Defect detection method and system for high-precision transmission lead screw of automobile seat

Abstract

The invention discloses a defect detection method and system for a high-precision transmission lead screw of an automobile seat, and relates to the technical field of lead screw defects, an operation stability coefficient Zwxs and a self-locking coefficient Zsxs are constructed by monitoring and analyzing defects in the aspects of transmission stability and self-locking performance of the lead screw, and the operation stability coefficient Zwxs and the self-locking coefficient Zsxs are used as key input parameters; and inputting into a defect prediction model based on a CNN (Convolutional Neural Network). The CNN model can output a balance evaluation index Pgzs after linear normalization processing by using the nonlinear fitting capability of big data training, the balance evaluation index Pgzs quantifies the balance condition of the lead screw between the transmission stability and the self-locking performance, the comprehensive evaluation of the lead screw between the transmission performance and the self-locking performance is realized, and the prediction result is more comprehensive and reliable. In addition, by setting a performance balance threshold value Q, the balance evaluation index Pgzs is matched and analyzed with the performance balance threshold value Q, and automatic screening and accurate judgment of the performance of the lead screw are achieved.

Description

Technical Field

[0001] The invention relates to the technical field of screw rod defects, and in particular to a method and system for detecting defects in a high-precision transmission screw rod of an automobile seat. Background Art

[0002] In recent years, with the improvement of the automotive industry's requirements for comfort and safety, the demand for adjustability and precision control of automotive seat systems has increased significantly. In order to achieve precise adjustment and position control of seats, the transmission screw, as an important mechanical transmission component in the automotive seat system, is required to have stable transmission performance and good self-locking properties. Specifically, the high-precision transmission screw used in automotive seats needs to find a balance between stability and self-locking to ensure stable operation during seat adjustment and to maintain the set position without external force. Therefore, in practical applications, defect detection of the screw becomes a key task to ensure the reliability of the seat transmission system, especially the evaluation and analysis of the balance between its self-locking and stability.

[0003] The shortcomings of the current defect detection methods mainly come from the limitations of traditional methods in high-precision and dynamic detection. This detection method is mostly based on static analysis, lacking intelligent feature extraction and high-precision real-time monitoring capabilities. Once the defects of the transmission screw are not discovered in time, especially when the self-locking and stability are unbalanced, it may cause problems such as abnormal noise, position drift or even jamming during seat adjustment. These abnormalities will not only affect the user experience of the seat, but may also affect the safety of passengers in serious cases, especially when the car is impacted or stopped suddenly. Therefore, it is crucial to dynamically detect and comprehensively analyze the balance of the transmission screw in terms of self-locking and stability, and to discover and deal with defects in a timely manner to ensure the reliability and safety of the seat transmission system. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a method and system for detecting defects in a high-precision transmission screw of an automobile seat, which solves the problems in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for detecting defects of a high-precision transmission screw rod of an automobile seat comprises the following steps:

[0006] S1. Rotate the transmission screw and capture the video stream in real time from multiple angles to capture the dynamic characteristics of the transmission screw. After image processing, generate a detection data set. Based on the detection data set, determine whether the actual rotation axis of the transmission screw used in the car seat coincides with the standard axis. If not, trigger a sorting instruction.

[0007] S2. If they coincide, further defect monitoring and analysis of the transmission screw is performed. Based on the detection data set, the transmission stability of the transmission screw when the car seat is adjusted is evaluated to construct the operation stability coefficient Zwxs. At the same time, the self-locking coefficient Zsxs of the transmission screw is constructed by analyzing the appearance conditions of the transmission screw.

[0008] S3. Use convolutional neural network technology to build a defect prediction model, and input the operation stability coefficient Zwxs and the self-locking coefficient Zsxs of the corresponding transmission screw into the defect prediction model. After linear normalization, fit the output balance evaluation index Pgzs;

[0009] S4. Pre-set the performance balance threshold Q and match it with the balance evaluation index Pgzs for analysis to comprehensively judge the balance state between the self-locking property and the transmission stability of the transmission screw. Based on the balance state, comprehensively judge whether the transmission screw installed in the car seat has any defects in performance.

[0010] Preferably, the specific steps of S1 include:

[0011] S11, rotating the transmission screw in advance, and while the transmission screw is rotating, dynamically acquiring a video stream through a multi-view image acquisition device, and extracting a plurality of groups of image frames from the video stream;

[0012] S12. Based on the several groups of image frames obtained in S11, the noise components are removed by using bilateral filtering method, and the pixel values ​​in the several groups of image frames are adjusted to be uniformly distributed by using histogram equalization, and the several groups of image frames after image processing are aggregated into a detection data set, and the detection data set is subjected to feature extraction to obtain the measurement period T, the instantaneous value v(t) of the vibration velocity, the thread angle, the number of intervals J between the threads, and the pitch Jjz at the corresponding interval, and according to the standard transmission screw, the pitch Jjz between the internal threads of the standard transmission screw is obtained. ideal And by applying pressure to the transmission screw, the friction coefficient Mc is obtained;

[0013] S13, pre-divide the transmission screw into two sections evenly and mark them as the front end section and the rear end section respectively, and perform image extraction on several groups of image frames processed by step S12 to obtain the center point coordinates of the front end section and the rear end section of the transmission screw at four angles of 0°, 90°, 180° and 270° respectively.

[0014] Preferably, the specific step S1 also includes:

[0015] S14. Based on the center point coordinates of the front and rear cross sections of the transmission screw at four angles of 0°, 90°, 180° and 270° obtained in S13, the average value of the four angles is taken on each cross section to obtain the center point of the corresponding cross section in three-dimensional space, and the actual rotation axis of the transmission screw is constructed according to the center point of the corresponding cross section in three-dimensional space. The actual rotation axis of the transmission screw It is formed by the following equation:

[0016]

[0017] In the formula, Expressed as the three-dimensional coordinates of any point on the actual rotation axis; Expressed as the coordinates of the starting point on the actual rotation axis; It is represented as the direction vector of the actual rotation axis; d is represented as a scalar;

[0018] In three-dimensional space, we usually use a point (i.e., the starting point) and a direction vector to define a straight line. This straight line (i.e., the axis of rotation) can extend to the entire length of the screw;

[0019] S141. Analyze the deviation trend of the actual rotation axis of the transmission screw in three-dimensional space through the vector change between the center points of the front section and the rear section, calculate the vector from the front end to the rear end, and record it as the direction vector of the actual rotation axis. The direction vector of the actual rotation axis Obtained through: Among them, I 2 Represented as the center point of the rear section, I 1 It is represented as the center point of the front section;

[0020] S15, based on the actual rotation axis of the transmission screw obtained in S14 Combined with the standard axis, compare the actual rotation axis The direction deviation between the two axes is obtained by:

[0021]

[0022] In the formula, Expressed as the direction vector of the standard axis; Expressed as the magnitude of the direction vector of the actual rotation axis; Expressed as the magnitude of the direction vector of the standard axis; Expressed as the direction vector of the actual rotation axis; is the dot product of the direction vector of the actual rotation axis and the direction vector of the standard axis; cos(θ) represents the actual rotation axis The cosine of the deviation from the standard axis;

[0023] S16, pre-set the safety threshold, by comparing the safety threshold with the actual rotation axis The cosine value cosθ of the deviation from the standard axis is compared to determine whether the actual rotation axis of the transmission screw used in the car seat coincides with the standard axis. The specific determination is as follows:

[0024] If the actual rotation axis If the cosine value cos(θ) of the deviation from the standard axis falls within the safety threshold, it is determined that the actual rotation axis of the transmission screw used in the car seat coincides with the standard axis, and the transmission screw is further analyzed.

[0025] If the actual rotation axis If the cosine value cos(θ) of the deviation from the standard axis does not fall within the safety threshold, it is determined that the actual rotation axis of the transmission screw used in the car seat does not coincide with the standard axis, and the sorting instruction will be triggered;

[0026] S17. After receiving the sorting instruction, the current transmission screw is transmitted to the area to be manually inspected.

[0027] Preferably, the specific step S1 also includes:

[0028] S18. If it is determined that the actual rotation axis of the transmission screw used in the car seat coincides with the standard axis, the position offset between the actual rotation axis of the transmission screw used in the car seat and the standard axis is analyzed in combination with the center point of the corresponding cross section obtained in S14 in the three-dimensional space to obtain the eccentricity Pxj. The eccentricity Pxj is obtained in the following manner:

[0029]

[0030] Where n represents the number of center points obtained in different cross-sectional images, i = 1, 2, 3, ..., n, I i Represented as the center point obtained in the i-th section image, Represented as standard axis, Represented as the actual axis of rotation The center point obtained in the i-th section image and the standard axis The vertical distance between.

[0031] Preferably, the specific steps of S2 include:

[0032] S21. Based on the detection data set, the transmission stability of the transmission screw when the automobile seat is adjusted in position is evaluated, and after linear normalization processing, an operation stability coefficient Zwxs is constructed. The operation stability coefficient Zwxs is obtained by the following formula:

[0033]

[0034] In the formula, Zfz represents the vibration amplitude; Pxj represents the eccentricity, Cx represents the transmission efficiency, α and β are weight values, and C represents the first correction constant. The specific values ​​of α and β are set by the user according to the situation.

[0035] S211, the vibration amplitude Zfz is obtained by the following formula:

[0036]

[0037] In the formula, T represents the measurement period, and v(t) represents the instantaneous value of the vibration velocity;

[0038] S212. The transmission efficiency Cx is obtained by the following formula:

[0039]

[0040] Wherein, Mc represents the friction coefficient, θ represents the thread angle, cos(θ) represents the cosine value of the thread angle, and sin(θ) represents the sine value of the thread angle.

[0041] Preferably, the specific step S2 also includes:

[0042] S22. Based on the detection data set, analyze the appearance condition of the transmission screw, and after linear normalization, construct the self-locking coefficient Zsxs of the transmission screw. The self-locking coefficient Zsxs is obtained by the following formula:

[0043]

[0044] Where Bccd is the roughness, θ is the thread angle, Lpyz is the pitch deviation factor, and a 1 、a 2 and a 3 are weight values, where a 1 、a 2 and a 3 The specific value is set by the user according to the situation, and V is represented by the second correction constant.

[0045] Preferably, the specific step S2 also includes:

[0046] S221. Using the image Fourier transform technology, perform Fourier transform on several groups of image frames to obtain amplitude spectrum, wherein the amplitude spectrum includes intensity information of all frequency components of several groups of image frames, and the intensity information includes frequency coordinates (u, r) and the total number m of high-frequency components. Based on the amplitude spectrum, obtain the roughness Bccd of several groups of image frames, specifically in the following manner:

[0047]

[0048] Wherein, m represents the total number of high-frequency components; (u, r) represents the frequency coordinate, and Fourier transform converts several groups of image frames from the spatial domain to the frequency domain, where u and r represent the horizontal and vertical frequencies in the frequency domain, respectively; |G(u, r)| represents the amplitude value at the frequency coordinate (u, r), that is, the signal strength (or amplitude) at the frequency coordinate (u, r); H represents the range of the high-frequency component;

[0049] S222, the pitch deviation factor Lpyz is obtained by the following formula:

[0050]

[0051] Where J represents the number of intervals between threads, j = 1, 2, 3, ..., J, Jjz j Expressed as the pitch at the jth interval, Jjz ideal Expressed as the pitch between the internal threads of a standard transmission screw.

[0052] Preferably, the specific steps of S3 include:

[0053] S31, using convolutional neural network technology to build a preliminary model, and using the detection data set to train and test the preliminary model, and using the trained preliminary model as a recognition model, respectively obtaining feature information in the recognition model, and using the obtained feature information to train and test the recognition model, combined with whether the sorting instruction is triggered in S1, using the trained recognition model as a defect prediction model, after linear normalization processing, fitting the output balance evaluation index Pgzs, the balance evaluation index Pgzs is obtained in the following manner:

[0054]

[0055] In the formula, F 1 and F 2 are all weight values, R represents the third correction constant, where F 1 and F 2 The specific value is set by the user according to the situation.

[0056] Preferably, the specific steps of S4 include:

[0057] S41. By matching and analyzing the balance evaluation index Pgzs with the performance balance threshold Q, the balance state between the self-locking property and the transmission stability of the transmission screw is comprehensively judged. The specific contents are as follows:

[0058] S411, if the balance evaluation index Pgzs falls within the performance balance threshold Q, it is comprehensively judged that the transmission screw is in a balanced state between self-locking and transmission stability, and the current transmission screw is transmitted to the next defect detection link;

[0059] S412, if the balance evaluation index Pgzs does not fall within the performance balance threshold Q, it is comprehensively judged that the transmission screw is in an unbalanced state between self-locking and transmission stability, and the current transmission screw is transmitted to the area for manual quality inspection.

[0060] A high-precision transmission screw defect detection system for automobile seats, comprising a dynamic image capture module, an axis detection module, a continuous detection module, a defect comprehensive analysis module and a defect calibration module;

[0061] The dynamic image capture module is used to rotate the transmission screw and capture the video stream in real time from multiple angles to capture the dynamic characteristics of the transmission screw, and generate a detection data set after image processing;

[0062] The axis detection module will determine whether the actual rotation axis of the transmission screw used in the car seat coincides with the standard axis based on the detection data set, and if not, trigger a sorting instruction;

[0063] The continuous detection module is used to analyze when the overlap occurs, further perform defect monitoring analysis on the transmission screw, and evaluate the transmission stability of the transmission screw when the car seat is adjusted based on the detection data set to construct the operation stability coefficient Zwxs. At the same time, by analyzing the appearance condition of the transmission screw, the self-locking coefficient Zsxs of the transmission screw is constructed;

[0064] The defect comprehensive analysis module uses convolutional neural network technology to build a defect prediction model, and inputs the operation stability coefficient Zwxs and the self-locking coefficient Zsxs of the corresponding transmission screw into the defect prediction model. After linear normalization processing, the balance evaluation index Pgzs is fitted and output;

[0065] The defect calibration module is used to pre-set the performance balance threshold Q and match it with the balance evaluation index Pgzs for analysis, so as to comprehensively judge the balance state between the self-locking property and the transmission stability of the transmission screw. Based on the balance state, it is comprehensively judged whether there are any defects in the performance of the transmission screw arranged in the car seat.

[0066] The present invention provides a method and system for detecting defects of a high-precision transmission screw rod of an automobile seat, which has the following beneficial effects:

[0067] (1) This method can capture the dynamic characteristics of the screw during the rotation operation and generate a detection data set through the capture of multi-angle real-time video streams and image processing technology. This real-time, multi-angle detection method ensures the integrity and accuracy of the data, so that it can more accurately determine whether the actual rotation axis of the screw coincides with the standard axis. If a deviation is detected, the sorting instruction can be triggered in time to separate the defective screw, thereby improving the detection efficiency. Furthermore, through the defect monitoring and analysis of the screw transmission stability and self-locking performance, the operation stability coefficient Zwxs and the self-locking coefficient Zsxs are constructed, and they are used as key input parameters and input into the defect prediction model based on the convolutional neural network CNN. The CNN model uses the nonlinear fitting ability of big data training. After linear normalization, it can output the balance evaluation index Pgzs, which quantifies the balance between the screw transmission stability and self-locking performance, and realizes the comprehensive evaluation of the screw transmission performance and self-locking performance, making the prediction result more comprehensive and reliable. In addition, by setting the performance balance threshold Q, the method matches and analyzes the balance evaluation index Pgzs with it, realizing the automatic screening and accurate judgment of the screw performance. When the balance evaluation index Pgzs meets the threshold, it indicates that the transmission screw has met the established balance requirements in terms of performance and can be safely used for high-precision adjustment of car seats; otherwise, it can be determined that the screw has potential defects in self-locking or transmission stability, and risky screw products can be eliminated in time, thereby improving the safety and adjustment accuracy of car seats, further reducing production costs, and ensuring the stability and consistency of product quality.

[0068] (2) The actual rotation axis of the transmission screw is calculated by collecting the center point data of the front section and the rear section at different angles. The detection system realizes the quantification of the direction deviation by calculating the cosine value of the angle between the actual rotation axis and the standard axis direction vector. The cosine value of the angle is used as the basis for deviation judgment to ensure the measurement accuracy of the deviation. By setting a safety threshold, the system can automatically compare the cosine value of the deviation between the actual rotation axis and the standard axis. If the deviation value falls within the safety threshold range, the actual rotation axis is judged to meet the standard; otherwise, the sorting instruction will be automatically triggered. This automated judgment mechanism reduces the error of manual judgment and improves the objectivity and standardization of judgment. The automated judgment process effectively reduces the dependence on manual intervention, enhances the reliability and repeatability of the detection process, and improves efficiency. When the deviation value exceeds the safety threshold, the system immediately triggers the sorting instruction and sends the current transmission screw to the manual quality inspection area for further inspection. This mechanism ensures that only those screws with deviations will enter the manual inspection stage, further reducing the workload of quality inspection, while ensuring that all screws with large deviations can be identified and isolated in time. In short, this method ensures the accuracy of the actual rotation axis of the transmission screw through precise detection and screening, further avoiding the risk of wear and failure of the transmission system caused by eccentricity or misalignment. The accuracy of detection and screening directly improves the service life of the transmission screw and the overall reliability of the system. By preventing potential installation deviations and structural abnormalities, the reliability of the car seat transmission system is improved, the failure rate is reduced, and the stability of the overall seat is guaranteed.

[0069] (3) The system uses the convolution neural network technology to build a preliminary model, trains and tests the preliminary model through the detection data set, and obtains the recognition model. On the basis of the recognition model, it further obtains and extracts feature information, and deeply trains the model to build the final defect prediction model. The model can automatically identify the defect state of the transmission screw, and fit the balance evaluation index Pgzs based on this. By using deep learning technology to build a defect prediction model, the system has adaptive capabilities in the defect detection process, can efficiently capture the potential defects of the transmission screw, and significantly improves the accuracy and efficiency of defect detection. Through the extraction of feature information and model training, the balance evaluation index Pgzs output by the system can quantify the balance state of the transmission screw between self-locking and transmission stability. The index integrates multiple characteristic factors of self-locking and stability. After linear normalization, it can perform a comprehensive quantitative evaluation of the balance state of the screw. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 A schematic diagram of a process flow of a method for detecting defects in a high-precision transmission screw rod of an automobile seat according to the present invention;

[0071] Figure 2The present invention is a block diagram of a high-precision transmission screw defect detection system for an automobile seat. DETAILED DESCRIPTION

[0072] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0073] Example 1

[0074] See also Figure 1 The present invention provides a method for detecting defects of a high-precision transmission screw rod of an automobile seat, comprising the following steps:

[0075] S1. Rotate the transmission screw and capture the video stream in real time from multiple angles to capture the dynamic characteristics of the transmission screw. After image processing, generate a detection data set. Based on the detection data set, determine whether the actual rotation axis of the transmission screw used in the car seat coincides with the standard axis. If not, trigger a sorting instruction.

[0076] S2. If they coincide, further defect monitoring and analysis of the transmission screw is performed. Based on the detection data set, the transmission stability of the transmission screw when the car seat is adjusted is evaluated to construct the operation stability coefficient Zwxs. At the same time, the self-locking coefficient Zsxs of the transmission screw is constructed by analyzing the appearance conditions of the transmission screw.

[0077] S3. Use convolutional neural network technology to build a defect prediction model, and input the operation stability coefficient Zwxs and the self-locking coefficient Zsxs of the corresponding transmission screw into the defect prediction model. After linear normalization, fit the output balance evaluation index Pgzs;

[0078] S4. Pre-set the performance balance threshold Q and match it with the balance evaluation index Pgzs for analysis to comprehensively judge the balance state between the self-locking property and the transmission stability of the transmission screw. Based on the balance state, comprehensively judge whether the transmission screw installed in the car seat has any defects in performance.

[0079] In this embodiment, in step S1, the method can capture the dynamic characteristics of the screw in the rotation process in many aspects by capturing the video stream in real time from multiple angles, and generate a detection data set. Further, by analyzing the overlap with the standard axis, it is determined whether the screw deviates from the preset standard axis. If the axis deviation is found, the screw with problems can be marked and sent to manual inspection through sorting instructions, further reducing the risk of defects such as installation eccentricity and assembly error, and effectively improving the centering accuracy of the screw. In step S2, the method conducts an in-depth analysis of the transmission stability and appearance conditions of the screw through the detection data set, constructs the operation stability coefficient Zwxs and the self-locking coefficient Zsxs respectively, and forms a more comprehensive performance evaluation system. This method further breaks through the limitation of the singleness of traditional detection methods, and can simultaneously consider the two key parameters of transmission stability and self-locking, ensuring that the screw can maintain stable transmission and effectively self-lock when the power is cut off, further improving the overall transmission efficiency and safety. In steps S3 and S4, the method uses convolutional neural network technology to build a defect prediction model, takes the operation stability coefficient Zwxs and the self-locking coefficient Zsxs as input features, and outputs the balance evaluation index Pgzs after linear normalization. By matching and analyzing the index with the preset performance balance threshold Q, the system can comprehensively judge the balance state between the self-locking and transmission stability of the transmission screw, further avoiding the risk of possible product defects flowing into the use link. In short, this method further realizes the balance detection and evaluation of the multi-dimensional performance of the transmission screw through multi-angle video acquisition, characteristic coefficient analysis and intelligent defect prediction, meets the requirements of high-precision automotive seats for the stability and self-locking of the transmission screw, and provides reliable protection for the safety and comfort of automotive seats.

[0080] Example 2

[0081] Please refer to Figure 1 , specifically: S1 specific steps include:

[0082] S11, rotating the transmission screw in advance, and while the transmission screw is rotating, dynamically acquiring a video stream through a multi-view image acquisition device, and extracting a plurality of groups of image frames from the video stream;

[0083] S12. Based on the several groups of image frames obtained in S11, the noise components are removed by using bilateral filtering method, and the pixel values ​​in the several groups of image frames are adjusted to be uniformly distributed by histogram equalization to improve the contrast of the image, so that the brightness distribution is more uniform, which is suitable for images with uneven illumination. The several groups of image frames after image processing are aggregated into a detection data set, and the detection data set is subjected to feature extraction to obtain the measurement period T, the instantaneous value v(t) of the vibration speed, the thread angle, the number of intervals J between the threads and the pitch Jjz at the corresponding interval, and according to the standard transmission screw, the pitch Jjz between the internal threads of the standard transmission screw is obtained. ideal And by applying pressure to the transmission screw, the friction coefficient Mc is obtained;

[0084] S13. Divide the transmission screw into two sections in advance and mark them as the front end section and the rear end section respectively, and perform image extraction on several groups of image frames processed by step S12 to obtain the center point coordinates of the front end section and the rear end section of the transmission screw at four angles of 0°, 90°, 180° and 270° respectively; wherein the center point coordinates refer to the coordinates of the geometric center of each cross section of the transmission screw at different angles, which is a point in three-dimensional space. Specifically, the center point is calculated based on the cross-sectional image of the screw by image processing technology.

[0085] In dynamic acquisition, a rotary encoder will be connected to the lead screw to monitor its real-time rotation speed and angle. Whenever the lead screw rotates to a specific angle, the encoder sends a trigger signal to the high-speed camera to ensure that each frame of the image is captured at the same rotation position. The external trigger controls the start and end time of the shooting, which is suitable for capturing the dynamic performance of the lead screw during acceleration or deceleration.

[0086] In this embodiment, in S11, a multi-view image acquisition device is used to dynamically acquire a video stream, and several groups of image frames are extracted, so that the state of the transmission screw in the actual rotation operation is continuously captured. This dynamic acquisition method can ensure that stable geometric information is obtained when the screw is in working state, especially when it rotates at high frequency, key details can still be extracted, providing a reliable data source for subsequent analysis and evaluation. In S12, the image noise is removed by bilateral filtering, and histogram equalization is adjusted to improve the contrast and clarity of the image. This processing makes the brightness of the image more uniform, and even in the case of unstable lighting conditions or local shadows, the structural information of the screw can be accurately extracted. Therefore, even in a complex lighting environment, the quality of the data is effectively guaranteed, which helps to achieve more accurate detection results. S13 divides the screw into two sections, front and back, and obtains the geometric center point coordinates of the front and rear ends at four angles respectively. Using this multi-angle, two-section center point data, the actual rotation axis of the screw can be accurately described. These precise center point coordinates provide basic data for judging the coaxiality, misalignment and eccentricity of the screw. In particular, by analyzing the center point positions of the front and rear sections, it is possible to evaluate whether the screw has bending, eccentricity or other structural defects. Through the image processing and coordinate extraction of steps S12 and S13, the multi-frame images are converted into a structured detection data set, which directly reflects the spatial geometric characteristics of the screw and can be used for further numerical analysis, modeling and prediction. The high efficiency of this process lays the foundation for the realization of subsequent defect detection and prediction models, reduces data processing time, and improves the real-time performance of the detection system. The image preprocessing technology included in this detection method, combined with the acquisition of dual-segment multi-angle center point coordinates, ensures the reliability of the detection system in complex environments. This method can not only be used for static detection, but also meet the precise measurement requirements in dynamic rotation environments, effectively improving the applicability of the transmission screw detection system.

[0087] Example 3

[0088] Please refer to Figure 1 Specifically: S1 includes the following steps:

[0089] S14. Based on the center point coordinates of the front and rear cross sections of the transmission screw at four angles of 0°, 90°, 180° and 270° obtained in S13, the average value of the four angles is taken on each cross section to obtain the center point of the corresponding cross section in three-dimensional space, and the actual rotation axis of the transmission screw is constructed according to the center point of the corresponding cross section in three-dimensional space. Actual axis of rotation of the drive screw It is formed by the following equation:

[0090]

[0091] In the formula, Expressed as the three-dimensional coordinates of any point on the actual rotation axis; Expressed as the coordinates of the starting point on the actual rotation axis; denoted as the direction vector of the actual rotation axis; d is denoted as a scalar, which is used to determine the parameters of different positions on the actual rotation axis to adjust the distance moved in the direction;

[0092] In three-dimensional space, we usually use a point (i.e., the starting point) and a direction vector to define a straight line. This straight line (i.e., the axis of rotation) can extend to the entire length of the screw. The coordinates of the starting point on the actual axis of rotation are This is a fixed point selected on a straight line. You can choose any point on the straight line (the center point of the front section and the rear section) as the starting point, as long as this point is indeed on the straight line, because no matter which center point is selected, the actual rotation axis is the same, just the starting point is different, and the direction is the same;

[0093] Example: x 0 +d*d x Expressed as the coordinate component along the x direction, y 0 +d*d y Expressed as the coordinate component along the y direction, z 0 +d*d z is expressed as the coordinate component along the z direction; therefore, is the coordinate position of any point on the actual rotation axis in three-dimensional space, and its direction is determined by the direction vector controlled, in space along Directionally extended;

[0094] S141. Analyze the deviation trend of the actual rotation axis of the transmission screw in three-dimensional space through the vector change between the center points of the front section and the rear section, calculate the vector from the front end to the rear end, and record it as the direction vector of the actual rotation axis. Direction vector of the actual rotation axis Obtained through: Among them, I 2 Represented as the center point of the rear section, I 1 It is represented as the center point of the front section; the direction vector is determined by using the trends of the two center points (center point I of the front section and the rear section);

[0095] S15, based on the actual rotation axis of the transmission screw obtained in S14 Combined with the standard axis, compare the actual rotation axis The direction deviation between the two axes is obtained by:

[0096]

[0097] In the formula, Expressed as the direction vector of the standard axis; Expressed as the magnitude (length) of the direction vector of the actual rotation axis; The magnitude (length) of the direction vector expressed as the standard axis; Expressed as the direction vector of the actual rotation axis; It is the dot product of the direction vector of the actual rotation axis and the direction vector of the standard axis, indicating the directional projection relationship between the two vectors. The larger the dot product result, the closer the two vectors are to the same direction. cos(θ) represents the actual rotation axis. The cosine of the deviation from the standard axis;

[0098] S16, pre-set the safety threshold, by comparing the safety threshold with the actual rotation axis The cosine value cosθ of the deviation from the standard axis is compared to determine whether the actual rotation axis of the transmission screw used in the car seat coincides with the standard axis. The specific determination is as follows:

[0099] If the actual rotation axis If the cosine value cos(θ) of the deviation from the standard axis falls within the safety threshold, it is determined that the actual rotation axis of the transmission screw used in the car seat coincides with the standard axis, and the transmission screw is further analyzed.

[0100] If the actual rotation axis If the cosine value cos(θ) of the deviation from the standard axis does not fall within the safety threshold, it is determined that the actual rotation axis of the transmission screw used in the car seat does not coincide with the standard axis, and the sorting instruction will be triggered;

[0101] The above safety threshold is set as follows: according to the corresponding actual rotation axis obtained by several groups of transmission screws during defect detection The cosine value cos(θ) of the deviation from the standard axis, the mean value of the cosine value cos(θ) and the standard deviation of the cosine value cos(θ) are obtained, and the lower limit of the safety threshold is set by the mean value of the cosine value cos(θ) and the standard deviation of the cosine value cos(θ): the lower limit is: the mean value of the cosine value cos(θ) - K* the standard deviation of the cosine value cos(θ); the upper limit is: the maximum value of the cosine value cos(θ), that is, the value 1; wherein K is a constant, usually taking a value of 1-3, corresponding to different confidence levels, and the specific value is set by the user (according to the actual situation); according to the setting method of the safety threshold here, the balance threshold Q in S4 is obtained in the same way;

[0102] Note: When the cos(θ) value is close to 1, it means that the direction of the actual rotation axis is very close to the standard axis, almost parallel, and the direction deviation is small; when the cos(θ) value is much smaller than 1, it means that the direction of the actual rotation axis deviates greatly from the standard axis, and there may be eccentricity or installation error. This value helps us determine whether the direction of the actual rotation axis is consistent with the standard axis in the design.

[0103] S17. After receiving the sorting instruction, the current transmission screw is transmitted to the area to be manually inspected.

[0104] In this embodiment, the detection system obtains the center point data of the front and rear end sections of the screw rod at multiple angles, and calculates the center position of the section based on this, constructs the actual rotation axis of the transmission screw rod, and can improve the accuracy of axis detection as much as possible compared to the traditional single point or simple manual visual method, especially in the scene where the detection space is small and the installation requirements are high, it can accurately evaluate the centering and precision of the screw rod. By constructing the actual rotation axis and comparing the standard axis, the system can accurately calculate the deviation angle between the actual axis and the standard axis. The cosine value of this direction deviation can reflect whether the screw rod has installation eccentricity or directional deviation, providing a highly sensitive installation status judgment standard to ensure stable operation of the equipment. The system presets a safety threshold. When the cosine value of the deviation between the actual axis and the standard axis exceeds the threshold, it means that the screw rod has serious eccentricity or directional error and does not meet the installation standard. At this time, the system will automatically trigger the sorting instruction and send the transmission screw rod to the manual quality inspection area for further detection and analysis. Through such an intelligent sorting mechanism, unqualified screw rods can be effectively filtered out, the risk of equipment failure can be reduced, and the stability of the transmission system can be improved. The automated detection and sorting function of the system reduces the dependence on manual detection.

[0105] Through high-precision axis deviation detection, the smooth operation of the transmission screw in actual installation can be ensured. Since the screw is a crucial transmission component in components such as car seats, ensuring its axis alignment can directly affect the transmission effect and the service life of the product. The application of this system improves the stability of the overall quality of the product and ensures high-standard assembly quality. By archiving the test data, including the deviation cosine value, the position of the center point of each section and the axis direction, a complete test data record library can be established. These data can be used for subsequent quality traceability and analysis, and provide important reference for subsequent product improvement and fault prediction. The system can effectively detect the axis alignment of the transmission screw, determine whether the direction meets the standard through the deviation cosine value, and automatically sort out unqualified products. The entire process reduces manual errors, improves detection accuracy and efficiency, ensures the installation quality of the screw and the stability of the system, and maximizes the automation and reliability of production.

[0106] Example 4

[0107] Please refer to Figure 1 Specifically: S1 includes the following steps:

[0108] S18. If it is determined that the actual rotation axis of the transmission screw used in the car seat coincides with the standard axis, the position offset between the actual rotation axis of the transmission screw used in the car seat and the standard axis is analyzed in combination with the center point of the corresponding cross section obtained in S14 in the three-dimensional space to obtain the eccentricity Pxj. The eccentricity Pxj is obtained in the following manner:

[0109]

[0110] Where n represents the number of center points obtained in different cross-sectional images, i = 1, 2, 3, ..., n, I i Represented as the center point obtained in the i-th section image, Represented as standard axis, Represented as the actual axis of rotation The center point obtained in the i-th section image and the standard axis The vertical distance between.

[0111] The specific steps of S2 include:

[0112] S21. Based on the test data set, the transmission stability of the transmission screw when the automobile seat is adjusted is evaluated. After linear normalization, the operation stability coefficient Zwxs is constructed. The operation stability coefficient Zwxs is obtained by the following formula:

[0113]

[0114] In the formula, Zfz is the vibration amplitude, which reflects the average vibration capacity; Pxj is the eccentricity, Cx is the transmission efficiency, α and β are weight values, and C is the first correction constant. The specific values ​​of α and β are set by the user according to the situation.

[0115] S211, the vibration amplitude Zfz is obtained by the following formula:

[0116]

[0117] In the formula, T represents the measurement period, v(t) represents the instantaneous value of the vibration velocity, dt is the small increment of the time variable t, and in the integral symbol, it means that the entire measurement period T is divided into countless tiny time segments, and the square value of the instantaneous vibration velocity v(t) in each time segment is accumulated (integrated);

[0118] S212, transmission efficiency Cx is obtained by the following formula:

[0119]

[0120] Where Mc is the friction coefficient, θ is the thread angle, the transmission efficiency Cx is between 0 and 1, the closer it is to 1, the higher the efficiency; cos(θ) is the cosine value of the thread angle, and sin(θ) is the sine value of the thread angle.

[0121] The above friction coefficient Mc is obtained by the following formula: F f Expressed as friction, that is, the parallel resistance acting on the contact surface; F n Expressed as normal force, that is, the pressure applied perpendicular to the contact surface.

[0122] In this embodiment, after analyzing the coincidence of the actual rotation axis of the transmission screw and the standard axis, the eccentricity of each section relative to the standard axis is further calculated. The eccentricity Pxj is obtained by the vertical distance from each center point to the standard axis in the multi-section data, reflecting the position offset of the actual rotation axis of the screw relative to the standard axis. The eccentricity Pxj quantifies the eccentricity of the transmission screw and can identify it before a slight eccentricity occurs, which helps to improve the installation and centering accuracy of the transmission screw and ensure the stability of the car seat adjustment system. By comprehensively considering the three factors of vibration amplitude, eccentricity and transmission efficiency, this scheme generates an operation stability coefficient Zwxs after linear normalization. This coefficient quantifies the stability of the transmission screw during the car seat adjustment process, ensuring that the operation stability is more comprehensive and reliable. The calculation of the vibration amplitude can more truly reflect the vibration characteristics and transmission stability of the transmission screw in the actual working process, which helps to identify vibration anomalies in advance and prevent potential failures, thereby extending the service life of the screw and the seat adjustment system. The calculation of transmission efficiency comprehensively considers two key factors: friction coefficient and thread angle, and can truly reflect the loss situation in the transmission process. The closer the transmission efficiency is to 1, the smaller the transmission loss and the higher the efficiency.

[0123] Example 5

[0124] Please refer to Figure 1 Specifically: S2 includes the following specific steps:

[0125] S22. Based on the detection data set, the appearance condition of the transmission screw is analyzed, and after linear normalization processing, the self-locking coefficient Zsxs of the transmission screw is constructed. The self-locking coefficient Zsxs is obtained by the following formula:

[0126]

[0127] Where Bccd is the roughness, θ is the thread angle, Lpyz is the pitch deviation factor, and a 1 、a 2 and a 3 are weight values, where a 1 、a 2 and a 3 The specific value is set by the user according to the situation, and V is represented by the second correction constant.

[0128] The specific steps of S2 also include:

[0129] S221. Using the image Fourier transform technology, perform Fourier transform on several groups of image frames to obtain amplitude spectrum, wherein the amplitude spectrum contains the intensity information of all frequency components of several groups of image frames, and the intensity information includes the frequency coordinates (u, r) and the total number m of high-frequency components. The roughness is usually reflected in the amplitude of the high-frequency components. Based on the amplitude spectrum, the roughness Bccd of several groups of image frames is obtained, which is specifically obtained in the following manner:

[0130]

[0131] Wherein, m represents the total number of high-frequency components, which represents the number of frequency components in the high-frequency range in the amplitude spectrum after Fourier transform; (u, r) represents the frequency coordinate, and Fourier transform converts several groups of image frames from the spatial domain to the frequency domain, where u and r represent the horizontal and vertical frequencies in the frequency domain, respectively; |G(u, r)| represents the amplitude value at the frequency coordinate (u, r), that is, the signal strength (or amplitude) at the frequency coordinate (u, r); H represents the range of high-frequency components, which is the frequency range specified in the frequency domain, including the higher frequency part. Generally, the high-frequency components are distributed in the four corners of the spectrum diagram, representing the details of the image that change dramatically.

[0132] The surface roughness of the transmission screw is indirectly represented by the roughness Bccd of several groups of image frames.

[0133] S222, pitch deviation factor Lpyz is obtained by the following formula:

[0134]

[0135] Where J represents the number of intervals between threads, j = 1, 2, 3, ..., J, Jjz j Expressed as the pitch at the jth interval, Jjz ideal Expressed as the pitch between the internal threads of a standard transmission screw.

[0136] In this embodiment, the system calculates the self-locking coefficient Zsxs by analyzing the appearance conditions of the transmission screw, combining multiple factors such as roughness, thread angle, pitch deviation factor, etc. The construction of this coefficient combines the surface roughness and thread geometric characteristics, making the self-locking performance evaluation more comprehensive and accurate. The quantitative evaluation of the self-locking coefficient Zsxs improves the judgment accuracy of the self-locking performance of the screw, ensuring that the transmission screw can be stably locked in actual operation, further avoiding slippage or loosening, thereby improving the safety and reliability of the car seat in use. Through multi-level appearance inspection, including analysis of multiple factors such as roughness, thread angle, pitch deviation factor, etc., the system can evaluate the quality of the transmission screw from multiple dimensions. Compared with single parameter detection, this multi-factor evaluation method is more comprehensive and can capture slight changes in the appearance and geometric parameters of the screw. Multi-level analysis improves the coverage and accuracy of transmission screw detection, helps to identify possible defects early, ensure that the product meets quality standards, and reduce the risk of failure after installation.

[0137] Example 6

[0138] Please refer to Figure 1 , specifically: S3 specific steps include:

[0139] S31. Use convolutional neural network technology to build a preliminary model, train and test the preliminary model with the detection data set, and use the trained preliminary model as the recognition model, respectively obtain feature information in the recognition model, and train and test the recognition model with the obtained feature information, and combine whether the sorting instruction is triggered in S1 to use the trained recognition model as the defect prediction model. After linear normalization, fit the output balance evaluation index Pgzs, and the balance evaluation index Pgzs is obtained in the following way:

[0140]

[0141] In the formula, F 1 and F 2 are all weight values, R represents the third correction constant, where F 1 and F 2 The specific value is set by the user according to the situation.

[0142] The specific steps of S4 include:

[0143] S41. By matching and analyzing the balance evaluation index Pgzs with the performance balance threshold Q, the balance state between the self-locking property and the transmission stability of the transmission screw is comprehensively judged. The specific contents are as follows:

[0144] S411, if the balance evaluation index Pgzs falls within the performance balance threshold Q, it is comprehensively judged that the transmission screw is in a balanced state between self-locking and transmission stability, and the current transmission screw is transmitted to the next defect detection link;

[0145] S412, if the balance evaluation index Pgzs does not fall within the performance balance threshold Q, it is comprehensively judged that the transmission screw is in an unbalanced state between self-locking and transmission stability, and the current transmission screw is transmitted to the area for manual quality inspection.

[0146] In this embodiment, a preliminary recognition model is constructed by convolutional neural network, and the model is trained and tested in combination with the detection data set, so that the model has the ability to automatically identify defects. The recognition model is further developed into a defect prediction model through the extraction and training of feature information, and finally the balance evaluation index Pgzs is fitted. This CNN-based prediction model not only improves the accuracy of defect detection, but also realizes automated defect prediction. The defect prediction model automatically identifies potential defects in the transmission screw, which significantly improves the detection efficiency, reduces the need for manual inspection, and ensures efficient and high-precision quality control. The system quantifies the balance state between the self-locking property and the transmission stability of the transmission screw by calculating the balance evaluation index Pgzs. The system sets a performance balance threshold Q, and by matching the balance evaluation index Pgzs with the threshold, it determines whether the self-locking property and the transmission stability of the transmission screw have reached the expected balance state, further realizing the compatibility verification between the self-locking property and the transmission stability, ensuring the safety of the transmission screw in operation, reducing the risk caused by imbalance, and effectively extending the service life of the transmission system. When the balance evaluation index Pgzs does not fall within the performance balance threshold Q, the system will automatically send the transmission screw to the manual quality inspection area again to further confirm its defect status. This intelligent sorting mechanism effectively filters out screws that do not meet the standards and reduces the burden of manual quality inspection. In summary, this method further realizes the intelligent comprehensive judgment of the self-locking and transmission stability of the transmission screw through the feature extraction and prediction model of the convolutional neural network, combined with the matching analysis of the balance evaluation index and the performance balance threshold. Through the automated defect prediction and sorting mechanism, the system significantly improves the detection efficiency, reduces the need for manual operation, ensures the high quality and stability of the transmission screw, and provides a strong guarantee for the safe operation of the transmission system.

[0147] Example 7

[0148] Please refer to Figure 2 ,Specifically: A high-precision transmission screw defect detection system for an automobile seat includes a dynamic image capture module, an axis detection module, a continuous detection module, a defect comprehensive analysis module and a defect calibration module;

[0149] The dynamic image capture module is used to rotate the transmission screw and capture the video stream in real time from multiple angles to capture the dynamic characteristics of the transmission screw. After image processing, a detection data set is generated;

[0150] The axis detection module will determine whether the actual rotation axis of the transmission screw used in the car seat coincides with the standard axis based on the detection data set. If not, the sorting instruction will be triggered;

[0151] The continuous detection module is used to analyze the overlap and further conduct defect monitoring analysis on the transmission screw. Based on the detection data set, the transmission stability of the transmission screw when the car seat is adjusted is evaluated to construct the operation stability coefficient Zwxs. At the same time, by analyzing the appearance conditions of the transmission screw, the self-locking coefficient Zsxs of the transmission screw is constructed;

[0152] The defect comprehensive analysis module uses convolutional neural network technology to build a defect prediction model, and inputs the operation stability coefficient Zwxs and the self-locking coefficient Zsxs of the corresponding transmission screw into the defect prediction model. After linear normalization processing, the output balance evaluation index Pgzs is fitted;

[0153] The defect calibration module is used to pre-set the performance balance threshold Q and match it with the balance evaluation index Pgzs for analysis, so as to comprehensively judge the balance state of the transmission screw between self-locking and transmission stability. Based on the balance state, it is comprehensively judged whether there are any performance defects in the transmission screw installed in the car seat.

[0154] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for detecting defects in high-precision transmission screws of automobile seats, characterized in that: The following steps are included: S1. Rotate the transmission screw and capture the video stream in real time from multiple angles to capture the dynamic characteristics of the transmission screw. After image processing, generate a detection data set. Based on the detection data set, determine whether the actual rotation axis of the transmission screw used in the car seat coincides with the standard axis. If not, trigger a sorting instruction. S2. If they coincide, further defect monitoring and analysis of the transmission screw is performed. Based on the detection data set, the transmission stability of the transmission screw when the car seat is adjusted is evaluated to construct the operation stability coefficient Zwxs. At the same time, the self-locking coefficient Zsxs of the transmission screw is constructed by analyzing the appearance conditions of the transmission screw. S3. Use convolutional neural network technology to build a defect prediction model, and input the operation stability coefficient Zwxs and the self-locking coefficient Zsxs of the corresponding transmission screw into the defect prediction model. After linear normalization, fit the output balance evaluation index Pgzs; S4. Pre-set the performance balance threshold Q and match it with the balance evaluation index Pgzs for analysis to comprehensively judge the balance state between the self-locking property and the transmission stability of the transmission screw. Based on the balance state, comprehensively judge whether the transmission screw installed in the car seat has any defects in performance.

2. The method for detecting defects of a high-precision transmission screw rod of a car seat according to claim 1, characterized in that: The specific steps of S1 include: S11, rotating the transmission screw in advance, and while the transmission screw is rotating, dynamically acquiring a video stream through a multi-view image acquisition device, and extracting a plurality of groups of image frames from the video stream; S12, based on the several groups of image frames obtained in S11, the noise components are removed by using bilateral filtering method, and the pixel values ​​in the several groups of image frames are adjusted to be uniformly distributed by histogram equalization, and the several groups of image frames after image processing are aggregated into a detection data set, and the detection data set is subjected to feature extraction to obtain the measurement period T, the instantaneous value v(t) of the vibration speed, and the thread angle The number of intervals J between threads and the pitch Jjz at the corresponding intervals, and according to the standard transmission screw, the pitch Jjz between the internal threads of the standard transmission screw is obtained ideal And by applying pressure to the transmission screw, the friction coefficient Mc is obtained; S13, pre-divide the transmission screw into two sections evenly and mark them as the front end section and the rear end section respectively, and perform image extraction on several groups of image frames processed by step S12 to obtain the center point coordinates of the front end section and the rear end section of the transmission screw at four angles of 0°, 90°, 180° and 270° respectively.

3. The method for detecting defects of a high-precision transmission screw rod of a car seat according to claim 2, characterized in that: The specific steps of S1 also include: S14. Based on the center point coordinates of the front and rear cross sections of the transmission screw at four angles of 0°, 90°, 180° and 270° obtained in S13, the average value of the four angles is taken on each cross section to obtain the center point of the corresponding cross section in three-dimensional space, and the actual rotation axis of the transmission screw is constructed according to the center point of the corresponding cross section in three-dimensional space. The actual rotation axis of the transmission screw It is formed by the following equation: In the formula, Expressed as the three-dimensional coordinates of any point on the actual rotation axis; Expressed as the coordinates of the starting point on the actual rotation axis; It is represented as the direction vector of the actual rotation axis; d is represented as a scalar; S141. Analyze the deviation trend of the actual rotation axis of the transmission screw in three-dimensional space through the vector change between the center points of the front section and the rear section, calculate the vector from the front end to the rear end, and record it as the direction vector of the actual rotation axis. The direction vector of the actual rotation axis Obtained through: Among them, I2 represents the center point of the rear section, and I1 represents the center point of the front section; S15, based on the actual rotation axis of the transmission screw obtained in S14 Combined with the standard axis, compare the actual rotation axis The direction deviation between the two axes is obtained by: In the formula, Expressed as the direction vector of the standard axis; Expressed as the magnitude of the direction vector of the actual rotation axis; Expressed as the magnitude of the direction vector of the standard axis; Expressed as the direction vector of the actual rotation axis; is the dot product of the direction vector of the actual rotation axis and the direction vector of the standard axis; cos(θ) represents the actual rotation axis The cosine of the deviation from the standard axis; S16, pre-set the safety threshold, by comparing the safety threshold with the actual rotation axis The cosine value cosθ of the deviation from the standard axis is compared to determine whether the actual rotation axis of the transmission screw used in the car seat coincides with the standard axis. The specific determination is as follows: If the actual rotation axis If the cosine value cos(θ) of the deviation from the standard axis falls within the safety threshold, it is determined that the actual rotation axis of the transmission screw used in the car seat coincides with the standard axis, and the transmission screw is further analyzed. If the actual rotation axis If the cosine value cos(θ) of the deviation from the standard axis does not fall within the safety threshold, it is determined that the actual rotation axis of the transmission screw used in the car seat does not coincide with the standard axis, and the sorting instruction will be triggered; S17. After receiving the sorting instruction, the current transmission screw is transmitted to the area to be manually inspected.

4. The method for detecting defects of a high-precision transmission screw rod of a car seat according to claim 3 is characterized in that: The specific steps of S1 also include: S18. If it is determined that the actual rotation axis of the transmission screw used in the car seat coincides with the standard axis, the position offset between the actual rotation axis of the transmission screw used in the car seat and the standard axis is analyzed in combination with the center point of the corresponding cross section obtained in S14 in the three-dimensional space to obtain the eccentricity Pxj. The eccentricity Pxj is obtained in the following manner: Where n represents the number of center points obtained in different cross-sectional images, i = 1, 2, 3, ..., n, I i Represented as the center point obtained in the i-th section image, Represented as standard axis, Represented as the actual axis of rotation The center point obtained in the i-th section image and the standard axis The vertical distance between.

5. The method for detecting defects of high-precision transmission screw rods of automobile seats according to claim 4 is characterized in that: The specific steps of S2 include: S21. Based on the detection data set, the transmission stability of the transmission screw when the automobile seat is adjusted in position is evaluated, and after linear normalization processing, an operation stability coefficient Zwxs is constructed. The operation stability coefficient Zwxs is obtained by the following formula: In the formula, Zfz represents the vibration amplitude; Pxj represents the eccentricity, Cx represents the transmission efficiency, α and β are weight values, and C represents the first correction constant. The specific values ​​of α and β are set by the user according to the situation. S211, the vibration amplitude Zfz is obtained by the following formula: In the formula, T represents the measurement period, and v(t) represents the instantaneous value of the vibration velocity; S212. The transmission efficiency Cx is obtained by the following formula: In the formula, Mc is the friction coefficient, Expressed as thread angle; Expressed as the cosine of the thread angle, Expressed as the sine of the thread angle.

6. The method for detecting defects of a high-precision transmission screw rod of a car seat according to claim 5, characterized in that: The specific steps of S2 also include: S22. Based on the detection data set, analyze the appearance condition of the transmission screw, and after linear normalization, construct the self-locking coefficient Zsxs of the transmission screw. The self-locking coefficient Zsxs is obtained by the following formula: Where Bccd represents the roughness, It is expressed as the thread angle, Lpyz is expressed as the pitch deviation factor, a1, a2 and a3 are all weight values, among which, the specific values ​​of a1, a2 and a3 are set by the user according to the situation, and V is expressed as the second correction constant.

7. The method for detecting defects of a high-precision transmission screw rod of a car seat according to claim 6, characterized in that: The specific steps of S2 also include: S221. Using the image Fourier transform technology, perform Fourier transform on several groups of image frames to obtain amplitude spectrum, wherein the amplitude spectrum includes intensity information of all frequency components of several groups of image frames, and the intensity information includes frequency coordinates (u, r) and the total number m of high-frequency components. Based on the amplitude spectrum, obtain the roughness Bccd of several groups of image frames, specifically in the following manner: Where m represents the total number of high-frequency components; (u, r) represents the frequency coordinate. Fourier transform converts several groups of image frames from the spatial domain to the frequency domain, where u and r represent the horizontal and vertical frequencies in the frequency domain, respectively; |G(u, r)| represents the amplitude value at the frequency coordinate (u, r); H represents the range of the high-frequency component; S222, the pitch deviation factor Lpyz is obtained by the following formula: Where J represents the number of intervals between threads, j = 1, 2, 3, ..., J, Jjz j Expressed as the pitch at the jth interval, Jjz ideal Expressed as the pitch between the internal threads of a standard transmission screw.

8. The method for detecting defects of a high-precision transmission screw rod of a car seat according to claim 1, characterized in that: The specific steps of S3 include: S31, using convolutional neural network technology to build a preliminary model, and using the detection data set to train and test the preliminary model, and using the trained preliminary model as a recognition model, respectively obtaining feature information in the recognition model, and using the obtained feature information to train and test the recognition model, combined with whether the sorting instruction is triggered in S1, using the trained recognition model as a defect prediction model, after linear normalization processing, fitting the output balance evaluation index Pgzs, the balance evaluation index Pgzs is obtained in the following manner: In the formula, F1 and F2 are weight values, R represents the third correction constant, and the specific values ​​of F1 and F2 are set by the user according to the situation.

9. The method for detecting defects of a high-precision transmission screw rod of a car seat according to claim 1, characterized in that: The specific steps of S4 include: S41. By matching and analyzing the balance evaluation index Pgzs with the performance balance threshold Q, the balance state between the self-locking property and the transmission stability of the transmission screw is comprehensively judged. The specific contents are as follows: S411, if the balance evaluation index Pgzs falls within the performance balance threshold Q, it is comprehensively judged that the transmission screw is in a balanced state between self-locking and transmission stability, and the current transmission screw is transmitted to the next defect detection link; S412, if the balance evaluation index Pgzs does not fall within the performance balance threshold Q, it is comprehensively judged that the transmission screw is in an unbalanced state between self-locking and transmission stability, and the current transmission screw is transmitted to the area for manual quality inspection.

10. A high-precision transmission screw defect detection system for automobile seats, used to implement the high-precision transmission screw defect detection method for automobile seats described in any one of claims 1 to 9, characterized in that: It includes dynamic image capture module, axis detection module, continuous detection module, defect comprehensive analysis module and defect calibration module; The dynamic image capture module is used to rotate the transmission screw and capture the video stream in real time from multiple angles to capture the dynamic characteristics of the transmission screw, and generate a detection data set after image processing; The axis detection module will determine whether the actual rotation axis of the transmission screw used in the car seat coincides with the standard axis based on the detection data set, and if not, trigger a sorting instruction; The continuous detection module is used to analyze when the overlap occurs, further perform defect monitoring analysis on the transmission screw, and evaluate the transmission stability of the transmission screw when the car seat is adjusted based on the detection data set to construct the operation stability coefficient Zwxs. At the same time, by analyzing the appearance condition of the transmission screw, the self-locking coefficient Zsxs of the transmission screw is constructed; The defect comprehensive analysis module uses convolutional neural network technology to build a defect prediction model, and inputs the operation stability coefficient Zwxs and the self-locking coefficient Zsxs of the corresponding transmission screw into the defect prediction model. After linear normalization processing, the balance evaluation index Pgzs is fitted and output; The defect calibration module is used to pre-set the performance balance threshold Q and match it with the balance evaluation index Pgzs for analysis, so as to comprehensively judge the balance state between the self-locking property and the transmission stability of the transmission screw. Based on the balance state, it is comprehensively judged whether there are any defects in the performance of the transmission screw arranged in the car seat.

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