Data acquisition method and system for frame sand blasting treatment and storage medium

Abstract

The invention relates to the technical field of data acquisition and processing, and discloses a data acquisition method and system for frame sand blasting treatment and a storage medium. The method comprises the following steps: acquiring electromagnetic interference spectrum data generated by sand collision to obtain interference spectrum distribution characteristics; the overlapping condition of the interference peak position and the vibration signal acquisition frequency range is judged, and if yes, the acquisition frequency is adjusted to a standby frequency bandwidth; original vibration signals of the frame sand blasting equipment are collected, noise suppression processing is carried out, and a de-noised vibration signal sequence is generated; key vibration characteristic parameters are extracted from the de-noised signal sequence, whether the key vibration characteristic parameters meet the preset standard of process quality monitoring or not is judged, and if not, data transmission configuration is dynamically updated, and a data transmission protocol is optimized; and the reliability of the signal is evaluated through the central processing unit. According to the method, electromagnetic interference in the sand blasting environment can be effectively coped with, the quality of vibration signal collection and the reliability of data transmission are remarkably improved, and accurate monitoring of the frame sand blasting process is guaranteed.

Description

Technical Field

[0001] This application relates to the field of data acquisition and processing technology, and in particular to a data acquisition method, system and storage medium for edge sandblasting. Background Technology

[0002] In the industrial manufacturing sector, edge sandblasting is a key surface treatment process, widely used in metal surface treatment, coating removal, cleaning, and strengthening processes in industries such as automotive, aerospace, and machinery. The quality of this process directly affects the final performance and appearance of the product. Therefore, real-time and accurate acquisition and analysis of vibration signals from sandblasting equipment and workpiece edges are crucial for monitoring process status, identifying equipment malfunctions, and ensuring processing uniformity.

[0003] However, in the specific application scenario of a sandblasting workshop, the continuous impact of high-speed sand particles on the metal frame generates two interrelated types of interference: strong mechanical vibration and broadband, high-intensity electromagnetic interference caused by metal friction and collision. This electromagnetic interference spectrum generated by sand particle collisions has high-intensity characteristics and dynamically drifts with changes in sandblasting pressure, sand particle material and size, and collision angle, forming a complex and time-varying interference environment.

[0004] With the rapid development of industrial automation and intelligent manufacturing technologies, higher requirements have been placed on vibration signal monitoring during sandblasting processes. However, existing data acquisition methods have significant limitations when facing such specific interference. Traditional vibration signal acquisition methods mostly rely on acquisition within a fixed frequency range, lacking the ability to actively sense and analyze the electromagnetic interference spectrum. When dynamically changing interference peaks happen to cover the acquisition frequency, the acquired vibration signal will be severely submerged, resulting in waveform distortion, feature loss, and other problems, making subsequent analysis and status judgments inaccurate. Furthermore, even if a valid signal is acquired, unstable electromagnetic environments can easily cause data transmission errors or interruptions during transmission to the processing unit, further affecting the reliability of the entire monitoring system.

[0005] Therefore, there is an urgent need in this field for a data acquisition method specifically designed to address the interference from sand particle collisions during edge sandblasting. The core of this method lies in its ability to accurately identify and track the time-varying characteristics of electromagnetic interference, and intelligently adjust signal acquisition and transmission strategies accordingly. This fundamentally solves the technical challenges of poor signal quality and unstable data transmission, thus providing a reliable data foundation for precise process control. How to effectively address electromagnetic interference generated by sand particle collisions, dynamically adjust the acquisition frequency, and ensure signal quality has become a key technical challenge for improving the quality monitoring of edge sandblasting processes. Summary of the Invention

[0006] This application provides a data acquisition method, system, and storage medium for edge sandblasting, aiming to solve the problem of unstable vibration signal quality caused by changes in the electromagnetic interference spectrum in the prior art. By combining electromagnetic interference spectrum analysis, frequency adaptive adjustment, and noise suppression technology, this invention can effectively remove environmental interference and ensure the accuracy and reliability of vibration signal acquisition.

[0007] In a first aspect, this application provides a data acquisition method for edge sandblasting processing, the method comprising: Step 1: In the sandblasting environment of the frame, collect electromagnetic interference spectrum data generated by sand particle collision, and process the data to obtain the interference spectrum distribution characteristics that characterize the current interference pattern. Step 2: Based on the characteristics of the interference spectrum distribution, determine the current interference intensity level and identify the peak location and core distribution width of the interference energy. Step 3: Determine whether the core distribution width overlaps with the preset vibration signal acquisition frequency range. If so, adaptively adjust the acquisition frequency to the preset backup acquisition frequency range. Step 4: In the spare acquisition frequency range, acquire the original vibration signal of the frame sandblasting equipment, and perform noise suppression processing on the original vibration signal to obtain a high-quality denoised vibration signal sequence. Step 5: Extract key vibration feature parameters from the denoised vibration signal sequence to characterize the equipment status, and determine whether the key vibration feature parameters meet the preset standards for process quality monitoring. If not, dynamically update the data transmission configuration based on the interference spectrum distribution characteristics. Step 6: Based on the updated data transmission configuration, send the denoised vibration signal sequence to the central processing unit and obtain the signal reliability assessment results for the sequence.

[0008] Secondly, this application provides a data acquisition system for edge sandblasting processing, the system comprising: The data acquisition module is used to collect electromagnetic interference spectrum data generated by sand particle collisions in the frame sandblasting environment, and process the data to obtain the interference spectrum distribution characteristics that characterize the current interference pattern. The interference determination module is used to determine the current interference intensity level based on the characteristics of the interference spectrum distribution, and to determine the location of the interference peak and the core distribution width of the interference energy. The frequency adjustment module is used to determine whether there is an overlap between the core distribution width and the preset vibration signal acquisition frequency range. If so, the acquisition frequency is adaptively adjusted to the preset backup acquisition frequency range. The signal denoising module is used to acquire the original vibration signal of the frame sandblasting equipment in the backup acquisition frequency range, and to perform noise suppression processing on the original vibration signal to obtain a high-quality denoised vibration signal sequence. The feature monitoring module is used to extract key vibration feature parameters that characterize the equipment status from the denoised vibration signal sequence, and to determine whether the key vibration feature parameters meet the preset standards for process quality monitoring. If not, the data transmission configuration is dynamically updated based on the interference spectrum distribution characteristics. The transmission evaluation module is used to send the denoised vibration signal sequence to the central processing unit according to the updated data transmission configuration, and obtain the signal reliability evaluation result of the sequence.

[0009] A third aspect of this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the aforementioned data acquisition method for edge sandblasting.

[0010] Compared with the prior art, the beneficial effects of the technical solution of this application are at least as follows: 1. By analyzing the electromagnetic interference spectrum in real time and adaptively switching the acquisition frequency, the main interference frequency bands generated by sand particle collisions are fundamentally avoided. At the same time, combined with dynamic noise suppression technology, the vibration signals collected from the source have a high signal-to-noise ratio and integrity, significantly improving data accuracy.

[0011] 2. By correlating the quality assessment results of vibration signals with interference spectrum characteristics, and through power control mechanisms and channel selection optimization, the data transmission configuration is dynamically adjusted to form a closed-loop control, effectively responding to the dynamic changes in industrial field interference and significantly reducing the bit error rate and packet loss rate of data transmission.

[0012] 3. By extracting and analyzing key vibration characteristic parameters to quantify signal quality and generate reliability assessment results, it provides accurate and reliable data for the status monitoring and quality assessment of the frame sandblasting process, enabling timely detection of potential problems and guiding production adjustment and maintenance decisions.

[0013] 4. This invention forms a complete adaptive closed loop from "environmental perception → decision adjustment → signal processing → transmission optimization → effect evaluation", which can make optimal decisions autonomously according to complex and ever-changing working environments. It not only reduces the dependence on manual intervention and improves the level of intelligent operation and maintenance, but also effectively solves the interference problem in vibration signal acquisition, enabling the frame sandblasting process to accurately grasp the equipment status and process parameters, thereby improving production efficiency and product quality. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a flowchart of the data acquisition method for edge sandblasting in this application; Figure 2 This is a diagram illustrating the comparison of data acquisition efficiency in this application; Figure 3 This is a diagram illustrating the comparison of data collection accuracy in this application; Figure 4 This is a comparative diagram of the overall performance scores of this application; Figure 5 This is a schematic diagram of the data acquisition system used in the sandblasting process of the border according to this application. Detailed Implementation

[0016] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms “comprising” or “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0017] For ease of understanding, the specific process of the embodiments of this application is described below. Figure 1 The diagram shows a flowchart of a data acquisition method for edge sandblasting provided by the present invention. The flowchart specifically includes the following steps: Step 1: In the sandblasting environment of the frame, collect electromagnetic interference spectrum data generated by sand particle collisions, and process the data to obtain the interference spectrum distribution characteristics that characterize the current interference pattern.

[0018] In one specific embodiment, the process of performing step 1 may specifically include the following steps: The electromagnetic interference spectrum data generated by sand particle collisions is collected through a real-time monitoring module. The electromagnetic interference spectrum data is processed using a spectrum analysis algorithm. The time-domain signal is converted into a frequency-domain representation, the amplitude of each frequency point is calculated, and it is used as a characteristic value of the spectrum distribution. The eigenvalues ​​are arranged in frequency order to form a spectrum vector, and a Gaussian fitting algorithm is used to fit the spectrum vector to generate the initial interference spectrum distribution characteristics. The initial interference spectrum distribution characteristics are analyzed to identify frequency bands whose amplitude exceeds the first preset amplitude threshold. Based on this, the main frequency range of the interference signal is determined, and the intensity distribution of each main frequency range is quantified by calculating the integral of the amplitude curve within the frequency band. The main frequency range is compared with the preset frequency range to obtain the preliminary classification results of the interference signals; The preliminary classification results are normalized to generate the final interference spectrum distribution characteristics.

[0019] Specifically, sensor arrays and real-time monitoring modules are deployed at key locations in the sandblasting workshop. These devices capture the time-domain waveforms of electromagnetic signals excited when sand particles collide with metal frames at specific sampling frequencies. Due to the randomness and wide bandwidth characteristics of sand particle collisions, the sampling frequency needs to cover the typical interference range from low to medium frequency (e.g., 50Hz to 500Hz) to ensure that electromagnetic interference components generated by sand particles of different sizes and different collision velocities can be captured.

[0020] The collected raw electromagnetic interference spectrum data is a time-domain signal and needs to be processed using spectrum analysis algorithms. Specifically, the time-domain signal is converted into a frequency-domain representation using a Fast Fourier Transform (FFT), and the amplitude value corresponding to each frequency point is calculated. These amplitude values ​​directly reflect the energy intensity of the interference signal at different frequencies and are used as characteristic values ​​of the spectrum distribution to establish a "frequency-amplitude" correspondence. For example, in a sandblasting workshop environment, this transformation may reveal significant amplitude peaks at 50Hz, 100Hz, and 150Hz, which correspond to electromagnetic interference excited by different mechanical vibration modes.

[0021] The amplitude values ​​at each frequency point are arranged from low to high frequency, forming a spectrum vector of dimension N×2 (N is the number of frequency points), where each row of data corresponds to a frequency point and its corresponding amplitude feature value. This vector is then fitted using a Gaussian fitting algorithm, and the Gaussian function parameters are optimized using the least squares method. The expression for the Gaussian function is: ,in, Let A represent the frequency variable, A be the peak amplitude after fitting, and μ be the center frequency after fitting. , where A is the standard deviation, used to describe the degree of interference diffusion. During the fitting process, the amplitude value in the spectrum vector is used as the target value, and A, μ, and are iteratively adjusted. Three parameters are used until the sum of squared errors between the fitted curve and the spectral vector is minimized, generating the initial interference spectral distribution characteristics. These initial characteristics include parameters such as the fitted amplitude peak, center frequency, and standard deviation, and are derived from these parameters. A defined continuous Gaussian curve can initially characterize the concentration and spread range of interference signals.

[0022] When analyzing the initial interference spectrum distribution characteristics, a first preset amplitude threshold is set to 1.5 times the average amplitude value. This threshold is derived from historical data statistics of sand particle collision interference in the frame sandblasting environment, effectively distinguishing effective interference signals from background noise. The entire frequency range is scanned to identify continuous frequency intervals in the initial characteristics where the amplitude exceeds the first preset amplitude threshold; these intervals are determined as the main interference frequency ranges. For each identified interference frequency band, the area under its amplitude curve is calculated, i.e., the amplitude function is integrated within the frequency band boundary to obtain the intensity distribution. Through the correspondence between "main frequency range - intensity integral value," the interference intensity of different frequency intervals is quantified, reflecting the total interference energy generated by sand particle collisions within a specific frequency range.

[0023] The identified main interference frequency range is compared with the preset vibration signal acquisition frequency range, and the interference signals are classified according to the degree of overlap. The preset frequency range is divided according to the vibration signal acquisition requirements of the frame sandblasting equipment. For example, it is divided into low frequency band (50Hz~150Hz), mid frequency band (150Hz~300Hz), and high frequency band (300Hz~500Hz). Different frequency bands correspond to the vibration signals of different components of the equipment (e.g., the low frequency band corresponds to the vibration of the frame transmission mechanism, and the mid frequency band corresponds to the vibration of the sandblasting gun). If the main frequency range falls in the low frequency band, the preliminary classification result is "low frequency interference"; if it falls in the mid frequency band, it is classified as "mid frequency interference"; if it spans multiple frequency bands, it is classified as "wideband interference". This classification result can quickly locate the potential impact of interference on the vibration signal acquisition of different components of the equipment.

[0024] Preferably, the interference signals can also be classified according to the degree of overlap. This classification is based on the overlap ratio calculation. For example, when the overlap ratio exceeds 60%, it is classified as severe interference, 30% to 60% as moderate interference, and less than 30% as mild interference.

[0025] The preliminary classification results include category labels and key quantitative parameters supporting the category. These key quantitative parameters include the main interference frequency range, the integral intensity of the frequency range, the overlap ratio with the preset acquisition range, the amplitude peak value within the interference frequency range, and the corresponding frequency point.

[0026] The preliminary classification results are normalized to map the values ​​of various interference features to a standard range of 0-1. After normalization, the intensity values ​​of different classification results have a unified comparison dimension, eliminating the incomparability of intensity values ​​caused by differences in frequency ranges, and generating the final interference spectrum distribution characteristics. This final characteristic includes a complete data structure containing the classification results, normalized core spectral parameters (such as center frequency, bandwidth, and intensity value), main frequency ranges, spectrum fitting parameters, and original quantization parameters.

[0027] By converting the original time-domain signal into frequency-domain features and then establishing an interference distribution model through Gaussian fitting, the electromagnetic interference characteristics caused by sand particle collisions can be accurately captured. Quantifying the interference intensity distribution and establishing a classification system provides a data foundation for subsequent adaptive adjustment of the acquisition frequency, overcoming the technical shortcomings of traditional methods where fixed acquisition frequencies are easily affected by interference. Normalization processing ensures the consistency and comparability of interference data acquired under different operating conditions, creating conditions for the stable operation of the system under different sandblasting parameters.

[0028] Step 2: Based on the characteristics of the interference spectrum distribution, determine the current interference intensity level and identify the peak location and core distribution width of the interference energy.

[0029] In one specific embodiment, the process of performing step 2 may specifically include the following steps: The amplitude value in the interference spectrum distribution characteristics is compared with the second preset amplitude threshold. The integral of all amplitude values ​​exceeding the threshold is calculated to obtain the quantized value of the interference intensity. Based on the preset quantized value range, the interference intensity level is divided to determine the interference level of the current frame sandblasting environment. Based on the interference level, a peak extraction threshold is adaptively selected. Based on the selected extraction threshold, frequency points with amplitudes exceeding the threshold are extracted from the interference spectrum distribution characteristics as peak data. Analyze the peak data to locate the current peak interference position and determine the core distribution width.

[0030] Specifically, the quantification of amplitude information in the interference spectrum distribution characteristics involves systematically comparing the amplitude values ​​of each frequency point in the spectrum with a second preset amplitude threshold. This second preset amplitude threshold is determined based on historical interference data from the sandblasting environment, for example, set to -55dBm. This value avoids misclassifying background noise as valid interference while also preventing the omission of medium-to-high intensity interference generated by high-speed sand particle collisions. All frequency points exceeding this threshold are identified, and integration is performed on these exceeding amplitude values. The quantified value of the interference intensity is obtained through integration, reflecting the total energy level of electromagnetic interference in the sandblasting environment.

[0031] The preset quantization range is divided according to the vibration signal acquisition requirements of the frame sandblasting equipment. For example, 0-30 is classified as Level 1 interference (low intensity), 31-60 as Level 2 interference (medium intensity), and above 61 as Level 3 interference (high intensity). By assigning the calculated interference intensity quantization value to the corresponding range, the interference level of the current frame sandblasting environment is determined. This classification method allows the system to dynamically assess the severity of electromagnetic interference based on the actual sandblasting conditions. Clear quantization values ​​and level classifications provide a basis for subsequent targeted processing, avoiding mismatches in processing solutions due to ambiguity in interference intensity judgment.

[0032] Based on the determined interference level, an adaptive peak extraction threshold is selected. For Level 1 interference (low intensity), where the interference signal is weak and discrete, the peak extraction threshold is set to -50dBm to ensure that all potential interference peaks are captured, avoiding the omission of weak but potentially impactful interference points due to an excessively high threshold. For Level 2 interference (medium intensity), where the interference signal concentration increases, the peak extraction threshold is adjusted to -45dBm to retain effective peaks while reducing the false extraction of noise points. For Level 3 interference (high intensity), where the interference signal energy is concentrated and the amplitude is high, the peak extraction threshold is set to -40dBm to quickly locate the main interference peaks and reduce redundant data processing. This threshold adjustment mechanism ensures effective identification of interference components with practical impact under different interference environments. Based on the selected extraction threshold, all frequency points with amplitudes exceeding the threshold are selected from the interference spectrum distribution characteristics to form a peak data set. ,in, Represents frequency coordinates. This indicates the corresponding amplitude value.

[0033] For sand collision interference scenarios with concentrated peak data and no significant dispersion (such as stable low-frequency interference generated by uniform sand jetting), a weighted average algorithm is used to calculate the frequency-weighted mean as the center location of the interference peak, with the amplitude value of the peak data as the weight. The formula is The weighting coefficient is determined by the amplitude value of each peak point. The decision is made based on this weighting method. This method ensures that peak points with larger amplitudes contribute more significantly to location determination, making the location results more representative of the main interference sources. In practical applications in sandblasting workshops, this process may identify peak points with a concentrated distribution in the 80Hz to 120Hz frequency band. Through weighted calculation, the location of the interference peak with a center frequency of approximately 95Hz is obtained. After obtaining the weighted mean, the weighted standard deviation of the peak data set is calculated. The calculation formula is: Then the interval As the core distribution width.

[0034] For complex interference environments where peak data exhibits a certain degree of dispersion, Gaussian fitting methods can be further introduced into peak data analysis. A Gaussian function is applied to the peak dataset P, and the fitted Gaussian function is optimized using the least squares method to generate a fitted distribution curve. The mean parameter of the curve is used as the center position of the interference peaks. For example, after sorting and filtering, the peak data group is obtained as follows: , , After fitting the above example data, the mean is 201.5Hz, the standard deviation is 18.2Hz, and the peak amplitude of the fitted curve is -46.8dBm. Therefore, the interference peak position is 201.5Hz. This method can effectively reduce the influence of discrete peak data on the positioning results and improve the accuracy of the interference peak position, especially suitable for scenarios where the frequency of sand collision interference has a small drift. The interval jointly determined by the mean μ1 and the standard deviation σ1 is... The core distribution width of the interference energy.

[0035] Step 3: Determine whether the core distribution width overlaps with the preset vibration signal acquisition frequency range. If so, adaptively adjust the acquisition frequency to the preset backup acquisition frequency range.

[0036] In one specific embodiment, the process of performing step 3 may specifically include the following steps: Calculate the degree of overlap between the core distribution width and the vibration signal acquisition frequency range. If the degree of overlap exceeds the preset overlap threshold, activate the adaptive adjustment mechanism. After the adaptive adjustment mechanism is activated, the characteristics of the interference spectrum distribution are smoothed, and continuous frequency regions with amplitudes lower than the third preset amplitude threshold are identified as candidate non-interference frequency regions. From the candidate non-interference frequency domains, select the domain with the largest width as the initial backup acquisition frequency domain; Scan the initial backup acquisition frequency range. If a local energy peak is identified, shift its frequency to the edge of the range with a preset protection bandwidth to determine the optimized backup acquisition frequency range. A bandwidth allocation optimization algorithm is used to divide the optimized backup acquisition frequency domain into subbands and allocate resources, thereby determining and outputting the final backup acquisition frequency domain.

[0037] Specifically, the degree of overlap is quantified by calculating the ratio of the intersection length of the core distribution width of the interference energy and the acquisition range to the total length of the core distribution width. When this overlap ratio exceeds a preset overlap threshold, it indicates that the interference has a substantial impact on the current acquisition frequency band, and an adaptive adjustment mechanism is activated to find a usable clean frequency band. By calculating the overlap of the core distribution width, the coverage of the acquisition range by the concentrated interference energy region is accurately quantified, avoiding over- or under-processing caused by judging a single peak point.

[0038] After the adaptive adjustment mechanism is activated, the system switches to analyzing interference spectrum distribution characteristic data containing complete spectral information. To avoid misjudgment of idle frequency bands due to noise fluctuations, the spectrum data is preprocessed using a Gaussian smoothing filter. The smoothed spectrum curve effectively suppresses random fluctuations and more clearly shows the spectral envelope contour, making the interference spectrum distribution characteristics more consistent with the actual interference energy change trend. Based on the smoothed spectrum, a third preset amplitude threshold characterizing the noise floor is set. This threshold is determined based on the background noise level of the frame sandblasting environment, such as -60dBm. All continuous frequency intervals with amplitudes lower than the third preset amplitude threshold are marked as a set of candidate non-interference frequency domains. Each candidate region From its lower boundary frequency and upper boundary frequency definition.

[0039] From the set of candidate non-interference frequency bands In the middle, select the width value. The largest candidate frequency band is selected as the initial backup acquisition frequency band. This selection strategy aims to provide the maximum operating bandwidth for vibration signal acquisition. If multiple largest frequency bands of the same width exist, the frequency band closest to the original acquisition frequency range is selected first.

[0040] The initial backup frequency band is scanned, and a peak detection algorithm is used to identify local energy peaks. The algorithm logic compares the amplitude values ​​of each frequency point with the amplitude values ​​of its two adjacent frequency points. If the amplitude value of a frequency point is greater than the amplitude values ​​of its two adjacent points, it is determined to be a local energy peak. For example, in the range of 120Hz to 150Hz, the amplitude value at 135Hz is -58dBm, while its adjacent values ​​at 134Hz and 136Hz are -59dBm and -59dBm respectively. Therefore, 135Hz is a local energy peak. The preset protection bandwidth is set according to the diffusion range of interference peaks in the frame sandblasting environment, such as 5Hz. The 135Hz frequency is shifted 5Hz towards the edge of the frequency band according to the preset rules. The shift operation forms the boundary of the optimized backup acquisition frequency band. This frequency shift effectively avoids residual interference components that may exist in the candidate frequency band. For example, the above preset rules are to shift uniformly towards higher frequencies, uniformly towards lower frequencies, or towards the nearest boundary (a default direction needs to be specified when equidistant). For example, when the preset protection bandwidth is 5Hz, 135Hz is shifted to the upper limit of the range, 150Hz, and after the shift, 140Hz is obtained, so that the optimized backup acquisition frequency range is adjusted to 140Hz~150Hz, avoiding interference from local energy peaks.

[0041] After obtaining a clearly defined backup acquisition frequency band, a bandwidth allocation algorithm based on the best-fit principle is used to sub-band this continuous frequency band. For example, when the device sampling rate is 1000Hz, each sub-band needs to have a bandwidth of at least 20Hz to ensure signal integrity. If the optimized backup band of 130Hz–150Hz has a width of 20Hz, it is divided into one sub-band; if the backup band of 220Hz–260Hz has a width of 40Hz, it is divided into two sub-bands (220Hz–240Hz and 240Hz–260Hz). Resource allocation is based on the priority of the equipment components corresponding to the sub-bands. For example, the sub-band corresponding to the sandblasting gun is allocated 60% of the transmission bandwidth, and the sub-band corresponding to the transmission mechanism is allocated 40% of the bandwidth, ensuring priority transmission of vibration signals from critical components. Based on the sub-band division and resource allocation results, the backup acquisition frequency band is determined and output. This allocation scheme, together with the optimized backup acquisition frequency band, constitutes a complete frequency switching command, directly controlling the vibration sensor to switch to the specified interference-free frequency band for signal acquisition.

[0042] By employing an overlap-based quantitative triggering mechanism based on energy distribution, the system transitions from passive reception to active avoidance. Smoothing processing and threshold identification are used to construct candidate non-interference bands, ensuring the reliability of backup frequency resources. Local peak scanning and protective bandwidth offset eliminate potential regional interference. Channel partitioning and resource allocation guarantee the transmission efficiency of the acquisition system in new frequency bands. The synergistic effect of these technical features enables the vibration signal acquisition system to maintain stable operation in the complex electromagnetic environment generated by sand particle collisions, providing a continuous and reliable data source for process quality monitoring.

[0043] Step 4: In the spare acquisition frequency range, acquire the original vibration signal of the frame sandblasting equipment, and perform noise suppression processing on the original vibration signal to obtain a high-quality denoised vibration signal sequence.

[0044] In one specific embodiment, the process of performing step 4 may specifically include the following steps: Within the backup acquisition frequency range, the original vibration signal of the frame sandblasting equipment is acquired by a vibration sensor. An adaptive filtering algorithm based on the minimum mean square error criterion is used to suppress noise in the original vibration signal. The filter coefficients are iteratively adjusted according to the autocorrelation function of the signal to filter out interference components and obtain a preliminary denoised signal. The initial denoised signal is subjected to amplitude equalization to correct the vibration amplitude deviation, and frequency domain correction technology is used to compensate for the frequency shift in the signal. The signal after amplitude equalization and frequency correction is reconstructed to generate a high-quality denoised vibration signal sequence.

[0045] Specifically, the backup frequency band effectively avoids the main electromagnetic interference frequency bands caused by sand particle collisions, thus ensuring that the acquired raw vibration signal has a high initial signal-to-noise ratio. Vibration sensors are deployed in key parts of the frame sandblasting equipment (such as the sandblasting gun mounting bracket and the frame transmission track). The vibration sensors capture the vibration signal of the frame sandblasting equipment in real time during operation. This signal contains the vibration components generated during normal equipment operation and the interference components remaining from sand particle collisions, forming the raw vibration signal. The raw vibration signal is stored in the form of time-domain data, with each data point containing a timestamp and the corresponding vibration amplitude value. By using the backup acquisition frequency band to avoid the core distribution area of ​​interference, it is ensured that the acquired raw vibration signal is less affected by interference. However, it still contains residual interference that has not been completely avoided and inherent sensor noise.

[0046] An adaptive filtering algorithm based on the minimum mean square error criterion is used to suppress noise in the original vibration signal. The core of the algorithm is to minimize the mean square error between the output signal and the desired signal by iteratively adjusting the filter coefficients. First, the autocorrelation function of the original vibration signal is calculated. The autocorrelation function describes the similarity of the signal at different time points, and the formula is: Where x(n) is the amplitude value of the original vibration signal at the nth sampling point. Where N is the time delay and N is the total number of sampling points. For example, for an original vibration signal containing 1000 sampling points, calculate... Autocorrelation function value at time The average power of the signal is obtained; calculation is performed. time The correlation between adjacent sampling points is obtained. The statistical characteristics of the adaptive filtering module are estimated by calculating the autocorrelation matrix of the input signal, and a transverse filter is constructed based on this. The core of the filter lies in its dynamic update mechanism of the weight vector. This mechanism continuously calculates the error between the filter output and the desired signal through an iterative algorithm, and adjusts the weight coefficients along the steepest descent direction of the error performance surface based on this error value. The statistical characteristics of the noise are estimated based on the autocorrelation function results. The initial filter coefficients are set to a set of preset values ​​(such as an all-1 vector), and then the iterative formula is used to... Adjustment coefficient, where, Let be the filter coefficient vector for the k-th iteration. Let be the step size factor (set to 0.01 based on the environmental interference fluctuations of the frame sandblasting), e(k) be the error signal of the k-th iteration (equal to the difference between the expected signal and the filtered output signal, the expected signal being obtained through smoothing the original vibration signal), and x(k) be the input signal vector of the k-th iteration. The mean square error is calculated after each iteration. When the mean square error is less than a preset threshold (e.g., ...), ... The iteration stops when the filter coefficients are reached. At this point, the filter coefficients can effectively filter out the interference components in the original vibration signal, and output a preliminary denoised signal. Through autocorrelation function analysis and iteration coefficient adjustment, the filtering effect can match the changes in interference in the frame sandblasting environment in real time, retaining the effective vibration components while suppressing noise, thereby suppressing the interference components related to the reference noise to the maximum extent at the output end, and obtaining a preliminary denoised signal.

[0047] The initial denoised signal then enters the signal enhancement stage to correct distortion introduced by the transmission link. The amplitude equalization processing unit first analyzes the envelope of the signal, detects amplitude deviations caused by channel attenuation or gain fluctuations, and corrects the signal amplitude to a preset dynamic range through a gain mapping function driven by an automatic gain control loop or lookup table. After amplitude recovery, the processing flow moves to the frequency domain correction stage. This stage performs a discrete Fourier transform on the signal to map it to the frequency domain. In the frequency domain, it identifies the frequency offset of the fundamental frequency and its harmonics caused by equipment speed fluctuations or sampling clock drift, and applies a phase rotation factor or spectrum shift operation to compensate for the identified offset, ensuring the frequency accuracy of the vibration characteristics. The offset frequency domain signal is then converted back to the time domain through an inverse fast Fourier transform to obtain the frequency-corrected signal.

[0048] The signal, after amplitude equalization and frequency correction, is reconstructed. The reconstruction process involves rearranging the time-domain signal according to the sampling order, removing redundant data points that may have been generated during processing (such as invalid data generated by zero-padding during Fast Fourier Transform and Inverse Transform), and retaining valid data with the same number of original sampling points, forming a high-quality denoised vibration signal sequence. Each data point in this sequence contains a timestamp and a corresponding corrected vibration amplitude value. The amplitudes of all data points are within the target range, and the frequency has no offset, which can be used for subsequent extraction of key vibration characteristic parameters.

[0049] The adaptive filtering algorithm, through its dynamic adjustment characteristics, effectively addresses the time-varying nature of residual interference in the sandblasting environment; amplitude equalization technology compensates for the nonlinear attenuation during signal transmission, ensuring the accuracy of vibration amplitude measurement; and frequency domain correction eliminates the impact of frequency drift on equipment status analysis. The synergistic effect of these technical features ensures that the final vibration signal sequence accurately and reliably reflects the mechanical operating status of the frame sandblasting equipment, providing a solid data foundation for subsequent feature extraction and process monitoring.

[0050] Step 5: Extract key vibration characteristic parameters for characterizing equipment status from the denoised vibration signal sequence, and determine whether the key vibration characteristic parameters meet the preset standards for process quality monitoring. If not, dynamically update the data transmission configuration based on the interference spectrum distribution characteristics.

[0051] In one specific embodiment, step 5 involves extracting key vibration characteristic parameters for characterizing the equipment state from the denoised vibration signal sequence and determining whether the key vibration characteristic parameters meet the preset standards for process quality monitoring, including: Time-frequency analysis was performed on the denoised vibration signal sequence to extract key vibration characteristic parameters, including signal peak amplitude, average amplitude, and phase sequence. The phase variance is calculated based on the phase sequence to quantify the phase instability of the signal, and the attenuation ratio of the peak amplitude to the average amplitude of the signal is calculated to quantify the attenuation of the signal. Based on phase instability and attenuation, the waveform distortion index is calculated by a weighted fusion method. The waveform distortion index is compared with the preset standard to determine whether it meets the process quality monitoring requirements. If the waveform distortion index exceeds the preset standard, an anomaly marker data containing the distortion index value and a timestamp is generated to trigger a data transmission configuration update.

[0052] Specifically, the denoised vibration signal sequence is discrete time-domain data, containing timestamps and corresponding vibration amplitude values. The denoised vibration signal sequence is sent to the time-frequency analysis module, where the time-domain waveform is converted into a frequency-domain representation through discrete Fourier transform. Three key vibration characteristic parameters are extracted from the transformation results: peak amplitude of the signal, i.e. the maximum instantaneous amplitude in the sequence; average amplitude of the signal, obtained by calculating the arithmetic mean of the amplitudes of all sampling points in the sequence; and phase sequence, the set of phase values ​​corresponding to each frequency point.

[0053] Based on the extracted phase sequence, the phase variance is calculated to quantify the signal's phase instability. This variance is obtained by taking the arithmetic mean of the phase sequence, then calculating the sum of squared deviations of each phase point from the mean, and dividing by the sequence length. A larger phase variance indicates more severe phase fluctuations and greater equipment instability. Simultaneously, the signal attenuation is calculated, which is the ratio of the difference between the peak amplitude and the average amplitude to the peak amplitude. This ratio reflects the concentration of signal energy. Phase Instability With attenuation The waveform distortion index DI is calculated by weighted summation after input to the feature fusion module. The calculation formula is as follows: ,in, and The weighting coefficients are set based on historical data or determined according to the process quality requirements of the frame sandblasting equipment. The Distortion Index (DI), as a comprehensive indicator, reflects the degree of distortion in vibration signals caused by transmission interference and medium attenuation.

[0054] The calculated waveform distortion index DI is compared with a preset process quality monitoring standard threshold. This preset threshold is set according to the vibration signal fidelity requirements of the frame sandblasting process. When the waveform distortion index is greater than the standard threshold, the current vibration signal quality is determined to not meet the monitoring requirements, and a value containing the current distortion index DI and a processing timestamp is generated. The abnormal flag data packet serves as a trigger signal to initiate the dynamic update process of the data transmission configuration.

[0055] By extracting peak amplitude, average amplitude, and phase sequences through time-frequency analysis, signal characteristics are extended from a single domain to a joint time-frequency domain. The timing jitter and energy attenuation characteristics of the signal are quantified by calculating phase variance and attenuation ratio, respectively. A waveform distortion index is generated through weighted fusion, establishing a quantitative correlation model between signal quality and transmission interference. The synergistic effect of these technical features enables the system to accurately identify signal degradation caused by sand particle collision interference and provides a precise triggering basis for adaptive optimization of subsequent transmission links, thereby ensuring the effectiveness and reliability of process monitoring data.

[0056] In one specific embodiment, step 5, dynamically updating the data transmission configuration based on the interference spectrum distribution characteristics, includes: Data analysis was performed on the characteristics of the interference spectrum distribution. Frequency components with amplitudes exceeding the average level were extracted as the main interference components, and their frequency distribution and intensity were analyzed. Based on the frequency distribution of the main interference components, the modulation and coding scheme in the data transmission protocol is adjusted. At the same time, based on the intensity of the main interference components, the transmission power is dynamically increased through a power control mechanism. Scan all available channels and select the channel with the highest signal-to-noise ratio as the target transmission channel; The adjusted modulation and coding scheme, transmission power, and target transmission channel are integrated to generate an updated data transmission configuration.

[0057] Specifically, if key vibration characteristic parameters do not meet preset standards, it means that the current vibration signal quality is unreliable and cannot accurately reflect the mechanical state of the frame sandblasting equipment, affecting process quality analysis. Such signal quality problems are often caused by interference from sand particle collisions during signal acquisition / transmission. Updating data transmission configurations (adjusting modulation coding, transmission power, channel, etc.) can specifically avoid interference, reduce signal contamination, restore signal fidelity and availability, and ensure that subsequently acquired vibration signals can accurately and effectively transmit equipment status information, providing reliable data support for process quality monitoring. Therefore, when key vibration characteristic parameters do not meet the preset standards for process quality monitoring, a deep analysis process targeting the interference spectrum distribution characteristics should be initiated.

[0058] This analysis process performs a Fast Fourier Transform on the stored interference spectrum distribution characteristic data, converting the spectrum data to the frequency domain. It then calculates the average amplitude across the entire frequency band, subsequently iterating through all frequency points and filtering out all frequency points whose amplitude exceeds the average amplitude. These frequency components are the main interference components, forming the main interference component set. The frequency distribution of the main interference components is analyzed, and the start and end frequencies and bandwidth of each frequency band are recorded. and center frequency Meanwhile, the interference intensity level is quantized by calculating the sum of squared amplitudes of frequency points within the set.

[0059] Preferably, when multiple major interference frequency bands exist, priority should be given to adjusting the modulation and coding scheme and transmission power in the data transmission protocol based on the interference frequency bands with higher interference intensity, wider bandwidth, and greater overlap with the current data transmission frequency band, while also considering the characteristics of other interference frequency bands to achieve comprehensive anti-interference. For example, the frequency band with higher interference intensity is used as the core adjustment basis; if the intensity of multiple interference frequency bands is similar, priority should be given to the frequency band with wider bandwidth or greater overlap with the transmission frequency band.

[0060] Based on the characteristics of the main interference components obtained from the analysis, dual-path adjustment of transmission parameters is initiated. In the modulation and coding path, the bandwidth is adjusted according to the interference frequency distribution. Adjusting the physical layer protocol: When When the frequency is less than 40Hz, QPSK modulation and convolutional code are combined. When the frequency is between 40Hz and 100Hz, BPSK modulation combined with Reed-Solomon code is used. For frequencies exceeding 100Hz, spread spectrum communication and concatenated coding schemes are employed. In the power control path, adjustments are made based on the interference intensity level. Dynamically adjust transmission power Its adjustment strategy follows The relationship, among which, The reference power is denoted by k, which is the power adjustment coefficient to ensure that the output power matches the interference intensity.

[0061] While adjusting the transmission parameters, a multi-channel scan is performed. By measuring the signal and noise power of each candidate channel, the signal-to-noise ratio of each channel is calculated. Choose to satisfy The target transmission channel is selected as the primary channel. If multiple channels have the same and highest SNR, the channel with the largest frequency separation from the main interference component is prioritized to reduce the potential impact of interference on channel transmission. Finally, the system integrates the adjusted modulation and coding scheme, optimized transmit power parameters, and selected target channel information to generate a new data transmission configuration table. This configuration table is written into the configuration register of the communication module, completing the real-time reconstruction of the transmission link.

[0062] By establishing a correlation analysis between signal quality deviation and interference characteristics, a shift from passive reception to active adaptation was achieved. Dynamic adjustment of modulation and coding based on interference characteristics enhanced the anti-interference capability of the transmission link. Interference intensity-driven power control optimized energy efficiency while ensuring transmission reliability. Channel scanning and selection mechanisms effectively avoided frequency bands with concentrated interference. The synergistic effect of these technical features enabled the data acquisition system to maintain stable data transmission in a time-varying interference environment caused by sand particle collisions, providing a continuous and reliable data link guarantee for the process monitoring system.

[0063] Step 6: Based on the updated data transmission configuration, send the denoised vibration signal sequence to the central processing unit and obtain the signal reliability assessment results for the sequence.

[0064] In one specific embodiment, the process of performing step 6 may specifically include the following steps: Based on the updated data transmission configuration, the signal transmission power and transmission channel are adjusted, and the denoised vibration signal sequence is sent to the central processing unit using the adjusted parameters. During transmission, a dynamic safeguard mechanism is implemented. This mechanism includes real-time monitoring of channel interference intensity and dynamic adjustment of the transmission rate based on the interference spectrum distribution characteristics to optimize data transmission efficiency. Simultaneously, the frequency drift at the interference peak position is detected, and a backup transmission channel is switched or forward error correction coding is incorporated accordingly to address the peak drift problem. In the central processing unit, the received denoised vibration signal sequence is subjected to reliability analysis, and signal reliability assessment results are generated.

[0065] Specifically, the communication module reads the configuration parameters and sets the transmit power to the optimized power level. And lock the carrier frequency to the selected target channel. The hardware parameters were then reconfigured. Subsequently, the denoised vibration signal sequence was encapsulated into data packets and transmitted to the central processing unit using the re-initialized communication link.

[0066] During data transmission, a dynamic safeguard mechanism is executed in parallel to maintain link stability. This safeguard mechanism comprises two cooperating closed loops: 1. A rate control closed loop that monitors the Received Signal Strength Indicator (RSSI) to assess channel interference levels in real time. The measured value is matched with historical patterns in the interference spectrum distribution feature database, and the physical layer transmission rate is dynamically adjusted according to the preset rate-interference mapping relationship. Its regulation logic follows Where k is the rate decay coefficient, This refers to the highest physical layer transmission rate that the communication module can stably support under current channel conditions and hardware configuration. This mechanism ensures that connection reliability is maintained by reducing the data rate when interference increases. 2. The frequency domain tracking closed loop continuously monitors the frequency coordinates of known interference peak locations. Calculate its offset from the initial position. ,when Exceeding the channel width Specific proportion When a channel switching operation is triggered, a new working channel is selected from the pre-configured list of backup channels. If channel switching is not feasible, a Reed-Solomon forward error correction coding block is incorporated into the data stream to combat sudden errors caused by frequency drift by adding redundant information.

[0067] Once the denoised vibration signal sequence arrives at the central processing unit, the signal reliability analysis process is initiated. This process performs integrity verification and quality assessment on the received sequence, verifies data integrity by calculating the cyclic redundancy check (CRC) code of the sequence, and simultaneously extracts the bit error rate (BER) and signal-to-noise ratio (SNR) of the received signal. The Waveform Similarity Index (WSI) is used as a quality metric. These quality metrics are compared with preset reliability thresholds, and a weighted scoring algorithm is used to generate quantified signal reliability assessment results. The result represents the reliability of the current data transmission as a percentage, and is stored in the system database along with a timestamp and details of quality parameters.

[0068] Stable initial transmission conditions were established by resetting the parameters driven by configuration; proactive adaptation and dynamic compensation to time-varying interference during transmission were achieved through a dual closed-loop guarantee mechanism; and end-to-end reliability quantification provided feedback for closed-loop optimization of the system. The integration and coordination of these technical features enabled vibration data to maintain complete and reliable transmission in the non-stationary electromagnetic environment generated by sand particle collisions, providing high-quality data input for the process monitoring system and effectively overcoming the link interruption and data distortion problems caused by fixed transmission parameters in traditional methods.

[0069] Figures 2 to 4 This is a performance verification diagram for this application, wherein, Figure 2 This is a diagram illustrating the comparison of data collection efficiency. Figure 3 This is a diagram illustrating the comparison of data collection accuracy. Figure 4 This is a diagram showing the overall performance score comparison. Figure 2Traditional techniques, which use fixed-frequency acquisition (without avoiding interference bands), result in a large amount of invalid interference data in the acquired data, with only 0.8 MB / s of effective data per unit time. This patented method identifies the main interference bands (such as 93.75 Hz to 103.75 Hz) through interference spectrum analysis and adaptively switches to a backup acquisition range (such as 120 Hz to 140 Hz). The proportion of invalid data is reduced from 35% to 8%, and the effective data volume is increased to 1.5 MB / s, improving efficiency by 87.5%, highlighting the technical advantages of "actively avoiding interference and reducing invalid acquisition". Figure 3 Traditional techniques lack adaptive filtering and signal correction, resulting in signal acquisition being affected by electromagnetic interference, leading to waveform distortion rates of up to 28% and a matching degree of only 72% with the actual vibration state of the equipment. This makes it impossible to accurately reflect the operating status of sandblasting equipment (such as transmission mechanisms and sandblasting guns). This patented method, through adaptive filtering based on minimum mean square error (filtering out residual interference), amplitude equalization (correcting amplitude deviation), and frequency domain correction (compensating for frequency shift), reduces the signal phase variance from 0.01 rad² to 0.0025 rad² after noise reduction. The waveform distortion index is controlled within a reasonable range, achieving a matching degree of 96% with the actual vibration state of the equipment and improving accuracy by 24 percentage points. This solves the core problem of inaccurate acquisition caused by signal distortion in traditional techniques. Figure 4 Traditional technologies, due to their lack of interference sensing capability, simple signal processing, and fixed transmission configuration, achieve an overall performance score of only 55 points across interference avoidance (10 points), signal quality (15 points), transmission reliability (20 points), and process adaptability (10 points), which is insufficient to meet the high-precision data acquisition requirements of the frame sandblasting process. This patented method, through a complete closed loop of "interference spectrum analysis → frequency adaptive adjustment → signal optimization processing → dynamic update of transmission configuration → reliability assessment," achieves significantly improved interference avoidance (25 points, successfully avoiding over 95% of major interferences), signal quality (30 points, improving signal-to-noise ratio by 30% after denoising), and transmission reliability (27 points, reducing bit error rate from 1×10⁻⁶). -2 Reduced to 1.2×10 -3 The overall performance score reached 92 points, which is 10 points for process adaptability (adapting to different sandblasting pressures and sand particle sizes), surpassing traditional technologies in all aspects and highlighting the technical value of "full-process adaptive optimization".

[0070] The data acquisition method for edge sandblasting in the embodiments of this application has been described above. The data acquisition system for edge sandblasting in the embodiments of this application is described below. Please refer to [link to relevant documentation]. Figure 5 The present application provides a schematic diagram of the structure of a data acquisition system for edge sandblasting, the system comprising: The data acquisition module 10 is used to acquire electromagnetic interference spectrum data generated by sand particle collision in the frame sandblasting environment, and process the data to obtain the interference spectrum distribution characteristics that characterize the current interference pattern.

[0071] The interference determination module 20 is used to determine the current interference intensity level based on the characteristics of the interference spectrum distribution, and to determine the location of the interference peak and the core distribution width of the interference energy.

[0072] The frequency adjustment module 30 is used to determine whether the core distribution width overlaps with the preset vibration signal acquisition frequency range. If so, the acquisition frequency is adaptively adjusted to the preset backup acquisition frequency range.

[0073] The signal denoising module 40 is used to acquire the original vibration signal of the frame sandblasting equipment in the backup acquisition frequency range, and to perform noise suppression processing on the original vibration signal to obtain a high-quality denoised vibration signal sequence.

[0074] The feature monitoring module 50 is used to extract key vibration feature parameters for characterizing the equipment status from the denoised vibration signal sequence, and to determine whether the key vibration feature parameters meet the preset standards for process quality monitoring. If not, the data transmission configuration is dynamically updated based on the interference spectrum distribution characteristics.

[0075] The transmission evaluation module 60 is used to send the denoised vibration signal sequence to the central processing unit according to the updated data transmission configuration, and obtain the signal reliability evaluation result of the sequence.

[0076] This application also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the steps of the data acquisition method for the edge sandblasting process.

[0077] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A data acquisition method for edge sandblasting, characterized in that, The method includes: Step 1: In the sandblasting environment of the frame, collect electromagnetic interference spectrum data generated by sand particle collision, and process the data to obtain the interference spectrum distribution characteristics that characterize the current interference pattern. Step 2: Based on the interference spectrum distribution characteristics, determine the current interference intensity level, and identify the interference peak location and the core distribution width of the interference energy; Step 3: Determine whether the core distribution width overlaps with the preset vibration signal acquisition frequency range. If so, adaptively adjust the acquisition frequency to the preset backup acquisition frequency range. Step 4: Within the spare acquisition frequency range, acquire the original vibration signal of the frame sandblasting equipment, and perform noise suppression processing on the original vibration signal to obtain a high-quality denoised vibration signal sequence. Step 5: Extract key vibration feature parameters for characterizing the equipment status from the denoised vibration signal sequence, and determine whether the key vibration feature parameters meet the preset standards for process quality monitoring. If not, dynamically update the data transmission configuration based on the interference spectrum distribution characteristics. Step 6: According to the updated data transmission configuration, send the denoised vibration signal sequence to the central processing unit and obtain the signal reliability evaluation result of the sequence.

2. The method according to claim 1, characterized in that, Step 1 includes: The electromagnetic interference spectrum data generated by sand particle collisions is collected through a real-time monitoring module. The electromagnetic interference spectrum data is processed using a spectrum analysis algorithm to convert the time-domain signal into a frequency-domain representation, calculate the amplitude of each frequency point, and use it as a characteristic value of the spectrum distribution. The feature values ​​are arranged in frequency order to form a spectrum vector, and the spectrum vector is fitted using a Gaussian fitting algorithm to generate initial interference spectrum distribution features. The initial interference spectrum distribution characteristics are analyzed to identify frequency bands whose amplitude exceeds a first preset amplitude threshold. Based on this, the main frequency range of the interference signal is determined, and the intensity distribution of each main frequency range is quantified by calculating the integral of the amplitude curve within the frequency band. The main frequency range is compared with the preset frequency range to obtain the preliminary classification results of the interference signals; The preliminary classification results are normalized to generate the final interference spectrum distribution characteristics.

3. The method according to claim 1, characterized in that, Step 2 includes: The amplitude value in the interference spectrum distribution characteristics is compared with the second preset amplitude threshold, and the integral of all amplitude values ​​exceeding the threshold is calculated to obtain the quantized value of the interference intensity. Based on the preset quantized value range, the interference intensity level is divided to determine the interference level of the current frame sandblasting environment. Based on the interference level, a peak extraction threshold is adaptively selected. Based on the selected extraction threshold, frequency points with amplitudes exceeding the threshold are extracted from the interference spectrum distribution characteristics as peak data. The peak data is analyzed to locate the current interference peak position and determine the core distribution width.

4. The method according to claim 1, characterized in that, Step 3 includes: Calculate the degree of overlap between the core distribution width and the vibration signal acquisition frequency range. If the degree of overlap exceeds a preset overlap threshold, activate the adaptive adjustment mechanism. After the adaptive adjustment mechanism is activated, the interference spectrum distribution characteristics are smoothed, and continuous frequency regions with amplitudes lower than the third preset amplitude threshold are identified as candidate non-interference frequency regions. From the candidate non-interference frequency domains, select the domain with the largest width as the initial backup acquisition frequency domain; Scan the initial backup acquisition frequency range. If a local energy peak is identified, shift its frequency to the edge of the range with a preset protection bandwidth to determine the optimized backup acquisition frequency range. A bandwidth allocation optimization algorithm is used to divide the optimized backup acquisition frequency domain into subbands and allocate resources, thereby determining and outputting the final backup acquisition frequency domain.

5. The method according to claim 1, characterized in that, Step 4 includes: Within the backup acquisition frequency range, the original vibration signal of the frame sandblasting equipment is acquired by a vibration sensor. An adaptive filtering algorithm based on the minimum mean square error criterion is used to suppress noise in the original vibration signal. The filter coefficients are iteratively adjusted according to the autocorrelation function of the signal to filter out interference components and obtain a preliminary denoised signal. The initial denoised signal is subjected to amplitude equalization processing to correct the vibration amplitude deviation, and frequency domain correction technology is used to compensate for the frequency shift in the signal. The signal after amplitude equalization and frequency correction is reconstructed to generate a high-quality denoised vibration signal sequence.

6. The method according to claim 1, characterized in that, In step 5, key vibration characteristic parameters for characterizing the equipment status are extracted from the denoised vibration signal sequence, and it is determined whether the key vibration characteristic parameters meet the preset standards for process quality monitoring, including: Time-frequency analysis was performed on the denoised vibration signal sequence to extract key vibration characteristic parameters, including signal peak amplitude, average amplitude, and phase sequence. The phase variance is calculated based on the phase sequence to quantify the phase instability of the signal, and the attenuation ratio of the peak amplitude to the average amplitude of the signal is calculated to quantify the attenuation of the signal. Based on the phase instability and the attenuation, the waveform distortion index is calculated by a weighted fusion method. The waveform distortion index is compared with a preset standard to determine whether it meets the process quality monitoring requirements. If the waveform distortion index exceeds the preset standard, an anomaly marker data containing the distortion index value and a timestamp is generated to trigger a data transmission configuration update.

7. The method according to claim 1, characterized in that, In step 5, dynamically updating the data transmission configuration based on the interference spectrum distribution characteristics includes: Data analysis was performed on the interference spectrum distribution characteristics to extract frequency components with amplitudes exceeding the average level as the main interference components, and their frequency distribution and intensity were analyzed. Based on the frequency distribution of the main interference components, the modulation and coding scheme in the data transmission protocol is adjusted. At the same time, based on the intensity of the main interference components, the transmission power is dynamically increased through a power control mechanism. Scan all available channels and select the channel with the highest signal-to-noise ratio as the target transmission channel; The adjusted modulation and coding scheme, transmission power, and target transmission channel are integrated to generate an updated data transmission configuration.

8. The method according to claim 1, characterized in that, Step 6 includes: Based on the updated data transmission configuration, the signal transmission power and transmission channel are adjusted, and the denoised vibration signal sequence is sent to the central processing unit using the adjusted parameters. During transmission, a dynamic safeguard mechanism is implemented. This safeguard mechanism includes real-time monitoring of channel interference intensity and dynamic adjustment of the transmission rate based on the interference spectrum distribution characteristics to optimize data transmission efficiency. Simultaneously, the frequency drift at the interference peak position is detected, and a backup transmission channel is switched or forward error correction coding is incorporated accordingly to address the peak drift problem. The central processing unit performs reliability analysis on the received denoised vibration signal sequence and generates the signal reliability assessment result.

9. A data acquisition system for edge sandblasting, used to implement the method as described in any one of claims 1 to 8, characterized in that, The system includes: The data acquisition module is used to collect electromagnetic interference spectrum data generated by sand particle collisions in the frame sandblasting environment, and process the data to obtain the interference spectrum distribution characteristics that characterize the current interference pattern. The interference determination module is used to determine the current interference intensity level based on the interference spectrum distribution characteristics, and to determine the interference peak position and the core distribution width of the interference energy. The frequency adjustment module is used to determine whether the core distribution width overlaps with the preset vibration signal acquisition frequency range. If so, the acquisition frequency is adaptively adjusted to the preset backup acquisition frequency range. The signal denoising module is used to acquire the original vibration signal of the frame sandblasting equipment in the backup acquisition frequency range, and to perform noise suppression processing on the original vibration signal to obtain a high-quality denoised vibration signal sequence. The feature monitoring module is used to extract key vibration feature parameters for characterizing the equipment status from the denoised vibration signal sequence, and to determine whether the key vibration feature parameters meet the preset standards for process quality monitoring. If not, the data transmission configuration is dynamically updated based on the interference spectrum distribution characteristics. The transmission evaluation module is used to send the denoised vibration signal sequence to the central processing unit according to the updated data transmission configuration, and obtain the signal reliability evaluation result of the sequence.

10. A computer-readable storage medium storing instructions thereon, characterized in that, When the instruction is executed by the processor, it implements the data acquisition method for frame sandblasting as described in any one of claims 1 to 8.

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