Stay cable real-time time-varying cable force identification method based on adaptive frequency modulation mode decomposition

Abstract

The invention provides a stay cable real-time time-varying cable force identification method based on adaptive frequency modulation mode decomposition, and the method comprises the steps: firstly carrying out the online segmentation of vibration data through employing a sliding window technology, determining the time-invariant mode frequency of a target mode component in each sliding window through employing a scale space peak value pickup algorithm SSPP, and carrying out the recognition of the time-invariant cable force of the target mode component; and identifying the time-varying frequency of the stay cable through an adaptive frequency modulation mode decomposition (ACMD) algorithm.

Description

Technical Field

[0001] The invention belongs to the technical field of civil engineering structure health monitoring and signal processing interaction, and in particular relates to a method for identifying real-time time-varying cable forces of a stay cable based on adaptive frequency modulation mode decomposition. Background Art

[0002] At present, the cable force identification methods of inclined cables can be roughly divided into two categories: direct method and indirect method. The direct method directly measures the cable force through various sensors such as force sensors, fiber Bragg grating (OFBG) sensors and electromagnetic (EM) sensors. Although sensors have the advantages of direct and high precision in measuring cable force, they also have shortcomings. For example, force sensors and OFBG sensors need to be installed during the construction phase, which makes replacement difficult, has poor durability, and is not suitable for existing cables without sensors installed. EM sensors are expensive and are only suitable for the measurement of static cable force. The indirect method based on the vibration principle has attracted widespread attention as an economical, convenient and non-destructive cable force identification method. The idea of ​​this type of method is to obtain the instantaneous vibration frequency of the cable through signal analysis methods, and then calculate the time-varying cable force based on the relationship between the cable force and its vibration frequency.

[0003] The key to the time-varying cable force identification based on the vibration method is to accurately identify the instantaneous vibration frequency of the cable. Identifying the instantaneous frequency of the inclined cable is the core step of the time-varying cable force identification based on the vibration method. For the real-time time-varying cable force identification of the cable, the existing technology has proposed a variety of solutions, but they all have their own obvious defects, such as:

[0004] (1) The cable force identification method based on the CP algorithm requires at least two accelerometers to be installed on the cable or a video monitoring system to extract the multi-point displacement of the cable, which is not convenient for practical application. (2) In the half-wave cable force calculation method, in order to ensure the accuracy, the high-order modal frequency of the cable needs to be obtained, which requires a higher vibration response sampling rate. (3) The real-time time-varying cable force identification method based on the Capon algorithm and the block recursive amplitude phase estimation algorithm requires a very short sliding window step or update block. Due to the low signal-to-noise ratio of the structural health monitoring signal, these methods are prone to calculation errors in actual engineering. (4) The cable force identification method based on the Hilbert transform has a prominent endpoint effect problem. Summary of the invention

[0005] Considering that identifying the instantaneous frequency of a cable-stayed cable is a core step in identifying the time-varying cable tension in the cable-stayed cable based on the vibration method and the defects and shortcomings of the prior art, the present invention proposes a real-time time-varying cable tension identification method for a cable-stayed cable based on adaptive frequency modulation modal decomposition, which adopts a high-performance adaptive frequency modulation modal decomposition (ACMD). In order to improve the applicability of ACMD to online tension identification, the present invention also adopts a sliding window technology to segment vibration data online, and a scale-space peak picking (SSPP) algorithm is used to determine the key parameters of the ACMD algorithm.

[0006] The technical solution specifically adopted by the present invention to solve the technical problem is:

[0007] A real-time time-varying cable force identification method based on adaptive frequency modulation modal decomposition is proposed. The sliding window technology is used to segment the vibration data online. In each sliding window, the time-invariant modal frequency of the target modal component is determined by the scale space peak picking algorithm SSPP, and the time-varying frequency of the cable is identified by the adaptive frequency modulation modal decomposition algorithm ACMD.

[0008] Furthermore, the present invention measures the vibration acceleration time history data of the inclined cable by collecting data from an acceleration sensor on the cable, and performs detrending preprocessing on the collected data. Then, based on a certain length of collected signals, the PSD (power spectral density) is used to select those modes that are excited and / or well measured as target identification modal components of the cable.

[0009] Furthermore, the process of updating the vibration signal in real time by the sliding window is as follows:

[0010] At the beginning: Select L w The SSPP+ACMD algorithm is executed within the sliding window of length.

[0011] In the middle step: the sliding window skips the sliding step length L relative to the previous step s Length, perform SSPP+ACMD algorithm in the sliding window; for the first intermediate step, take the distance L from the end of the window in the starting step W -(L W -L S ) / 2 distance from the previous output result and the first intermediate step distance from the window head position L W -(L W -L S ) / 2 distance after the output results; the connection position of the output results of any two adjacent intermediate steps is L away from the end of the window in the previous window W -(L W -L S ) / 2 position;

[0012] End step: When the sliding window slides to the last segment of collected data, the SSPP+ACMD algorithm is executed in the sliding window; the distance L from the window head in the end step is taken W -(L W -L S ) / 2 distance after the output result and the last intermediate step distance window tail L W -(L W -L S ) / 2 before the output result is connected;

[0013] This enables real-time updated cable force identification of the sliding window.

[0014] Furthermore, in each sliding window, after the time-invariant modal frequency of the target modal component is determined by using the scale space peak picking algorithm SSPP, the time-invariant modal frequency is passed to the ACMD algorithm as the initial value of the frequency of the modal component; the SSPP algorithm identifies the time-invariant frequency of the cable by identifying the peak point of the PSD (power spectral density) analysis data of the vibration signal.

[0015] Furthermore, the method for identifying the time-varying frequency of the cable by using the adaptive frequency modulation mode decomposition algorithm ACMD is specifically as follows:

[0016] The vibration signal of the cable is expressed as follows after demodulation:

[0017]

[0018] in

[0019]

[0020] Where y(t) is the cable vibration signal, m is the number of modal components contained in the vibration signal, For the mediation operator and The frequency function, p i (t) and q i (t) is the modulation signal, θ i is the initial phase, and the instantaneous amplitude of the modulation signal is the instantaneous frequency of the modulation signal; ACMD decomposes the signal by minimizing the bandwidth of the modulation signal, and the optimization goal of ACMD is determined by combining the greedy search algorithm:

[0021]

[0022] In the formula, ||·||2 represents the l2 norm, is the residual energy of the vibration signal after removing the estimated component, and α is the weight factor; ACMD continuously searches for the signal component that can take away the most energy from the input signal; for the time series t=t0,…,t N-1 For a discrete signal, N is the number of sampling points, and equation (3) can be rewritten as:

[0023]

[0024] In the formula, Ω is the second-order difference matrix, y=[y(t0),…,y(t N-1 )] T , G i =[C i ,S i ], p i =[p i (t0),…,p i (t N-1 )] T ,q i =[p i (t0),…,p i (t N-1 )] T ;

[0025] The optimization problem corresponding to the above optimization objective is solved by alternately updating the demodulated signal and the frequency function; the estimation of the i-th component signal at the j-th iteration is:

[0026]

[0027] The instantaneous frequency of the component signal is updated as

[0028]

[0029] In the formula, is the frequency increment matrix, I is the identity matrix, and β is the penalty coefficient;

[0030] When two consecutive estimated signals When the difference of the iterations is less than the preset value or the number of iterative calculations reaches the preset maximum number of iterations, stop estimating the i-th signal component; based on the recursive idea, after obtaining the i-th signal component, subtract it from the original signal, and continue to decompose the residual signal as the new initial signal; finally, when the energy of the residual signal is less than the preset value, the ACMD algorithm stops calculating.

[0031] Furthermore, after each iterative calculation exits the ACMD algorithm, the instantaneous frequency of each window is connected according to the connection method defined by the sliding window.

[0032] Furthermore, after the ACMD algorithm is solved, the modal order corresponding to the time-varying instantaneous frequency of the cable is determined according to the law of the frequency doubling phenomenon of the cable, and then the instantaneous frequency of the cable is substituted into the tension string formula of the cable: T = 4ml (f n / n) 2 , the real-time time-varying cable force identification result of the cable is obtained; where m is the mass per meter of the cable, l is the length of the cable, n is the modal order, and f n is the vibration frequency of the nth mode.

[0033] And, a real-time time-varying cable force identification system for a stay cable based on adaptive frequency modulation modal decomposition, comprising: a sliding window module, an SSPP module and an ACMD module; the sliding window module uses a sliding window technology to segment vibration data online;

[0034] In each sliding window, the SSPP module is called to determine the time-invariant modal frequency of the target modal component using the scale space peak picking algorithm SSPP, and the ACMD module is called to identify the time-varying frequency of the cable through the adaptive frequency modulation modal decomposition algorithm ACMD.

[0035] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the method for identifying real-time time-varying cable forces of a stay cable based on adaptive frequency modulation modal decomposition as described above are implemented.

[0036] A non-transitory computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for identifying real-time time-varying cable forces of a stay cable based on adaptive frequency modulation modal decomposition as described above.

[0037] Compared with the prior art, the key design points of the present invention and its preferred solution include:

[0038] (1) The present invention adopts the ACMD algorithm with high resolution, strong robustness, computational efficiency and the ability to target specific modal components to identify the time-varying frequency of the cable.

[0039] (2) To enhance the applicability of ACMD in real-time time-varying cable potentials, SSPP determines the time-invariant modal frequencies of the target modal components within each sliding window and then passes them to ACMD as key parameters.

[0040] Key benefits include:

[0041] 1) The real-time computing feature of the present invention makes it suitable for streaming data computing, which solves the key problem of real-time time-varying frequency identification of the cable force, and realizes the intelligent perception of real-time changes from the cable monitoring signal to the cable vibration frequency and then to the cable time-varying cable force.

[0042] 2) The present invention only requires data from one acceleration sensor on the cable.

[0043] 3) The present invention is efficient and accurate in calculation: The present invention uses ACMD to identify the time-varying frequency of the vibration signal. Since ACMD has the advantages of high resolution, strong robustness, high calculation efficiency and the ability to target specific modal components, the present invention is efficient and accurate in calculation when used for real-time time-varying cable force identification.

[0044] 4) The invention has strong applicability: the invention adopts the sliding window technology to segment the cable vibration signal online, and solves the boundary effect of ACMD by setting the overlapping signal. In addition, the invention adopts the SSPP algorithm to determine the key parameters of ACMD, which enhances the applicability of ACMD in cable force identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0046] Figure 1 This is a flowchart of the overall framework of an embodiment of the present invention;

[0047] Figure 2 This is a flow chart of updating vibration signals of a sliding window according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to make the features and advantages of the present invention more clearly understood, the following embodiments are specifically described in detail as follows:

[0049] It should be noted that the following detailed descriptions are illustrative and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used in this specification have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0050] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0051] Figure 1 The present invention provides a method flow for identifying the real-time time-varying cable force of a stay cable based on adaptive modulation mode decomposition (ACMD). The method of the present invention comprises the following steps:

[0052] Firstly, the vibration acceleration time history data of the cable is measured using an accelerometer, and the collected data is preprocessed for detrending.

[0053] In order to realize real-time cable force identification, the cable vibration signal needs to be updated in real time. The embodiment of the present invention adopts sliding window technology to achieve this goal. The schematic diagram of the sliding window updating the vibration signal in real time is as follows: Figure 2 As shown, where L w and L s They are the length of the sliding window and the sliding step size respectively:

[0054] The update process is:

[0055] At the beginning: Select L w The SSPP+ACMD algorithm is executed within the sliding window of length.

[0056] In the middle step: the sliding window skips the sliding step length L relative to the previous step s The length of the window (for the first intermediate step, it is relative to the starting step) is used to perform the SSPP+ACMD algorithm in the sliding window. For the first intermediate step, the distance from the end of the window to the starting step is L. W -(L W -L S ) / 2 distance from the previous output result and the first intermediate step distance from the window head position L W -(L W -L S ) / 2 distance after the output results; the connection position of the output results of any two adjacent intermediate steps is L away from the end of the window in the previous window W -(L W -L S ) / 2 position;

[0057] End step: When the sliding window slides to the last segment of collected data, the SSPP+ACMD algorithm is executed in the sliding window; the distance L from the window head in the end step is taken W -(L W -L S ) / 2 distance after the output result and the last intermediate step distance window tail L W -(L W -L S ) / 2 before the output result is connected;

[0058] This enables real-time updated cable force identification of the sliding window.

[0059] As the focus of the present invention, a key parameter of the ACMD algorithm is the initial value of the target modal component, and the rationality of the initial value setting directly affects the performance of the ACMD algorithm. In order to adaptively set the initial value of the target modal component and avoid cumbersome manual participation, the present invention uses the scale space peak recognition (SSPP, from the document LIUTK USA. Scale-Space Peak Picking [R]: Inria Nancy-Grand Est (Villers-lès-Nancy, France), 2015) algorithm in each sliding window to adaptively determine the time-invariant modal frequency of the cable, and these time-invariant modal frequencies are passed to the ACMD algorithm as the frequency initial value of the modal component. The SSPP algorithm identifies the time-invariant frequency of the cable by identifying the peak point of the spectrum analysis data of the vibration signal.

[0060] ACMD is used to extract the instantaneous frequency of the cable from each real-time analysis window data. The main principles involved in this process are as follows:

[0061] The vibration signal of the cable is expressed as follows after demodulation:

[0062]

[0063] in

[0064]

[0065] Where y(t) is the cable vibration signal, m is the number of modal components contained in the vibration signal, For the mediation operator and The frequency function, p i (t) and q i (t) is the modulation signal, θ i is the initial phase, and the instantaneous amplitude of the modulation signal is the instantaneous frequency of the modulation signal. ACMD decomposes the signal by minimizing the bandwidth of the modulation signal. Combined with the greedy search algorithm, the optimization goal of ACMD is determined as:

[0066]

[0067] In the formula, ||·||2 represents the l2 norm, is the residual energy of the vibration signal after removing the estimated component, and α is the weight factor. ACMD continuously searches for the signal component that can take away the most energy from the input signal. For the time series t=t0,…,t N-1 For a discrete signal (N is the number of sampling points), equation (3) can be rewritten as

[0068]

[0069] In the formula, Ω is the second-order difference matrix, y=[y(t0),…,y(t N-1 )] T , G i =[C i ,S i ], p i =[p i (t0),…,p i (t N-1 )] T ,q i =[p i (t0),…,p i (t N-1 )] T .

[0070] This optimization problem is solved by alternately updating the demodulated signal and the frequency function. The estimate of the i-th component signal at the j-th iteration is:

[0071]

[0072] The instantaneous frequency of the component signal is updated as

[0073]

[0074] In the formula, is the frequency increment matrix, I is the identity matrix, and β is the penalty coefficient.

[0075] When two consecutive estimated signals When the difference of the iterations is less than the preset value or the number of iterative calculations reaches the preset maximum number of iterations, the estimation of the i-th signal component is stopped. Based on the recursive idea, after obtaining the i-th signal component, it is subtracted from the original signal, and the residual signal is used as the new initial signal to continue decomposition. Finally, when the energy of the residual signal is less than the preset value, the ACMD algorithm stops calculating.

[0076] After exiting the ACMD algorithm, the instantaneous frequency of each window is calculated according to Figure 2 to connect using the connection method specified in the table below.

[0077] Based on the rule that the cable frequency roughly shows a frequency doubling phenomenon, the modal order corresponding to the time-varying cable instantaneous frequency is determined, and then the cable instantaneous frequency is substituted into the cable tension string formula: T = 4ml (f n / n) 2 , and the real-time time-varying cable force identification results are obtained. Where m is the mass per meter of the cable, l is the length of the cable, n is the modal order, and f is the nis the vibration frequency of the nth mode.

[0078] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0079] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0080] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0081] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0082] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0083] The above is only a preferred embodiment of the present invention, and does not limit the present invention in other forms. Any technician familiar with the profession may use the above disclosed technical content to change or modify it into an equivalent embodiment with equivalent changes. However, any simple modification, equivalent change and modification made to the above embodiment according to the technical essence of the present invention without departing from the technical solution of the present invention still belongs to the protection scope of the technical solution of the present invention.

[0084] The present invention is not limited to the above-mentioned optimal implementation mode. Anyone can derive other various forms of real-time time-varying cable force identification methods based on adaptive frequency modulation modal decomposition under the inspiration of the present invention. All equal changes and modifications made within the scope of the patent application of the present invention should fall within the scope of the present invention.

Claims

1. A method for identifying real-time time-varying cable forces in a stay cable based on adaptive frequency modulation modal decomposition, characterized in that: The sliding window technology is used to segment the vibration data online. In each sliding window, the scale space peak picking algorithm SSPP is used to determine the time-invariant modal frequency of the target modal component, and the adaptive frequency modulation modal decomposition algorithm ACMD is used to identify the time-varying frequency of the cable.

2. The method for identifying real-time time-varying cable forces of a stay cable based on adaptive frequency modulation modal decomposition according to claim 1 is characterized by: The vibration acceleration time history data of the inclined cable is measured by collecting data from an acceleration sensor on the cable, and the collected data is preprocessed to remove the trend; then, based on a certain length of collected signals, the target identification modal component of the cable is selected through the power spectral density.

3. The method for identifying real-time time-varying cable forces of a stay cable based on adaptive frequency modulation modal decomposition according to claim 1 is characterized by: The process of updating the vibration signal in real time with the sliding window is as follows: At the beginning: Select L w The SSPP+ACMD algorithm is executed within the sliding window of length. In the middle step: the sliding window skips the sliding step length L relative to the previous step s Length, perform SSPP+ACMD algorithm in the sliding window; for the first intermediate step, take the distance L from the end of the window in the starting step W -(L W -L S ) / 2 distance from the previous output result and the first intermediate step distance from the window head position L W -(L W -L S ) / 2 distance after the output results; the connection position of the output results of any two adjacent intermediate steps is L away from the end of the window in the previous window W -(L W -L S ) / 2 position; End step: When the sliding window slides to the last segment of collected data, the SSPP+ACMD algorithm is executed in the sliding window; the distance L from the window head in the end step is taken W -(L W -L S ) / 2 distance after the output result and the last intermediate step distance window tail L W -(L W -L S ) / 2 before the output result is connected; This enables real-time updated cable force identification of the sliding window.

4. The method for identifying real-time time-varying cable forces of a stay cable based on adaptive frequency modulation modal decomposition according to claim 1 is characterized by: In each sliding window, after determining the time-invariant modal frequency of the target modal component by using the scale space peak picking algorithm SSPP, the time-invariant modal frequency is passed to the ACMD algorithm as the initial value of the frequency of the modal component; The SSPP algorithm identifies the time-invariant frequency of the cable by identifying the peak points of the power spectral density analysis data of the vibration signal.

5. The method for identifying real-time time-varying cable forces of a stay cable based on adaptive frequency modulation modal decomposition according to claim 1 is characterized by: The method for identifying the time-varying frequency of the cable by using the adaptive frequency modulation mode decomposition algorithm ACMD is specifically as follows: The vibration signal of the cable is expressed as follows after demodulation: in Where y(t) is the cable vibration signal, m is the number of modal components contained in the vibration signal, For the mediation operator and The frequency function, p i (t) and q i (t) is the modulation signal, θ i is the initial phase, and the instantaneous amplitude of the modulation signal is the instantaneous frequency of the modulation signal; ACMD decomposes the signal by minimizing the bandwidth of the modulation signal, and the optimization goal of ACMD is determined by combining the greedy search algorithm: In the formula, ·2 represents the l2 norm, is the residual energy of the vibration signal after removing the estimated component, and α is the weight factor; ACMD continuously searches for the signal component that can take away the most energy from the input signal; for the time series t=t0,…,t N-1 For a discrete signal, N is the number of sampling points, and equation (3) can be rewritten as: In the formula, Ω is the second-order difference matrix, y=[y(t0),…,y(t N-1 )] T , G i =[C i ,S i ], p i =[p i (t0),…,p i (t N-1 )] T ,q i =[p i (t0),…,p i (t N-1 )] T ; The optimization problem corresponding to the above optimization objective is solved by alternately updating the demodulated signal and the frequency function; the estimation of the i-th component signal at the j-th iteration is: The instantaneous frequency of the component signal is updated as In the formula, is the frequency increment matrix, I is the identity matrix, and β is the penalty coefficient; When two consecutive estimated signals When the difference of the iterations is less than the preset value or the number of iterative calculations reaches the preset maximum number of iterations, stop estimating the i-th signal component; based on the recursive idea, after obtaining the i-th signal component, subtract it from the original signal, and continue to decompose the residual signal as the new initial signal; finally, when the energy of the residual signal is less than the preset value, the ACMD algorithm stops calculating.

6. The method for identifying real-time time-varying cable forces of a stay cable based on adaptive frequency modulation modal decomposition according to claim 5 is characterized by: After each iterative calculation exits the ACMD algorithm, the instantaneous frequency of each window is connected according to the connection method defined by the sliding window.

7. The method for identifying real-time time-varying cable forces of a stay cable based on adaptive frequency modulation modal decomposition according to claim 1 is characterized by: After the ACMD algorithm is solved, the modal order corresponding to the time-varying instantaneous frequency of the cable is determined according to the law of the frequency doubling phenomenon of the cable, and then the instantaneous frequency of the cable is substituted into the tension string formula of the cable: T = 4ml (f n / n) 2 , the real-time time-varying cable force identification result of the cable is obtained; where m is the mass per meter of the cable, l is the length of the cable, n is the modal order, and f n is the vibration frequency of the nth mode.

8. A real-time time-varying cable force identification system based on adaptive frequency modulation mode decomposition, characterized in that: include: Sliding window module, SSPP module and ACMD module; The sliding window module uses sliding window technology to segment vibration data online; In each sliding window, the SSPP module is called to determine the time-invariant modal frequency of the target modal component using the scale space peak picking algorithm SSPP, and the ACMD module is called to identify the time-varying frequency of the cable through the adaptive frequency modulation modal decomposition algorithm ACMD.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the method for real-time time-varying cable force identification based on adaptive frequency modulation modal decomposition as described in any one of claims 1 to 7 are implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for identifying real-time time-varying cable forces of a stay cable based on adaptive frequency modulation mode decomposition as described in any one of claims 1 to 7 are implemented.

Images