The method for automatic identification of modal parameters based on the SSI algorithm according to claim 2 is characterized in that: The covariance matrix expression is: Among them, E[] is the expectation operator, T is the transposition operator, y k is the measured structural response vector at discrete time k, A d is the state matrix, C d is the output matrix.

4. The method for automatic identification of modal parameters based on the SSI algorithm according to claim 2 is characterized in that: The method of obtaining modal parameters by using the system matrix and constructing a stability diagram includes: The identification results are arranged and summarized to generate a stability diagram, and the stability axis of the stability diagram is used to determine the real modal parameters of the structure, wherein the preset value of the stable modal point of the stability diagram is: Where H is the Hermitian transpose operator, MAC is the modal determination criterion, Δf lim is the receiving threshold of the frequency, ξ lim is the receiving threshold of the damping ratio, is the acceptance threshold of the vibration mode.

5. The method for automatic identification of modal parameters based on the SSI algorithm according to claim 1 is characterized in that: The using of the DBSCAN algorithm to calculate the distance matrix of the modal points in the stability graph to obtain the data clustering result includes: Using the modal points in the stability diagram as input to the DBSCAN algorithm, adjusting the preset parameter values, and calculating the distance between every two modal points; Determine the modal point and confirm the type of the modal point. If the modal point is a core modal point, construct a new cluster using all the modal points reachable from the core modal point. Deleting the modal points in the new cluster, repeatedly determining the modal points and confirming the modal point types, and marking the remaining modal points as noise points; A clustering diagram is drawn based on the noise points, and a clustering result is obtained.

6. The method for automatic identification of modal parameters based on the SSI algorithm according to claim 5 is characterized in that: The distance calculation formula between every two modal points is: Among them, MAC is the mode determination criterion, and q,r are the mode points.

7. A modal parameter automatic identification device based on SSI algorithm, characterized in that: include: The preprocessing module filters and eliminates the trend of the current dam data to obtain preprocessed data; A stability map drawing module, based on the pre-processed data, draws a stability map using an SSI algorithm; A clustering module, using a DBSCAN algorithm to calculate a distance matrix of modal points in the stability graph to obtain a data clustering result; The modal parameter acquisition module calculates the average value of each cluster in the data clustering result to obtain the identified modal parameters.

8. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 6 when executed by a processor.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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