Methods, devices, equipment, storage media, and products for identifying the fundamental frequency of bridge cables.

Abstract

This invention relates to the field of cable force detection technology, and in particular to a method, apparatus, equipment, storage medium, and product for identifying the fundamental frequency of bridge cables. The method involves converting the vibration acceleration data of the bridge cable structure under monitoring over a duration into a power spectrum data set, calculating the noise floor using the power spectrum data set to improve noise estimation accuracy, then filtering the power spectrum data for energy peaks to obtain a frequency-energy peak array. This array, along with pre-determined candidate fundamental frequencies, is then used to perform energy assessment and scoring using a preset fundamental frequency scoring model, improving the accuracy of energy assessment and scoring for candidate frequencies. Finally, the actual fundamental frequency of the cable under test is determined based on the score value of the candidate fundamental frequencies. This eliminates the need for manual screening, improving identification efficiency while achieving high-precision identification of the cable fundamental frequency.

Description

Technical Field

[0001] This invention relates to the field of cable tension detection technology, and in particular to methods, devices, equipment, storage media, and products for identifying the fundamental frequency of bridge cables. Background Technology

[0002] Bridge structural health monitoring is an important research direction in the field of modern civil engineering. Among them, cable-stayed structures are key components of long-span bridges, and their health status is directly related to the safety and service life of the entire structure. The fundamental frequency of the cable, as a key parameter characterizing its dynamic characteristics, is an important indicator for assessing the health status of the cable.

[0003] In traditional technologies, the identification of the fundamental frequency of cable structures relies on manual analysis or simple algorithms. These methods are often inefficient and inaccurate when faced with noise interference and complex vibration modes.

[0004] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main objective of this invention is to provide a method, apparatus, device, storage medium, and product for identifying the fundamental frequency of bridge cables, aiming to solve the technical problems of low efficiency and low accuracy in identifying the fundamental frequency of bridge cable structures in the prior art.

[0006] To achieve the above objectives, the present invention provides a method for identifying the fundamental frequency of bridge cables, the method comprising the following steps: Acquire the vibration acceleration data of the cable to be tested and the power spectrum data set corresponding to the vibration acceleration data; The noise floor is calculated based on the power spectrum data set; Based on the noise floor, the energy peak values ​​of each power spectrum data group in the power spectrum data group are filtered to obtain a frequency-energy peak value array; The frequency-energy peak array and the predetermined candidate fundamental frequencies are scored using a preset fundamental frequency scoring model to obtain the score value of each candidate fundamental frequency; The actual fundamental frequency of the cable to be tested is determined based on the score value of each candidate fundamental frequency.

[0007] Optionally, the step of scoring the frequency-energy peak array and the pre-determined candidate fundamental frequencies using a preset fundamental frequency scoring model to obtain a score value for each candidate fundamental frequency includes: Determine the window width coefficient and the maximum verification harmonic order for each candidate fundamental frequency; The harmonic energy and frequency-energy peak array of each candidate fundamental frequency are matched, wherein the harmonic energy is the energy peak of the target fundamental frequency within the window width coefficient range, which is an integer multiple of the candidate fundamental frequency; The number of target fundamental frequencies matching the harmonic energy in the frequency-energy peak array and the cumulative harmonic energy are counted, where the cumulative harmonic energy is the sum of the energy of the candidate fundamental frequency and the energy of the target fundamental frequency; The candidate fundamental frequencies are scored using a preset fundamental frequency scoring model based on the total energy of the frequency-energy peak array, the number of target harmonics, the maximum number of verified harmonics, the energy of the candidate fundamental frequencies, and the cumulative energy of the harmonics, to obtain a score value for each candidate fundamental frequency.

[0008] Optionally, before scoring each candidate fundamental frequency using a preset fundamental frequency scoring model based on the total energy of the frequency-energy peak array, the number of target harmonics, the maximum verified harmonic order, the energy of the candidate fundamental frequency, and the cumulative harmonic energy, the step further includes: The harmonic weight and harmonic energy proportion weight are calculated by a preset harmonic parameter weighting processing strategy. The harmonic energy proportion weight is the proportion of the cumulative harmonic energy to all energy within the window width coefficient range from the candidate fundamental frequency to the target fundamental frequency. Accordingly, based on the total energy of the frequency-energy peak array, the number of target harmonics, the maximum verified harmonic order, the energy of the candidate fundamental frequency, and the cumulative harmonic energy, a score is obtained for each candidate fundamental frequency using a preset fundamental frequency scoring model, including: The candidate fundamental frequencies are scored using a preset fundamental frequency scoring model based on the total energy of the frequency-energy peak array, the number of target harmonics, the maximum number of verified harmonics, the energy of the candidate fundamental frequencies, the cumulative energy of the harmonics, the harmonic weights, and the harmonic energy percentage weights, to obtain a score value for each candidate fundamental frequency. Specifically, the calculation formula for the preset fundamental frequency scoring model is as follows:

[0009] in, This is the final score. For the target harmonic number, Maximum number of verified harmonics The energy of the candidate fundamental frequency. To accumulate energy for harmonics, This is the sum of the energy values ​​in the frequency-energy peak array.

[0010] Optionally, the step of filtering the energy peaks of each group of power spectrum data in the power spectrum data set according to the noise floor to obtain a frequency-energy peak array includes: The local peak values ​​and offsets of each power spectrum data group in the power spectrum data group are fitted by the three-point parabolic interpolation method. The local peak value is the maximum energy value corresponding to each frequency within a preset window range, and the maximum energy value is greater than the energy peak value of the noise substrate. The center frequency is determined based on the offset. The cumulative energy of the center frequency within a preset window range is calculated. Based on the energy accumulation and updating of the local peak values ​​of each group of power spectrum data, a frequency-energy peak array is obtained.

[0011] Optionally, the step of fitting the local peak values ​​and the offset of each local peak value in the power spectrum data set using the three-point parabolic interpolation method includes: The power spectrum data set was filtered to obtain a target power spectrum data set after removing the DC frequency and Nyquist frequency. Based on a preset window range, the target power spectrum data group is traversed to determine multiple local energy peaks; By comparing the local peak values ​​of each energy level with the noise floor, target local peak values ​​that are greater than the noise floor are selected. Determine the frequency corresponding to the local peak value of the target energy; Calculate the offset between the frequency corresponding to the local peak of the target energy and the initial frequency in the power spectrum data.

[0012] Optionally, calculating the noise floor based on the power spectrum data set includes: Determine the energy peak value of each power spectrum data in the power spectrum data group; Arrange the energy peaks in descending order from high to low to obtain an energy sorting array; Based on a preset filtering strategy, some energy peaks in the energy arrangement array are removed to obtain the target energy arrangement array; Calculate the energy mean of the standard energy permutation array and output it as the noise floor.

[0013] Furthermore, to achieve the above objectives, the present invention also proposes a bridge cable fundamental frequency identification device, the bridge cable fundamental frequency identification device comprising: The acquisition module is used to acquire the vibration acceleration data of the cable to be tested and the power spectrum data group corresponding to the vibration acceleration data; The calculation module is used to calculate the noise floor based on the power spectrum data set; The evaluation module is used to filter the energy peaks of each group of power spectrum data in the power spectrum data group according to the noise basis to obtain a frequency-energy peak array; The scoring module is used to score the frequency-energy peak array and the pre-determined candidate fundamental frequencies through a preset fundamental frequency scoring model to obtain the score value of each candidate fundamental frequency; The determination module is used to determine the actual fundamental frequency of the cable to be tested based on the score value of each candidate fundamental frequency.

[0014] Furthermore, to achieve the above objectives, the present invention also proposes a bridge cable fundamental frequency identification device, which includes: a memory, a processor, and a bridge cable fundamental frequency identification program stored in the memory and executable on the processor. The bridge cable fundamental frequency identification program is configured to implement the steps of the bridge cable fundamental frequency identification method described above.

[0015] In addition, to achieve the above objectives, the present invention also proposes a storage medium storing a bridge cable fundamental frequency identification program, wherein when the bridge cable fundamental frequency identification program is executed by a processor, the steps of the bridge cable fundamental frequency identification method described above are implemented.

[0016] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the bridge cable fundamental frequency identification method described above.

[0017] This invention acquires vibration acceleration data of the cable under test and the corresponding power spectrum data set; calculates a noise floor based on the power spectrum data set; filters the energy peaks of each power spectrum data set according to the noise floor to obtain a frequency-energy peak array; scores the frequency-energy peak array and predetermined candidate fundamental frequencies using a preset fundamental frequency scoring model to obtain a score value for each candidate fundamental frequency; and determines the actual fundamental frequency of the cable under test based on the score values ​​of each candidate fundamental frequency. Compared with the prior art, this invention transforms the vibration acceleration data of the bridge cable structure under test over a duration into... The system generates a power spectrum data set and calculates the noise floor using this data set to improve noise estimation accuracy. Then, it filters the energy peaks in the power spectrum data to obtain a frequency-energy peak array. This array, along with pre-determined candidate fundamental frequencies, is used to perform energy assessment and scoring using a preset fundamental frequency scoring model. This improves the accuracy of energy assessment and scoring for candidate frequencies. Finally, the actual fundamental frequency of the cable to be tested is determined based on the score of the candidate fundamental frequencies. This eliminates the need for manual screening, improving identification efficiency and achieving high-precision identification of the cable fundamental frequency. It avoids the technical problems of low efficiency and low accuracy in fundamental frequency identification of bridge cable structures in existing technologies. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the first embodiment of the bridge cable fundamental frequency identification method of the present invention; Figure 2 This is a flowchart illustrating the second embodiment of the bridge cable fundamental frequency identification method of the present invention; Figure 3 This is a schematic diagram of the operational logic flow of fundamental frequency identification in an embodiment of the bridge cable fundamental frequency identification method of the present invention; Figure 4 This is a flowchart illustrating the third embodiment of the bridge cable fundamental frequency identification method of the present invention; Figure 5 This is a schematic diagram of the control logic flow for filtering and sorting energy peaks in an embodiment of the bridge cable fundamental frequency identification method of the present invention. Figure 6 This is a structural block diagram of the first embodiment of the bridge cable fundamental frequency identification device of the present invention; Figure 7 This is a schematic diagram of the structure of a bridge cable base frequency identification device in the hardware operating environment involved in the embodiments of the present invention.

[0021] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0022] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0023] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0024] Based on this, embodiments of the present invention provide a method for identifying the fundamental frequency of bridge cables, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of a bridge cable fundamental frequency identification method according to the present invention.

[0025] In this embodiment, the bridge cable fundamental frequency identification method includes: Step S10: Obtain the vibration acceleration data of the cable to be tested and the power spectrum data set corresponding to the vibration acceleration data.

[0026] Step S20: Calculate the noise floor based on the power spectrum data set.

[0027] Step S30: Based on the noise floor, filter the energy peak values ​​of each power spectrum data group in the power spectrum data group to obtain a frequency-energy peak value array.

[0028] Step S40: The frequency-energy peak array and the predetermined candidate fundamental frequencies are scored using a preset fundamental frequency scoring model to obtain the score value of each candidate fundamental frequency.

[0029] Step S50: Determine the actual fundamental frequency of the cable to be tested based on the score values ​​of each candidate fundamental frequency.

[0030] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device or control computer capable of performing the above functions. The following description uses a control computer as an example to illustrate this embodiment and the subsequent embodiments.

[0031] Acquiring vibration acceleration data of the cable to be tested refers to collecting vibration acceleration data of the cable by using vibration acceleration sensors installed on the bridge cable. In order to improve the reliability of the data, in this embodiment, after the acceleration data is collected, the acceleration data can also be filtered and smoothed. The processed signal is then subjected to Fast Fourier Transform (FFT) to obtain the power spectrum data group power in the frequency domain, with a length of N.

[0032] The noise floor is used to characterize the minimum noise energy value when there is no signal input.

[0033] Further, the calculation of the noise basis based on the power spectrum data set includes: Determine the energy peak value of each power spectrum data in the power spectrum data group; Arrange the energy peaks in descending order from high to low to obtain an energy sorting array; Based on a preset filtering strategy, some energy peaks in the energy arrangement array are removed to obtain the target energy arrangement array; Calculate the energy mean of the standard energy permutation array and output it as the noise floor.

[0034] In the specific implementation, the energy peaks of each power spectrum data in the power spectrum data group are first determined, and the energy peaks are arranged in descending order. In order to improve the reliability of the noise base, this embodiment removes some energy peaks from the energy arrangement array and calculates the average energy of the remaining energy peaks as the noise base, so as to determine the energy peak of the minimum noise to the greatest extent. Since the noise base represents the energy value of the minimum noise, the preset screening strategy can be to determine the number of peaks to be excluded, Np, where Np is greater than or equal to 5, or to exclude 5% of the total length of the energy arrangement array, skip the first Np largest peaks, and calculate the average value of the remaining part to more accurately estimate the noise level.

[0035] Since noise may be caused by environmental factors, hardware structure, etc., there may be interference from the noise floor even when there is no signal input. Therefore, in order to improve the accuracy of determining the fundamental frequency of the cable, this embodiment can first perform peak filtering on the power spectrum data group based on the noise floor, and remove the frequency part that has no signal input and only the noise floor remains, to obtain the frequency-energy peak array.

[0036] The preset fundamental frequency scoring model can be used to calculate the reliability score of each predetermined candidate fundamental frequency. Finally, the frequency with the highest probability of the cable to be detected is selected as the actual fundamental frequency based on the score values ​​of each candidate fundamental frequency. The fundamental frequency of a cable refers to its first natural frequency, which is the inherent vibration frequency of the cable when it is not subjected to other external forces. It is the lowest frequency in the cable's vibration frequency. Changes in the fundamental frequency of a cable can reflect changes in the cable's tension, and thus reflect the health of the bridge structure. If there is an abnormal change in the fundamental frequency of a cable, it may mean that there is a problem with the cable's tension, such as cable slack or excessive tension. This helps to detect potential problems in the bridge structure in a timely manner and ensure the safe operation of the bridge.

[0037] This embodiment converts the vibration acceleration data of the bridge cable structure under monitoring over a duration into a power spectrum data set, and calculates the noise floor using the power spectrum data set to improve the accuracy of noise estimation. Then, the power spectrum data is filtered for energy peaks to obtain a frequency-energy peak array. The frequency-energy peak array and a pre-determined candidate fundamental frequency are used to evaluate and score the energy of the candidate frequencies using a preset fundamental frequency scoring model to improve the accuracy of energy evaluation and scoring of the candidate frequencies. Finally, the actual fundamental frequency of the cable under test is determined by the score value of the candidate fundamental frequency. This eliminates the need for manual screening, improves identification efficiency, and achieves high-precision identification of the cable fundamental frequency.

[0038] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 Step S40 includes: Step S401: Determine the window width coefficient and the maximum verification harmonic order for each candidate fundamental frequency.

[0039] Step S402: Match the harmonic energy and frequency-energy peak array of each candidate fundamental frequency.

[0040] Step S403: Count the number of target fundamental frequencies that match the harmonic energy in the frequency-energy peak array and the cumulative harmonic energy.

[0041] Step S404: Based on the total energy of the frequency-energy peak array, the number of target harmonics, the maximum verification harmonic order, the energy of the candidate fundamental frequency, and the cumulative energy of the harmonics, a score is obtained for each candidate fundamental frequency by using a preset fundamental frequency scoring model.

[0042] It should be noted that the window width coefficient for each candidate fundamental frequency is generally within ±3% of the candidate fundamental frequency, and can be dynamically adjusted as the frequency changes. The target fundamental frequency whose harmonic energy is an integer multiple of the candidate fundamental frequency has a peak energy value within the window width coefficient range. Since the energy is concentrated in the low harmonics, the maximum verification harmonic number exceeding 5 may be submerged by noise, making it easier to misjudge. Under normal health monitoring, the maximum verification harmonic number MaxHarmonics ranges from 3 to 5, preferably 5, and can be dynamically adjusted according to the signal sampling frequency.

[0043] Harmonics are wave components in a periodic waveform whose frequencies are integer multiples of the fundamental frequency. If the fundamental frequency of a periodic waveform is f, then the frequency of its nth harmonic is n×f, where n is a positive integer. For example, if the fundamental frequency of a waveform is 100 Hz, then its second harmonic frequency is 200 Hz, the third harmonic frequency is 300 Hz, and so on. This embodiment does not impose specific limitations on this.

[0044] The cumulative harmonic energy is the sum of the energy of the candidate fundamental frequency and the energy of the target fundamental frequency, that is, the sum of the energy of the candidate fundamental frequency and an integer multiple of the candidate fundamental frequency, and the energy that matches the frequency-energy peak array.

[0045] Further, before the step of scoring each candidate fundamental frequency using a preset fundamental frequency scoring model based on the total energy of the frequency-energy peak array, the number of target harmonics, the maximum verified harmonic order, the energy of the candidate fundamental frequency, and the cumulative harmonic energy, the step further includes: The harmonic weight and harmonic energy proportion weight are calculated by a preset harmonic parameter weighting processing strategy. The harmonic energy proportion weight is the proportion of the cumulative harmonic energy to all energy within the window width coefficient range from the candidate fundamental frequency to the target fundamental frequency. Accordingly, based on the total energy of the frequency-energy peak array, the number of target harmonics, the maximum verified harmonic order, the energy of the candidate fundamental frequency, and the cumulative harmonic energy, a score is obtained for each candidate fundamental frequency using a preset fundamental frequency scoring model, including: The candidate fundamental frequencies are scored using a preset fundamental frequency scoring model based on the total energy of the frequency-energy peak array, the number of target harmonics, the maximum number of verified harmonics, the energy of the candidate fundamental frequencies, the cumulative energy of the harmonics, the harmonic weights, and the harmonic energy percentage weights, to obtain a score value for each candidate fundamental frequency. Specifically, the calculation formula for the preset fundamental frequency scoring model is as follows:

[0046] in, This is the final score. For the target harmonic number, Maximum number of verified harmonics The energy of the candidate fundamental frequency. To accumulate energy for harmonics, This is the sum of the energy values ​​in the frequency-energy peak array.

[0047] In the specific implementation, by comparing the harmonics of each candidate fundamental frequency with all peak frequency groups, it is found whether there is a matching peak within the window width coefficient multiple of the candidate frequency at integer multiples of n (where n takes 1 to the maximum verification harmonic order). If there is, the number of matching harmonics is counted. At the same time, the energy of the candidate fundamental frequency and its harmonics within this range is counted, accumulated, and the energy percentage (EnergyScore) is calculated.

[0048] Based on this, a scoring formula is established by combining harmonics and energy.

[0049] in For the final score, For the number of harmonics, Maximum number of verified harmonics The energy of the candidate fundamental frequency. Accumulate energy for harmonics matching the candidate fundamental frequency. The total energy accumulated over all peak frequencies.

[0050] Furthermore, this embodiment employs the Sigmoid function to achieve a smooth transition. The introduction of the Sigmoid function establishes a harmonic and energy scoring mechanism, effectively reducing the impact of factors such as low fundamental frequency energy and environmental noise interference, thus improving the sensitivity and accuracy of fundamental frequency identification. The harmonic weighting formula is as follows:

[0051] in, For harmonic weights, This represents the minimum value within the harmonic weight range. is the maximum value of the harmonic weight range, k is the slope, and SNR is the signal-to-noise ratio.

[0052] Accordingly, the formula for the energy proportion weighting is:

[0053] Among them, W energy This represents the weighting of energy percentage.

[0054] refer to Figure 3 , Figure 3 This is a schematic diagram of the operating logic of the fundamental frequency identification in this embodiment. When the score of a candidate fundamental frequency exceeds a specified threshold and the score is the highest, or exceeds a specified number of harmonics, the candidate fundamental frequency can be determined as the actual fundamental frequency.

[0055] This embodiment scores each candidate fundamental frequency based on the sum of the energy of the frequency-energy peak array, the number of target harmonics, the maximum number of verified harmonics, the energy of the candidate fundamental frequency, the cumulative energy of the harmonics, the harmonic weight, and the harmonic energy percentage weight, using a preset fundamental frequency scoring model. Simultaneously, it uses a combination of harmonics and energy percentage as criteria, taking into account both the continuity of signal harmonics and energy concentration, thus improving the sensitivity and accuracy of fundamental frequency identification.

[0056] Based on the second embodiment of this application, in the third embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 4 Step S30 includes: Step S301: Fit the local peak values ​​and offsets of each power spectrum data group in the power spectrum data group using the three-point parabolic interpolation method.

[0057] Step S302: Determine the center frequency based on the offset.

[0058] Step S303: Calculate the cumulative energy of the center frequency within a preset window range.

[0059] Step S304: Based on the energy accumulation and updating the local peak values ​​of each group of power spectrum data, obtain the frequency-energy peak array.

[0060] It should be noted that the local peak value is the maximum energy value corresponding to each frequency within a preset window range, and the maximum energy value is greater than the energy peak value of the noise base. Since the energy value corresponding to the candidate fundamental frequency may not be the energy peak value within the window range, and there is a deviation from the energy corresponding to the actual harmonic frequency, this embodiment calculates the cumulative energy of the center frequency corresponding to the offset within the preset window range, stores the cumulative energy in the peak frequency object peek, and adds it to the peak array peeks one by one to obtain the statistical frequency-energy peak array with energy.

[0061] Furthermore, the fitting of the local peak values ​​and the offset of each local peak value in the power spectrum data set using the three-point parabolic interpolation method includes: The power spectrum data set was filtered to obtain a target power spectrum data set after removing the DC frequency and Nyquist frequency. Based on a preset window range, the target power spectrum data group is traversed to determine multiple local energy peaks; By comparing the local peak values ​​of each energy level with the noise floor, target local peak values ​​that are greater than the noise floor are selected. Determine the frequency corresponding to the local peak value of the target energy; Calculate the offset between the frequency corresponding to the local peak of the target energy and the initial frequency in the power spectrum data.

[0062] In the specific implementation, refer to Figure 5 , Figure 5 This is a schematic diagram of the control logic for filtering and sorting energy peaks in this embodiment. For the power spectrum data group power from n = 1 to n = N - 2 (excluding DC and Nyquist frequencies), it is polled to find whether the power at that point is a local peak and is greater than the noise floor. If not, the search continues to the next one. If the condition is met, the peak is recorded. Compared with the traditional method of determining the energy peak by the maximum value, the peak position may not be accurate due to the limitation of the sampling interval. Therefore, this embodiment improves the accuracy of energy peak location by continuously comparing local energy within the window range.

[0063] This embodiment fits the local peak values ​​and offsets of each group of power spectrum data in the power spectrum data set using a three-point parabolic interpolation method. The local peak value is the maximum energy value corresponding to each frequency within a preset window range, and the maximum energy value is greater than the energy peak value of the noise floor. The center frequency is determined based on the offset. The cumulative energy of the center frequency within the preset window range is calculated. The local peak values ​​of each group of power spectrum data are updated based on the cumulative energy sum, resulting in a dynamic balance between the harmonic tolerance and energy window of the frequency-energy peak array. This allows for slight offsets in the fundamental frequency and harmonics, avoiding missed detections. It is particularly effective in improving the robustness of actual signals under non-ideal environments. At the same time, by only taking the energy near the peak frequency, the impact of harmonic energy leakage on detection can be effectively reduced.

[0064] This application also provides a bridge cable fundamental frequency identification device, please refer to... Figure 6 The bridge cable fundamental frequency identification device includes: The acquisition module 10 is used to acquire the vibration acceleration data of the cable to be tested and the power spectrum data group corresponding to the vibration acceleration data.

[0065] The calculation module 20 is used to calculate the noise floor based on the power spectrum data set.

[0066] Evaluation module 30 is used to filter the energy peaks of each group of power spectrum data in the power spectrum data group according to the noise basis to obtain a frequency-energy peak array.

[0067] The scoring module 40 is used to score the frequency-energy peak array and the predetermined candidate fundamental frequencies through a preset fundamental frequency scoring model to obtain the score value of each candidate fundamental frequency.

[0068] The determination module 50 is used to determine the actual fundamental frequency of the cable to be tested based on the score value of each candidate fundamental frequency.

[0069] This embodiment converts the vibration acceleration data of the bridge cable structure under monitoring over a duration into a power spectrum data set, and calculates the noise floor using the power spectrum data set to improve the accuracy of noise estimation. Then, the power spectrum data is filtered for energy peaks to obtain a frequency-energy peak array. The frequency-energy peak array and a pre-determined candidate fundamental frequency are used to evaluate and score the energy of the candidate frequencies using a preset fundamental frequency scoring model to improve the accuracy of energy evaluation and scoring of the candidate frequencies. Finally, the actual fundamental frequency of the cable under test is determined by the score value of the candidate fundamental frequency. This eliminates the need for manual screening, improves identification efficiency, and achieves high-precision identification of the cable fundamental frequency.

[0070] In one embodiment, the scoring module 40 is further configured to determine the window width coefficient and the maximum verified harmonic order of each candidate fundamental frequency; match the harmonic energy of each candidate fundamental frequency with a frequency-energy peak array, wherein the harmonic energy is the energy peak of the target fundamental frequency that is an integer multiple of the candidate fundamental frequency within the window width coefficient range; count the number of target fundamental frequencies that match the harmonic energy in the frequency-energy peak array and the cumulative harmonic energy, wherein the cumulative harmonic energy is the sum of the energy of the candidate fundamental frequency and the energy of the target fundamental frequency; and score each candidate fundamental frequency using a preset fundamental frequency scoring model based on the total energy of the frequency-energy peak array, the number of target harmonics, the maximum verified harmonic order, the energy of the candidate fundamental frequency, and the cumulative harmonic energy to obtain a score value for each candidate fundamental frequency.

[0071] In one embodiment, the scoring module 40 is further configured to calculate harmonic weights and harmonic energy proportion weights using a preset harmonic parameter weighting strategy. The harmonic energy proportion weight is the ratio of the cumulative harmonic energy to all energy within a window width coefficient range from the candidate fundamental frequency to the target fundamental frequency. Correspondingly, a score value for each candidate fundamental frequency is obtained by scoring it using a preset fundamental frequency scoring model based on the total energy of the frequency-energy peak array, the number of target harmonics, the maximum number of verified harmonics, the energy of the candidate fundamental frequency, and the cumulative harmonic energy. This includes: scoring it using a preset fundamental frequency scoring model based on the total energy of the frequency-energy peak array, the number of target harmonics, the maximum number of verified harmonics, the energy of the candidate fundamental frequency, the cumulative harmonic energy, the harmonic weight, and the harmonic energy proportion weight to obtain a score value for each candidate fundamental frequency. Specifically, the calculation formula for the preset fundamental frequency scoring model is as follows:

[0072] in, This is the final score. For the target harmonic number, Maximum number of verified harmonics The energy of the candidate fundamental frequency. To accumulate energy for harmonics, This is the sum of the energy values ​​in the frequency-energy peak array.

[0073] In one embodiment, the evaluation module 30 is further configured to fit the local peak values ​​and offsets of each group of power spectrum data in the power spectrum data set using a three-point parabolic interpolation method, wherein the local peak value is the maximum energy value corresponding to each frequency within a preset window range, and the maximum energy value is greater than the energy peak value of the noise floor; determine the center frequency based on the offset; calculate the cumulative energy of the center frequency within the preset window range; and update the local peak values ​​of each group of power spectrum data based on the cumulative energy to obtain a frequency-energy peak value array.

[0074] In one embodiment, the evaluation module 30 is further configured to: filter the power spectrum data set to obtain a target power spectrum data set with DC frequency and Nyquist frequency removed; traverse the target power spectrum data set based on a preset window range to determine multiple local energy peaks; compare each local energy peak with a noise floor to filter out target local energy peaks that are greater than the noise floor; determine the frequency corresponding to the target local energy peak; and calculate the offset between the frequency corresponding to the target local energy peak and the initial frequency in the power spectrum data.

[0075] In one embodiment, the calculation module 20 is further configured to determine the energy peak value of each power spectrum data in the power spectrum data group; arrange each energy peak value in descending order from high to low to obtain an energy arrangement array; remove some energy peak values ​​in the energy arrangement array based on a preset screening strategy to obtain a target energy arrangement array; calculate the energy mean of the target energy arrangement array and output it as a noise floor.

[0076] This application provides a bridge cable base frequency identification device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the bridge cable base frequency identification method in the above embodiment 1.

[0077] The following is for reference. Figure 7 The diagram illustrates a structural schematic suitable for implementing the bridge cable baseband identification device in the embodiments of this application. The bridge cable baseband identification device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), vehicle terminals (e.g., vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 7 The bridge cable base frequency identification device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0078] like Figure 7 As shown, the bridge cable baseband identification device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the bridge cable baseband identification device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the bridge cable baseband identification device to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows a bridge cable baseband identification device with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0079] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0080] The bridge cable fundamental frequency identification device provided in this application, employing the bridge cable fundamental frequency identification method described in the above embodiments, can solve the technical problem of bridge cable fundamental frequency identification. Compared with the prior art, the beneficial effects of the bridge cable fundamental frequency identification device provided in this application are the same as those of the bridge cable fundamental frequency identification method provided in the above embodiments, and other technical features of this bridge cable fundamental frequency identification device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0081] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0082] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0083] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the bridge cable fundamental frequency identification method in the above embodiments.

[0084] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0085] The aforementioned computer-readable storage medium may be included in the bridge cable fundamental frequency identification device; or it may exist independently and not assembled into the bridge cable fundamental frequency identification device.

[0086] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0087] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0088] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0089] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described bridge cable fundamental frequency identification method, thereby solving the technical problem of bridge cable fundamental frequency identification. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the bridge cable fundamental frequency identification method provided in the above embodiments, and will not be repeated here.

[0090] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the bridge cable fundamental frequency identification method described above.

[0091] The computer program product provided in this application can solve the technical problem of fundamental frequency identification of bridge cables. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the bridge cable fundamental frequency identification method provided in the above embodiments, and will not be repeated here.

[0092] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for identifying the fundamental frequency of bridge cables, characterized in that, The bridge cable fundamental frequency identification method includes: Acquire the vibration acceleration data of the cable to be tested and the power spectrum data set corresponding to the vibration acceleration data; The noise floor is calculated based on the power spectrum data set; Based on the noise floor, the energy peak values ​​of each power spectrum data group in the power spectrum data group are filtered to obtain a frequency-energy peak value array; The frequency-energy peak array and the predetermined candidate fundamental frequencies are scored using a preset fundamental frequency scoring model to obtain the score value of each candidate fundamental frequency; The actual fundamental frequency of the cable to be tested is determined based on the score value of each candidate fundamental frequency; The step of scoring the frequency-energy peak array and the pre-determined candidate fundamental frequencies using a preset fundamental frequency scoring model to obtain a score value for each candidate fundamental frequency includes: Determine the window width coefficient and the maximum verification harmonic order for each candidate fundamental frequency; The harmonic energy and frequency-energy peak array of each candidate fundamental frequency are matched, wherein the harmonic energy is the energy peak of the target fundamental frequency within the window width coefficient range, which is an integer multiple of the candidate fundamental frequency; The number of target fundamental frequencies matching the harmonic energy in the frequency-energy peak array and the cumulative harmonic energy are counted, where the cumulative harmonic energy is the sum of the energy of the candidate fundamental frequency and the energy of the target fundamental frequency; The candidate fundamental frequencies are scored using a preset fundamental frequency scoring model based on the total energy of the frequency-energy peak array, the number of target harmonics, the maximum number of verified harmonics, the energy of the candidate fundamental frequencies, and the cumulative energy of the harmonics, to obtain a score value for each candidate fundamental frequency. Before the step of scoring each candidate fundamental frequency using a preset fundamental frequency scoring model based on the total energy of the frequency-energy peak array, the number of target harmonics, the maximum verified harmonic order, the energy of the candidate fundamental frequency, and the cumulative harmonic energy, the following steps are included: The harmonic weight and harmonic energy proportion weight are calculated by a preset harmonic parameter weighting processing strategy. The harmonic energy proportion weight is the proportion of the cumulative harmonic energy to all energy within the window width coefficient range from the candidate fundamental frequency to the target fundamental frequency. Accordingly, based on the total energy of the frequency-energy peak array, the number of target harmonics, the maximum verified harmonic order, the energy of the candidate fundamental frequency, and the cumulative harmonic energy, a score is obtained for each candidate fundamental frequency using a preset fundamental frequency scoring model, including: The candidate fundamental frequencies are scored using a preset fundamental frequency scoring model based on the total energy of the frequency-energy peak array, the number of target harmonics, the maximum number of verified harmonics, the energy of the candidate fundamental frequencies, the cumulative energy of the harmonics, the harmonic weights, and the harmonic energy percentage weights, to obtain a score value for each candidate fundamental frequency. Specifically, the calculation formula for the preset fundamental frequency scoring model is as follows: in, This is the final score. For the target harmonic number, To maximize the number of harmonics verified, The energy of the candidate fundamental frequency. To accumulate energy for harmonics, The sum of the energy values ​​in the frequency-energy peak array; For harmonic weights, W energy This represents the weighting of energy percentage.

2. The bridge cable fundamental frequency identification method as described in claim 1, characterized in that, The step of filtering the power spectrum data in the power spectrum data group according to the noise floor to obtain a frequency-energy peak array includes: The local peak values ​​and offsets of each power spectrum data group in the power spectrum data group are fitted by the three-point parabolic interpolation method. The local peak value is the maximum energy value corresponding to each frequency within a preset window range, and the maximum energy value is greater than the energy peak value of the noise substrate. The center frequency is determined based on the offset. The cumulative energy of the center frequency within a preset window range is calculated. Based on the energy accumulation and updating of the local peak values ​​of each group of power spectrum data, a frequency-energy peak array is obtained.

3. The bridge cable fundamental frequency identification method as described in claim 2, characterized in that, The fitting of local peak values ​​and offsets of each power spectrum data group in the power spectrum data set using the three-point parabolic interpolation method includes: The power spectrum data set was filtered to obtain a target power spectrum data set after removing the DC frequency and Nyquist frequency. Based on a preset window range, the target power spectrum data group is traversed to determine multiple local energy peaks; By comparing the local peak values ​​of each energy level with the noise floor, target local peak values ​​that are greater than the noise floor are selected. Determine the frequency corresponding to the local peak value of the target energy; Calculate the offset between the frequency corresponding to the local peak of the target energy and the initial frequency in the power spectrum data.

4. The bridge cable fundamental frequency identification method as described in claim 1, characterized in that, The calculation of the noise floor based on the power spectrum data set includes: Determine the energy peak value of each power spectrum data in the power spectrum data group; Arrange the energy peaks in descending order from high to low to obtain an energy sorting array; Based on a preset filtering strategy, some energy peaks in the energy arrangement array are removed to obtain the target energy arrangement array; Calculate the mean energy of the target energy permutation array and output it as the noise floor.

5. A bridge cable fundamental frequency identification device, used to implement the bridge cable fundamental frequency identification method according to any one of claims 1-4, characterized in that, The bridge cable fundamental frequency identification device includes: The acquisition module is used to acquire the vibration acceleration data of the cable to be tested and the power spectrum data group corresponding to the vibration acceleration data; The calculation module is used to calculate the noise floor based on the power spectrum data set; The evaluation module is used to filter the energy peaks of each group of power spectrum data in the power spectrum data group according to the noise basis to obtain a frequency-energy peak array; The scoring module is used to score the frequency-energy peak array and the pre-determined candidate fundamental frequencies through a preset fundamental frequency scoring model to obtain the score value of each candidate fundamental frequency; The determination module is used to determine the actual fundamental frequency of the cable to be tested based on the score value of each candidate fundamental frequency.

6. A bridge cable fundamental frequency identification device, characterized in that, The bridge cable fundamental frequency identification device includes: a memory, a processor, and a bridge cable fundamental frequency identification program stored in the memory and executable on the processor, wherein the bridge cable fundamental frequency identification program is configured to implement the bridge cable fundamental frequency identification method as described in any one of claims 1 to 4.

7. A storage medium, characterized in that, The storage medium stores a bridge cable fundamental frequency identification program, which, when executed by a processor, implements the bridge cable fundamental frequency identification method as described in any one of claims 1 to 4.

8. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the bridge cable fundamental frequency identification method as described in any one of claims 1 to 4.

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