Method and device for detecting critical dimension of large prefabricated part

Abstract

The invention provides a large prefabricated part critical dimension detection method and device, and relates to the technical field of dimension detection methods, and the method comprises the steps: synchronously obtaining the original time domain signal of the vibration of each measurement point, uniformly dividing the power spectrum density curve of each measurement point into a plurality of sub-bands, selecting the sub-band with the lowest energy as a noise reference value, setting a signal-to-noise ratio threshold value, constructing a formant extraction rule, and constructing an actual resonant frequency feature vector of the measurement point of the prefabricated part; constructing a resonant frequency prediction model by adopting a finite element method, judging a vibration type through a finite element solver, and constructing a predicted resonant frequency vector; and calculating a frequency difference value to obtain the final size of the prefabricated part. The accuracy of feature extraction is improved by obtaining the original time domain signal of each measuring point and combining material parameters and adopting wavelet denoising and fast Fourier transform; and the actual resonance frequency is compared with the predicted resonance frequency, so that the final size is corrected.

Description

Technical Field

[0001] The present invention relates to the technical field of size detection methods, and in particular to a method and device for detecting key dimensions of large prefabricated components. Background Art

[0002] In the production of prefabricated components, the accuracy of key dimensions is crucial to the safety and performance of the structure. However, traditional dimensional inspection methods often rely on manual measurement, which is susceptible to human error and fails to fully exploit the relationship between material properties and vibration characteristics, resulting in insufficient inspection accuracy. This makes it difficult to effectively identify and correct component dimensional errors during construction, potentially leading to safety hazards and reduced structural performance.

[0003] In the prior art, publication number CN119554967A discloses a method of constructing a BIM model through building information modeling technology, using drone aerial survey technology and 3D laser scanners to construct a point cloud model and a centimeter-level and millimeter-level real-life 3D model of the entire prefabricated component, to inspect and test the key dimensions of the prefabricated component. Through BIM technology, the internal detailed structure of the prefabricated component and the collision problem of the hidden component are processed and solved in advance, the complex segment structure is pre-cleared through visualization, and the automatically calculated engineering quantity is imported into the factory for processing to control the cost and improve the standardization of prefabricated components; 3D laser scanning technology and drone aerial survey technology are used, and the two are used for complementary modeling. However, this method is very expensive, and the inspection of the key dimensions of the prefabricated component requires a lot of time and manpower costs. It does not comprehensively consider the physical properties of the material and the geometric parameters of the component. The inspection process increases the uncertainty of human intervention. Therefore, an efficient and reliable method for key dimension detection is urgently needed.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and device for detecting key dimensions of large prefabricated components to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: A method for detecting key dimensions of large prefabricated components, comprising the following steps: S1: setting multiple measuring points on the surface of the prefabricated component to be inspected, synchronously acquiring the original time domain signals of the vibrations of each measuring point, and obtaining the material parameters of the prefabricated component, the material parameters including elastic modulus, density, and Poisson's ratio; S2: Normalize the original time domain signal of each measuring point, use wavelet denoising to reduce the noise of the original time domain signal of each measuring point, and use fast Fourier transform to calculate the power spectrum density curve of the original time domain signal of each measuring point after noise reduction; S3: Evenly divide the power spectrum density curve of each measuring point into multiple sub-bands, select the sub-band with the lowest energy as the noise reference value and set the signal-to-noise ratio threshold. Based on the signal-to-noise ratio threshold, construct a resonance peak extraction rule to extract the peak of the resonance frequency of each measuring point. Based on the peak of the resonance frequency of each measuring point, construct the actual resonance frequency feature vector of the measuring point of the prefabricated component; S4: defining a dimension vector for each measuring point, the dimension vector including the length, width, and thickness of the prefabricated component, constructing a material parameter vector based on the material parameters, concatenating the dimension vector and the material parameter vector, and constructing a resonance frequency prediction model using the finite element method. The concatenated comprehensive vector is used as the input of the model, and the corresponding predicted resonance frequency is used as the output of the model. Based on the material parameters, the vibration type is determined by the finite element solver, and the vibration type includes bending, torsion, and expansion states of the prefabricated component. Based on the predicted resonance frequency and the vibration type, a predicted resonance frequency vector is constructed. S5: Based on the actual resonant frequency eigenvector and the predicted resonant frequency vector, the frequency difference is calculated, the frequency difference is logarithmically mapped to the dimensional error, the actual size is summed with the dimensional error, and the final size of the prefabricated component is obtained.

[0007] Furthermore, wavelet denoising is used to perform noise reduction processing on the original time domain signals of each measuring point, which specifically includes the following steps: The normalized original time domain signal of each measuring point is subjected to multi-scale wavelet decomposition to obtain multi-scale detail coefficients and approximate coefficients. The multi-scale detail coefficients are soft-thresholded to obtain detail coefficients after threshold processing. The detail coefficients and approximate coefficients after threshold processing are used to reconstruct the noise reduction signal. The original time domain signals at the bending, torsion and expansion locations are subjected to independent denoising by wavelet inverse transform to obtain the noise reduction signal. The time domain signal of a measurement point ,The fast Fourier transform is used to calculate the power spectral density curve of the original time domain signal of each measurement point after noise reduction.

[0008] Furthermore, the power spectrum density curve of the original time domain signal of each measurement point after noise reduction is calculated using fast Fourier transform, which specifically includes the following steps: Get the noise-reduced The time domain signal of each measuring point is At sampling frequency Discretize it and get the length discrete sequence; perform fast Fourier transform on the discrete signal and calculate the square of the spectrum amplitude; retain the positive frequency part and double the energy to generate the corresponding frequency value.

[0009] Furthermore, based on the signal-to-noise ratio threshold, a resonance peak extraction rule is constructed to extract the peak of the resonance frequency of each measuring point. Based on the peak of the resonance frequency of each measuring point, the actual resonance frequency feature vector of the measuring point of the prefabricated component is constructed. The specific steps are as follows: Calculate the total energy of each subband: ; Indicates the The first measurement point The energy of each sub-band; Indicates the The unilateral power spectral density of each measuring point; Indicates the length of the discrete sequence; represents the discrete sampling frequency; Indicates the A set of frequency indices of subbands; Indicates the subband index number; Choose the one with the lowest energy subbands as noise reference: ; in, The set of indices representing the lowest energy subbands; For each subband, calculate its signal-to-noise ratio: ; Indicates the The first measurement point The signal-to-noise ratio of each sub-band; Setting the global threshold , filter out effective resonance peaks, build resonance peak extraction rules, and extract the peaks of resonance frequencies: ; in, Indicates the The first measurement point The frequency points corresponding to the resonance peaks detected in each sub-band; represents peak width; Indicates the preset minimum peak width; Indicates the The measuring points are The signal power at Indicates the power ratio threshold; According to the structural dynamics model, the theoretical frequency range of the original time domain signal of each measuring point is defined: ; in, represents the frequency of the theoretical bend; represents the frequency at which the theoretical torsion occurs; Indicates the frequency of theoretical expansion and contraction; The extracted resonance peaks Match with the theoretical interval, filter the effective frequency, for each measuring point , construct a three-dimensional feature vector: ; in, Indicates the Frequency value at the bend of each measuring point; Indicates the Frequency value of the torsion of each measuring point; Indicates the Frequency value at the expansion point of each measuring point; Indicates the The actual resonant frequency eigenvector of each measuring point.

[0010] Furthermore, a material parameter vector is constructed based on the material parameters, and the size vector and the material parameter vector are concatenated. The specific steps are as follows: Set each measuring point to have a finite local area on the component surface, with a size of The cubic range of: Use laser scanner to obtain the surrounding area of ​​the measuring point For the point cloud data of the area, perform point cloud projection along the main axis direction, take the 95% confidence interval of the projection interval, and calculate the length as the length of the measuring point; Set up five parallel measurement lines in the horizontal range, use a contact displacement sensor to measure the actual width of each line, and select the median of the width interval with the most occurrences through histogram analysis as the width of the measuring point; In the cube range centered on the measuring point, select 4 additional measuring points with symmetrical distribution, and perform 5 thickness measurements in total, 1 at the center point and 4 at symmetrical points. Use Gaussian weights to calculate the average thickness of the measuring points, and use the average thickness as the final thickness of the measuring points. Define the size vector: ; in, Indicates the The size vector of each measuring point; Indicates the The length of the material at each measuring point; Indicates the The width of the material at each measuring point; Indicates the The thickness of the material at each measuring point; Construct the material parameter vector based on the material's elastic modulus, density, and Poisson's ratio: ; in, represents the material parameter vector; represents the elastic modulus; Indicates density; represents Poisson's ratio; Concatenate the size vector and the material parameter vector to obtain the concatenated comprehensive vector: ; in, Represents the combined vector after concatenation.

[0011] Furthermore, the finite element method is used to construct a resonance frequency prediction model, the spliced ​​comprehensive vector is used as the input of the model, and the corresponding predicted resonance frequency is used as the output of the model. The specific steps are as follows: Computer programming is used to automatically generate the geometric model of the prefabricated component, assign material parameters to the generated geometric model, divide the geometric model into grids, and construct the eigenvalue equation: ; in, ; ; Indicates the The cross-sectional area of ​​each measuring point; Indicates the Stiffness matrix of each measuring point; Indicates the The quality matrix of each measuring point; Indicates the The natural angular frequency of the first mode; Indicates the The vibration shape vector of the first mode; Solving the resonance equation yields the resonant frequency: ; Indicates the Resonant frequencies; represents the resonant frequency index; The model output is Resonant frequency vectors: ; Indicates the The resonance vector of each measuring point.

[0012] Furthermore, according to the material parameters, the vibration type is determined by a finite element solver. The vibration type includes bending, torsion, and expansion states of the prefabricated component. Based on the predicted resonant frequency and the vibration type, a predicted resonant frequency vector is constructed. Specifically, the following steps are included: Define the displacement direction weight ratio as the modal discrimination index and use the finite element solver to determine the vibration type: ; ; ; represents the axial displacement component; represents the lateral displacement component; represents the angular displacement component; Indicates the proportion of axial displacement energy to total energy; It represents the ratio of lateral displacement energy to total energy; It represents the ratio of angular displacement energy to total energy; represents the Euclidean norm; Design judgment rules: ; Indicates the vibration type; Based on the predicted resonant frequency and vibration type, the predicted resonant frequency vector of each measuring point is constructed: ; Indicates the The predicted resonance frequency vector matrix of each measuring point; Indicates the The bending resonance frequency of each measuring point; Indicates the The torsional resonance frequency of each measuring point; Indicates the The expansion and contraction resonance frequency of each measuring point.

[0013] Furthermore, the frequency difference is calculated, the frequency difference is logarithmically mapped to the dimensional error, the actual size and the dimensional error are summed to obtain the final size of the prefabricated component, which specifically includes the following steps: Get the frequency difference between the actual resonance frequency eigenvector of each measuring point and the vibration type corresponding to the predicted resonance frequency vector: ; in, ; in, represents the mapping correlation coefficient, obtained by linearly fitting the size error and frequency error; Indicates the Measuring point Frequency difference of each vibration type; Indicates the Measuring point The actual resonant frequency of each vibration type; Shidi Measuring point Predicted resonant frequencies for each vibration type; Indicates the Measuring point Dimensional error of each vibration type; represents the correction constant to ensure that the input of the logarithmic function is positive; Add the dimensional error to the original measured size to get the corrected size, then: ; in, Indicates the revised The size of each measuring point; Indicates the The original size of each measuring point.

[0014] The present invention further provides a device for detecting key dimensions of large prefabricated components, the device being used to perform the above-mentioned detection method, comprising: A data acquisition module is used to set multiple measuring points on the surface of the prefabricated component to be inspected, synchronously obtain the original time domain signal of the vibration of each measuring point, and obtain the material parameters of the prefabricated component, wherein the material parameters include elastic modulus, density and Poisson's ratio; The power spectrum solution module is used to normalize the original time domain signal of each measuring point, use wavelet denoising to reduce the noise of the original time domain signal of each measuring point, and use fast Fourier transform to calculate the power spectrum density curve of the original time domain signal of each measuring point after noise reduction; The actual resonance frequency module is used to evenly divide the power spectrum density curve of each measuring point into multiple sub-bands, select the sub-band with the lowest energy as the noise reference value and set the signal-to-noise ratio threshold. Based on the signal-to-noise ratio threshold, a resonance peak extraction rule is constructed to extract the peak of the resonance frequency of each measuring point. Based on the peak of the resonance frequency of each measuring point, the actual resonance frequency feature vector of the measuring point of the prefabricated component is constructed; A resonance frequency prediction module is used to define a dimension vector for each measuring point, the dimension vector including the length, width, and thickness of the prefabricated component, construct a material parameter vector based on the material parameters, concatenate the dimension vector and the material parameter vector, and construct a resonance frequency prediction model using the finite element method. The concatenated comprehensive vector is used as the input of the model, and the corresponding predicted resonance frequency is used as the output of the model. Based on the material parameters, a finite element solver is used to determine the vibration type, which includes bending, torsion, and expansion states of the prefabricated component. A predicted resonance frequency vector is constructed based on the predicted resonance frequency and vibration type. The dimensional error module is used to calculate the frequency difference based on the actual resonant frequency eigenvector and the predicted resonant frequency vector, map the frequency difference to the dimensional error through logarithm, and sum the actual size and the dimensional error to obtain the final size of the prefabricated component. Compared with the existing technology, the present invention has the following advantages: Wavelet denoising and fast Fourier transform are performed on the original time domain signal of the measuring point to extract the accurate resonant frequency characteristics. Compared with existing technologies, the efficiency and accuracy of detection are improved. Wavelet denoising can effectively remove random noise in the signal and improve the signal-to-noise ratio, while fast Fourier transform quickly generates power spectrum density, providing a reliable foundation for subsequent frequency extraction and a solid data foundation for subsequent finite element analysis and dimensional correction. The overall solution can achieve high-precision resonant frequency detection and analysis, thereby promoting the dimensional optimization and quality control of prefabricated components; through systematic signal processing and model prediction, the impact of human error is significantly reduced and detection efficiency is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Schematic diagram of the overall method of the present invention.

[0016] Figure 2 It is a schematic diagram of the overall system flow of the present invention. DETAILED DESCRIPTION

[0017] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to specific embodiments.

[0018] It should be noted that, unless otherwise defined, the technical or scientific terms used in the present invention should have the usual meanings 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 position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0019] Example: See also Figure 1 , the present invention provides a technical solution: A method for detecting key dimensions of large prefabricated components, comprising the following steps: S1: setting multiple measuring points on the surface of the prefabricated component to be inspected, synchronously acquiring the original time domain signals of the vibrations of each measuring point, and obtaining the material parameters of the prefabricated component, the material parameters including elastic modulus, density, and Poisson's ratio; S2: Normalize the original time domain signals of the measurement points, use wavelet denoising to reduce the noise of the original time domain signals of each measurement point, and use fast Fourier transform to calculate the power spectrum density curve of the original time domain signals of each measurement point after noise reduction; The wavelet denoising is used to perform noise reduction processing on the original time domain signal of each measuring point, specifically comprising the following steps: Perform multi-scale wavelet decomposition on the normalized original time domain signal of the measurement point to obtain multi-scale detail coefficients and approximate coefficients: ; in, ; ; Indicates the The first measurement point Layer detail coefficient; Indicates the The first measurement point Layer approximation coefficient; represents the wavelet basis function; represents the scaling function; Indicates the Tier Detail coefficient; Indicates the layer index; Represents the detail coefficient index; Indicates the Tier Detail coefficient; Indicates the maximum number of decomposition levels; Indicates the The normalized original time domain signal of each measurement point; Perform soft thresholding on the multi-scale detail coefficients to obtain the detail coefficients after threshold processing: ; in, ; Indicates the The first measurement point Standard deviation of layer noise; is the length of the signal; After threshold processing, The first measurement point Tier Detail coefficient; Indicates the The first measurement point Adaptive thresholding of layer wavelet decomposition; Reconstruct the denoised signal using the thresholded coefficients: ; in, ; Represents the thresholded The first measurement point Layer detail coefficient; Independently denoise the original time domain signals at bending, torsion, and stretching: ; in, Indicates the The thresholded Tier Detail coefficient; Indicates the The first measurement point Layer approximation coefficient; represents the inverse wavelet transform; After noise reduction, The time domain signal of each measurement point.

[0020] The method of using fast Fourier transform to calculate the power spectrum density of the original time domain signal of each measurement point after noise reduction specifically includes the following steps: Get the noise-reduced The time domain signal of each measuring point is At sampling frequency Discretize it and get the length A discrete sequence of : ; in, ; in, Represents the discrete signal sequence index; express Discrete signal of each measuring point; Perform a fast Fourier transform on a discrete signal: ; in, ; Indicates the The discrete signal after Fourier transform of the measurement points; Represents the index of scattered frequency components; Compute the square of the spectrum magnitude: ; in, Represents the square of the spectrum amplitude of a discrete signal; Keep the positive frequencies and double the energy: ; Indicates the The unilateral power spectral density of each measuring point; Generate the corresponding frequency values: ; Indicates the length of the discrete sequence; Indicates the The actual frequency value corresponding to each frequency point.

[0021] S3: Evenly divide the power spectrum density curve of each measuring point into multiple sub-bands, select the sub-band with the lowest energy as the noise reference value and set the signal-to-noise ratio threshold. Based on the signal-to-noise ratio threshold, construct a resonance peak extraction rule to extract the peak of the resonance frequency of each measuring point. Based on the peak of the resonance frequency of each measuring point, construct the actual resonance frequency feature vector of the measuring point of the prefabricated component; The method of constructing a resonance peak extraction rule based on the signal-to-noise ratio threshold, extracting the peak of the resonance frequency of each measuring point, and constructing the actual resonance frequency feature vector of the measuring point of the prefabricated component based on the peak of the resonance frequency of each measuring point, specifically comprises the following steps: Calculate the total energy of each subband: ; Indicates the The first measurement point The energy of each sub-band; Indicates the The unilateral power spectral density of each measuring point; Indicates the length of the discrete sequence; represents the discrete sampling frequency; Indicates the A set of frequency indices of subbands; Indicates the subband index number; Choose the one with the lowest energy subbands as noise reference: ; in, The set of indices representing the lowest energy subbands; For each subband, calculate its signal-to-noise ratio: ; Indicates the The first measurement point The signal-to-noise ratio of each sub-band; Setting the global threshold , filter out effective resonance peaks, build resonance peak extraction rules, and extract the peaks of resonance frequencies: ; in, Indicates the The first measurement point The frequency points corresponding to the resonance peaks detected in each sub-band; represents peak width; Indicates the preset minimum peak width; Indicates the The measuring points are The signal power at Indicates the power ratio threshold; According to the structural dynamics model, the theoretical frequency range of the original time domain signal of each measuring point is defined: ; in, represents the frequency of the theoretical bend; represents the frequency at which the theoretical torsion occurs; Indicates the frequency of theoretical expansion and contraction; The extracted resonance peaks Match with the theoretical interval, filter the effective frequency, for each measuring point , construct a three-dimensional feature vector: ; in, Indicates the Frequency value at the bend of each measuring point; Indicates the Frequency value of the torsion of each measuring point; Indicates the Frequency value at the expansion point of each measuring point; Indicates the The actual resonant frequency eigenvector of each measurement point.

[0022] In the above process, the energy of each frequency band is first calculated by sub-band energy analysis. , select the lowest energy subband to estimate the noise floor , and calculate the subband signal-to-noise ratio , and then based on the signal-to-noise ratio threshold , power ratio threshold and minimum peak width Extracting effective resonance peaks using triple constraints Finally, these resonance peaks are matched with the bending, torsion, and stretching frequency ranges predicted by structural dynamics theory to screen out characteristic frequencies that conform to physical laws. 、 、 , and finally construct a three-dimensional feature vector that characterizes the dynamic characteristics of the structure .

[0023] S4: defining a dimension vector, wherein the dimension vector includes the length, width, and thickness of the prefabricated component; constructing a material parameter vector based on the material parameters; concatenating the dimension vector and the material parameter vector; constructing a resonance frequency prediction model using the finite element method; using the concatenated comprehensive vector as the input of the model; and using the corresponding predicted resonance frequency as the output of the model; determining the vibration type using a finite element solver based on the material parameters; the vibration type includes bending, torsion, and expansion and contraction states of the prefabricated component; and constructing a predicted resonance frequency vector based on the predicted resonance frequency and the vibration type; The material parameter vector is constructed based on the material parameters, and the size vector and the material parameter vector are spliced. The specific steps are as follows: Set each measuring point to have a finite local area on the component surface, with a size of The cubic range of: Use laser scanner to obtain the surrounding area of ​​the measuring point For the point cloud data of the area, perform point cloud projection along the main axis direction, take the 95% confidence interval of the projection interval, and calculate the length as the length of the measuring point; The principal axis direction here refers to a limited local area on the surface of the component The main geometric extension direction within the cube usually corresponds to the maximum dimension axis of the component in space. For example, for a beam structure in the shape of an I-beam, the main axis direction is usually the length direction of the beam, that is, the longest dimension axis. For a plate structure such as a metal plate, the main axis is the normal or long side direction of the plate surface. After laser scanning the point cloud data, the point cloud is projected onto the coordinate axis in this direction, and the length dimension is determined by the projection distribution.

[0024] Calculating the length means projecting the point cloud data along the main axis to obtain a set of one-dimensional coordinate values, and calculating the 95% confidence interval of the projected coordinates. The span of this interval is the maximum value minus the minimum value, which is the measured point. The length of the measuring point; reflects the effective structural size of the local area in the main axis direction; For example, after the local point cloud of the I-beam is projected onto the main axis, the coordinate range is cm, remove outliers cm, the 95% confidence interval is cm, then Set five parallel measurement lines in the horizontal range and use a contact displacement sensor to measure the actual width of each line. The median of the width interval with the most occurrences is selected through histogram analysis as the width of the measuring point. The lateral range here refers to the plane perpendicular to the main axis direction, that is, The measurement range within the plane is set up with 5 measuring lines parallel to the main axis direction. Each line measures the actual width value through a contact displacement sensor. The final width Take the median of the highest frequency bin in the histogram of all measurements; For example, the main axis direction is along the length of the beam, which is the X axis, and the transverse plane perpendicular to the main axis direction is the YZ plane. Set 5 measurement lines parallel to the main axis direction at the following positions: cm, each line uses a displacement sensor to measure the Z-direction dimension, and the measured value is cm, where For outliers, histogram analysis is performed, where Occurs 4 times, take the median cm as In the cube range centered on the measuring point, select 4 additional measuring points with symmetrical distribution, and perform 5 thickness measurements in total, 1 at the center point and 4 at symmetrical points. Use Gaussian weights to calculate the average thickness of the measuring points, and use the average thickness as the final thickness of the measuring points. Define the size vector: ; in, Indicates the The size vector of each measuring point; Indicates the The length of the material at each measuring point; Indicates the The width of the material at each measuring point; Indicates the The thickness of the material at each measuring point; Construct the material parameter vector based on the material's elastic modulus, density, and Poisson's ratio: ; in, represents the material parameter vector; represents the elastic modulus; Indicates density; represents Poisson's ratio; Concatenate the size vector and the material parameter vector to obtain the concatenated comprehensive vector: ; in, Represents the combined vector after concatenation.

[0025] The finite element method is used to construct a resonance frequency prediction model, the spliced ​​comprehensive vector is used as the input of the model, and the corresponding predicted resonance frequency is used as the output of the model. The specific steps are: Use computer programming to automatically generate the geometric model of the prefabricated component, assign material parameters to the generated geometric model, and divide the geometric model into meshes: Use the programming language Python combined with the geometric modeling library to generate a parametric geometric model, save the generated parametric geometric model in a standardized format, and use the mesh generation library GMSH to discretize the geometric model and generate a mesh; Construct the eigenvalue equation: ; in, ; ; Indicates the The cross-sectional area of ​​each measuring point; Indicates the Stiffness matrix of each measuring point; Indicates the The quality matrix of each measuring point; Indicates the The natural angular frequency of the first mode; Indicates the The vibration shape vector of the first mode; Solving the resonance equation yields the resonant frequency: ; Indicates the Resonant frequencies; represents the resonant frequency index; The model output is Resonant frequency vectors: ; Indicates the The resonance vector of each measuring point.

[0026] The method comprises the following steps: determining the vibration type by using a finite element solver according to the material parameters, wherein the vibration type includes bending, torsion and expansion states of the prefabricated component; and constructing a predicted resonance frequency vector based on the predicted resonance frequency and the vibration type. Define the displacement direction weight ratio as the modal discrimination index and use the finite element solver to determine the vibration type: ; ; ; represents the axial displacement component; represents the lateral displacement component; represents the angular displacement component; Indicates the proportion of axial displacement energy to total energy; It represents the ratio of lateral displacement energy to total energy; It represents the ratio of angular displacement energy to total energy; represents the Euclidean norm; Design judgment rules: ; Indicates the vibration type; Based on the predicted resonant frequency and vibration type, the predicted resonant frequency vector of each measuring point is constructed: ; Indicates the The predicted resonance frequency vector matrix of each measuring point; Indicates the The bending resonance frequency of each measuring point; Indicates the The torsional resonance frequency of each measuring point; Indicates the The expansion and contraction resonance frequency of each measuring point.

[0027] In the above process, the vibration type discrimination rule ensures that each measurement point corresponds to only a single vibration type through the mutually exclusive threshold, and the prediction vector The three-dimensional structure of is essentially a standardized container of type-frequency, and its three elements are bound to specific type conditions, such as Only activated in the telescopic type, there is actually only one position to store the effective frequency, and the other positions are theoretically empty, which is convenient for comparison with the measured vector Alignment and avoid frequency coexistence conflicts through conditional constraints.

[0028] S5: Based on the actual resonant frequency eigenvector and the predicted resonant frequency vector, the frequency difference is calculated, the frequency difference is logarithmically mapped to the dimensional error, the actual size is summed with the dimensional error, and the final size of the prefabricated component is obtained.

[0029] Calculate the frequency difference, map the frequency difference to the dimensional error through logarithm, sum the actual size and the dimensional error to obtain the final size of the prefabricated component, specifically including the following steps: Get the frequency difference between the actual resonance frequency eigenvector of each measuring point and the vibration type corresponding to the predicted resonance frequency vector: ; in, ; in, represents the mapping correlation coefficient, obtained by linearly fitting the size error and frequency error; Indicates the Measuring point Frequency difference of each vibration type; Indicates the Measuring point The actual resonant frequency of each vibration type; Shidi Measuring point Predicted resonant frequencies for each vibration type; Indicates the Measuring point Dimensional error of each vibration type; represents the correction constant to ensure that the input of the logarithmic function is positive; when When , the actual resonance frequency is higher than the predicted frequency, resulting in a positive size correction; when When the actual frequency is lower than the predicted frequency, the size correction is negative; the dependent variable Specifically reflects the prefabricated components at specific points The dimensional error correction at the frequency difference is calculated by The difference between the actual size and the predicted size of the component is quantified.

[0030] Add the dimensional error to the original measured size to get the corrected size, then: ; in, Indicates the revised The size of each measuring point; Indicates the The original size of each measuring point.

[0031] The present invention further provides a device for detecting key dimensions of large prefabricated components, the device being used to perform the above-mentioned detection method, comprising: A data acquisition module is used to set multiple measuring points on the surface of the prefabricated component to be inspected, synchronously obtain the original time domain signal of the vibration of each measuring point, and obtain the material parameters of the prefabricated component, wherein the material parameters include elastic modulus, density and Poisson's ratio; The power spectrum solution module is used to normalize the original time domain signal of each measuring point, use wavelet denoising to reduce the noise of the original time domain signal of each measuring point, and use fast Fourier transform to calculate the power spectrum density curve of the original time domain signal of each measuring point after noise reduction; The actual resonance frequency module is used to evenly divide the power spectrum density curve of each measuring point into multiple sub-bands, select the sub-band with the lowest energy as the noise reference value and set the signal-to-noise ratio threshold. Based on the signal-to-noise ratio threshold, a resonance peak extraction rule is constructed to extract the peak of the resonance frequency of each measuring point. Based on the peak of the resonance frequency of each measuring point, the actual resonance frequency feature vector of the measuring point of the prefabricated component is constructed; A resonance frequency prediction module is used to define a dimension vector for each measuring point, the dimension vector including the length, width, and thickness of the prefabricated component, construct a material parameter vector based on the material parameters, concatenate the dimension vector and the material parameter vector, and construct a resonance frequency prediction model using the finite element method. The concatenated comprehensive vector is used as the input of the model, and the corresponding predicted resonance frequency is used as the output of the model. Based on the material parameters, a finite element solver is used to determine the vibration type, which includes bending, torsion, and expansion states of the prefabricated component. A predicted resonance frequency vector is constructed based on the predicted resonance frequency and vibration type. The dimensional error module calculates the frequency difference based on the actual resonant frequency eigenvector and the predicted resonant frequency vector, logarithmically maps the frequency difference to the dimensional error, and sums the actual size with the dimensional error to obtain the final dimensions of the prefabricated component. The above formulas are dimensionless and numerically calculated. These formulas are derived from software simulations of a large amount of data collected to obtain the most recent real-world results. The preset parameters in these formulas are set by those skilled in the art based on actual conditions.

[0032] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software depends on the specific application and design constraints of the technical solution.

[0033] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, and may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment as needed.

[0034] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A method for detecting key dimensions of large prefabricated components, characterized in that the steps include: S1: setting multiple measuring points on the surface of the prefabricated component to be inspected, synchronously acquiring the original time domain signals of the vibrations of each measuring point, and obtaining the material parameters of the prefabricated component, the material parameters including elastic modulus, density, and Poisson's ratio; S2: Normalize the original time domain signal of each measuring point, use wavelet denoising to reduce the noise of the original time domain signal of each measuring point, and use fast Fourier transform to calculate the power spectrum density curve of the original time domain signal of each measuring point after noise reduction; S3: Evenly divide the power spectrum density curve of each measuring point into multiple sub-bands, select the sub-band with the lowest energy as the noise reference value and set the signal-to-noise ratio threshold. Based on the signal-to-noise ratio threshold, construct a resonance peak extraction rule to extract the peak of the resonance frequency of each measuring point. Based on the peak of the resonance frequency of each measuring point, construct the actual resonance frequency feature vector of the measuring point of the prefabricated component; S4: defining a dimension vector for each measuring point, the dimension vector including the length, width, and thickness of the prefabricated component, constructing a material parameter vector based on the material parameters, concatenating the dimension vector and the material parameter vector, and constructing a resonance frequency prediction model using the finite element method. The concatenated comprehensive vector is used as the input of the model, and the corresponding predicted resonance frequency is used as the output of the model. Based on the material parameters, the vibration type is determined by the finite element solver, and the vibration type includes bending, torsion, and expansion states of the prefabricated component. Based on the predicted resonance frequency and the vibration type, a predicted resonance frequency vector is constructed. S5: Based on the actual resonant frequency eigenvector and the predicted resonant frequency vector, the frequency difference is calculated, the frequency difference is logarithmically mapped to the dimensional error, the actual size is summed with the dimensional error, and the final size of the prefabricated component is obtained.

2. A method for detecting key dimensions of large prefabricated components according to claim 1, characterized in that: The wavelet denoising is used to perform noise reduction processing on the original time domain signal of each measuring point, specifically comprising the following steps: The normalized original time domain signal of each measuring point is subjected to multi-scale wavelet decomposition to obtain multi-scale detail coefficients and approximate coefficients. The multi-scale detail coefficients are soft-thresholded to obtain detail coefficients after threshold processing. The detail coefficients and approximate coefficients after threshold processing are used to reconstruct the noise reduction signal. The original time domain signals at the bending, torsion and expansion locations are subjected to independent denoising by wavelet inverse transform to obtain the noise reduction signal. The time domain signal of a measurement point ,The fast Fourier transform is used to calculate the power spectral density curve of the original time domain signal of each measurement point after noise reduction.

3. A method for detecting key dimensions of large prefabricated components according to claim 2, characterized in that: The method of using fast Fourier transform to calculate the power spectrum density curve of the original time domain signal of each measurement point after noise reduction specifically includes the following steps: Get the noise-reduced The time domain signal of each measuring point is At sampling frequency Discretize it and get the length discrete sequence; perform fast Fourier transform on the discrete signal and calculate the square of the spectrum amplitude; retain the positive frequency part and double the energy to generate the corresponding frequency value.

4. A method for detecting key dimensions of large prefabricated components according to claim 1, characterized in that: The method of constructing a resonance peak extraction rule based on the signal-to-noise ratio threshold, extracting the peak of the resonance frequency of each measuring point, and constructing the actual resonance frequency feature vector of the measuring point of the prefabricated component based on the peak of the resonance frequency of each measuring point, specifically comprises the following steps: Calculate the total energy of each subband: ； Indicates the The first measurement point The energy of each sub-band; Indicates the The unilateral power spectral density of each measuring point; Indicates the length of the discrete sequence; represents the discrete sampling frequency; Indicates the A set of frequency indices of subbands; Indicates the subband index number; Choose the one with the lowest energy subbands as noise reference: ； in, The set of indices representing the lowest energy subbands; For each subband, calculate its signal-to-noise ratio: ； Indicates the The first measurement point The signal-to-noise ratio of each sub-band; Setting the global threshold , filter out effective resonance peaks, build resonance peak extraction rules, and extract the peaks of resonance frequencies: ； in, Indicates the The first measurement point The frequency points corresponding to the resonance peaks detected in each sub-band; represents peak width; Indicates the preset minimum peak width; Indicates the The measuring points are The signal power at Indicates the power ratio threshold; According to the structural dynamics model, the theoretical frequency range of the original time domain signal of each measuring point is defined: ； in, represents the frequency of the theoretical bend; represents the frequency at which the theoretical torsion occurs; Indicates the frequency of theoretical expansion and contraction; The extracted resonance peaks Match with the theoretical interval, filter the effective frequency, for each measuring point , construct a three-dimensional feature vector: ； in, Indicates the Frequency value at the bend of each measuring point; Indicates the Frequency value of the torsion of each measuring point; Indicates the Frequency value at the expansion point of each measuring point; Indicates the The actual resonant frequency eigenvector of each measurement point.

5. A method for detecting key dimensions of large prefabricated components according to claim 1, characterized in that: The material parameter vector is constructed based on the material parameters, and the size vector and the material parameter vector are spliced. The specific steps are as follows: Set each measuring point to have a finite local area on the component surface, with a size of The cubic range of: Use laser scanner to obtain the surrounding area of ​​the measuring point For the point cloud data of the area, perform point cloud projection along the main axis direction, take the 95% confidence interval of the projection interval, and calculate the length as the length of the measuring point; Set up five parallel measurement lines in the horizontal range, use a contact displacement sensor to measure the actual width of each line, and select the median of the width interval with the most occurrences through histogram analysis as the width of the measuring point; In the cube range centered on the measuring point, select 4 additional measuring points with symmetrical distribution, and perform 5 thickness measurements in total, 1 at the center point and 4 at symmetrical points. Use Gaussian weights to calculate the average thickness of the measuring points, and use the average thickness as the final thickness of the measuring points. Define the size vector: ； in, Indicates the The size vector of each measuring point; Indicates the The length of the material at each measuring point; Indicates the The width of the material at each measuring point; Indicates the The thickness of the material at each measuring point; Construct the material parameter vector based on the material's elastic modulus, density, and Poisson's ratio: ； in, represents the material parameter vector; represents the elastic modulus; Indicates density; represents Poisson's ratio; Concatenate the size vector and the material parameter vector to obtain the concatenated comprehensive vector: ； in, Represents the combined vector after concatenation.

6. A method for detecting key dimensions of large prefabricated components according to claim 1, characterized in that: The finite element method is used to construct a resonance frequency prediction model, the spliced ​​comprehensive vector is used as the input of the model, and the corresponding predicted resonance frequency is used as the output of the model. The specific steps are: Use computer software to generate the geometric model of the prefabricated component, assign material parameters to the generated geometric model, divide the geometric model into grids, and construct the eigenvalue equation: ； in, ； ； Indicates the The cross-sectional area of ​​each measuring point; Indicates the Stiffness matrix of each measuring point; Indicates the The quality matrix of each measuring point; Indicates the The natural angular frequency of the first mode; Indicates the The vibration shape vector of the first mode; Solving the resonance equation yields the resonant frequency: ； Indicates the Resonant frequencies; represents the resonant frequency index; The model output is Resonant frequency vectors: ； Indicates the The resonance vector of each measuring point.

7. A method for detecting key dimensions of large prefabricated components according to claim 1, characterized in that: According to the material parameters, the vibration type is judged by the finite element solver, and the vibration type includes the bending, torsion and expansion states of the prefabricated component. Based on the predicted resonance frequency and the vibration type, a predicted resonance frequency vector is constructed. The following steps are involved: Define the displacement direction weight ratio as the modal discrimination index and use the finite element solver to determine the vibration type: ； ； ； represents the axial displacement component; represents the lateral displacement component; represents the angular displacement component; Indicates the proportion of axial displacement energy to total energy; It represents the ratio of lateral displacement energy to total energy; It represents the ratio of angular displacement energy to total energy; represents the Euclidean norm; Design judgment rules: ； Indicates the vibration type; Based on the predicted resonant frequency and vibration type, the predicted resonant frequency vector of each measuring point is constructed: ； Indicates the The predicted resonance frequency vector matrix of each measuring point; Indicates the The bending resonance frequency of each measuring point; Indicates the The torsional resonance frequency of each measuring point; Indicates the The expansion and contraction resonance frequency of each measuring point.

8. A method for detecting key dimensions of large prefabricated components according to claim 1, characterized in that: The method of calculating the frequency difference, mapping the frequency difference to the dimensional error by logarithm, and summing the actual size and the dimensional error to obtain the final size of the prefabricated component specifically includes the following steps: Get the frequency difference between the actual resonance frequency eigenvector of each measuring point and the vibration type corresponding to the predicted resonance frequency vector: ； in, ； in, represents the mapping correlation coefficient, obtained by linearly fitting the size error and frequency error; Indicates the Measuring point Frequency difference of each vibration type; Indicates the Measuring point The actual resonant frequency of each vibration type; Shidi Measuring point Predicted resonant frequencies for each vibration type; Indicates the Measuring point Dimensional error of each vibration type; represents the correction constant to ensure that the input of the logarithmic function is positive; Add the dimensional error to the original measured size to get the corrected size, then: ； in, Indicates the revised The size of each measuring point; Indicates the The original size of each measuring point.

9. A device for detecting key dimensions of large prefabricated components, characterized by: The detection device is used to perform the detection method according to any one of claims 1 to 8, comprising: A data acquisition module is used to set multiple measuring points on the surface of the prefabricated component to be inspected, synchronously obtain the original time domain signal of the vibration of each measuring point, and obtain the material parameters of the prefabricated component, wherein the material parameters include elastic modulus, density and Poisson's ratio; The power spectrum solution module is used to normalize the original time domain signal of each measuring point, perform noise reduction processing on the original time domain signal of each measuring point using wavelet denoising, and calculate the power spectrum density curve of the original time domain signal of each measuring point after noise reduction using fast Fourier transform; The actual resonance frequency module is used to evenly divide the power spectrum density curve of each measuring point into multiple sub-bands, select the sub-band with the lowest energy as the noise reference value and set the signal-to-noise ratio threshold. Based on the signal-to-noise ratio threshold, a resonance peak extraction rule is constructed to extract the peak of the resonance frequency of each measuring point. Based on the peak of the resonance frequency of each measuring point, the actual resonance frequency feature vector of the measuring point of the prefabricated component is constructed; A resonance frequency prediction module is used to define a dimension vector for each measuring point, the dimension vector including the length, width, and thickness of the prefabricated component, construct a material parameter vector based on the material parameters, concatenate the dimension vector and the material parameter vector, and construct a resonance frequency prediction model using the finite element method. The concatenated comprehensive vector is used as the input of the model, and the corresponding predicted resonance frequency is used as the output of the model. Based on the material parameters, a finite element solver is used to determine the vibration type, which includes bending, torsion, and expansion states of the prefabricated component. A predicted resonance frequency vector is constructed based on the predicted resonance frequency and vibration type. The dimensional error module is used to calculate the frequency difference based on the actual resonant frequency eigenvector and the predicted resonant frequency vector, map the frequency difference to the dimensional error through logarithm, sum the actual size and the dimensional error, and obtain the final size of the prefabricated component.

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