Working mode parameter identification method, device and equipment based on power exponent window function

Abstract

The invention provides a working modal parameter identification method, device and equipment based on a power exponent window function, and relates to the technical field of modal parameter identification. The method comprises the following steps: collecting vibration response signals of a plurality of sensors under complex working condition excitation in real time, and performing sliding window segmentation on the vibration response signals; standardizing the signal in each sliding window, and weighting the standardized signal by using a power exponent window function to obtain a weighted signal; frequency spectrum features are extracted from the weighted signals through fast Fourier transform, and the interference ratio is calculated; according to the interference ratio, adjusting in combination with window function parameters so as to reduce negative frequency domain interference; according to the optimized spectrum features, the inherent frequency and the damping ratio of the signal in each sliding window are identified, and a modal shape is extracted by combining principal component analysis; and integrating identification results of all the signals in the sliding window to obtain modal parameter characteristics in a full time domain, and realizing online dynamic identification of working modal parameters. According to the invention, the accuracy of modal parameter identification can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of working modal parameter identification, and in particular to a working modal parameter identification method, device and equipment based on a power exponential window function. Background Art

[0002] In practical engineering applications, modal parameter identification of dynamic systems is an important part of system dynamics characteristics analysis and is widely used in structural health monitoring, damage identification, vibration control, model updating and other fields. However, modal parameter identification faces many challenges under complex working conditions, especially under time-varying and underdetermined conditions.

[0003] At present, the frequency domain modal parameter identification method uses the spectral characteristics of the signal to extract the modal parameters. It has certain anti-noise ability and the advantage of processing non-stationary signals, but it also faces the interference of spectral leakage and boundary effects. Although the commonly used window functions (such as Hanning window and Hamming window) can suppress spectral leakage to a certain extent, their inherent design characteristics limit the recognition accuracy. Especially in multi-modal and complex working conditions, traditional window functions cannot achieve high-precision extraction of modal parameters. In addition, the existing frequency domain methods lack a clear explanation of the physical meaning of the window function and cannot effectively associate the structural characteristics and spectral characteristics of the system.

[0004] In view of this, the applicant filed this application after studying the existing technology. Summary of the invention

[0005] The present invention aims to provide a method, device and equipment for identifying working modal parameters based on a power exponential window function, so as to solve the shortcomings of existing methods such as spectrum leakage and low recognition accuracy of modal parameters.

[0006] In order to solve the above technical problems, the present invention is implemented through the following technical solutions: A method for identifying working modal parameters based on a power exponential window function, comprising: S1, real-time acquisition of vibration response signals of multiple sensors under complex working conditions, and segmentation of them by sliding windows; S2, standardize the signal in each sliding window to obtain a segmented standardized signal; S3, applying a power exponential window function to the standardized signal for weighting to obtain a weighted signal, so as to enhance the spectral characteristics of the signal and suppress boundary effects and spectral leakage; S4, extracting spectrum features from the weighted signal using fast Fourier transform, thereby calculating the interference ratio; S5, adjusting according to the interference ratio in combination with window function parameters to reduce negative frequency domain interference and optimize the modal separation effect; S6, according to the optimized spectrum characteristics, the natural frequency and damping ratio of the signal in each sliding window are identified, and the modal vibration shape is extracted by combining the principal component analysis technology to obtain the identification result in each sliding window; S7, integrates the recognition results of all the signals in the sliding windows, obtains the modal parameter characteristics in the full time domain, and realizes the online dynamic recognition of the working modal parameters.

[0007] Preferably, the standardized signal is obtained by calculating the mean value of the signal in each sliding window. and standard deviation The formula is: ; ; ; in, , is the signal of the i-th sliding window; M is the total number of sliding windows, ; S is the sliding step length; L is the length of the sliding window; N represents the number of sensors deployed for detection; t represents time; is the standardized signal of the i-th sliding window; is the mean value of the signal in the i-th sliding window; is the standard deviation of the signal in the i-th sliding window.

[0008] Preferably, the expression of the weighted signal is: ; ; in, represents the weighted signal of the i-th sliding window; is the standardized signal of the i-th sliding window; t represents time; represents the power exponential window function; and are window function parameters, which control the exponential decay speed and power shape respectively.

[0009] Preferably, a fast Fourier transform is used to extract spectral features from the weighted signal, specifically: Weighted signal Perform fast Fourier transform to get the spectrum , the expression is: ; in, is the frequency variable of the signal, indicating the speed of signal vibration; j is the imaginary unit, and in Fourier transform, j is used to represent the complex part; Extract spectrum The positive frequency domain component of and negative frequency domain components , expressed as: ; .

[0010] Preferably, the expression of the interference ratio is: ; in, is the interference ratio; is the positive frequency domain component; is the negative frequency domain component.

[0011] Preferably, by adjusting the parameters of the window function and To maximize the interference ratio , to reduce negative frequency domain interference.

[0012] Preferably, the number of the sensors is greater than the number of degrees of freedom of the target structure so as to fully capture the dynamic characteristics of the target structure.

[0013] The present invention also provides a working modal parameter identification device based on a power exponential window function, comprising: The acquisition and segmentation unit is used to acquire the vibration response signals of multiple sensors under complex working conditions in real time and perform sliding window segmentation on them; A standardization unit, used to standardize the signal in each sliding window to obtain a segmented standardized signal; A power exponential window unit, used for applying a power exponential window function to the standardized signal for weighting to obtain a weighted signal, so as to enhance the spectral characteristics of the signal and suppress boundary effects and spectral leakage; An interference ratio calculation unit, used for extracting spectrum features from the weighted signal by fast Fourier transform, so as to calculate the interference ratio; A parameter adjustment unit, used to adjust according to the interference ratio in combination with window function parameters to reduce negative frequency domain interference and optimize the modal separation effect; The segmented recognition unit is used to identify the natural frequency and damping ratio of the signal in each sliding window according to the optimized spectrum characteristics, and extract the modal vibration shape by combining the principal component analysis technology to obtain the recognition result in each sliding window; The full time domain unit is used to integrate the recognition results of all signals in the sliding window, obtain the modal parameter characteristics in the full time domain, and realize the online dynamic recognition of the working modal parameters.

[0014] The present invention also provides a working modal parameter identification device based on a power exponential window function, comprising a processor and a memory, wherein the memory stores a computer program, and the computer program can be executed by the processor to implement a working modal parameter identification method based on a power exponential window function as described above.

[0015] The present invention also provides a computer-readable storage medium, on which computer-readable instructions are stored. When the computer-readable instructions are executed by a processor of a device where the computer-readable storage medium is located, a working modal parameter identification method based on a power exponential window function as described above is implemented.

[0016] In summary, compared with the prior art, the present invention has the following beneficial effects: The present invention weights the signal through a power exponential window function and uses the window function parameters for flexible adjustment to enhance the spectral characteristics of the signal, suppress boundary effects and spectrum leakage, thereby improving the accuracy of modal parameter identification and effectively adapting to the rapid change characteristics of dynamic system signals. The method of the present invention is particularly suitable for online monitoring and modal identification of large-scale structures (such as bridges, wind turbines, etc.), and can effectively improve the accuracy of modal parameter identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A schematic diagram of a working modal parameter identification method based on a power exponential window function provided in Example 1.

[0019] Figure 2 A flowchart of a method for identifying working modal parameters based on a power exponential window function is provided in Example 1.

[0020] Figure 3 This is a schematic diagram of the structure of the time-varying three-degree-of-freedom spring oscillator model provided in Example 1.

[0021] FIG4 (1) and FIG4 (2) are comparison diagrams of the natural vibration modes (i.e., theoretical values) of the three-degree-of-freedom time-varying system at different times of 168.5 s and 625.8 s respectively provided by the method of the present invention in Example 1 and the modal vibration modes (i.e., identification values) identified by the method of the present invention.

[0022] Figure 5This is a comparison diagram of the theoretical natural frequency of the three-degree-of-freedom time-varying structure provided in Example 1 and the natural frequency change curve identified by the method of the present invention.

[0023] Figure 6 This is a curve diagram of MAC value variation of a three-degree-of-freedom time-varying system identified by the method of the present invention provided in Example 1.

[0024] Figure 7 A schematic diagram of a working modal parameter identification device based on a power exponential window function provided in Example 2.

[0025] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the invention claimed for protection, but merely represents the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0027] Embodiment 1 Embodiment 1 of the present invention provides a method for identifying working modal parameters based on a power exponential window function, which can be implemented by a working modal parameter identification device based on a power exponential window function (hereinafter referred to as the identification device), and in particular, executed by one or more processors in the identification device.

[0028] In this embodiment, the identification device may be an electronic device equipped with a processor, the processor having a computer program of the working modal parameter identification method based on the power exponential window function and the computer program can be executed, such as a computer, a smart phone, a smart tablet, a workstation, etc., which is not limited here.

[0029] In this embodiment, spectrum leakage is also called frequency spectrum leakage, which is a common phenomenon in digital signal processing. It refers to the phenomenon that signal energy leaks to other frequency components due to signal bandgap, truncation, limited signal sampling window and signal boundary effect when performing spectrum analysis or digital signal processing.

[0030] In this embodiment, the power exponential window function is the product of a power function and an exponential function, and has two adjustable parameters. The selection of these two parameters will affect the characteristics of the response spectrum, thereby affecting the modal parameter identification effect of the peak picking method with the power exponential window.

[0031] like Figure 1-Figure 2 As shown, a method for identifying working modal parameters based on a power exponential window function includes steps S1 to S7.

[0032] S1, collects the vibration response signals of multiple sensors under complex working conditions in real time and performs sliding window segmentation on them.

[0033] In this embodiment, multiple sensors are arranged at key locations of the target structure (such as bridges, wind turbines, etc.) to collect the vibration response signals of the structure in real time when it is in working state. , ensuring that the number of sensors is greater than the number of degrees of freedom of the structure to fully capture the dynamic characteristics of the structure.

[0034] Set the sampling frequency (In this embodiment, is greater than twice the target frequency to meet the Nyquist criterion), the sampling time interval is , the total sampling time is T, the vibration signal is discretized into N data points, forming a time series vibration response signal , N represents the number of deployed sensor detections.

[0035] S2, standardize the signal in each sliding window to obtain a segmented standardized signal.

[0036] In this embodiment, the vibration response signal of the time series is , split according to a sliding window of length L.

[0037] Then, the mean of the signal in each sliding window is calculated and standard deviation , to obtain the segmented normalized signal.

[0038] The normalized signal is calculated by calculating the mean of the signal in each sliding window and standard deviation The formula is: ; ; ; in, , is the signal of the i-th sliding window; M is the total number of sliding windows, ; S is the sliding step length; L is the length of the sliding window; N is the number of sensors deployed; t is the time; is the standardized signal of the i-th sliding window; is the mean value of the signal in the i-th sliding window; is the standard deviation of the signal in the i-th sliding window.

[0039] S3, applying a power exponential window function to weight the standardized signal to obtain a weighted signal, so as to enhance the spectral characteristics of the signal and suppress boundary effects and spectral leakage.

[0040] In this embodiment, a power exponential window function is defined, and the power exponential window function W(t) is used to weight the standardized signal. Multiplying with the power exponential window function, a weighted signal is obtained. The expression of the weighted signal is: ; ; in, represents the weighted signal of the i-th sliding window; is the standardized signal of the i-th sliding window; t represents time; represents the power exponential window function; and are window function parameters, which control the exponential decay speed and power shape respectively.

[0041] S4, extracting frequency spectrum features from the weighted signal using fast Fourier transform, thereby calculating the interference ratio.

[0042] In this step, we first weight the signal Perform fast Fourier transform to get the spectrum , the expression is: ; in, is the frequency variable of the signal, indicating the speed of signal vibration; j is the imaginary unit, and in Fourier transform, j is used to represent the complex part; Then, extract the spectrum The positive frequency domain component of and negative frequency domain components , expressed as: ; .

[0043] Next, the interference ratio is calculated, and the expression is: ; in, is the interference ratio; is the positive frequency domain component; is the negative frequency domain component.

[0044] S5, adjusting according to the interference ratio in combination with window function parameters to reduce negative frequency domain interference and optimize the modal separation effect.

[0045] In this step, by adjusting the parameters of the window function and To maximize , in order to reduce negative frequency domain interference. Modal separation aims to extract each independent modal signal from the mixed signal. However, reducing negative frequency domain interference is the key to optimizing the modal separation effect.

[0046] S6, based on the optimized spectrum characteristics, the natural frequency and damping ratio of the signal in each sliding window are identified, and the modal vibration shape is extracted in combination with the principal component analysis technology to obtain the identification result in each sliding window.

[0047] According to the positive frequency domain The peak of the spectrum identifies the natural frequency , an automatic peak tracking algorithm (such as the fast peak detection algorithm) or manual visual detection can be used to identify the peak, and the frequency corresponding to the spectrum peak is the identified natural frequency.

[0048] Natural frequency is the basis of modal analysis, which reflects the frequency characteristics of the structure during free vibration.

[0049] The damping ratio reflects the energy dissipation characteristics of the structure during vibration. The damping ratio is calculated by the spectrum width. , find the peak value 1 / The two frequency components corresponding to and , then the spectrum width for and The difference is = - .

[0050] The modal vibration shapes are extracted through principal component analysis (PCA) of multi-sensor signals.

[0051] The identification results include natural frequency, damping ratio and mode shape.

[0052] S7, integrates the recognition results of all the signals in the sliding windows, obtains the modal parameter characteristics in the full time domain, and realizes the online dynamic recognition of the working modal parameters.

[0053] The modal parameters identified by each sliding window are stored as time series states to obtain the modal parameter characteristics in the full time domain for structural health monitoring and change trend analysis.

[0054] In addition, in another preferred embodiment of the present invention, a linear time-varying three-degree-of-freedom spring oscillator system is used to verify the modal parameter identification method proposed in the present invention.

[0055] like Figure 3 The linear time-varying three-degree-of-freedom spring oscillator system shown.

[0056] Set the sampling frequency The sampling interval is 40Hz. The sampling time is 0.025s and the sampling time is t=2000s.

[0057] Assume that the initial conditions of the three degrees of freedom displacement of the system are all zero, and give the block Apply Gaussian white noise excitation and set the quality to: ; 1kg, rigidity =1000N / m, =1000N / m, =1000N / m; Damping =0.01Ns / m, =0.01Ns / m, =0.01Ns / m.

[0058] Then experiments were conducted on the three-degree-of-freedom spring oscillator.

[0059] Using MATLAB / Newmark- The response signal is obtained by simulation. The sampling frequency of the signal is 40Hz, the sampling time is t=2000s, and the sampling interval is 0.025s.

[0060] This embodiment compares and evaluates the working modal parameter identification method of the present invention through the modal assurance criterion (MAC) and the relative error of the modal natural frequency.

[0061] Modal Assurance Criterion The formula is as follows: ; in, and Respectively represent The MAC value varies between 0 and 1. The larger the MAC value (closer to 1), the better the vibration shape.

[0062] Natural frequency relative error The formula of the evaluation method is as follows: 100% ; in, and Respectively represent The estimated modal natural frequencies and theoretical natural frequencies are The closer the value is to 0, the more accurate the identified natural frequency is.

[0063] The MAC value recognition results of the three-degree-of-freedom spring oscillator at the instantaneous moments of 480s and 830s obtained by the experiment using the method of the present invention are shown in Tables 1 and 2, and the natural frequency error is shown in Table 3.

[0064] Table 1. Confidence criteria of each modal at 480s

[0065] Table 2. Confidence criteria of each modal at 830s

[0066] Table 3. Natural frequency errors of each order

[0067] As shown in Figures 4(1) and 4(2), the natural vibration modes of the three-degree-of-freedom time-varying system at different times of 168.5s and 625.8s of the experimental method provided by an embodiment of the present invention are compared with the instantaneous vibration modes identified by the proposed method.

[0068] From Table 1, Table 2 and Figure 5 , Figure 6 It can be seen from the figure that the method proposed in the present invention can well realize the identification of modal parameters under under-time variation conditions.

[0069] In summary, compared with the prior art, the present invention has the following beneficial effects: The present invention weights the signal through a power exponential window function and flexibly adjusts the window function parameters to enhance the spectral characteristics of the signal, suppress boundary effects and spectrum leakage, thereby improving the accuracy of modal parameter identification and effectively adapting to the rapid change characteristics of dynamic system signals. The method of the present invention is particularly suitable for online monitoring and modal identification of large-scale structures (such as bridges, wind turbines, etc.), and can effectively improve the accuracy of modal parameter identification.

[0070] Embodiment 2 like Figure 7 As shown, the second embodiment of the present invention further provides a working modal parameter identification device based on a power exponential window function, comprising: The acquisition and segmentation unit is used to acquire the vibration response signals of multiple sensors under complex working conditions in real time and perform sliding window segmentation on them; A standardization unit, used to standardize the signal in each sliding window to obtain a segmented standardized signal; A power exponential window unit, used for applying a power exponential window function to the standardized signal for weighting to obtain a weighted signal, so as to enhance the spectral characteristics of the signal and suppress boundary effects and spectral leakage; An interference ratio calculation unit, used for extracting spectrum features from the weighted signal by fast Fourier transform, so as to calculate the interference ratio; A parameter adjustment unit, used to adjust according to the interference ratio in combination with window function parameters to reduce negative frequency domain interference and optimize the modal separation effect; The segmented recognition unit is used to identify the natural frequency and damping ratio of the signal in each sliding window according to the optimized spectrum characteristics, and extract the modal vibration shape by combining the principal component analysis technology to obtain the recognition result in each sliding window; The full time domain unit is used to integrate the recognition results of all signals in the sliding window, obtain the modal parameter characteristics in the full time domain, and realize the online dynamic recognition of the working modal parameters.

[0071] Embodiment 3 The third embodiment of the present invention also provides a working modal parameter identification device based on a power exponential window function, which includes a memory and a processor, wherein a computer program is stored in the memory, and the computer program can be executed by the processor to implement the working modal parameter identification method based on the power exponential window function as described above.

[0072] Embodiment 4 The fourth embodiment of the present invention also provides a computer-readable storage medium, on which computer-readable instructions are stored. When the computer-readable instructions are executed by a processor of a device where the computer-readable storage medium is located, the working modal parameter identification method based on the power exponential window function as described above is implemented.

[0073] In several embodiments provided in the embodiments of the present invention, it should be understood that the disclosed apparatus and method can also be implemented in other ways. The apparatus and method embodiments described above are merely schematic. For example, the flowcharts in the accompanying drawings show the possible architecture, functions and operations of the apparatus, method and computer program product according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, program segment or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0074] In addition, the functional modules in the various embodiments of the present invention may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

[0075] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, an electronic device, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program code. It should be noted that in this article, the term "include", "include" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements inherent to such process, method, article or device. Without more constraints, an element defined by the phrase "comprising a..." does not exclude the existence of other identical elements in the process, method, article or apparatus comprising the element.

[0076] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The singular forms "a", "said" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0077] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0078] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0079] The "first\second" mentioned in the embodiments is only to distinguish similar objects, and does not represent a specific order for the objects. It is understandable that the "first\second" can be interchanged with the specific order or sequence where permitted. It should be understood that the objects distinguished by "first\second" can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than those illustrated or described herein.

[0080] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for identifying working modal parameters based on a power exponential window function, characterized in that: include: S1, real-time acquisition of vibration response signals of multiple sensors under complex working conditions, and segmentation of them by sliding windows; S2, standardize the signal in each sliding window to obtain a segmented standardized signal; S3, applying a power exponential window function to the standardized signal for weighting to obtain a weighted signal, so as to enhance the spectral characteristics of the signal and suppress boundary effects and spectral leakage; S4, extracting spectrum features from the weighted signal using fast Fourier transform, thereby calculating the interference ratio; S5, adjusting according to the interference ratio in combination with window function parameters to reduce negative frequency domain interference and optimize the modal separation effect; S6, according to the optimized spectrum characteristics, the natural frequency and damping ratio of the signal in each sliding window are identified, and the modal vibration shape is extracted by combining the principal component analysis technology to obtain the identification result in each sliding window; S7, integrates the recognition results of all the signals in the sliding windows, obtains the modal parameter characteristics in the full time domain, and realizes the online dynamic recognition of the working modal parameters.

2. The method for identifying working modal parameters based on a power exponential window function according to claim 1 is characterized in that , the standardized signal is obtained by calculating the mean of the signal in each sliding window and standard deviation The formula is: ； ； ； in, , is the signal of the i-th sliding window; M is the total number of sliding windows, ; S is the sliding step length; L is the length of the sliding window; N is the number of sensors deployed; t is the time; is the standardized signal of the i-th sliding window; is the mean value of the signal in the i-th sliding window; is the standard deviation of the signal in the i-th sliding window.

3. The method for identifying working modal parameters based on a power exponential window function according to claim 1 is characterized in that , the expression of the weighted signal is: ； ； in, represents the weighted signal of the i-th sliding window; is the standardized signal of the i-th sliding window; t represents time; represents the power exponential window function; and are window function parameters, which control the exponential decay speed and power shape respectively.

4. The method for identifying working modal parameters based on a power exponential window function according to claim 1 is characterized in that , the frequency spectrum features are extracted from the weighted signal using fast Fourier transform, specifically: Weighted signal Perform fast Fourier transform to get the spectrum , the expression is: ； in, is the frequency variable of the signal, indicating the speed of signal vibration; j is the imaginary unit, and in Fourier transform, j is used to represent the complex part; Extract spectrum The positive frequency domain component of and negative frequency domain components , expressed as: ； ； in, is the spectrum; is the positive frequency domain component; is the negative frequency domain component.

5. The method for identifying working modal parameters based on a power exponential window function according to claim 1 is characterized in that , the expression of the interference ratio is: ； in, is the interference ratio; is the positive frequency domain component; is the negative frequency domain component.

6. The method for identifying working modal parameters based on a power exponential window function according to claim 5 is characterized in that , by adjusting the parameters of the window function and To maximize the interference ratio , to reduce negative frequency domain interference.

7. The method for identifying working modal parameters based on a power exponential window function according to claim 1, characterized in that ,The number of the sensors is greater than the number of ,degrees of freedom of the target structure to fully capture the ,dynamic characteristics of the target structure.

8. A working modal parameter identification device based on a power exponential window function, characterized in that: include: The acquisition and segmentation unit is used to acquire the vibration response signals of multiple sensors under complex working conditions in real time and perform sliding window segmentation on them; A standardization unit, used to standardize the signal in each sliding window to obtain a segmented standardized signal; A power exponential window unit, used for applying a power exponential window function to the standardized signal for weighting to obtain a weighted signal, so as to enhance the spectral characteristics of the signal and suppress boundary effects and spectral leakage; An interference ratio calculation unit, used for extracting spectrum features from the weighted signal by fast Fourier transform, so as to calculate the interference ratio; A parameter adjustment unit, used to adjust according to the interference ratio in combination with window function parameters to reduce negative frequency domain interference and optimize the modal separation effect; The segmented recognition unit is used to identify the natural frequency and damping ratio of the signal in each sliding window according to the optimized spectrum characteristics, and extract the modal vibration shape by combining the principal component analysis technology to obtain the recognition result in each sliding window; The full time domain unit is used to integrate the recognition results of all signals in the sliding window, obtain the modal parameter characteristics in the full time domain, and realize the online dynamic recognition of the working modal parameters.

9. The working modal parameter identification device based on power exponential window function according to claim 8 is characterized in that , the expression of the weighted signal is: ； ； in, represents the weighted signal of the i-th sliding window; is the standardized signal of the i-th sliding window; t represents time; represents the power exponential window function; and are window function parameters, which control the exponential decay speed and power shape respectively.

10. A working modal parameter identification device based on a power exponential window function, characterized in that: It comprises a processor and a memory, wherein the memory stores a computer program, and the computer program can be executed by the processor to implement a working modal parameter identification method based on a power exponential window function as described in any one of claims 1 to 7.

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