Screw machine regulation and control system and method based on machine vision

Abstract

The invention relates to the technical field of screw machine regulation and control, and discloses a screw machine regulation and control system and method based on machine vision, and the method comprises the steps: obtaining working area image information of a to-be-turned screw and a workpiece, inputting the working area image information into an image processing module to extract screw features, and matching the screw features with a preset screw feature set; therefore, the specification and geometric characteristics of the to-be-turned screw are determined. Based on the feature information, position features of the screw are determined, three-dimensional coordinate conversion is carried out, and the relative position relation between the to-be-turned screw and the workpiece is obtained. And determining an initial alignment path of the screw by using the relative position relationship, and setting tightening parameters of the screw machine according to the workpiece surface state and the screw specification. Subsequently, the tightening parameters are adjusted in real time according to the material hardness, the thread friction coefficient and the real-time temperature of the screw, the tightening parameters are dynamically adjusted by obtaining and analyzing the screw characteristics, the position relation and the environmental factors in real time, the precision and stability of the screw tightening process are improved, and the screw assembling quality is effectively guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of screw machine control, and in particular to a screw machine control system and method based on machine vision. Background Art

[0002] In the process of automated assembly, the tightening quality of screws plays a key role in the reliability, stability and safety of the product. As connectors, screws are widely used in the fields of machinery, electronics, automobiles, aviation, etc. They form a stable mechanical bond by tightening, so that the components can maintain structural stability under different load conditions. However, if the tightening quality is poor, it may lead to loose connections, uneven stress, and even serious problems such as falling off and breaking under high load or vibration environments. Therefore, the stability of the tightening quality is directly related to the durability, safety and service life of the product.

[0003] At present. In the traditional screw tightening process, manual operation or fixed parameters set based on experience are usually relied on to control the tightening force and rotation speed. However, due to the differences in materials, sizes, thread types, etc. of screws of different specifications, screws of different materials and geometric features have different requirements for the tightening process. For example, large-sized or high-hardness screws require higher torque during the tightening process, while small-sized or low-hardness screws may require smaller tightening force and more precise control. If the parameters are set improperly, the tightening force may be too large or insufficient, which not only affects the connection effect, but may also cause damage to the screws and the workpiece surface. In addition, with the development of industrial automation and intelligent manufacturing, the complexity of the product assembly process and the requirements for high precision and low error are constantly increasing. In automated assembly, if the parameters such as tightening force, torque, and rotation speed cannot be accurately controlled, errors are likely to occur in the assembly process, which directly affects the overall performance and quality of the product.

[0004] Therefore, there is an urgent need to invent a screw machine control technology to solve the problem that the screw tightening method of the screw machine in the existing technology relies on fixed parameter settings, which is difficult to adapt to the material and geometric characteristics of screws of different specifications, resulting in parameters such as tightening force and torque being difficult to accurately control, thereby affecting assembly quality and product performance. Summary of the invention

[0005] In view of this, the present invention proposes a screw machine control system and method based on machine vision, aiming to solve the problem that the screw tightening method of the screw machine in the current technology relies on fixed parameter settings, which is difficult to adapt to the material and geometric characteristics of screws of different specifications, resulting in parameters such as tightening force and torque being difficult to accurately control, thereby affecting the assembly quality and product performance.

[0006] The present invention proposes a screw machine control method based on machine vision, comprising:

[0007] Acquire image information of the working area of ​​the screw to be tightened and the workpiece, and input the image information into a preset image processing module for image processing, wherein the image processing includes denoising, filtering and contrast enhancement processing of the image information based on the image processing module;

[0008] Extracting feature information of the screw to be tightened in the image information after image processing, matching the feature information with screws of various specifications in a pre-established screw feature set, and obtaining specifications and geometric features of the screw to be tightened;

[0009] According to the type and geometric features of the screw to be tightened, position feature information of the screw to be tightened is determined, and the position feature information is converted into three-dimensional coordinates to obtain the relative position relationship between the screw to be tightened and the workpiece, wherein the position feature information includes: the center position, hole diameter, inclination angle and surface features of the screw to be tightened;

[0010] According to the relative position relationship, the initial alignment path of the screw to be tightened is determined, and according to the surface state of the workpiece and the specification of the screw to be tightened, the tightening parameters of the screw machine are determined, wherein the tightening parameters of the screw machine include: the clamping force, torque and rotation speed when the screw machine is tightened;

[0011] The material hardness, thread friction coefficient and real-time temperature of the screw to be tightened are obtained, and the tightening parameters are adjusted according to the material hardness, thread friction coefficient and real-time temperature.

[0012] Furthermore, the pre-established screw feature set includes:

[0013] Acquire geometric parameters and material parameters of the screws of each specification, establish a characteristic formula of the screw according to the geometric parameters and material parameters of the screw, and establish the screw feature set according to the characteristic formula of the screws of each specification;

[0014] The geometric parameters of the screw are the diameter of the screw, the length of the screw, the pitch of the screw, the head type of the screw and the thread type of the screw;

[0015] The material parameters of the screw include the material type of the screw and the hardness of the screw.

[0016] Further, when determining the initial alignment path of the screw to be tightened according to the relative position relationship, it includes:

[0017] The spatial three-dimensional coordinates of the screw to be tightened and the three-dimensional coordinates of the workpiece in the image information are obtained, and the spatial three-dimensional coordinates of the screw to be tightened and the three-dimensional coordinates of the workpiece are substituted into Formula I to determine the relative position relationship between the screw to be tightened and the workpiece. Formula I is as follows:

[0018]

[0019] Among them, x t ,y t ,z t is the relative position relationship between the screw to be tightened and the workpiece, x s ,y s ,z s is the three-dimensional coordinate of the screw to be tightened, x w ,y w ,z w is the three-dimensional coordinate of the workpiece, and R is the rotation matrix of the workpiece relative to the world coordinate system;

[0020] Obtain the initial position of the tightening tool of the screw machine, and compare the initial position of the tightening tool with the relative position relationship x t ,y t ,z t Substitute into formula II to determine the initial alignment path of the screw to be tightened, where formula II is as follows:

[0021]

[0022] Wherein, P is the initial alignment path of the screw to be tightened, and x0, y0, z0 are the initial positions of the tightening tool.

[0023] Further, when adjusting the tightening parameters of the screw machine according to the material hardness, thread friction coefficient and real-time temperature, it includes:

[0024] Obtaining the real-time material hardness of the screw to be tightened, and determining whether to adjust the tightening parameters of the screw machine according to the relationship between the real-time material hardness and the preset hardness of the tightening screw specification;

[0025] When the real-time material hardness is consistent with the preset hardness, it is determined that the tightening parameters of the screw machine are not adjusted;

[0026] When the real-time material hardness is inconsistent with the preset hardness, an adjustment coefficient is determined according to the hardness difference between the real-time material hardness and the preset hardness, and the tightening parameters of the screw machine are adjusted according to the adjustment coefficient.

[0027] Furthermore, when determining the adjustment coefficient according to the hardness difference between the real-time material hardness and the preset hardness, it includes:

[0028] Determine the adjustment coefficient according to the relationship between the hardness difference and a preconfigured first preset hardness difference and a second preset hardness difference;

[0029] When the hardness difference is less than the first preset hardness difference, the adjustment coefficients of the clamping force and torque when the screw machine is tightened are determined to be M4, and the adjustment coefficient of the rotation speed when the screw machine is tightened is determined to be N1;

[0030] When the hardness difference is greater than or equal to the first preset hardness difference, and the hardness difference is less than zero, the adjustment coefficients of the clamping force and torque when the screw machine is tightened are determined to be M3, and the adjustment coefficient of the rotation speed when the screw machine is tightened is determined to be N2;

[0031] When the hardness difference is greater than zero and the hardness difference is less than the second preset hardness difference, the adjustment coefficients of the clamping force and torque when the screw machine is tightened are determined to be M2, and the adjustment coefficient of the rotation speed when the screw machine is tightened is determined to be N3;

[0032] When the hardness difference is greater than or equal to the second preset hardness difference, the adjustment coefficients of the clamping force and torque when the screw machine is tightened are determined to be M1, and the adjustment coefficient of the rotation speed when the screw machine is tightened is determined to be N4;

[0033] Wherein, the first preset hardness difference value < zero < the second preset hardness difference value, and M1 < M2 < M3 < M4 < 1, 1 < N1 < N2 < N3 < N4.

[0034] Further, when the adjustment coefficient of the clamping force and torque when the screw machine is tightened is determined to be Mi, and the adjustment coefficient of the rotation speed when the screw machine is tightened is Ni, i=1, 2, 3, 4, including:

[0035] The real-time thread friction coefficient of the screw to be tightened is obtained, and according to the relationship between the real-time thread friction coefficient and the preset preset thread friction coefficient, it is determined whether to correct the adjustment coefficient Mi of the clamping force and torque when the screw machine is tightened and the adjustment coefficient Ni of the rotation speed when the screw machine is tightened:

[0036] When the real-time thread friction coefficient is consistent with the preset thread friction coefficient, it is determined that the adjustment coefficient Mi of the pressing force and torque when the screw machine is tightening and the adjustment coefficient Ni of the rotation speed when the screw machine is tightening are not corrected;

[0037] When the real-time thread friction coefficient is inconsistent with the preset thread friction coefficient, a correction coefficient is determined based on the friction coefficient difference between the real-time thread friction coefficient and the preset thread friction coefficient, and the adjustment coefficient Mi of the clamping force and torque when the screw machine is tightened is corrected based on the correction coefficient.

[0038] Furthermore, when determining the correction coefficient according to the friction coefficient difference between the real-time thread friction coefficient and the preset thread friction coefficient, it includes:

[0039] Determining the correction coefficient according to the relationship between the friction coefficient difference and a preconfigured first preset friction coefficient difference and a second preset friction coefficient difference;

[0040] When the friction coefficient difference is less than the first preset friction coefficient difference, the correction coefficient of the pressing force and torque when the screw machine is tightening is determined to be L1;

[0041] When the friction coefficient difference is greater than or equal to the first preset friction coefficient difference, and the friction coefficient difference is less than zero, the correction coefficient of the clamping force and torque when the screw machine is tightening is determined to be L2;

[0042] When the friction coefficient difference is greater than zero and the friction coefficient difference is less than the second preset friction coefficient difference, the correction coefficient of the clamping force and torque when the screw machine is tightened is determined to be 1 1;

[0043] When the friction coefficient difference is greater than or equal to the second preset friction coefficient difference, the correction coefficient of the clamping force and torque when the screw machine is tightened is determined to be 1 2;

[0044] Wherein, the first preset friction coefficient difference value < zero < the second preset friction coefficient difference value, and L1 < L2 < 1, 1 < l 1 < l 2.

[0045] Further, when the correction coefficient of the clamping force and torque when the screw machine is tightened is determined to be ΔLj, ΔLj=L1,L2,l1,l2 includes:

[0046] Obtaining the real-time temperature of the screw to be tightened, and determining whether to correct the correction coefficient ΔLj of the pressing force and torque when the screw machine is tightening and the adjustment coefficient Ni of the rotation speed when the screw machine is tightening according to the relationship between the real-time temperature of the screw to be tightened and the preset temperature;

[0047] When the real-time temperature is consistent with the preset temperature, it is determined that the correction coefficient ΔLj of the pressing force and torque when the screw machine is tightening and the adjustment coefficient Ni of the rotation speed when the screw machine is tightening are not corrected;

[0048] When the real-time temperature is inconsistent with the preset temperature, a correction coefficient is determined based on the temperature difference between the real-time temperature and the preset temperature, and the correction coefficient △Lj of the clamping force when the screw machine is tightening and the adjustment coefficient Mi of the rotation speed are corrected based on the correction coefficient.

[0049] Furthermore, when determining the correction coefficient according to the temperature difference between the real-time temperature and the preset temperature, it includes:

[0050] Determining the correction coefficient according to a relationship between the temperature difference and a preconfigured first preset temperature difference and a second preset temperature difference;

[0051] When the temperature difference is less than the first preset temperature difference, the correction coefficient is determined to be A1;

[0052] When the temperature difference is greater than or equal to the first preset temperature difference, and the temperature difference is less than zero, the correction coefficient is determined to be A2;

[0053] When the temperature difference is greater than zero and the temperature difference is less than the second preset temperature difference, the correction coefficient is determined to be a1;

[0054] When the temperature difference is greater than or equal to the second preset temperature difference, the correction coefficient is determined to be a2;

[0055] Wherein, the first preset friction coefficient difference value < zero < the second preset friction coefficient difference value, and A1 < A2 < 1, 1 < a1 < a2.

[0056] Compared with the prior art, the beneficial effect of the present invention is that: by obtaining image information of the working area of ​​the screw to be tightened and the workpiece, the image information is input into a preset image processing module for denoising, filtering and contrast enhancement. This step ensures the accuracy of subsequent feature extraction, so that the geometric shape, size and position features of the screw can be effectively identified. By improving the clarity and recognizability of the image, subtle feature changes can be better captured, thereby providing a reliable data basis for subsequent analysis. Secondly, when extracting feature information after image processing, matching is performed according to a pre-established screw feature set. This process not only improves the accuracy of screw identification, but also can identify screws of various specifications. By conducting an in-depth analysis of the type and geometric features of the screw to be tightened, a three-dimensional coordinate of the relative position relationship with the workpiece can be generated. The acquisition of this relative position relationship is a key step in achieving precise tightening, because it allows the alignment path of the screw to be adjusted in real time during the tightening process, thereby ensuring the correct insertion and tightening of the screw. In addition, by using the acquired relative position relationship, the initial alignment path of the screw to be tightened can be accurately determined, and on this basis, the tightening parameters of the screw machine can be intelligently adjusted according to the surface state of the workpiece and the specifications of the screw. These tightening parameters, including clamping force, torque and rotation speed, are dynamically calculated based on the characteristics of the screw, thus avoiding the parameter mismatch problem that may occur in traditional methods. Through this dynamic adjustment, not only can the accuracy of tightening be improved, but also the risk of damage to the screw and workpiece surface can be significantly reduced, and the overall assembly quality can be improved. Finally, by taking into account key factors such as the material hardness of the screw, real-time temperature and thread friction coefficient. Fine adjustments can be made during the tightening process to ensure the best tightening effect under different working conditions. This dynamic feedback mechanism enables the screw machine to adapt to various complex environments and conditions in actual applications, greatly improving the reliability and safety of the product.

[0057] On the other hand, the present application also provides a screw machine control system based on machine vision. When the screw machine control system based on machine vision is running, it is used to execute a screw machine control method based on machine vision of the above embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0059] Figure 1 A flowchart of a screw machine control method based on machine vision provided by an embodiment of the present invention;

[0060] Figure 2 A functional block diagram of a screw machine control system based on machine vision provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0061] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0062] like Figure 1 As shown, in some embodiments of the present application, this embodiment provides a screw machine control method based on machine vision, including:

[0063] Step S100: acquiring image information of the working area of ​​the screw to be tightened and the workpiece, and inputting the image information into a preset image processing module for image processing.

[0064] Specifically, image processing includes denoising, filtering and contrast enhancement of image information based on an image processing module.

[0065] It is understandable that by acquiring the image information of the working area of ​​the screws to be tightened and the workpiece, a series of image processing operations are performed using the preset image processing module, including denoising, filtering and contrast enhancement. These processing steps are aimed at improving image quality, removing noise and interference in the image, and enhancing the visibility of target features to ensure that subsequent feature extraction and recognition can be more accurate and reliable. Denoising technology effectively eliminates random noise in the image, filtering operations further smooth the image and reduce unnecessary details, while contrast enhancement increases the contrast between important features and the background, making the screws to be tightened and their geometric features more prominent in the image, thereby providing clear basic data for subsequent automated analysis.

[0066] Step S200: extracting feature information of the screw to be tightened from the image information after image processing, matching the feature information with screws of various specifications in a pre-established screw feature set, and obtaining the specifications and geometric features of the screw to be tightened.

[0067] Specifically, the pre-established screw feature set includes: obtaining the geometric parameters and material parameters of screws of various specifications, establishing the screw feature formula according to the screw geometric parameters and material parameters, and establishing the screw feature set according to the screw feature formula of various specifications. The screw geometric parameters are the screw diameter, screw length, screw pitch, screw head type and screw thread type. The screw material parameters are the screw material type and the screw hardness.

[0068] It can be seen that by extracting the feature information of the screws to be tightened after image processing and matching it with the pre-established screw feature set, the specifications and geometric features of the screws to be tightened are obtained. Specifically, the pre-established screw feature set contains the geometric parameters (such as diameter, length, pitch, head type and thread type) and material parameters (such as material type and hardness) of screws of various specifications. These parameters are represented by establishing a characteristic formula for the screws to form a comprehensive feature database. The construction of this feature set can effectively identify and classify the screws to be tightened, and accurately match them according to their geometric and material characteristics.

[0069] It can be understood that by extracting the feature information of the screws to be tightened after image processing and matching it with the pre-established screw feature set, the specifications and geometric features of the screws to be tightened can be accurately identified. The extraction of the feature information of the screws to be tightened relies on advanced image processing technology, which can identify key geometric parameters from complex images. This process includes the accurate capture of geometric parameters such as the diameter, length, pitch, head type and thread type of the screw. These parameters not only determine the basic shape of the screw, but also directly affect its performance during the tightening process. In addition, the material parameters of the screw, such as material type and hardness, are also key factors affecting the strength and durability of the screw connection. Therefore, establishing a comprehensive and accurate screw feature set is the basis for efficient identification and matching. Secondly, in order to construct the screw feature set, the geometric and material parameters of screws of different specifications are collected and converted into feature formulas. These feature formulas not only include simple geometric measurements, but also should comprehensively consider the performance of the screws in different application environments. For example, under high temperature or high load conditions, screws of different materials may exhibit different hardness and tensile strength. Therefore, the screw feature set should fully consider these variables. This multi-dimensional feature description enables the screw feature set to cover a wide range of screw specifications, facilitating subsequent automated identification and classification. Finally, during the matching process, the feature information of the screw to be tightened will be compared with the features of screws of various specifications in the screw feature set. By using an efficient matching algorithm, the specific specifications and geometric features of the screw to be tightened can be quickly identified. This process not only improves the speed of recognition, but also ensures the accuracy of the matching results. For example, after identifying the diameter and length of the screw to be tightened, it can quickly find the corresponding feature formula and then determine the specifications of the screw. This fast and accurate matching mechanism enables the automated assembly process to respond in real time, improving overall production efficiency.

[0070] Step S300: Determine the position feature information of the screw to be tightened according to the type and geometric features of the screw to be tightened, and perform three-dimensional coordinate conversion on the position feature information to obtain the relative position relationship between the screw to be tightened and the workpiece.

[0071] Specifically, the position feature information includes: the center position, hole diameter, inclination angle and surface features of the screw to be tightened.

[0072] Specifically, when obtaining the relative position relationship between the screw to be tightened and the workpiece, it includes: obtaining the spatial three-dimensional coordinates of the screw to be tightened and the three-dimensional coordinates of the workpiece in the image information, and substituting the spatial three-dimensional coordinates of the screw to be tightened and the three-dimensional coordinates of the workpiece into formula I to determine the relative position relationship between the screw to be tightened and the workpiece. Formula I is as follows: Among them, x t ,y t ,z tis the relative position relationship between the screw to be tightened and the workpiece, x s ,y s ,z s is the three-dimensional coordinate of the screw to be tightened, x w ,y w ,z w is the three-dimensional coordinate of the workpiece, and R is the rotation matrix of the workpiece relative to the world coordinate system.

[0073] It can be understood that the position feature information of the screw to be tightened is obtained through the type and geometric features of the screw to be tightened, and the three-dimensional coordinate conversion is performed to determine the relative position relationship between the screw to be tightened and the workpiece. Specifically, the position feature information of the screw to be tightened includes the center position, aperture, tilt angle and surface features, which are essential for correctly positioning the screw. When obtaining the relative position relationship between the screw to be tightened and the workpiece, the three-dimensional coordinates of the two are first extracted, and then the spatial three-dimensional coordinates of the screw to be tightened and the three-dimensional coordinates of the workpiece are calculated using formula I, combined with the rotation matrix R of the workpiece relative to the world coordinate system to determine their relative position relationship. Secondly, its dynamic position in actual operation is considered. This dynamic positioning capability is particularly important in complex automated assembly environments, because the assembly positions of many workpieces may deviate due to manufacturing tolerances, assembly errors or environmental factors. Therefore, real-time monitoring and dynamic adjustment of the position of the screw to be tightened can effectively improve the accuracy of assembly. In addition, in practical applications, image processing technology can help the system extract the spatial coordinates of the screw to be tightened. By using high-resolution cameras and advanced image processing algorithms, the position and posture information of the screw can be accurately captured. After denoising, filtering and enhancement, the extracted feature information is more accurate and reliable. Subsequently, the information is converted into a format consistent with the workpiece coordinate system through three-dimensional coordinate transformation, making the subsequent tightening operation more efficient. Finally, the relative position relationship calculated by formula I provides important basic data for the subsequent screw alignment and tightening process. This data can help the control system accurately adjust the working parameters of the screw machine, such as clamping force and rotation speed, to ensure the best connection between the screw and the workpiece.

[0074] Step S400: determining an initial alignment path of the screw to be tightened according to the relative position relationship, and determining the tightening parameters of the screw machine according to the surface state of the workpiece and the specifications of the screw to be tightened.

[0075] Specifically, the screw machine tightening parameters include: the clamping force, torque and rotation speed when the screw machine is tightening.

[0076] Specifically, the initial position of the tightening tool of the screw machine is obtained, and the initial position of the tightening tool is compared with the relative position relationship x t ,y t ,z tSubstitute into Formula II to determine the initial alignment path of the screw to be tightened, where Formula II is as follows:

[0077]

[0078] Among them, P is the initial alignment path of the screw to be tightened, and x0, y0, z0 are the initial positions of the tightening tool.

[0079] It can be seen that the initial alignment path of the screw to be tightened is determined by the relative position relationship, and the tightening parameters of the screw machine are accurately set in combination with the surface state of the workpiece and the specifications of the screw to be tightened. Specifically, by obtaining the initial position of the screw machine tightening tool and comparing it with the relative position relationship between the screw to be tightened and the workpiece (x t ,y t ,z t ) is substituted into Formula II to calculate the initial alignment path P of the screw to be tightened. This process ensures that the tightening tool can accurately align with the screw to be tightened during the tightening process, improving the accuracy and efficiency of the tightening process. In actual operation, the tightening tool of the screw machine needs to be dynamically adjusted according to the surface condition of the workpiece to cope with possible defects or unevenness on the surface. By accurately calculating the initial alignment path, the screw machine can position the tool to the optimal operating position before entering the tightening stage. In addition, the determined tightening parameters (such as clamping force, torque and rotational speed) will be optimized based on the type, geometric characteristics and material properties of the screw to be tightened to ensure the stability and connection strength of the screw during the tightening process, thereby avoiding the problem of over- or under-tightening.

[0080] It is understandable that by accurately calculating the relative position relationship, engineers can achieve efficient screw alignment in complex assembly environments. This not only reduces the possibility of misoperation, but also significantly improves assembly efficiency. Especially in large-scale production, automated screw machines can complete the tightening tasks of a large number of screws in a short time without frequent manual intervention, thereby reducing labor costs and the risk of human error. Secondly, the calculation of the initial alignment path needs to consider multiple factors, including the geometry of the workpiece, the specifications of the screws to be tightened, and the initial position of the screw machine tool. The application of Formula II makes this calculation more systematic and standardized. By combining the initial position of the tightening tool with the relative position relationship between the screw and the workpiece, the screw machine can automatically generate an optimal path. This process is not only fast but also highly flexible and can adapt to different types and specifications of screws. Such intelligent adjustment enables the screw machine to maintain a high level of accuracy and stability in different production environments. Finally, in the specific implementation, by analyzing the state of the workpiece surface and the characteristics of the screws to be tightened, the clamping force, torque and rotation speed can be reasonably set. These parameters not only directly affect the connection strength of the screw, but also are related to the fit between the screw and the workpiece. For example, for larger screws or those with higher hardness, higher torque and appropriate clamping force are usually required to ensure that they can effectively enter the workpiece and achieve the desired connection effect. For smaller screws or those with softer screws, more precise control is required to prevent damage to the screws or deformation of the material due to over-tightening.

[0081] Step S500, obtaining the material hardness, thread friction coefficient and real-time temperature of the screw to be tightened when the screw is tightened, and adjusting the tightening parameters according to the material hardness, thread friction coefficient and real-time temperature.

[0082] Specifically, when adjusting the tightening parameters of the screw machine according to the material hardness, thread friction coefficient and real-time temperature, it includes: obtaining the real-time material hardness of the screw to be tightened, and determining whether to adjust the tightening parameters of the screw machine according to the relationship between the real-time material hardness and the preset hardness of the tightening screw specification: when the real-time material hardness is consistent with the preset hardness, it is determined that the tightening parameters of the screw machine are not adjusted. When the real-time material hardness is inconsistent with the preset hardness, the adjustment coefficient is determined according to the hardness difference between the real-time material hardness and the preset hardness, and the tightening parameters of the screw machine are adjusted according to the adjustment coefficient.

[0083] Specifically, when determining the adjustment coefficient according to the hardness difference between the real-time material hardness and the preset hardness, it includes: determining the adjustment coefficient according to the relationship between the hardness difference and the pre-configured first preset hardness difference and the second preset hardness difference. When the hardness difference is less than the first preset hardness difference, the adjustment coefficient of the clamping force and torque when the screw machine is tightened is determined to be M4, and the adjustment coefficient of the rotation speed when the screw machine is tightened is N1. When the hardness difference is greater than or equal to the first preset hardness difference, and the hardness difference is less than zero, the adjustment coefficient of the clamping force and torque when the screw machine is tightened is determined to be M3, and the adjustment coefficient of the rotation speed when the screw machine is tightened is N2. When the hardness difference is greater than zero, and the hardness difference is less than the second preset hardness difference, the adjustment coefficient of the clamping force and torque when the screw machine is tightened is determined to be M2, and the adjustment coefficient of the rotation speed when the screw machine is tightened is N3. When the hardness difference is greater than or equal to the second preset hardness difference, the adjustment coefficient of the clamping force and torque when the screw machine is tightened is determined to be M1, and the adjustment coefficient of the rotation speed when the screw machine is tightened is N4. The first preset hardness difference value is less than zero and less than the second preset hardness difference value, and M1 is less than M2 is less than M3 is less than M4 is less than 1, and 1 is less than N1 is less than N2 is less than N3 is less than N4.

[0084] It is understandable that by real-time monitoring of the material hardness of the screw to be tightened, information about the actual state of the screw can be obtained in a timely manner. This information not only covers the physical properties of the screw, but also involves the behavior of different materials when subjected to force. For example, screws of different materials may exhibit different deformation characteristics under heat or force, which will directly affect the connection effect after tightening. It can also achieve a more efficient assembly process, thereby reducing the product failure rate caused by improper tightening. Secondly, in the implementation process, the setting of the hardness difference and the configuration of the adjustment coefficient are the key to achieving dynamic adjustment. Specifically, by comparing the real-time material hardness with the preset hardness, it is possible to quickly determine whether the material of the screw meets the design requirements. The basis of this comparison is based on multiple pre-defined hardness difference ranges, which can effectively cover the changes under different materials and usage conditions. For example, when the real-time material hardness is consistent with the preset hardness, there is no need to adjust the tightening parameters, thereby reducing unnecessary adjustments and improving production efficiency; when the hardness difference is large, the tightening parameters need to be adjusted accordingly to ensure the tightening quality of the screw. Through such a mechanism, the screw machine can not only adapt to the performance changes of different materials during assembly, but also improve the safety of operation to a certain extent and reduce losses caused by improper operation. Finally, the process of dynamically adjusting the tightening parameters of the screw machine involves comprehensive consideration of the clamping force, torque and rotation speed. By formulating specific adjustment coefficients for each situation according to the different hardness differences, it is ensured that it can be flexibly responded to in actual operation. For example, when the hardness difference is small, the clamping force and torque of the tightening tool are kept at a low level to avoid excessive pressure on the screw, while when the hardness difference is large, these parameters are increased to ensure that the screw can be fully locked. This refined control strategy enables every link in the automated assembly process to be carried out more scientifically and effectively, thereby improving the quality management level of the overall production line.

[0085] Specifically, when it is determined that the adjustment coefficient of the clamping force and torque when the screw machine is tightened is Mi, and the adjustment coefficient of the rotational speed when the screw machine is tightened is Ni, i=1,2,3,4, it includes: obtaining the real-time thread friction coefficient of the screw to be tightened, and determining whether to correct the adjustment coefficient Mi of the clamping force and torque when the screw machine is tightened and the adjustment coefficient Ni of the rotational speed when the screw machine is tightened according to the relationship between the real-time thread friction coefficient and the preset thread friction coefficient: when the real-time thread friction coefficient is consistent with the preset thread friction coefficient, it is determined not to correct the adjustment coefficient Mi of the clamping force and torque when the screw machine is tightened and the adjustment coefficient Ni of the rotational speed when the screw machine is tightened. When the real-time thread friction coefficient is inconsistent with the preset thread friction coefficient, it is determined that the correction coefficient is determined according to the friction coefficient difference between the real-time thread friction coefficient and the preset thread friction coefficient, and the adjustment coefficient Mi of the clamping force and torque when the screw machine is tightened is corrected according to the correction coefficient.

[0086] Specifically, when determining the correction coefficient according to the friction coefficient difference between the real-time thread friction coefficient and the preset thread friction coefficient, it includes: determining the correction coefficient according to the relationship between the friction coefficient difference and the pre-configured first preset friction coefficient difference and the second preset friction coefficient difference. When the friction coefficient difference is less than the first preset friction coefficient difference, the correction coefficient of the clamping force and torque when the screw machine is tightened is determined to be L1. When the friction coefficient difference is greater than or equal to the first preset friction coefficient difference, and the friction coefficient difference is less than zero, the correction coefficient of the clamping force and torque when the screw machine is tightened is determined to be L2. When the friction coefficient difference is greater than zero, and the friction coefficient difference is less than the second preset friction coefficient difference, the correction coefficient of the clamping force and torque when the screw machine is tightened is determined to be l 1. When the friction coefficient difference is greater than or equal to the second preset friction coefficient difference, the correction coefficient of the clamping force and torque when the screw machine is tightened is determined to be l 2. Among them, the first preset friction coefficient difference < zero < second preset friction coefficient difference, and L1 < L2 < 1, 1 < l 1 < l 2.

[0087] It is understandable that by comparing with the preset thread friction coefficient, adjustments can be made when the difference in friction coefficient reaches a certain threshold. Specifically, when the friction coefficient is consistent with the preset value, the current tightening parameters are kept unchanged to ensure normal tightening operations under standard friction conditions. However, when the real-time friction coefficient deviates from the preset value, the correction coefficient is determined by evaluating the degree of deviation, so as to dynamically adjust key parameters such as the clamping force and rotation speed of the screw machine to adapt to the current friction conditions. Secondly, this process sets multiple levels of correction coefficients, such as L1, L2, l 1 and l2, and applies different correction coefficients according to the deviation value range of the friction coefficient. These correction coefficients are designed for hierarchical control to ensure that the clamping force and torque of the screw machine can be reasonably adapted under different friction conditions. Specifically, if the difference in friction coefficient is small, only the tightening force and speed need to be fine-tuned, and a lower correction coefficient will be selected to avoid excessive impact on the tightening process; on the contrary, if the deviation is large, a higher correction coefficient will be selected to make more significant adjustments. This graded control not only effectively improves the flexibility of the tightening process, but also ensures the tightness and stability of the threaded connection. Finally, the real-time adjustment mechanism of the friction coefficient difference can effectively avoid the risk of unstable connection or damage caused by excessive or insufficient friction. For example, if the friction coefficient is too high, excessive friction resistance may be generated during the tightening process, resulting in excessive tightening force or even damage to the thread or workpiece surface; conversely, if the friction coefficient is too low, the screws may not be fully tightened, affecting the connection strength and stability. By setting the graded correction coefficient, the tightening parameters can be adjusted in a targeted manner, so that the screw machine can maintain the best tightening effect under different friction coefficient conditions.

[0088] Specifically, when it is determined that the correction coefficient of the clamping force and torque when the screw machine is tightened is △Lj, △Lj=L1,L2,l 1,l 2 includes: obtaining the real-time temperature of the screw to be tightened, and determining whether to correct the correction coefficient △Lj of the clamping force and torque when the screw machine is tightened and the adjustment coefficient Ni of the rotation speed when the screw machine is tightened according to the relationship between the real-time temperature of the screw to be tightened and the preset temperature: when the real-time temperature is consistent with the preset temperature, it is determined not to correct the correction coefficient △Lj of the clamping force and torque when the screw machine is tightened and the adjustment coefficient Ni of the rotation speed when the screw machine is tightened. When the real-time temperature is inconsistent with the preset temperature, the correction coefficient is determined according to the temperature difference between the real-time temperature and the preset temperature, and the correction coefficient △Lj of the clamping force when the screw machine is tightened and the adjustment coefficient Mi of the rotation speed are corrected according to the correction coefficient.

[0089] Specifically, when determining the correction coefficient according to the temperature difference between the real-time temperature and the preset temperature, it includes: determining the correction coefficient according to the relationship between the temperature difference and the pre-configured first preset temperature difference and the second preset temperature difference. When the temperature difference is less than the first preset temperature difference, the correction coefficient is determined to be A1. When the temperature difference is greater than or equal to the first preset temperature difference, and the temperature difference is less than zero, the correction coefficient is determined to be A2. When the temperature difference is greater than zero, and the temperature difference is less than the second preset temperature difference, the correction coefficient is determined to be a1. When the temperature difference is greater than or equal to the second preset temperature difference, the correction coefficient is determined to be a2. Among them, the first preset friction coefficient difference < zero < second preset friction coefficient difference, and A1 < A2 < 1, 1 < a1 < a2.

[0090] It is understandable that by real-time monitoring of the temperature information of the screw to be tightened, the clamping force and rotation speed of the screw machine are dynamically adjusted to ensure the stability and accuracy of the tightening quality. When the real-time temperature of the screw to be tightened is inconsistent with the preset temperature, the temperature difference between the two is analyzed, and it is determined whether the tightening parameters need to be adjusted based on the size of the difference. If the temperature difference is large, it means that the contact surface characteristics of the screw and the workpiece may change due to thermal expansion or contraction, and insufficient torque or small clamping force may occur during the tightening process. Therefore, a larger correction coefficient is automatically selected for adaptation to increase the clamping force and adjust the rotation speed. This correction mechanism can not only improve the reliability of assembly, but also reduce assembly quality problems caused by temperature fluctuations. Specifically, according to the difference between the real-time temperature and the preset temperature, multiple temperature difference thresholds (such as a first preset temperature difference and a second preset temperature difference) are set to classify and adjust the correction coefficients of the tightening parameters. When the temperature difference is small, only minor adjustments to the parameters are required, so a smaller correction coefficient (such as A1 or A2) is selected; when the temperature difference is large, a larger correction coefficient (such as a1 or a2) is selected to ensure that the tightening effect meets the requirements under different temperature environments. This method achieves more detailed adjustments through the judgment of multi-level temperature differences to adapt to complex temperature changes. This temperature difference-driven correction mechanism not only ensures the safety of the assembly process, but also significantly improves the consistency of the assembly, especially in an environment with high precision requirements, and more effectively maintains the stability of the tightening operation. Finally, by effectively utilizing the differentiated design of the correction coefficient, the impact of temperature differences can be intelligently distinguished. The grading mechanism of the correction coefficient (such as different levels from A1 to a2) can accurately respond to different scenarios of temperature fluctuations and ensure that the tightening parameters can accurately adapt to the screw and workpiece status at the current ambient temperature. This subdivision adjustment based on temperature differences not only ensures the precise control of the clamping force and rotation speed during the tightening process, but also further reduces the assembly error caused by temperature changes. It is particularly suitable for working environments with large temperature fluctuations and workpiece materials that are sensitive to temperature.

[0091] In the above embodiment, the image information of the working area of ​​the screw to be tightened and the workpiece is obtained, and the image information is input into the preset image processing module for denoising, filtering and contrast enhancement. This step ensures the accuracy of subsequent feature extraction, so that the geometric shape, size and position features of the screw can be effectively identified. By improving the clarity and recognizability of the image, subtle feature changes can be better captured, thereby providing a reliable data basis for subsequent analysis. Secondly, when extracting the feature information after image processing, matching is performed according to the pre-established screw feature set. This process not only improves the accuracy of screw identification, but also can identify screws of various specifications. By conducting an in-depth analysis of the type and geometric features of the screw to be tightened, a three-dimensional coordinate of the relative position relationship with the workpiece can be generated. The acquisition of this relative position relationship is a key step in achieving precise tightening, because it allows the alignment path of the screw to be adjusted in real time during the tightening process, thereby ensuring the correct insertion and tightening of the screw. In addition, according to the acquired relative position relationship, the initial alignment path of the screw to be tightened can be accurately determined, and on this basis, the tightening parameters of the screw machine can be intelligently adjusted according to the surface state of the workpiece and the specifications of the screw. These tightening parameters, including clamping force, torque and rotation speed, are dynamically calculated based on the characteristics of the screw, thus avoiding the parameter mismatch problem that may occur in traditional methods. Through this dynamic adjustment, not only can the accuracy of tightening be improved, but also the risk of damage to the screw and workpiece surface can be significantly reduced, and the overall assembly quality can be improved. Finally, by taking into account key factors such as the material hardness of the screw, real-time temperature and thread friction coefficient. Fine adjustments can be made during the tightening process to ensure the best tightening effect under different working conditions. This dynamic feedback mechanism enables the screw machine to adapt to various complex environments and conditions in actual applications, greatly improving the reliability and safety of the product.

[0092] In another preferred embodiment based on the above embodiments, this embodiment provides a screw machine control system based on machine vision, including a screw machine control method based on machine vision for executing the above embodiments when the screw machine control system based on machine vision is running.

[0093] like Figure 2As shown, a screw machine control system based on machine vision in an embodiment of the present invention includes: an acquisition module to obtain image information of the working area of ​​the screw to be tightened and the workpiece. The image processing module is electrically connected to the acquisition module, and the image processing module is used to perform denoising, filtering and contrast enhancement processing on the image information. The matching module is electrically connected to the image processing module, and the matching module is used to extract the feature information of the screw to be tightened in the image information after image processing. The matching module is also used to match the feature information with the screws of each specification in the pre-established screw feature set, and obtain the specifications and geometric features of the screw to be tightened. The first calculation module is electrically connected to the matching module, and the first calculation module is used to determine the position feature information of the screw to be tightened according to the type and geometric features of the screw to be tightened, and perform three-dimensional coordinate conversion on the position feature information to obtain the relative position relationship between the screw to be tightened and the workpiece. The second calculation module is electrically connected to the first calculation module, and the second calculation module is used to determine the initial alignment path of the screw to be tightened according to the relative position relationship, and determine the screw machine tightening parameters according to the surface state of the workpiece and the specifications of the screw to be tightened. The ultrasonic detection module is used to obtain the material hardness of the screw to be tightened when tightening. The vibration detection module is used to obtain the vibration state of the screw to be tightened when the screw is tightened, and determine the thread friction coefficient when the screw is tightened according to the vibration state. The infrared detection module is used to obtain the real-time temperature of the screw to be tightened when the screw is tightened. The central control module is electrically connected to the second calculation module, the ultrasonic detection module, the vibration detection module, the infrared detection module and the control system of the screw machine respectively. The central control module is used to adjust the tightening parameters according to the material hardness, the thread friction coefficient and the real-time temperature.

[0094] It can be understood that the machine vision-based screw machine control system and method in the above-mentioned embodiments have the same beneficial effects and will not be described in detail.

[0095] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0096] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0097] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0098] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0099] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A screw machine control method based on machine vision, characterized in that: include: Acquire image information of the working area of ​​the screw to be tightened and the workpiece, and input the image information into a preset image processing module for image processing, wherein the image processing includes denoising, filtering and contrast enhancement processing of the image information based on the image processing module; Extracting feature information of the screw to be tightened in the image information after image processing, matching the feature information with screws of various specifications in a pre-established screw feature set, and obtaining specifications and geometric features of the screw to be tightened; According to the type and geometric features of the screw to be tightened, position feature information of the screw to be tightened is determined, and the position feature information is converted into three-dimensional coordinates to obtain the relative position relationship between the screw to be tightened and the workpiece, wherein the position feature information includes: the center position, hole diameter, inclination angle and surface features of the screw to be tightened; According to the relative position relationship, the initial alignment path of the screw to be tightened is determined, and according to the surface state of the workpiece and the specification of the screw to be tightened, the tightening parameters of the screw machine are determined, wherein the tightening parameters of the screw machine include: the clamping force, torque and rotation speed when the screw machine is tightened; The material hardness, thread friction coefficient and real-time temperature of the screw to be tightened are obtained, and the tightening parameters are adjusted according to the material hardness, thread friction coefficient and real-time temperature.

2. The screw machine control method based on machine vision according to claim 1, characterized in that: The pre-built screw feature set includes: Acquire geometric parameters and material parameters of the screws of each specification, establish a characteristic formula of the screw according to the geometric parameters and material parameters of the screw, and establish the screw feature set according to the characteristic formula of the screws of each specification; The geometric parameters of the screw are the diameter of the screw, the length of the screw, the pitch of the screw, the head type of the screw and the thread type of the screw; The material parameters of the screw include the material type of the screw and the hardness of the screw.

3. The screw machine control method based on machine vision according to claim 1, characterized in that: Determining the initial alignment path of the screw to be tightened according to the relative position relationship includes: The spatial three-dimensional coordinates of the screw to be tightened and the three-dimensional coordinates of the workpiece in the image information are obtained, and the spatial three-dimensional coordinates of the screw to be tightened and the three-dimensional coordinates of the workpiece are substituted into Formula I to determine the relative position relationship between the screw to be tightened and the workpiece. Formula I is as follows: Among them, x t ,y t ,z t is the relative position relationship between the screw to be tightened and the workpiece, x s ,y s ,z s is the three-dimensional coordinate of the screw to be tightened, x w ,y w ,z w is the three-dimensional coordinate of the workpiece, and R is the rotation matrix of the workpiece relative to the world coordinate system; Obtain the initial position of the tightening tool of the screw machine, and compare the initial position of the tightening tool with the relative position relationship x t ,y t ,z t Substitute into formula II to determine the initial alignment path of the screw to be tightened, where formula II is as follows: Wherein, P is the initial alignment path of the screw to be tightened, and x0, y0, z0 are the initial positions of the tightening tool.

4. The screw machine control method based on machine vision according to claim 1, characterized in that: When adjusting the tightening parameters of the screw machine according to the material hardness, thread friction coefficient and real-time temperature, it includes: Obtaining the real-time material hardness of the screw to be tightened, and determining whether to adjust the tightening parameters of the screw machine according to the relationship between the real-time material hardness and the preset hardness of the tightening screw specification; When the real-time material hardness is consistent with the preset hardness, it is determined that the tightening parameters of the screw machine are not adjusted; When the real-time material hardness is inconsistent with the preset hardness, an adjustment coefficient is determined according to the hardness difference between the real-time material hardness and the preset hardness, and the tightening parameters of the screw machine are adjusted according to the adjustment coefficient.

5. The screw machine control method based on machine vision as claimed in claim 4, characterized in that: When determining the adjustment coefficient according to the hardness difference between the real-time material hardness and the preset hardness, it includes: Determine the adjustment coefficient according to the relationship between the hardness difference and a preconfigured first preset hardness difference and a second preset hardness difference; When the hardness difference is less than the first preset hardness difference, the adjustment coefficients of the clamping force and torque when the screw machine is tightened are determined to be M4, and the adjustment coefficient of the rotation speed when the screw machine is tightened is determined to be N1; When the hardness difference is greater than or equal to the first preset hardness difference, and the hardness difference is less than zero, the adjustment coefficients of the clamping force and torque when the screw machine is tightened are determined to be M3, and the adjustment coefficient of the rotation speed when the screw machine is tightened is determined to be N2; When the hardness difference is greater than zero and the hardness difference is less than the second preset hardness difference, the adjustment coefficients of the clamping force and torque when the screw machine is tightened are determined to be M2, and the adjustment coefficient of the rotation speed when the screw machine is tightened is determined to be N3; When the hardness difference is greater than or equal to the second preset hardness difference, the adjustment coefficients of the clamping force and torque when the screw machine is tightened are determined to be M1, and the adjustment coefficient of the rotation speed when the screw machine is tightened is determined to be N4; Wherein, the first preset hardness difference value < zero < the second preset hardness difference value, and M1 < M2 < M3 < M4 < 1, 1 < N1 < N2 < N3 < N4.

6. The method for controlling a screw machine based on machine vision as claimed in claim 5, characterized in that: When the adjustment coefficients of the clamping force and torque when the screw machine is tightened are determined to be Mi, and the adjustment coefficient of the rotation speed when the screw machine is tightened is determined to be Ni, i=1, 2, 3, 4, including: The real-time thread friction coefficient of the screw to be tightened is obtained, and according to the relationship between the real-time thread friction coefficient and the preset preset thread friction coefficient, it is determined whether to correct the adjustment coefficient Mi of the clamping force and torque when the screw machine is tightened and the adjustment coefficient Ni of the rotation speed when the screw machine is tightened: When the real-time thread friction coefficient is consistent with the preset thread friction coefficient, it is determined that the adjustment coefficient Mi of the pressing force and torque when the screw machine is tightening and the adjustment coefficient Ni of the rotation speed when the screw machine is tightening are not corrected; When the real-time thread friction coefficient is inconsistent with the preset thread friction coefficient, a correction coefficient is determined based on the friction coefficient difference between the real-time thread friction coefficient and the preset thread friction coefficient, and the adjustment coefficient Mi of the clamping force and torque when the screw machine is tightened is corrected based on the correction coefficient.

7. The method for controlling a screw machine based on machine vision as claimed in claim 6, characterized in that: When determining the correction coefficient according to the friction coefficient difference between the real-time thread friction coefficient and the preset thread friction coefficient, it includes: Determining the correction coefficient according to the relationship between the friction coefficient difference and a preconfigured first preset friction coefficient difference and a second preset friction coefficient difference; When the friction coefficient difference is less than the first preset friction coefficient difference, the correction coefficient of the pressing force and torque when the screw machine is tightening is determined to be L1; When the friction coefficient difference is greater than or equal to the first preset friction coefficient difference, and the friction coefficient difference is less than zero, the correction coefficient of the clamping force and torque when the screw machine is tightening is determined to be L2; When the friction coefficient difference is greater than zero and the friction coefficient difference is less than the second preset friction coefficient difference, the correction coefficient of the clamping force and torque when the screw machine is tightened is determined to be 1 1; When the friction coefficient difference is greater than or equal to the second preset friction coefficient difference, the correction coefficient of the clamping force and torque when the screw machine is tightened is determined to be 1 2; Wherein, the first preset friction coefficient difference value < zero < the second preset friction coefficient difference value, and L1 < L2 < 1, 1 < l 1 < l 2.

8. The method for controlling a screw machine based on machine vision as claimed in claim 7, characterized in that: When the correction coefficient of the clamping force and torque when the screw machine is tightened is determined to be △Lj, △Lj=L1,L2,l 1,l 2 includes: Obtaining the real-time temperature of the screw to be tightened, and determining whether to correct the correction coefficient △Lj of the pressing force and torque when the screw machine is tightening and the adjustment coefficient Ni of the rotation speed when the screw machine is tightening according to the relationship between the real-time temperature of the screw to be tightened and the preset temperature; When the real-time temperature is consistent with the preset temperature, it is determined that the correction coefficient △Lj of the pressing force and torque when the screw machine is tightened and the adjustment coefficient Ni of the rotation speed when the screw machine is tightened are not corrected; When the real-time temperature is inconsistent with the preset temperature, a correction coefficient is determined based on the temperature difference between the real-time temperature and the preset temperature, and the correction coefficient △Lj of the clamping force when the screw machine is tightening and the adjustment coefficient Mi of the rotation speed are corrected based on the correction coefficient.

9. The method for controlling a screw machine based on machine vision as claimed in claim 8, characterized in that: When determining the correction coefficient according to the temperature difference between the real-time temperature and the preset temperature, it includes: Determining the correction coefficient according to a relationship between the temperature difference and a preconfigured first preset temperature difference and a second preset temperature difference; When the temperature difference is less than the first preset temperature difference, the correction coefficient is determined to be A1; When the temperature difference is greater than or equal to the first preset temperature difference, and the temperature difference is less than zero, the correction coefficient is determined to be A2; When the temperature difference is greater than zero and the temperature difference is less than the second preset temperature difference, the correction coefficient is determined to be a1; When the temperature difference is greater than or equal to the second preset temperature difference, the correction coefficient is determined to be a2; Wherein, the first preset friction coefficient difference value < zero < the second preset friction coefficient difference value, and A1 < A2 < 1, 1 < a1 < a2.

10. A screw machine control system based on machine vision, characterized in that: When the machine vision-based screw machine control system is in operation, it is used to execute a machine vision-based screw machine control method as described in claims 1-9.

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