A method and system for detecting the surface quality of a motherboard of a Bluetooth headset

Abstract

The present invention discloses a method and system for detecting the surface quality of a Bluetooth headset mainboard, which relate to the technical field of quality detection. The method and system comprise the following steps: collecting mechanical vibration signals of a Bluetooth headset production line to generate a vibration spectrum matrix, and obtaining an anti-vibration imaging sequence based on the vibration spectrum matrix; performing time alignment and matching on the anti-vibration imaging sequence based on a DTW algorithm to obtain vibration compensation coefficients corresponding to different DTW thresholds and constructing a plurality of detection strategy combinations; applying each detection strategy combination to collect an image of the Bluetooth headset mainboard, synchronously obtaining image clarity data and processing delay data, and constructing a vibration interference evaluation model; performing data analysis on the output of the vibration interference evaluation model to obtain an optimal detection strategy combination, and applying the optimal detection strategy combination to the surface quality detection of the Bluetooth headset mainboard, thereby solving the problem that traditional methods cannot dynamically obtain the optimal detection strategy, resulting in reduced accuracy in the surface quality detection of the Bluetooth headset mainboard.

Description

Technical Field

[0001] The present invention relates to the technical field of quality detection, and more particularly to a method and system for detecting the surface quality of a Bluetooth headset mainboard. Background Art

[0002] With the rapid development of the Bluetooth headset industry, especially its widespread application in consumer electronics and smart devices, the quality control and testing technology for Bluetooth headsets is also constantly improving and refining. As a highly integrated, small consumer product, Bluetooth headsets must meet not only stringent functional requirements but also complex production environments and external interference.

[0003] For example, the invention patent announcement with announcement number: CN118858298B 202411320132.2 A headphone charging stand detection method, system and device, which first obtains detection feature information, wherein the detection feature information includes the camera's shooting feature information, the headphone charging stand feature information and the light source position coordinates, and then obtains the camera shooting position coordinates according to the shooting feature information, and then obtains the detected position coordinates based on the headphone charging stand feature information, and finally determines whether the detected position coordinates, the light source position coordinates and the camera shooting position coordinates are collinear. If they are collinear, the camera angle is adjusted to make the camera and the light source position coordinates not on the same straight line, thereby avoiding the reflection from the side of the headphone charging stand affecting the camera shooting, so that the captured photos will not have strong reflection spots due to the reflection from the side of the headphone charging stand, and thus will not seriously affect the problem of headphone charging stand defect detection.

[0004] The above disclosed technical solutions have at least the following technical problems:

[0005] Traditional Bluetooth headset motherboard quality inspection relies on empirical parameters under the interference of mechanical vibration on the production line. It does not build an evaluation model for vibration interference, image quality, and image detection delay, and is unable to dynamically obtain the optimal inspection strategy, resulting in reduced accuracy of Bluetooth headset motherboard surface quality inspection.

[0006] In view of the above problems, the present invention proposes a solution. Summary of the Invention

[0007] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method and system for detecting the surface quality of a Bluetooth headset motherboard. By constructing a vibration interference evaluation model to perform data analysis on multiple detection strategy combinations, the optimal detection strategy combination is effectively screened out to solve the problem that the traditional method relies on empirical parameters and cannot dynamically obtain the optimal detection strategy, resulting in reduced accuracy of Bluetooth headset motherboard surface quality detection.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A method for detecting the surface quality of a Bluetooth headset motherboard comprises the following steps: collecting mechanical vibration signals from a Bluetooth headset production line to generate a vibration spectrum matrix, and obtaining an anti-vibration imaging sequence based on the vibration spectrum matrix; performing time alignment and matching on the anti-vibration imaging sequence based on a DTW algorithm to obtain vibration compensation coefficients corresponding to different DTW thresholds; constructing several detection strategy combinations based on the vibration compensation coefficients corresponding to different DTW thresholds; applying each detection strategy combination to capture an image of the Bluetooth headset motherboard, and synchronously obtaining image clarity data and processing delay data to construct a vibration interference assessment model; and performing data analysis on the output of the vibration interference assessment model to obtain an optimal detection strategy combination, which is then applied to the surface quality detection of the Bluetooth headset motherboard.

[0010] In a preferred embodiment, the mechanical vibration signal of the Bluetooth headset production line is collected to generate a vibration spectrum matrix, specifically as follows: the mechanical vibration signal of the Bluetooth headset production line is collected, and the mechanical vibration signal of the production line is Fourier transformed to obtain a frequency domain spectrum; based on the frequency domain spectrum, the frequency band is divided according to a preset bandwidth to obtain a plurality of first frequency bands; the energy fluctuation variance of each first frequency band is calculated and based on a preset first threshold, the plurality of first frequency bands are screened to obtain a plurality of screened first frequency bands; feature extraction is performed on the plurality of screened first frequency bands to obtain frequency domain feature vectors; the frequency domain feature vectors are arranged in a time window sequence to obtain a vibration spectrum matrix.

[0011] In a preferred embodiment, the vibration-resistant imaging sequence is obtained based on the vibration spectrum matrix, specifically: the vibration spectrum matrix is divided according to time windows, and the vibration main frequency offset in each time window is calculated; according to the vibration main frequency offset in each time window, a vibration phase angle sequence is constructed; and the vibration phase angle sequence is used to obtain the vibration-resistant imaging sequence based on an interpolation algorithm.

[0012] In a preferred embodiment, the method of performing time alignment and matching on the anti-vibration imaging sequence based on the DTW algorithm to obtain vibration compensation coefficients corresponding to different DTW thresholds is specifically as follows: constructing a standard Bluetooth headset motherboard surface anti-vibration imaging sequence library and dividing the anti-vibration imaging sequence according to a preset time window to obtain a plurality of segmented sequences; using the DTW algorithm to calculate the path offset between each segmented sequence and the standard Bluetooth headset motherboard surface anti-vibration imaging sequence library to obtain a DTW distance matrix; based on the DTW distance matrix, dividing the DTW distance into a plurality of initial DTW thresholds according to a preset ratio; and calculating the vibration compensation coefficients corresponding to different DTW thresholds based on a preset vibration compensation coefficient calculation formula.

[0013] In a preferred embodiment, the vibration compensation coefficients corresponding to different DTW thresholds are used to construct several detection strategy combinations, specifically: historical mechanical vibration data of the Bluetooth headset production line is obtained, and the initial detection frame rate and camera exposure time are determined based on the historical mechanical vibration data of the Bluetooth headset production line; the initial detection frame rate and exposure time are randomly gained based on preset constraints to obtain several detection frame rates and exposure times; the vibration compensation coefficients corresponding to different DTW thresholds are respectively arranged and combined with several detection frame rates and exposure times to obtain several detection strategy combinations.

[0014] In a preferred embodiment, the image clarity data includes an image clarity influence coefficient, and a specific method for obtaining the image clarity influence coefficient is as follows: continuously collect N frames of Bluetooth headset mainboard surface images for each detection strategy combination, and calculate the Sobel edge gradient amplitude mean of each frame of the Bluetooth headset mainboard surface image in the PCB solder joint area; use the Sobel edge gradient amplitude mean as the first image clarity value to obtain N first image clarity values; calculate the average and variance of the N first image clarity values, and use the ratio of the average to the variance as the image clarity evaluation coefficient; calculate the standard deviation of the vibration angular velocity in the corresponding time window according to the vibration phase angle sequence; input the vibration compensation coefficient, image clarity evaluation coefficient and vibration angular velocity standard deviation of the current detection strategy combination into a preset image clarity influence coefficient calculation formula to obtain the image clarity influence coefficient.

[0015] In a preferred embodiment, the processing delay data includes a processing delay influence coefficient, and the specific method for obtaining the processing delay influence coefficient is as follows: obtain the camera parameters, detection frame rate and exposure time of the current detection camera, and input the camera parameters, detection frame rate and exposure time of the current detection camera into a preset frame rate delay calculation formula to obtain a frame rate delay factor; obtain the timing data of the Bluetooth headset motherboard image from acquisition to the end of quality inspection, and the timing data includes image acquisition time, image preprocessing time and defect recognition time; perform statistical analysis on the timing data, calculate the ratio of the standard deviation of the timing data to the mean, and obtain the delay fluctuation coefficient; input the frame rate delay factor and the delay fluctuation coefficient into the preset processing delay influence coefficient calculation formula to obtain the processing delay influence coefficient.

[0016] In a preferred embodiment, the output of the vibration interference assessment model is subjected to data analysis to obtain the optimal detection strategy combination, and the combination is applied to the surface quality detection of the Bluetooth headset motherboard, specifically: based on the vibration interference assessment model, a vibration interference assessment value corresponding to each detection strategy combination is obtained; the multidimensional parameters of each detection strategy combination are mapped into a detection strategy combination identification code according to a hash function, and the multidimensional parameters include detection frame rate, exposure time and vibration compensation coefficient; a two-dimensional curve is constructed with the detection strategy combination identification code as the x-axis and the vibration interference assessment value as the y-axis; data analysis is performed on the two-dimensional curve, and the detection strategy combination identification code corresponding to the lowest point of the two-dimensional curve is used as the optimal detection strategy combination; and the optimal detection strategy combination is applied to the surface quality detection of the Bluetooth headset motherboard.

[0017] The technical effects and advantages of the method and system for detecting the surface quality of a Bluetooth headset motherboard of the present invention are as follows:

[0018] 1. This invention uses the DTW algorithm to calculate the path offset between each segmented sequence and a library of standard vibration-resistant imaging sequences, generating a DTW distance matrix. By using thresholding and compensation coefficient calculations based on the DTW distance matrix, the method improves the accuracy of surface quality inspection for Bluetooth headset motherboards in various vibration environments, reduces vibration interference with inspection results, and optimizes production quality control.

[0019] 2. The present invention constructs multiple detection strategy combinations based on vibration compensation coefficients corresponding to different DTW thresholds, which can adapt to detection requirements under different vibration environments and improve the anti-interference ability of the system. In the actual detection process, each detection strategy combination is applied to collect images of the Bluetooth headset motherboard, and image clarity data and processing delay data are obtained synchronously to ensure a comprehensive evaluation of image quality and system response speed under different detection conditions; secondly, by constructing a vibration interference evaluation model, the detection effects of different strategy combinations are quantitatively analyzed, the vibration interference effects of each combination are accurately identified, and the optimal detection strategy combination is determined through data analysis; finally, the optimal detection strategy combination is applied to the surface quality detection of the Bluetooth headset motherboard, which not only effectively reduces the impact of vibration interference on detection accuracy, but also improves the stability and reliability of the detection system, thereby improving the production quality of Bluetooth headsets. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 The present invention is a flow chart of a method for detecting the surface quality of a Bluetooth headset mainboard.

[0021] Figure 2 The present invention is a schematic structural diagram of a Bluetooth headset mainboard surface quality detection system. DETAILED DESCRIPTION

[0022] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0023] Example 1, Figure 1 A method for detecting the surface quality of a Bluetooth headset motherboard is provided, comprising the following steps:

[0024] S1, collecting mechanical vibration signals from the Bluetooth headset production line to generate a vibration spectrum matrix, and obtaining an anti-vibration imaging sequence based on the vibration spectrum matrix;

[0025] In this example, the mechanical vibration signal of the Bluetooth headset production line is collected to generate a vibration spectrum matrix, specifically:

[0026] Collect the mechanical vibration signal of the Bluetooth headset production line and perform Fourier transform on the mechanical vibration signal of the production line to obtain the frequency domain spectrum;

[0027] Based on the frequency domain spectrum, the frequency band is divided according to a preset bandwidth to obtain a plurality of first frequency bands;

[0028] Calculating the energy fluctuation variance of each first frequency band and screening the plurality of first frequency bands based on a preset first threshold value to obtain a plurality of screened first frequency bands;

[0029] Perform feature extraction on the first frequency bands after several screening to obtain frequency domain feature vectors;

[0030] The frequency domain eigenvectors are arranged in a time window sequence to obtain the vibration spectrum matrix.

[0031] It should be noted that the mechanical vibration signals of the Bluetooth headset production line are first collected through high-precision vibration sensors, and the time domain signals are converted into frequency domain spectra using Fourier transform to reduce the randomness of the time domain signals and improve the feasibility and accuracy of data analysis.

[0032] After acquiring the frequency domain spectrum, the frequency bands are divided based on a preset bandwidth, allowing the vibration signal to be classified and processed according to different frequency intervals, thereby reducing data redundancy and improving computational efficiency. Secondly, the energy fluctuation variance of each first frequency band is calculated to quantify the fluctuation of the vibration signal in each frequency band. The frequency bands primarily affected by vibration are screened based on a preset first threshold, ensuring the effectiveness and pertinence of subsequent data analysis and preventing interference from irrelevant frequency band signals on the detection results.

[0033] Finally, feature extraction is performed on the filtered first frequency band to obtain frequency domain feature vectors that can characterize the vibration characteristics. The extracted frequency domain feature vectors are arranged according to a time window sequence to construct a vibration spectrum matrix, thereby maintaining the integrity of the vibration data in the time series dimension. This enables the detection system to more comprehensively analyze the vibration change trend and provide high-quality input data for subsequent anti-vibration imaging, vibration compensation, and detection strategy optimization.

[0034] Furthermore, compared to traditional single-point vibration measurement methods, this method can represent the global characteristics of the vibration signal in matrix form, enabling the detection system to more comprehensively adapt to vibration interference under different operating conditions. By adopting frequency domain analysis and matrix construction, not only is the utilization rate of the vibration signal improved, but the identifiability of the vibration characteristics is also enhanced, ensuring clear and stable detection data even in complex vibration environments. This effectively improves detection accuracy, reduces false and missed detections, and enhances production quality control capabilities.

[0035] In this example, the anti-vibration imaging sequence is obtained based on the vibration spectrum matrix, specifically:

[0036] Divide the vibration spectrum matrix into time windows and calculate the vibration main frequency offset in each time window;

[0037] Construct a vibration phase angle sequence based on the vibration main frequency offset in each time window;

[0038] The vibration phase angle sequence is used to obtain the anti-vibration imaging sequence based on the interpolation algorithm.

[0039] It should be noted that by constructing an anti-vibration imaging sequence based on the vibration spectrum matrix and applying the DTW (Dynamic Time Warping) algorithm to time-align the anti-vibration imaging sequence, vibration compensation coefficients corresponding to different DTW thresholds are obtained, thereby improving the accuracy and stability of surface quality inspection of Bluetooth headset motherboards. In this method, the vibration spectrum matrix is first divided into time windows, and the offset of the main vibration frequency within each time window is calculated. This allows the vibration signal to be precisely segmented in the time dimension and the change in the main vibration frequency within each time window is quantified. This process ensures the targeted nature of subsequent anti-vibration processing and enables the system to effectively identify the dynamic characteristics of production line vibration.

[0040] Subsequently, a vibration phase angle sequence is constructed based on the vibration main frequency offset in each time window, quantifying the phase of the vibration signal. Because vibration signals often exhibit nonlinear and complex fluctuations during the actual Bluetooth headset production process, directly using the original signal for anti-vibration processing can result in significant errors. However, by constructing a vibration phase angle sequence, the vibration information can be transformed into a mathematical model.

[0041] Furthermore, using the DTW algorithm to perform temporal alignment of vibration-resistant imaging sequences effectively addresses the problem of imaging sequence deformation caused by vibration. By calculating the optimal matching path between sequences, the DTW algorithm automatically adjusts the alignment of different time windows, resulting in more accurate timing matching of vibration signals. By setting different DTW thresholds and calculating the corresponding vibration compensation coefficients, the vibration compensation strategy can be dynamically adjusted under different detection environments, ensuring the adaptability and robustness of the detection system.

[0042] S2, based on the DTW algorithm, performs time alignment and matching on the anti-vibration imaging sequence to obtain the vibration compensation coefficients corresponding to different DTW thresholds;

[0043] In this example, based on the DTW algorithm, the anti-vibration imaging sequence is time-aligned and matched to obtain the vibration compensation coefficients corresponding to different DTW thresholds. Specifically,

[0044] Construct a standard Bluetooth headset motherboard surface anti-vibration imaging sequence library and segment the anti-vibration imaging sequence according to a preset time window to obtain several segmented sequences;

[0045] The DTW algorithm is used to calculate the path offset between each segment sequence and the standard Bluetooth headset motherboard surface anti-vibration imaging sequence library to obtain the DTW distance matrix.

[0046] Based on the DTW distance matrix, the DTW distance is divided into several initial DTW thresholds according to a preset ratio;

[0047] The vibration compensation coefficients corresponding to different DTW thresholds are calculated based on a preset vibration compensation coefficient calculation formula.

[0048] It should be noted that a standard Bluetooth headset motherboard surface vibration imaging sequence library was constructed as a benchmark dataset. This library contains standard imaging data acquired under different vibration conditions, providing stable and reliable reference information. By segmenting the vibration imaging sequence according to preset time windows, a number of segmented sequences are obtained, breaking down complex vibration effects into multiple time-series segments. This allows for more refined data analysis and facilitates subsequent matching calculations.

[0049] Based on this, the DTW algorithm was used to calculate the path offset between each segmented sequence and a standard Bluetooth headset motherboard surface vibration-resistant imaging sequence library, and a DTW distance matrix was constructed. The DTW algorithm flexibly adjusts data alignment along the time axis, eliminating imaging sequence deformation or temporal misalignment caused by vibration, thereby ensuring the accuracy of comparative analysis. The DTW distance matrix provides the degree of temporal offset between the vibration-resistant imaging sequence and the standard imaging sequence, enabling the system to quantify the changes in imaging quality under different vibration influences.

[0050] Based on the DTW distance matrix, the DTW distance is divided into several initial DTW thresholds according to a preset ratio, enabling the system to perform graded matching based on different vibration levels. Appropriate threshold division helps optimize the vibration compensation strategy and improve the detection system's adaptability to different vibration environments. Finally, based on a preset vibration compensation coefficient calculation formula, the vibration compensation coefficients corresponding to different DTW thresholds are calculated, thereby establishing a dynamic compensation mechanism for different vibration conditions. The introduction of the vibration compensation coefficients enables the detection system to automatically adjust imaging parameters to offset the impact of vibration on imaging quality, ensuring the clarity and consistency of the captured images.

[0051] The preset vibration compensation coefficient calculation formula is as follows:

[0052]

[0053] in, is the vibration compensation coefficient, is the eigenvalue of the kth segment sequence, is the number of segment sequences, is the eigenvalue of the kth standard Bluetooth headset motherboard surface anti-vibration imaging sequence segment, is the preset DTW maximum distance.

[0054] S3, construct several detection strategy combinations based on the vibration compensation coefficients corresponding to different DTW thresholds;

[0055] In this example, several detection strategy combinations are constructed based on the vibration compensation coefficients corresponding to different DTW thresholds, specifically:

[0056] Obtain historical mechanical vibration data from the Bluetooth headset production line and determine the initial detection frame rate and camera exposure time based on the historical mechanical vibration data of the Bluetooth headset production line;

[0057] Perform random gain on the initial detection frame rate and exposure time based on preset constraints to obtain several detection frame rates and exposure times;

[0058] The vibration compensation coefficients corresponding to different DTW thresholds are arranged and combined with several detection frame rates and exposure times to obtain several detection strategy combinations.

[0059] It's important to note that by acquiring historical mechanical vibration data from the Bluetooth headset production line, the system can analyze the vibration characteristics of the production environment over different time periods. Based on this historical data, the system then determines the initial detection frame rate and camera exposure time. Properly setting these initial detection frame rate and exposure time helps ensure that the imaging system can capture clear and stable image data under typical vibration conditions, providing foundational parameters for subsequent optimization.

[0060] On this basis, random gains are applied to the initial detection frame rate and exposure time based on preset constraints to generate multiple different parameter combinations. The introduction of random gains allows for dynamic adjustment of the frame rate and exposure time within a certain range to adapt to varying vibration environments, improving the flexibility and robustness of the detection strategy. The vibration compensation coefficients corresponding to different DTW thresholds are then permuted and combined with various detection frame rates and exposure times to generate multiple detection strategy combinations. This permutation and combination approach fully accounts for the impact of varying vibration environments on image clarity and processing latency, ensuring that the detection system selects the optimal detection parameters under various operating conditions.

[0061] By combining historical vibration data, DTW thresholds, and vibration compensation coefficients, the system can dynamically adjust detection strategies for varying vibration conditions, ensuring the stability and reliability of detection results. Compared to traditional fixed parameter setting methods, this solution optimizes detection parameters based on real-time vibration characteristics, effectively reducing the impact of vibration interference on imaging quality and improving defect detection accuracy. Furthermore, the establishment of a combination of multiple detection strategies enables the system to continuously optimize detection parameters based on feedback from the vibration interference assessment model in actual applications, improving detection efficiency and reducing false positives.

[0062] S4, apply each detection strategy combination to collect images of the Bluetooth headset motherboard, and simultaneously obtain image clarity data and processing delay data to build a vibration interference assessment model;

[0063] In this example, the image clarity data includes an image clarity influence coefficient, and a specific method for obtaining the image clarity influence coefficient is as follows:

[0064] For each detection strategy combination, N frames of Bluetooth headset motherboard surface images are continuously collected, and the mean Sobel edge gradient amplitude of each frame of the Bluetooth headset motherboard surface image in the PCB solder joint area is calculated;

[0065] Taking the mean value of the Sobel edge gradient amplitude as the first image clarity value, and obtaining N first image clarity values;

[0066] Calculating the average and variance of the N first image clarity values, and using the ratio of the average to the variance as an image clarity evaluation coefficient;

[0067] Calculate the standard deviation of the vibration angular velocity in the corresponding time window according to the vibration phase angle sequence;

[0068] The vibration compensation coefficient, image clarity evaluation coefficient and vibration angular velocity standard deviation of the current detection strategy combination are input into the preset image clarity influence coefficient calculation formula to obtain the image clarity influence coefficient.

[0069] The preset image clarity influence coefficient calculation formula is as follows:

[0070]

[0071] in, is the image clarity influence coefficient, is the image clarity evaluation coefficient, is the standard deviation of the vibration angular velocity, is the exposure time, is the vibration compensation coefficient of the current detection strategy combination.

[0072] It should be noted that for each detection strategy combination, N frames of Bluetooth headset motherboard surface images were continuously collected. The average Sobel edge gradient amplitude of each frame was calculated in the PCB solder joint area, and this was used as the first image clarity value. Because the Sobel edge gradient can effectively measure the edge sharpness of an image, this method can relatively accurately reflect the clarity of the image.

[0073] After obtaining N first image clarity values, they are statistically analyzed to calculate their average and variance, and the ratio of the average to the variance is used as the image clarity evaluation coefficient. This process can avoid the influence of single-frame image clarity and comprehensively evaluate the overall contribution of the detection strategy combination to image clarity. In addition, it is necessary to combine the vibration factor and calculate the standard deviation of the vibration angular velocity in the corresponding time window through the vibration phase angle sequence to quantify the degree of vibration impact. Finally, the vibration compensation coefficient, image clarity evaluation coefficient, and vibration angular velocity standard deviation of the current detection strategy combination are input into the preset image clarity influence coefficient calculation formula to obtain the image clarity influence coefficient, which provides a quantitative basis for the optimization of the detection strategy.

[0074] In this example, the processing delay data includes a processing delay impact coefficient. The specific method for obtaining the processing delay impact coefficient is as follows:

[0075] Obtain the camera parameters, detection frame rate, and exposure time of the current detection camera, and input the camera parameters, detection frame rate, and exposure time of the current detection camera into a preset frame rate delay calculation formula to obtain a frame rate delay factor;

[0076] Obtaining time series data from the acquisition of the Bluetooth headset motherboard image to the end of quality inspection, the time series data includes image acquisition time, image preprocessing time, and defect recognition time;

[0077] Perform statistical analysis on the time series data, calculate the ratio of the standard deviation to the mean of the time series data, and obtain the delay fluctuation coefficient;

[0078] The frame rate delay factor and the delay fluctuation coefficient are input into a preset processing delay influence coefficient calculation formula to obtain the processing delay influence coefficient.

[0079] The preset frame rate delay calculation formula is as follows:

[0080]

[0081] in, is the delay fluctuation coefficient, is the current detection frame rate, is the exposure time, It is the camera interface constant.

[0082] The preset calculation formula for the processing delay impact coefficient is as follows:

[0083]

[0084] in, To handle the delay effect coefficient, is the preset weight coefficient, is the preset baseline delay, is the delay fluctuation coefficient, is the delay fluctuation coefficient.

[0085] It is important to note that we collected time series data from the acquisition of Bluetooth headset motherboard images to the completion of quality inspection, including image acquisition time, image preprocessing time, and defect identification time. By statistically analyzing this time series data and calculating the ratio of its standard deviation to the mean, we calculated the delay fluctuation coefficient. This coefficient reflects the stability of the system across different inspection batches and mitigates fluctuations in inspection efficiency caused by uneven processing time.

[0086] Finally, the frame rate delay factor and the delay fluctuation coefficient are input into a preset processing delay impact coefficient calculation formula to obtain the overall processing delay impact coefficient, providing data support for detection strategy optimization. The beneficial effect of this invention is that by accurately calculating and quantifying the processing delays at each stage, it achieves real-time optimization of the detection system, reduces misjudgments caused by time delay fluctuations, and improves the efficiency and reliability of Bluetooth headset motherboard surface quality inspection.

[0087] S5, performs data analysis on the output of the vibration interference evaluation model to obtain the optimal detection strategy combination, which is then applied to the surface quality inspection of the Bluetooth headset motherboard.

[0088] In this example, the output of the vibration interference assessment model is analyzed to obtain the optimal detection strategy combination, which is then applied to the surface quality inspection of Bluetooth headset motherboards. Specifically,

[0089] Based on the vibration interference evaluation model, the vibration interference evaluation value corresponding to each detection strategy combination is obtained;

[0090] Mapping the multidimensional parameters of each detection strategy combination into a detection strategy combination identification code according to a hash function, wherein the multidimensional parameters include a detection frame rate, an exposure time, and a vibration compensation coefficient;

[0091] A two-dimensional curve is constructed with the detection strategy combination identification code as the x-axis and the vibration interference assessment value as the y-axis;

[0092] Perform data analysis on the two-dimensional curve, and use the detection strategy combination identification code corresponding to the lowest point of the two-dimensional curve as the optimal detection strategy combination;

[0093] The optimal detection strategy combination is applied to the surface quality inspection of Bluetooth headset motherboard.

[0094] It should be noted that, first, based on the vibration interference evaluation model, the vibration interference evaluation value corresponding to each detection strategy combination is calculated. The vibration interference evaluation value can reflect the stability of different strategy combinations in a vibration interference environment, thus providing data support for selecting the optimal strategy.

[0095] Next, the multi-dimensional parameters of each detection strategy combination (including detection frame rate, exposure time, and vibration compensation coefficient) are mapped into a unique detection strategy combination identification code using a hash function. This mapping method not only improves data processing efficiency but also ensures the uniqueness of each detection strategy. Subsequently, a two-dimensional curve is constructed with the detection strategy combination identification code as the x-axis and the vibration interference assessment value as the y-axis. This curve intuitively demonstrates the performance of each detection strategy combination under different vibration interference conditions, providing a graphical reference for subsequent strategy selection.

[0096] By analyzing the two-dimensional curve, the system can find the identification code for the detection strategy combination corresponding to the lowest point and determine that combination as the optimal strategy. Finally, this optimal detection strategy combination is applied to the surface quality inspection of Bluetooth headset motherboards, thereby maximizing the accuracy of the inspection process.

[0097] Example 2, Figure 2 The present invention provides a Bluetooth headset motherboard surface quality detection system, which includes a data acquisition module, a vibration compensation module, a strategy combination module, an interference assessment module, and a quality detection module:

[0098] The data acquisition module is used to collect the mechanical vibration signals of the Bluetooth headset production line to generate a vibration spectrum matrix, and obtain an anti-vibration imaging sequence based on the vibration spectrum matrix;

[0099] The vibration compensation module is used to perform time alignment and matching on the anti-vibration imaging sequence based on the DTW algorithm to obtain the vibration compensation coefficients corresponding to different DTW thresholds;

[0100] Strategy combination module, used to construct several detection strategy combinations based on vibration compensation coefficients corresponding to different DTW thresholds;

[0101] The interference assessment module is used to apply each detection strategy combination to collect images of the Bluetooth headset motherboard, and simultaneously obtain image clarity data and processing delay data to build a vibration interference assessment model;

[0102] The quality detection module is used to analyze the output of the vibration interference assessment model to obtain the optimal detection strategy combination and apply it to the surface quality inspection of Bluetooth headset motherboards.

[0103] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0104] The above embodiments may be implemented in whole or in part through software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0105] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0106] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0107] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0108] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for detecting the surface quality of a Bluetooth headset motherboard, characterized in that: The following steps are involved: The mechanical vibration signals of the Bluetooth headset production line are collected to generate a vibration spectrum matrix, and an anti-vibration imaging sequence is obtained based on the vibration spectrum matrix. Based on the DTW algorithm, the anti-vibration imaging sequence is time-aligned and matched to obtain the vibration compensation coefficients corresponding to different DTW thresholds. Specifically, Construct a standard Bluetooth headset motherboard surface anti-vibration imaging sequence library and segment the anti-vibration imaging sequence according to a preset time window to obtain several segmented sequences; The DTW algorithm is used to calculate the path offset between each segment sequence and the standard Bluetooth headset motherboard surface anti-vibration imaging sequence library to obtain the DTW distance matrix. Based on the DTW distance matrix, the DTW distance is divided into several initial DTW thresholds according to a preset ratio; Calculate the vibration compensation coefficients corresponding to different DTW thresholds based on the preset vibration compensation coefficient calculation formula; Several detection strategy combinations are constructed based on the vibration compensation coefficients corresponding to different DTW thresholds, specifically: Obtain historical mechanical vibration data from the Bluetooth headset production line and determine the initial detection frame rate and camera exposure time based on the historical mechanical vibration data of the Bluetooth headset production line; Perform random gain on the initial detection frame rate and exposure time based on preset constraints to obtain several detection frame rates and exposure times; The vibration compensation coefficients corresponding to different DTW thresholds are arranged and combined with several detection frame rates and exposure times to obtain several detection strategy combinations; Each detection strategy combination is applied separately to collect images of the Bluetooth headset motherboard, and the image clarity data and processing delay data are obtained simultaneously to build a vibration interference assessment model; The output of the vibration interference assessment model is analyzed to obtain the optimal detection strategy combination, which is then applied to the surface quality inspection of Bluetooth headset motherboards.

2. The method for detecting the surface quality of a Bluetooth headset motherboard according to claim 1, wherein: The mechanical vibration signal of the Bluetooth headset production line is collected to generate a vibration spectrum matrix, specifically: Collect the mechanical vibration signal of the Bluetooth headset production line and perform Fourier transform on the mechanical vibration signal of the production line to obtain the frequency domain spectrum; Based on the frequency domain spectrum, the frequency band is divided according to a preset bandwidth to obtain a plurality of first frequency bands; Calculating the energy fluctuation variance of each first frequency band and screening the plurality of first frequency bands based on a preset first threshold value to obtain a plurality of screened first frequency bands; Perform feature extraction on the first frequency bands after several screening to obtain frequency domain feature vectors; The frequency domain eigenvectors are arranged in a time window sequence to obtain the vibration spectrum matrix.

3. The method for detecting the surface quality of a Bluetooth headset motherboard according to claim 2, wherein: The vibration-resistant imaging sequence is obtained based on the vibration spectrum matrix, specifically: Divide the vibration spectrum matrix into time windows and calculate the vibration main frequency offset in each time window; Construct a vibration phase angle sequence based on the vibration main frequency offset in each time window; The vibration phase angle sequence is used to obtain the anti-vibration imaging sequence based on the interpolation algorithm.

4. The method for detecting the surface quality of a Bluetooth headset motherboard according to claim 3, wherein: The image clarity data includes an image clarity influence coefficient, and a specific method for obtaining the image clarity influence coefficient is as follows: For each detection strategy combination, N frames of Bluetooth headset motherboard surface images are continuously collected, and the mean Sobel edge gradient amplitude of each frame of the Bluetooth headset motherboard surface image in the PCB solder joint area is calculated; Taking the mean value of the Sobel edge gradient amplitude as the first image clarity value, and obtaining N first image clarity values; Calculating the average and variance of the N first image clarity values, and using the ratio of the average to the variance as an image clarity evaluation coefficient; Calculate the standard deviation of the vibration angular velocity in the corresponding time window according to the vibration phase angle sequence; The vibration compensation coefficient, image clarity evaluation coefficient and vibration angular velocity standard deviation of the current detection strategy combination are input into the preset image clarity influence coefficient calculation formula to obtain the image clarity influence coefficient.

5. The method for detecting the surface quality of a Bluetooth headset motherboard according to claim 4, wherein: The processing delay data includes a processing delay impact coefficient, and a specific method for obtaining the processing delay impact coefficient is as follows: Obtain the camera parameters, detection frame rate, and exposure time of the current detection camera, and input the camera parameters, detection frame rate, and exposure time of the current detection camera into a preset frame rate delay calculation formula to obtain a frame rate delay factor; Obtaining time series data from the acquisition of the Bluetooth headset motherboard image to the end of quality inspection, the time series data includes image acquisition time, image preprocessing time, and defect recognition time; Perform statistical analysis on the time series data, calculate the ratio of the standard deviation to the mean of the time series data, and obtain the delay fluctuation coefficient; The frame rate delay factor and the delay fluctuation coefficient are input into a preset processing delay influence coefficient calculation formula to obtain the processing delay influence coefficient.

6. The method for detecting the surface quality of a Bluetooth headset motherboard according to claim 5, wherein: The output of the vibration interference assessment model is analyzed to obtain the optimal detection strategy combination, which is applied to the surface quality inspection of the Bluetooth headset motherboard, specifically: Based on the vibration interference evaluation model, the vibration interference evaluation value corresponding to each detection strategy combination is obtained; Mapping the multidimensional parameters of each detection strategy combination into a detection strategy combination identification code according to a hash function, wherein the multidimensional parameters include a detection frame rate, an exposure time, and a vibration compensation coefficient; A two-dimensional curve is constructed with the detection strategy combination identification code as the x-axis and the vibration interference assessment value as the y-axis; Perform data analysis on the two-dimensional curve, and use the detection strategy combination identification code corresponding to the lowest point of the two-dimensional curve as the optimal detection strategy combination; The optimal detection strategy combination is applied to the surface quality inspection of Bluetooth headset motherboard.

7. A Bluetooth headset motherboard surface quality detection system, applied to a Bluetooth headset motherboard surface quality detection method according to any one of claims 1 to 6, characterized in that: It includes data acquisition module, vibration compensation module, strategy combination module, interference assessment module and quality detection module: The data acquisition module is used to collect the mechanical vibration signals of the Bluetooth headset production line to generate a vibration spectrum matrix, and obtain an anti-vibration imaging sequence based on the vibration spectrum matrix; The vibration compensation module is used to perform time alignment and matching on the anti-vibration imaging sequence based on the DTW algorithm to obtain the vibration compensation coefficients corresponding to different DTW thresholds; Strategy combination module, used to construct several detection strategy combinations based on vibration compensation coefficients corresponding to different DTW thresholds; The interference assessment module is used to apply each detection strategy combination to collect images of the Bluetooth headset motherboard, and simultaneously obtain image clarity data and processing delay data to build a vibration interference assessment model; The quality detection module is used to analyze the output of the vibration interference assessment model to obtain the optimal detection strategy combination and apply it to the surface quality inspection of Bluetooth headset motherboards.

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