A bridge cable fundamental frequency identification method, device, computer equipment and storage medium

Abstract

The present invention discloses a bridge cable fundamental frequency identification method, device, computer equipment and storage medium, belonging to the field of image processing technology. Aiming at the problem that the fundamental frequency of the bridge cable cannot be accurately identified in the spectrum diagram in the prior art, the present invention extracts the time domain vibration signal in the vibration video, converts the time domain vibration signal into the time domain vibration spectrum, extracts the characteristic peak point in the time domain vibration spectrum, sets the assumed fundamental frequency sequence interval, searches for the characteristic peak point that satisfies the assumed fundamental frequency sequence interval, and makes the characteristic peak point that satisfies the assumed fundamental frequency sequence interval as the assumed frequency point. The assumed frequency point set constitutes the assumed fundamental frequency sequence, and the difference proximity score, peak width score and spectrum peak steepness score of the assumed frequency point in the assumed fundamental frequency sequence are added to obtain the total score of the assumed frequency point. The fundamental frequency of the bridge cable is obtained according to the total score of the assumed frequency point and the order judgment, thereby effectively improving the accuracy of the recognition of the fundamental frequency of the bridge cable.

Description

Technical Field

[0001] The present invention relates to the field of image processing technology, and more specifically to a method, device, computer equipment and storage medium for identifying fundamental frequency of bridge cables. Background Art

[0002] Cable force is crucial to the stability and safe operation of bridges. As an important component of bridge structures, bridge cables can help bridges cross large-span rivers, valleys or other terrain obstacles, and play a role in stabilizing and supporting the bridge body when the bridge encounters external loads. It is of great significance for the healthy operation of bridges. In practical applications, how to detect the cable force of bridge cables in a timely and accurate manner has become the focus of ensuring the safe operation of bridges. Fundamental frequency is one of the important parameters for measuring the cable force of bridge cables. It is closely related to many important bridge factors such as bridge cable length, linear density, and bending stiffness. When bridge cables are subjected to external loads, their vibration frequency will change. The fundamental frequency of bridge cables must be captured quickly and accurately to calculate the cable force of bridge cables and then determine whether the bridge body is safe. Therefore, it is crucial to accurately and timely identify the fundamental frequency of bridge cables.

[0003] However, in actual monitoring, factors such as complex background and noise will affect the acquisition and processing of the fundamental frequency of bridge cable vibration, which in turn affects the accuracy of bridge cable force detection. In order to overcome these difficulties, appropriate signal processing techniques and algorithms, such as modal analysis and wavelet analysis, need to be adopted to improve the quality and accuracy of the signal. At the same time, it is also necessary to continuously optimize monitoring schemes and equipment to meet the needs of practical applications.

[0004] At present, the commonly used bridge cable vibration frequency detection methods in engineering include traditional contact sensor monitoring such as accelerometers, vibrating wires, and dynamometers, as well as non-contact measurement methods such as radar microwaves and video phase analysis. However, the contact method requires manual installation of sensors, which is inconvenient for many complex terrains and requires a lot of manpower and material resources. Compared with contact measurement, non-contact measurement methods have many conveniences, but compared with contact measurement, they need to consider more appropriate measurement positions, measurement distances, measurement angles and other parameters. If the parameters are inaccurate or encounter large external interference, the spectrum quality will be poor, and the effective spectrum peak information will be submerged with the spectrum peak information generated by noise, which is not conducive to extracting the required fundamental frequency information.

[0005] The cepstrum method, hypothesis method and finite element simulation method proposed in the existing literature all have problems such as inaccurate recognition or weak anti-interference ability caused by subjective factors. In the related technology, for example, Chinese patent document CN116593102A provides a method for automatically identifying the vibration frequency of cable components of a cable system bridge, assuming that the highest peak is a multiple of the fundamental frequency and using integer division to find it; for example, Chinese patent document CN114049586A provides a method for automatically identifying vibration frequency based on computer vision and power spectrum transfer ratio, using the peak position and a peak detector to identify the physical peak, and finally determine the vibration frequency corresponding to the physical peak. However, these related technologies cannot stably and effectively control the spectrum quality or accurately extract the fundamental frequency of the bridge cable from the spectrum when using non-contact methods to measure the spectrum. Summary of the invention

[0006] 1. Technical issues to be solved

[0007] In view of the problem in the prior art that the fundamental frequency of the bridge cable cannot be accurately identified in the spectrum diagram due to large interference or low vibration frequency, the present invention provides a method, device, computer equipment and storage medium for identifying the fundamental frequency of the bridge cable, which can resist noise interference and accurately identify the fundamental frequency of the bridge cable without precisely formulating measurement parameters.

[0008] 2. Technical solution

[0009] The purpose of the present invention is achieved through the following technical solutions.

[0010] A bridge cable fundamental frequency identification method comprises the following steps:

[0011] Collect vibration video of the detected object;

[0012] Extracting the time-domain vibration signal in the vibration video, converting the time-domain vibration signal into a time-domain vibration spectrum, and extracting characteristic peak points in the time-domain vibration spectrum;

[0013] Setting an assumed fundamental frequency sequence interval, finding characteristic peak points that satisfy the assumed fundamental frequency sequence interval, setting the characteristic peak points that satisfy the assumed fundamental frequency sequence interval as assumed frequency points, and the assumed frequency point set constitutes an assumed fundamental frequency sequence;

[0014] Calculate the difference closeness score, peak width score and spectrum peak steepness score of the assumed frequency point in the assumed fundamental frequency sequence, and add the difference closeness score, peak width score and spectrum peak steepness score of the assumed frequency point to obtain the total score of the assumed frequency point;

[0015] The fundamental frequency of the bridge cable is obtained based on the total score of the assumed frequency points and the order.

[0016] Furthermore, the time domain vibration signal is converted into a time domain vibration spectrum, and characteristic peak points in the time domain vibration spectrum are extracted. The specific steps include:

[0017] Perform Fourier transform on the time domain vibration signal to obtain the time domain vibration spectrum;

[0018] Output point k in the time domain vibration spectrum n , k n =(x, y), all points k n Construct point set K i , K i =[k 1 , k…, k n ], where x represents frequency, y represents amplitude intensity, and n represents a natural number;

[0019] In the point set K i Extract the peak point x n , all peak points x n Constitute the peak point set X i , X i =[x 1 , x 2 …, x n ];

[0020] At the peak point set X i Extract the characteristic peak point z n , all characteristic peak points z n Constitute the characteristic peak point set Z i , Z i =[z 1 , z 2 …, z n ].

[0021] Furthermore, the assumed base frequency sequence interval is set, and the characteristic peak point satisfying the assumed base frequency sequence interval is found. The specific steps include:

[0022] Set the assumed base frequency sequence interval α;

[0023] Set the characteristic peak point set Z i The first characteristic peak point z in 1 To assume the first-order frequency, select the characteristic peak point z n ,like

[0024] and Then update the characteristic peak point z n is the assumed first-order frequency; where, Indicates the characteristic peak point z 1 The frequency, Indicates the characteristic peak point z n The frequency, Indicates the characteristic peak point z 1 The amplitude strength, Indicates the characteristic peak point z n The amplitude strength of

[0025] Repeat the above steps to obtain five characteristic peak points that meet the assumed base frequency sequence interval. Let the characteristic peak points that meet the assumed base frequency sequence interval be assumed frequency points, which are recorded as assumed frequency points z and z respectively. n , assuming frequency point z n+1 , assuming frequency point z n+2 , assuming frequency point z n+3 , assuming frequency point z n+4 .

[0026] Furthermore, assuming that the base frequency sequence interval α, assuming that the frequency point z n , assuming frequency point z n+1 , assuming frequency point z n+2 , assuming frequency point z n+3 , assuming frequency point z n+4 Construct the assumed fundamental frequency sequence.

[0027] Furthermore, adjacent assumed frequency points in the assumed base frequency sequence are sequentially differentiated, and then the separated assumed frequency points in the assumed base frequency sequence are sequentially differentiated and divided by the number of intervals to obtain different difference values, and the proximity of the different difference values ​​to the assumed frequency points in the assumed base frequency sequence is calculated.

[0028] Furthermore, it is assumed that the calculation formula for the proximity score of the assumed frequency point difference value in the base frequency sequence is:

[0029] 10-(errorPercentage*10.0)

[0030] Among them, errorPercentage represents the proximity;

[0031] The calculation formula for the peak width fraction of the assumed frequency point in the assumed base frequency sequence is:

[0032] 15*(1-0.6*newWidth)

[0033] Among them, newWidth represents the peak width;

[0034] The calculation formula for the spectrum peak steepness score of the assumed frequency point in the assumed base frequency sequence is:

[0035] 15*(1-0.6*Y)

[0036] Where Y represents the steepness of the spectrum peak.

[0037] Furthermore, the order of the assumed frequency points in the assumed fundamental frequency sequence is judged, and then the first-order frequency is determined according to the total score of the assumed frequency points, and the first-order frequency is the fundamental frequency.

[0038] A bridge cable fundamental frequency identification device, comprising:

[0039] A video acquisition module, which acquires vibration video of the detected object;

[0040] A characteristic peak point extraction module extracts the time domain vibration signal in the vibration video, converts the time domain vibration signal into a time domain vibration spectrum, and extracts characteristic peak points in the time domain vibration spectrum;

[0041] The assumed base frequency sequence construction module sets the assumed base frequency sequence interval, searches for characteristic peak points that satisfy the assumed base frequency sequence interval, and sets the characteristic peak points that satisfy the assumed base frequency sequence interval as assumed frequency points. The assumed frequency point set constitutes the assumed base frequency sequence.

[0042] A frequency reliability calculation module calculates the difference closeness score, peak width score and spectrum peak steepness score of the assumed frequency point in the assumed fundamental frequency sequence, and adds the difference closeness score, peak width score and spectrum peak steepness score of the assumed frequency point to obtain the total score of the assumed frequency point;

[0043] The bridge cable fundamental frequency judgment module determines the bridge cable fundamental frequency based on the total score of the assumed frequency points and the order judgment.

[0044] A computer device comprises a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and the processor implements the above-mentioned method when executing the computer program.

[0045] A computer-readable storage medium stores a computer program, and the computer program executes the above method when executed by a processor.

[0046] 3. Beneficial effects

[0047] Compared with the prior art, the advantages of the present invention are:

[0048] A bridge cable fundamental frequency identification method, device, computer equipment and storage medium of the present invention screen out bridge cable signals and noise signals by extracting peak feature points and utilizing the principle that the difference between different order frequencies is approximately equal to the fundamental frequency, thereby ensuring that important vibration frequency information can be quickly extracted under different measurement conditions and in environments with different noise interferences, and effectively improving the accuracy of bridge cable fundamental frequency identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1This is a flow chart of a bridge cable fundamental frequency identification method in one embodiment of the present invention;

[0050] Figure 2 This is a schematic diagram of a bridge cable measurement scenario in an embodiment of the present invention;

[0051] Figure 3 It is a schematic diagram of the measured spectrum results of the bridge cable in one embodiment of the present invention;

[0052] Figure 4 It is a schematic diagram of the fundamental frequency identification result of the bridge cable in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0054] Example

[0055] like Figure 1 As shown, a bridge cable fundamental frequency identification method provided by the present embodiment comprises the following steps: collecting a vibration video of the detection object; extracting a time domain vibration signal in the vibration video, converting the time domain vibration signal into a time domain vibration spectrum, and extracting characteristic peak points in the time domain vibration spectrum; setting an assumed fundamental frequency sequence interval, finding characteristic peak points that satisfy the assumed fundamental frequency sequence interval, making the characteristic peak points that satisfy the assumed fundamental frequency sequence interval as assumed frequency points, and the assumed frequency point set constitutes an assumed fundamental frequency sequence; calculating the difference closeness score, peak width score, and spectral peak steepness score of the assumed frequency points in the assumed fundamental frequency sequence, adding the difference closeness score, peak width score, and spectral peak steepness score of the assumed frequency points to obtain a total score of the assumed frequency points; and determining the fundamental frequency of the bridge cable according to the total score of the assumed frequency points and the order.

[0056] Specifically in this embodiment, the vibration video of the detection object is first collected. It should be noted that the bridge cable fundamental frequency identification method provided in this embodiment is based on the frequency measurement of the video phase. Figure 2As shown, it is necessary to use a camera to shoot a vibration video of the detection object in an actual scene. In this embodiment, the detection object is a bridge cable. In this embodiment, the camera frame rate needs to be adjusted according to the actual situation. In order to meet the Nyquist sampling theorem, in this embodiment, the camera frame rate used should be greater than 2 times the maximum vibration frequency of the detection object. Therefore, the camera frame rate used in this embodiment is 50fps, and the number of video frames is between 500 and 550 frames. It should be noted that the vibration video data of the detection object can be saved in common video formats such as avi, mov, and mp4. In this embodiment, the vibration video data of the detection object is saved in avi video format. Thus, the vibration video of the bridge cable is collected by the camera, and a video image sequence containing the bridge cable and the background can be obtained in the vibration video of the bridge cable.

[0057] Furthermore, the time domain vibration signal in the vibration video is extracted, the time domain vibration signal is converted into a time domain vibration spectrum, and the characteristic peak points in the time domain vibration spectrum are extracted. Specifically, the time domain vibration signal is obtained by video image processing of a video image sequence containing bridge cables and background. In this embodiment, the corresponding time domain vibration signal is extracted using the video phase difference. After the time domain vibration signal in the vibration video is extracted, the time domain vibration signal is Fourier transformed to obtain the time domain vibration spectrum. It should be noted that in this embodiment, Fourier transforming the time domain vibration signal is a prior art. In this embodiment, the time domain vibration spectrum of the detection object is the frequency value of the detection object. Then, the points that make up the spectrum in the time domain vibration spectrum are output through the matlab program. Specifically, in this embodiment, the frequency information in the form of a point set can be obtained by cyclically outputting the frequency and the corresponding amplitude intensity using the matlab program. In this embodiment, the points that make up the spectrum in the time domain vibration spectrum are k n The form of storage, k n =(x, y), where x represents frequency, y represents amplitude, and n represents a natural number. Output point k in the time domain vibration spectrum n , k n =(x, y), all points k n Construct point set K i , K i =[k 1 , k…, k n ]. In this embodiment, by gathering all points in a point set, many peaks in the time domain vibration spectrum are converted into discrete data that are easy to analyze, which facilitates the subsequent extraction of peak features. It should be noted that in this embodiment, outputting the points that make up the spectrum in the time domain vibration spectrum through a matlab program is a prior art.

[0058] Furthermore, in the point set K i Extract the peak point x n , all peak points xn Constitute the peak point set X i , X i =[x 1 , x 2 …, x n ]. In the time domain vibration spectrum, the vibration frequency information is the peak information, and the spectrum peak generated by noise is generally wider than the spectrum peak of the vibration frequency, and the spectrum peak of noise is relatively smoother. Therefore, in this embodiment, the noise can be effectively screened out by judging the steepness of the spectrum peak and the peak width. Specifically, in the point set K i Filter out the peak point x n , that is, in the point set K i The maximum points are screened out. Therefore, the maximum point should satisfy the requirement that its amplitude intensity is greater than the amplitude intensity of the left and right adjacent points. Then, the peak width of the screened maximum point is calculated. Specifically, a rectangular coordinate system is established, and the spectrum peak is approximated as a triangle with the base of the triangle on the x-axis. The peak point is respectively combined with the left and right adjacent points and the linear function is solved. The intercepts of the two linear functions on the x-axis are calculated. The absolute value of the intercept difference can be approximately equivalent to the peak width. The peak width is recorded as newWidth. After the spectrum peak is approximated as a triangle, the two hypotenuses contain the steepness information of the spectrum peak. The absolute values ​​of the slopes of the two linear functions are added and divided by 2 to approximate the steepness of the spectrum peak. The steepness of the spectrum peak is recorded as Y. Therefore, in the point set K i Extract the peak point x n , all peak points x n Constitute the peak point set X i , X i =[x 1 , x 2 …, x n ].

[0059] At the peak point set X i Extract the characteristic peak point z n , all characteristic peak points z n Constitute the characteristic peak point set Z i , Z i =[z 1 , z 2 …, z n]. Specifically, in this embodiment, an exciter is used as a stable excitation source to excite a fixed rope at 1Hz to 2.1Hz. A non-contact measurement method is used to measure multiple times at different angles, distances, and interferences, and the spectrum is analyzed when the frequency is known. It can be concluded that if the peak width is too wide or the spectrum peak steepness is too low, the peak belongs to noise and is removed. In this embodiment, if the peak width newWidth>1 or the spectrum peak steepness Y<0.136, the peak is considered to be noise and is removed. This condition is used to screen the peak points to be analyzed, and finally the peak points that meet the conditions are set to Z i Defined as the characteristic peak point, all characteristic peak points z n Constitute the characteristic peak point set Z i , Z i =[z 1 , z 2 …, z n ]. Through this step, a large amount of useless peak point information can be eliminated from the numerous peak points that make up the spectrum graph, and a peak point set that may contain vibration frequency information can be extracted.

[0060] Furthermore, the interval of the assumed fundamental frequency sequence is set, and the characteristic peak points satisfying the interval of the assumed fundamental frequency sequence are found, and the characteristic peak points satisfying the interval of the assumed fundamental frequency sequence are set as the assumed frequency points, and the assumed frequency point set constitutes the assumed fundamental frequency sequence. It should be noted that in this embodiment, under the assumption of a small sag ratio, the influence of the bending stiffness and the sag of the deadweight of the bridge cable is ignored. After the two ends are fixed, a small section of the microelement is taken for analysis. According to the D'Alembert principle, an equilibrium equation is established, and the vibration of the bridge cable is regarded as a simple harmonic vibration. The frequency relationship of the bridge cable can be obtained, that is:

[0061] f1=fn / n

[0062] Wherein, f1 represents the first-order frequency, fn represents the nth-order frequency, and n represents the order. In this embodiment, the difference between the frequencies of the bridge cables is approximately equal to the first-order frequency f1, and thus, the frequency relationship of the bridge cables can be used to further distinguish the characteristic peak points, thereby extracting the frequencies of the various orders hidden in the spectrum.

[0063] Furthermore, the assumed fundamental frequency sequence interval α is set. It should be noted that in actual application, since the vibration frequencies of various bridge cables are significantly greater than 0.5 Hz, in this embodiment, the initial value of the assumed fundamental frequency sequence interval α is set to 0.5 Hz, that is, it is assumed that the difference between the frequencies of each order of vibration of the detection object is 0.5 Hz. Furthermore, the characteristic peak point set Z is set to i The first characteristic peak point z in 1 To assume the first-order frequency, select the characteristic peak point z n ,like and Then update the characteristic peak point z n is the assumed first-order frequency, where Indicates the characteristic peak point z 1 The frequency, Indicates the characteristic peak point z n The frequency, Indicates the characteristic peak point z 1 The amplitude strength, Indicates the characteristic peak point z n Specifically in this embodiment, the characteristic peak point set Z i The first characteristic peak point z in 1 Set to assume the first-order frequency, if the characteristic peak point z 1 Frequency and the characteristic peak point z n Frequency The difference is greater than the assumed base frequency sequence interval of 0.5Hz, and the characteristic peak point z 1 The amplitude strength Greater than the characteristic peak point z n The amplitude strength Then continue to compare the amplitude strength with the next point whose frequency difference does not exceed 0.5Hz; if the characteristic peak point z 1 Frequency and the characteristic peak point z n Frequency The difference is less than or equal to the assumed base frequency sequence interval of 0.5Hz, and the characteristic peak point z 1 The amplitude strength Less than or equal to the characteristic peak point z n The amplitude strength Then update the object and at the same time, set the feature peak point z n Set to assume the first-order frequency. Pass through the characteristic peak point z n Continue to compare the amplitude strength with the next point whose frequency difference is no more than 0.5Hz, until the peak feature point z with the largest amplitude strength within the 0.5Hz range is determined. n , at this time, the peak feature point z n Assuming the first-order frequency, then Z n+1 is determined as the assumed second-order frequency, and this is repeated until the assumed fifth-order frequency z is determined. n+4 Repeat the above steps to obtain five characteristic peak points that meet the assumed base frequency sequence interval. Let the characteristic peak points that meet the assumed base frequency sequence interval be assumed frequency points, which are recorded as: assumed frequency point z n , assuming frequency point z n+1 , assuming frequency point z n+2 , assuming frequency point z n+3 , assuming frequency point zn+4 In this embodiment, assuming that the base frequency sequence interval is α, it is assumed that the frequency point z n , assuming frequency point z n+1 , assuming frequency point z n+2 , assuming frequency point z n+3 , assuming frequency point z n+4 Constitute an assumed fundamental frequency sequence. It should be noted that these five assumed frequency points are the assumed fundamental frequency sequence under the assumption that the fundamental frequency sequence interval α is 0.5Hz. Since it is uncertain how much the frequencies of the vibrations of the detected object differ from each other, it is necessary to readjust the assumed fundamental frequency sequence interval α. In this embodiment, the assumed fundamental frequency sequence interval α increases from 0.5Hz to 2.5Hz in increments of 0.01Hz. Each time the assumed fundamental frequency sequence interval is changed, the above steps are repeated to find five new assumed frequency points, thereby obtaining the assumed fundamental frequency sequences under different assumed fundamental frequency sequence intervals.

[0064] Furthermore, the difference closeness score, peak width score and spectrum peak steepness score of the assumed frequency point in the assumed fundamental frequency sequence are calculated, and the difference closeness score, peak width score and spectrum peak steepness score of the assumed frequency point are added together to obtain the total score of the assumed frequency point.

[0065] It should be noted that there is an assumed base frequency sequence under a certain assumed base frequency sequence interval, and the differences between the assumed base frequency sequences are approximately equal to the assumed first-order frequency in the assumed base frequency sequence, that is, the first assumed frequency point in the assumed base frequency sequence, then the assumed frequency point is the true first-order frequency, and the first-order frequency is the bridge cable base frequency. If most of the differences are approximately equal to a certain assumed frequency point in the assumed base frequency sequence, it is considered that there are several assumed frequency points and interference points of unknown order in this set of assumed base frequency sequences, and further screening and order judgment are required based on the degree of proximity and the size of the close assumed frequency point itself. At this time, it is necessary to find the difference of the assumed base frequency sequence and calculate the degree of proximity of the difference.

[0066] In this embodiment, adjacent assumed frequency points in the assumed base frequency sequence are sequentially subtracted, and then the assumed frequency points separated in the assumed base frequency sequence are sequentially subtracted and divided by the number of separations to obtain different difference values, and the proximity of the different difference values ​​to the assumed frequency points in the assumed base frequency sequence is calculated. Specifically, for each group of assumed frequency points z in the assumed base frequency sequence, n , assuming frequency point z n+1 , assuming frequency point z n+2 , assuming frequency point z n+3 , assuming frequency point z n+4The difference of the frequencies of the adjacent assumed frequency points is calculated by directly making the difference and taking the absolute value. The difference between the assumed frequency points separated by each other is calculated by dividing the absolute value of the frequency difference by the number of intervals. For example, the difference between the fifth assumed frequency point and the first assumed frequency point is the absolute value of the subtraction of the frequencies divided by 4, thereby obtaining ten differences. The ten calculated differences are compared with the frequencies of each group of five assumed frequency points, and each difference is selected to be the closest assumed frequency point in the group of assumed base frequency sequences, and the proximity errorPercentage is calculated. In this embodiment, the proximity errorPercentage refers to the absolute value of the difference between the difference and the closest assumed frequency point / the closest assumed frequency. Through this step, the specific information of the difference in each assumed base frequency sequence under different assumed base frequency sequence intervals and the degree of proximity between each assumed frequency point in the assumed base frequency sequence and the difference can be obtained, which is conducive to subsequent quantitative analysis.

[0067] Then, the difference closeness score, peak width score and spectrum peak steepness score of the assumed frequency points in the assumed base frequency sequence are calculated. In this embodiment, the scores of each assumed frequency point in each group of assumed base frequency sequences are calculated, and after multiple tests, the calculation method of each assumed frequency point score is obtained. Figure 3 The figure shows the results of the measured spectrum of the bridge cable. Specifically, the calculation formula for the proximity score of the assumed frequency point difference value in the assumed base frequency sequence is:

[0068] 10-(errorPercentage*10.0)

[0069] In this embodiment, if the errorPercentage>0.7, it is considered that the error is large, the difference is meaningless, and is directly set to 0, and finally the score of each difference is added.

[0070] The calculation formula for the peak width fraction of the assumed frequency point in the assumed base frequency sequence is:

[0071] 15*(1-0.6*newWidth)

[0072] Wherein, newWidth represents the peak width.

[0073] The calculation formula for the spectrum peak steepness score of the assumed frequency point in the assumed base frequency sequence is:

[0074] 15*(1-0.6*Y)

[0075] Where Y represents the peak slope.

[0076] Therefore, the difference closeness score, peak width score and spectrum peak steepness score of the assumed frequency point are added to obtain the total score of the assumed frequency point. It should be noted that since the fundamental frequency, i.e. the first-order frequency, is affected by the measurement method, its peak characteristics are not obvious, while the spectrum peaks of the second-order frequency and the third-order frequency are more prominent and steep. At the same time, in a certain assumed fundamental frequency sequence interval, it is possible to include the second-order frequency to the fifth-order frequency, but not the first-order frequency, resulting in most of the difference being approximately equal to the second-order frequency, so its score may be higher than the first-order frequency. Therefore, the assumed frequency point with the highest score is not necessarily the fundamental frequency, and further order judgment is required.

[0077] Specifically, in this embodiment, the top three assumed frequency points with the highest scores under all assumed base frequency sequence intervals are output, and the top three assumed frequency points with the highest scores are assumed frequency point a, assumed frequency point b and assumed frequency point c. Among them, the assumed frequency a with the highest score is set as the nth order frequency. In order to determine its order, the scores of the assumed frequency point a are compared with those of the assumed frequency point b and the assumed frequency point c. If the difference between the scores of the assumed frequency point a and the assumed frequency point b and the assumed frequency point c is 5 points or more, the assumed frequency point a is discarded, and the scores of the assumed frequency point b and the assumed frequency point c are compared. At this time, the assumed frequency point with the higher score is divided by the assumed frequency point with the lower score. If the result m is approximately an integer M, and the absolute value difference between m and M does not exceed 0.3, the assumed frequency point with the lower score is considered to be the first order frequency, and the assumed frequency point with the higher score is the Mth order frequency. If the difference between the scores of the assumed frequency point a and the assumed frequency point b and the assumed frequency point c is less than 5 points, the sizes of the assumed frequency point a, the assumed frequency point b and the assumed frequency point c are compared. If the assumed frequency point a>the assumed frequency point b>the assumed frequency point c, then calculate the assumed frequency point a / the assumed frequency point b, the assumed frequency point a / the assumed frequency point c, and the assumed frequency point b / the assumed frequency point c. If the results of the assumed frequency point a / the assumed frequency point c and the assumed frequency point b / the assumed frequency point c are both approximately integers, and the difference with the absolute value of each integer does not exceed 0.3, then the assumed frequency point c is the first-order frequency. If not satisfied, then determine whether the fraction size of the assumed frequency point a / the assumed frequency point b or the assumed frequency point a / the assumed frequency point c meets the requirement. If satisfied, then determine that the assumed frequency point b or the assumed frequency point c is the first-order frequency. If both are not satisfied, then the assumed frequency point a is the first-order frequency.

[0078] like Figure 4 As shown, due to the different measurement methods and external interference, a large amount of noise will be caused, making it difficult to determine the fundamental frequency of the bridge cable. In this embodiment, the fundamental frequency of the bridge cable is analyzed and obtained by using a bridge cable fundamental frequency identification method based on peak feature extraction.

[0079] Therefore, the present embodiment provides a method for identifying the fundamental frequency of a bridge cable, which screens out the bridge cable signal and the noise signal by extracting peak feature points and utilizing the principle that the difference between different-order frequencies is approximately equal to the fundamental frequency, thereby ensuring that important vibration frequency information can be quickly extracted under different measurement conditions and in environments with different noise interferences, thereby effectively improving the accuracy of identifying the fundamental frequency of the bridge cable.

[0080] The present embodiment provides a bridge cable fundamental frequency identification method, which is a bridge cable fundamental frequency identification method based on peak feature extraction, which does not require the installation of sensors on the detection object, but only requires a general video acquisition device, and can better identify the vibration fundamental frequency of the bridge cable structure in a complex environment. Compared with the traditional method of determining the fundamental frequency of bridge cables, it eliminates the subjectivity of manual identification and effectively overcomes the high noise background, greatly improves the accuracy and convenience of bridge cable fundamental frequency identification, and has strong practicality and wide applicability.

[0081] This embodiment also provides a bridge cable fundamental frequency identification device, including a video acquisition module, a characteristic peak point extraction module, an assumed fundamental frequency sequence construction module, a frequency reliability calculation module and a bridge cable fundamental frequency judgment module. In the video acquisition module, a vibration video of the detection object is collected. In the characteristic peak point extraction module, the time domain vibration signal in the vibration video is extracted, the time domain vibration signal is converted into a time domain vibration spectrum, and the characteristic peak point in the time domain vibration spectrum is extracted. In the assumed fundamental frequency sequence construction module, the assumed fundamental frequency sequence interval is set, and the characteristic peak point that meets the assumed fundamental frequency sequence interval is found, and the characteristic peak point that meets the assumed fundamental frequency sequence interval is set as the assumed frequency point, and the assumed frequency point set constitutes the assumed fundamental frequency sequence. In the frequency reliability calculation module, the difference proximity score, peak width score and spectrum peak steepness score of the assumed frequency point in the assumed fundamental frequency sequence are calculated, and the difference proximity score, peak width score and spectrum peak steepness score of the assumed frequency point are added to obtain the total score of the assumed frequency point. In the bridge cable fundamental frequency judgment module, the bridge cable fundamental frequency is obtained according to the total score of the assumed frequency point and the order judgment. The bridge cable fundamental frequency identification device provided in this embodiment can implement any method of the bridge cable fundamental frequency identification method, and the specific working process of a bridge cable fundamental frequency identification device can refer to the corresponding process in the bridge cable fundamental frequency identification method embodiment. The method and device provided in this embodiment can be implemented in other ways. For example, the device embodiment described above is only schematic; for example, the division of a certain module is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual connection or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, or it can be an electrical, mechanical or other form of connection.

[0082] This embodiment also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the bridge cable fundamental frequency identification method when executing the computer program.

[0083] This embodiment also provides a computer-readable storage medium. A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, a bridge cable fundamental frequency identification method described in this embodiment is executed. The computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, device or device; the program code contained in the computer-readable medium can be transmitted using any appropriate medium, including but not limited to wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0084] The above schematically describes the invention and its implementation methods, which is not restrictive. Without departing from the spirit or basic features of the invention, the invention can be implemented in other specific forms. What is shown in the accompanying drawings is only one of the implementation methods of the invention. The actual structure is not limited thereto, and any figure mark in the claims should not limit the claims involved. Therefore, if a person of ordinary skill in the art is inspired by it, without departing from the purpose of the invention, a structural method and an embodiment similar to the technical solution are designed without creativity, which should all belong to the protection scope of the present invention. In addition, the word "including" does not exclude other elements or steps, and the word "one" before the element does not exclude the inclusion of "multiple" elements. The multiple elements stated in the product claim can also be implemented by one element through software or hardware. The words first, second, etc. are used to indicate the name, and do not indicate any specific order.

Claims

1. A bridge cable fundamental frequency identification method, comprising the following steps: Collect vibration video of the detected object; Extract the time domain vibration signal in the vibration video, convert the time domain vibration signal into a time domain vibration spectrum, and extract the characteristic peak points in the time domain vibration spectrum. The specific steps include: performing Fourier transform on the time domain vibration signal to obtain the time domain vibration spectrum; outputting the points in the time domain vibration spectrum , , all points Constitute the point set , ,in, Indicates frequency, Represents the amplitude intensity, n represents a natural number; in the point set Extract peak points , all peak points Peak point set , ; At the peak point set Extract characteristic peak points , all characteristic peak points Constructing the characteristic peak point set , ; Set the assumed base frequency sequence interval and find the characteristic peak points that meet the assumed base frequency sequence interval. The specific steps include: setting the assumed base frequency sequence interval α; setting the characteristic peak point set The first characteristic peak point in To assume a first-order frequency, select the characteristic peak point ,like ∣ - ∣≤α and ≤ , then update the feature peak point is the assumed first-order frequency; where, Indicates the characteristic peak point The frequency, Indicates the characteristic peak point The frequency, Indicates the characteristic peak point The amplitude strength, Indicates the characteristic peak point The amplitude strength of the value; repeat the above steps to obtain five characteristic peak points that meet the assumed fundamental frequency sequence interval; let the characteristic peak points that meet the assumed fundamental frequency sequence interval be assumed frequency points, respectively recorded as assumed frequency points , Assumed frequency point , Assumed frequency point , Assumed frequency point , Assumed frequency point ; Under the assumption that the base frequency sequence interval is α, the frequency point is assumed to be , Assumed frequency point , Assumed frequency point , Assumed frequency point , Assumed frequency point Constructing a hypothetical fundamental frequency sequence; Calculate the difference closeness score, peak width score and spectrum peak steepness score of the assumed frequency point in the assumed fundamental frequency sequence, and add the difference closeness score, peak width score and spectrum peak steepness score of the assumed frequency point to obtain the total score of the assumed frequency point; The fundamental frequency of the bridge cable is obtained based on the total score of the assumed frequency points and the order.

2. A bridge cable fundamental frequency identification method according to claim 1, characterized in that: The adjacent assumed frequency points in the assumed base frequency sequence are sequentially subtracted, and then the separated assumed frequency points in the assumed base frequency sequence are sequentially subtracted and divided by the number of intervals to obtain different difference values, and the proximity of the different difference values ​​to the assumed frequency points in the assumed base frequency sequence is calculated.

3. A bridge cable fundamental frequency identification method according to claim 2, characterized in that: The calculation formula for the closeness score of the assumed frequency point difference value in the assumed base frequency sequence is: Among them, errorPercentage represents the proximity; The calculation formula for the peak width fraction of the assumed frequency point in the assumed base frequency sequence is: Among them, newWidth represents the peak width; The calculation formula for the spectrum peak steepness score of the assumed frequency point in the assumed base frequency sequence is: Where Y represents the steepness of the spectrum peak.

4. A bridge cable fundamental frequency identification method according to claim 3, characterized in that: The order of the assumed frequency points in the assumed base frequency sequence is judged, and then the first-order frequency is determined according to the total score of the assumed frequency points, and the first-order frequency is the base frequency.

5. A bridge cable fundamental frequency identification device, characterized in that: include: A video acquisition module, which acquires vibration video of the detected object; The characteristic peak point extraction module extracts the time domain vibration signal in the vibration video, converts the time domain vibration signal into the time domain vibration spectrum, and extracts the characteristic peak point in the time domain vibration spectrum. The specific steps include: performing Fourier transform on the time domain vibration signal to obtain the time domain vibration spectrum; outputting the peak point in the time domain vibration spectrum; , , all points Constitute the point set , ,in, Indicates frequency, Represents the amplitude intensity, n represents a natural number; in the point set Extract peak points , all peak points Peak point set , ; At the peak point set Extract characteristic peak points , all characteristic peak points Constructing the characteristic peak point set , ; Assuming a base frequency sequence construction module, setting the assumed base frequency sequence interval, and finding the characteristic peak points that meet the assumed base frequency sequence interval. The specific steps include: setting the assumed base frequency sequence interval α; setting the characteristic peak point set The first characteristic peak point in To assume a first-order frequency, select the characteristic peak point ,like ∣ - ∣≤α and ≤ , then update the feature peak point is the assumed first-order frequency; where, Indicates the characteristic peak point The frequency, Indicates the characteristic peak point The frequency, Indicates the characteristic peak point The amplitude strength, Indicates the characteristic peak point The amplitude strength of the value; repeat the above steps to obtain five characteristic peak points that meet the assumed fundamental frequency sequence interval; let the characteristic peak points that meet the assumed fundamental frequency sequence interval be assumed frequency points, respectively recorded as assumed frequency points , Assumed frequency point , Assumed frequency point , Assumed frequency point , Assumed frequency point ; Under the assumption that the base frequency sequence interval is α, the frequency point is assumed to be , Assumed frequency point , Assumed frequency point , Assumed frequency point , Assumed frequency point Constructing a hypothetical fundamental frequency sequence; A frequency reliability calculation module calculates the difference closeness score, peak width score and spectrum peak steepness score of the assumed frequency point in the assumed fundamental frequency sequence, and adds the difference closeness score, peak width score and spectrum peak steepness score of the assumed frequency point to obtain the total score of the assumed frequency point; The bridge cable fundamental frequency judgment module determines the bridge cable fundamental frequency based on the total score of the assumed frequency points and the order judgment.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 4 is implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 4 is executed.

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