Electric tension equipment operation monitoring system and method based on machine vision

Abstract

The invention discloses an electric tension equipment operation monitoring system and method based on machine vision, and belongs to the field of general control systems.The method comprises the steps that operation process anomaly analysis is conducted according to an obtained tension equipment anomaly analysis result and an output end anomaly analysis result in the operation process; according to the operation process abnormity analysis method and device, comprehensive analysis is carried out on the safe operation condition of the equipment through the operation data of the operation process of the tension equipment and the output end connection data, so that the safety of the electric tension equipment in the operation process is improved; according to the method, the change condition of the output end of the tension equipment in the operation process is analyzed to obtain the characteristics influencing the safe operation of the output end, and then the obtained characteristics are subjected to comprehensive data evaluation and analysis, so that the monitoring efficiency and accuracy of the output end of the tension equipment are improved.

Description

Technical Field

[0001] The present invention belongs to the general field of control systems, and more specifically relates to an electric pulling equipment operation monitoring system and method based on machine vision. Background Art

[0002] Visual electric pulling equipment is used in materials science and materials research and development to test the mechanical properties of materials, such as tensile strength and elongation. In the field of mechanical manufacturing, it is used for product quality inspection and performance verification. During the operation of electric pulling equipment, it is necessary to monitor and warn the data of the operation process to avoid damage to the pulling equipment during operation, thereby preventing dangerous situations. Therefore, an electric pulling equipment operation monitoring system and method based on machine vision is required. However, the prior art does not have a system specifically used for monitoring the operation of electric pulling equipment. If monitoring systems in other fields are applied to the operation monitoring of electric pulling equipment, it is impossible to achieve a comprehensive analysis of the operation data of the pulling equipment and the output connection data during the operation process. It is easy for equipment aging to cause equipment failure or the output breakage process to be monitored poorly, which can easily lead to problems such as low monitoring safety and low efficiency. Most of the prior arts have the above problems. In order to solve the problems raised by this background technology, the present application designs an electric pulling equipment operation monitoring system and method based on machine vision. Summary of the invention

[0003] In view of the deficiencies in the prior art, the present invention proposes an electric pulling equipment operation monitoring system and method based on machine vision.

[0004] To achieve the above object, the present invention provides the following technical solutions: In a first aspect, a method for monitoring the operation of electric pulling equipment based on machine vision is provided, which comprises the following specific steps: Step 1: Install the workpiece to be inspected on the working end of the electric tension equipment, and obtain the operation data of the electric tension equipment during the working process and the image change data of the output end of the electric tension equipment; Step 2: Obtain the data of the electric pulling equipment during the working process and import it into the pulling equipment abnormality analysis model to perform pulling equipment abnormality analysis; Step 3: Import the acquired output end image change data of the electric pulling equipment into the output end change abnormality analysis model to perform output end abnormality analysis; Step 4: Performing operation process abnormality analysis based on the obtained abnormality analysis results of the tension equipment and the abnormality analysis results of the output end during the operation process; Step 5: Analyze and output the abnormal situation based on the abnormal analysis results of the operation process.

[0005] As a preferred technical solution of the method for monitoring the operation of electric pulling equipment based on machine vision, the specific contents of installing the workpiece to be detected on the working end of the electric pulling equipment and obtaining the operation data of the electric pulling equipment and the image change data of the output end of the electric pulling equipment during the working process are as follows: S11, a workpiece to be inspected is installed on the working end of an electric pulling device, and the electric device operates to pull the workpiece to be inspected to cause deformation; S12, acquiring deformation data and image data of the output end of the electric pulling device through the image acquisition terminal, acquiring crack change data of the output end of the electric pulling device, and storing them in the first storage component; The specific steps of acquiring deformation data and image data of the output end of the electric pulling device through the image acquisition terminal, and acquiring crack change data of the output end of the electric pulling device may include the following aspects: S121. Select a suitable image acquisition device: First, you need to select a high-resolution, high-sensitivity image acquisition device, such as a high-resolution industrial camera or camera, which can capture the subtle deformation and crack changes at the output end of the electric tension equipment; S122. Install and calibrate the image acquisition device: install the image acquisition device at a position where the output end of the electric pulling device can be clearly photographed, and calibrate it to ensure that the acquired image data is accurate and distortion-free; S123, collecting image data: during the working process of the electric tension device, the image data of the output end is continuously captured by the image acquisition device; this can be achieved by setting a suitable frame rate to ensure that sufficient deformation and crack change information can be captured; S124, processing and analyzing image data: the collected image data needs to be processed and analyzed to extract deformation and crack change information, which can be achieved through image processing software or algorithms, such as using image segmentation, edge detection, feature extraction and other technologies to identify and analyze deformation and cracks at the output end; S125, obtaining deformation data and crack change data: by analyzing the processed image data, the deformation data and crack change data of the output end of the electric tension device can be extracted; these data may include parameters such as displacement, velocity, acceleration, etc. of the deformation, as well as information such as length, width, depth, etc. of the crack; S13. Acquire the operating data of the electric pulling equipment through the data acquisition terminal, and store it in the second storage component.

[0006] As a preferred technical solution of the electric pulling equipment operation monitoring method based on machine vision, the method of obtaining the electric pulling equipment data during the working process and importing it into the pulling equipment abnormality analysis model to perform pulling equipment abnormality analysis includes the following specific steps: S21, obtaining speed, tension and deformation data of the output end of the electric pulling device during operation; S22, obtaining the speed and pulling force of the electric pulling device during operation, and importing the obtained speed and pulling force into the pulling process abnormal value calculation formula to calculate the pulling process abnormal value, wherein the pulling process abnormal value calculation formula is: , where T is the test time, a is the speed anomaly ratio, vt is the running speed data of the pulling equipment at time t, vm is the set standard pulling speed, ft is the running force data of the pulling equipment at time t, fm is the set standard pulling force, dt is the integral of time. It should be noted that in order to perform abnormal analysis on the operation of the pulling equipment during the running time, the form of integrating time is used here, and the abnormality of the running time is divided into the integral of the abnormality of the time. vt-vm is the deviation between the running speed data of the pulling equipment at time t and the standard pulling speed, ft-fm is the deviation between the running force data of the pulling equipment at time t and the standard pulling force, so as to accurately perform abnormal analysis on the output of the electric pulling equipment during the running time.

[0007] As a preferred technical solution of the electric pulling equipment operation monitoring method based on machine vision, the obtained output end image change data of the electric pulling equipment is imported into the output end change abnormality analysis model to perform output end abnormality analysis, which includes the following specific steps: S31, obtaining deformation data and image data of the output end of the electric pulling device, and obtaining the initial image of the output end of the electric pulling device at the same time, importing the obtained real-time deformation image data of the output end of the electric pulling device and the initial image data of the output end of the electric pulling device into the deformation abnormal value calculation formula to calculate the deformation abnormal value, wherein the deformation abnormal value calculation formula is: , where m is the number of cameras that acquire images, s() is the area of ​​the image, pi is the starting image of the output end of the device shot at the i-th angle, and mi is the real-time deformation image of the output end of the electric pulling device shot at the i-th angle. is the intersection of two images, is the union of two images, where i is any item from 1 to m; S32, obtaining image data of the output end of the electric pulling device, obtaining crack length and maximum width data on the image, and importing the obtained crack length and maximum width data on the image into a crack change abnormal value calculation formula to calculate the crack change abnormal value, wherein the crack change abnormal value calculation formula is: , where cj is the distance between the jth crack and the workpiece contact point, n is the number of cracks, mj is the length of the jth crack, zj is the width of the jth crack, mc is the crack length safety value, zc is the crack width safety value, and j is any one of 1 to n. Since the closer the distance to the workpiece contact point, the more dangerous it is, this parameter is used here to weight cracks at different positions.

[0008] As a preferred technical solution of the method for monitoring the operation of electric pulling equipment based on machine vision, the obtained output end image change data of the electric pulling equipment is imported into the output end change abnormality analysis model for output end abnormality analysis, and the following specific steps are also included: S33, obtaining the calculated deformation abnormal value and crack change abnormal value, and at the same time obtaining the deformation abnormal value and crack change abnormal value at the start time of this pulling process, and importing them into the pulling output end abnormal value calculation formula to calculate the pulling output end abnormal value, wherein the pulling output end abnormal value calculation formula is: , where kxm is the safety value of abnormal deformation, kxzm is the safety value of deformation change under standard tension, kxz is the abnormal deformation value at the beginning of this pulling process, F is the standard tension, Fm is the average tension during the pulling process, klm is the safety value of abnormal crack change, and klzm is the safety value of crack change under standard tension. is the deformation anomaly ratio.

[0009] As a preferred technical solution of the electric pulling equipment operation monitoring method based on machine vision, the abnormal analysis of the operation process by obtaining the abnormal analysis results of the pulling equipment during operation and the abnormal analysis results of the output end includes the following specific contents: Obtain the calculated pulling process abnormal value and the calculated pulling output end abnormal value, and substitute them into the running process abnormal value calculation formula to calculate the running process abnormal value, wherein the running process abnormal value calculation formula is: ,in, is the abnormal proportion coefficient of the tension process.

[0010] As a preferred technical solution of the electric pulling equipment operation monitoring method based on machine vision, the analysis and output of abnormal conditions according to the abnormal analysis results of the operation process obtained include the following specific contents: The calculated operation process abnormal value is compared with the set operation process abnormal threshold. If the obtained operation process abnormal value is greater than or equal to the set operation process abnormal threshold, an operation abnormality warning is issued. If the obtained operation process abnormal value is less than the set operation process abnormal threshold, the operation is displayed as normal.

[0011] In a second aspect, a machine vision-based electric pulling equipment operation monitoring system is provided, which is implemented based on the above-mentioned machine vision-based electric pulling equipment operation monitoring method, and specifically includes a data acquisition module, a pulling equipment abnormality analysis module, an output end abnormality analysis module, an operation process abnormality analysis module, an abnormal situation output module and a control module; The data acquisition module is used to install the workpiece to be detected on the working end of the electric pulling device, and acquire the operation data of the electric pulling device and the image change data of the output end of the electric pulling device during the working process; The tension equipment abnormality analysis module is used to obtain the electric tension equipment data during the working process and import it into the tension equipment abnormality analysis model to perform tension equipment abnormality analysis; The output end abnormality analysis module is used to obtain the image change data of the output end of the electric pulling equipment and import it into the output end change abnormality analysis model to perform output end abnormality analysis; The operation process abnormality analysis module is used to perform operation process abnormality analysis based on the obtained tensile equipment abnormality analysis results and output end abnormality analysis results during the operation process; The abnormal situation output module is used to analyze and output the abnormal situation according to the obtained abnormal analysis result of the operation process; The control module is used for controlling the operation of the data acquisition module, the tension equipment abnormality analysis module, the output end abnormality analysis module, the operation process abnormality analysis module and the abnormal situation output module.

[0012] In a third aspect, an electronic device is provided, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the above-mentioned method for monitoring the operation of electric pulling equipment based on machine vision by calling the computer program stored in the memory.

[0013] In a fourth aspect, a computer-readable storage medium is provided, storing instructions, which, when executed on a computer, enable the computer to execute the above-mentioned method for monitoring the operation of electric pulling equipment based on machine vision.

[0014] Compared with the prior art, the beneficial effects of this application are: The present application installs the workpiece to be detected on the working end of the electric tension equipment, obtains the operation data of the electric tension equipment during the operation process and the image change data of the output end of the electric tension equipment, obtains the data of the electric tension equipment during the operation process and imports it into the abnormal analysis model of the tension equipment to perform abnormal analysis of the tension equipment, obtains the image change data of the output end of the electric tension equipment and imports it into the abnormal analysis model of the output end change to perform abnormal analysis of the output end, performs abnormal analysis of the operation process through the obtained abnormal analysis results of the tension equipment during the operation process and the abnormal analysis results of the output end, analyzes and outputs the abnormal situation according to the obtained abnormal analysis results of the operation process, and improves the safety of the electric tension equipment during the operation process by comprehensively analyzing the safe operation of the equipment through the operation data and the output end connection data of the operation process; The present application analyzes the changes in the output end of the tension equipment during operation, obtains the characteristics that affect the safe operation of the output end, and then performs comprehensive data evaluation and analysis on the obtained characteristics, thereby improving the monitoring efficiency and accuracy of the output end of the tension equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the overall flow of the method for monitoring the operation of electric pulling equipment based on machine vision of the present invention; Figure 2 This is a flow chart of step 3 of the method for monitoring the operation of electric pulling equipment based on machine vision of the present invention; Figure 3 It is a schematic diagram of the overall framework of the electric pulling equipment operation monitoring system based on machine vision of the present invention; Figure 4 It is a schematic diagram of the deformation data collection process of the present invention; Figure 5 This is a schematic diagram of the adaptation scenario of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present application and its application or use.

[0017] Example 1

[0018] In order to solve the technical problems raised in the background technology, the present invention provides a preferred embodiment: Figure 1-Figure 2 As shown, the electric pulling equipment operation monitoring method based on machine vision includes the following specific steps: Step 1: Install the workpiece to be inspected on the working end of the electric tension equipment, and obtain the operation data of the electric tension equipment during the working process and the image change data of the output end of the electric tension equipment; The usage scenarios of this embodiment are as follows Figure 5 As shown, the test workpiece is installed between two tension output ends. The tension equipment control system controls the tension output end to output tension and move at a set speed on the guide rail of the equipment body. The image acquisition terminal collects image change data of the tension output end during operation to perform abnormal analysis on the tension output end to avoid dangerous situations caused by excessive aging test. In one specific embodiment, the workpiece to be inspected is mounted on the working end of the electric pulling device, and the specific contents of obtaining the operation data of the electric pulling device during the working process and the image change data of the output end of the electric pulling device are as follows: S11, a workpiece to be inspected is installed on the working end of an electric pulling device, and the electric device operates to pull the workpiece to be inspected to cause deformation; S12, acquiring deformation data and image data of the output end of the electric pulling device through the image acquisition terminal, acquiring crack change data of the output end of the electric pulling device, and storing them in the first storage component; like Figure 4 As shown, the specific steps of acquiring deformation data and image data of the output end of the electric pulling device through the image acquisition terminal, and acquiring crack change data of the output end of the electric pulling device may include the following aspects: S121. Select a suitable image acquisition device: First, you need to select a high-resolution, high-sensitivity image acquisition device, such as a high-resolution industrial camera or camera, which can capture the subtle deformation and crack changes at the output end of the electric tension equipment; S122. Install and calibrate the image acquisition device: install the image acquisition device at a position where the output end of the electric pulling device can be clearly photographed, and calibrate it to ensure that the acquired image data is accurate and distortion-free; S123, collecting image data: during the working process of the electric tension device, the image data of the output end is continuously captured by the image acquisition device; this can be achieved by setting a suitable frame rate to ensure that sufficient deformation and crack change information can be captured; S124, processing and analyzing image data: the collected image data needs to be processed and analyzed to extract deformation and crack change information, which can be achieved through image processing software or algorithms, such as using image segmentation, edge detection, feature extraction and other technologies to identify and analyze deformation and cracks at the output end; S125, obtaining deformation data and crack change data: by analyzing the processed image data, the deformation data and crack change data of the output end of the electric tension device can be extracted; these data may include parameters such as displacement, velocity, acceleration, etc. of the deformation, as well as information such as length, width, depth, etc. of the crack; S13, acquiring the operating data of the electric pulling device through the data acquisition terminal, and storing the data in the second storage component; Step 2: Obtain the data of the electric pulling equipment during the working process and import it into the pulling equipment abnormality analysis model to perform pulling equipment abnormality analysis; In one specific embodiment, obtaining the electric pulling equipment data during operation and importing it into the pulling equipment abnormality analysis model to perform pulling equipment abnormality analysis includes the following specific steps: S21, obtaining speed, tension and deformation data of the output end of the electric pulling device during operation; S22, obtaining the speed and pulling force of the electric pulling device during operation, and importing the obtained speed and pulling force into the pulling process abnormal value calculation formula to calculate the pulling process abnormal value, wherein the pulling process abnormal value calculation formula is: , where T is the test time, a is the speed anomaly ratio, vt is the running speed data of the pulling equipment at time t, vm is the set standard pulling speed, ft is the running force data of the pulling equipment at time t, fm is the set standard pulling force, dt is the time integral, it should be noted that, in order to perform abnormal analysis on the operation of the pulling equipment during the running time, the form of integrating time is used here, and the abnormality of the running time is divided into the integral of the abnormality of the time, vt-vm is the deviation between the running speed data of the pulling equipment at time t and the standard pulling speed, ft-fm is the deviation between the running force data of the pulling equipment at time t and the standard pulling force, so as to accurately perform abnormal analysis on the output of the electric pulling equipment during the running time; Step 3: Import the acquired output end image change data of the electric pulling equipment into the output end change abnormality analysis model to perform output end abnormality analysis; In one specific embodiment, the obtained output end image change data of the electric pulling device is imported into the output end change abnormality analysis model to perform output end abnormality analysis, which includes the following specific steps: S31, obtaining deformation data and image data of the output end of the electric pulling device, and obtaining the initial image of the output end of the electric pulling device at the same time, importing the obtained real-time deformation image data of the output end of the electric pulling device and the initial image data of the output end of the electric pulling device into the deformation abnormal value calculation formula to calculate the deformation abnormal value, wherein the deformation abnormal value calculation formula is: , where m is the number of cameras that acquire images, s() is the area of ​​the image, pi is the starting image of the output end of the device shot at the i-th angle, and mi is the real-time deformation image of the output end of the electric pulling device shot at the i-th angle. is the intersection of two images, is the union of two images, where i is any item from 1 to m; S32, obtaining image data of the output end of the electric pulling device, obtaining crack length and maximum width data on the image, and importing the obtained crack length and maximum width data on the image into a crack change abnormal value calculation formula to calculate the crack change abnormal value, wherein the crack change abnormal value calculation formula is: , where cj is the distance between the jth crack and the contact point between the output end and the workpiece, n is the number of cracks, mj is the length of the jth crack, zj is the width of the jth crack, mc is the safety value of the crack length, zc is the safety value of the crack width, and j is any one of 1 to n. Since the closer the distance to the workpiece contact point, the more dangerous it is, this parameter is used here to weight the cracks at different positions; S33, obtaining the calculated deformation abnormal value and crack change abnormal value, and at the same time obtaining the deformation abnormal value and crack change abnormal value at the start time of this pulling process, and importing them into the pulling output end abnormal value calculation formula to calculate the pulling output end abnormal value, wherein the pulling output end abnormal value calculation formula is: , where kxm is the safety value of abnormal deformation, kxzm is the safety value of deformation change under standard tension, kxz is the abnormal deformation value at the beginning of this pulling process, F is the standard tension, Fm is the average tension during the pulling process, klm is the safety value of abnormal crack change, and klzm is the safety value of crack change under standard tension. is the deformation abnormality proportion coefficient. It should be specifically explained here that the deformation abnormality safety value, the deformation change safety value under standard tension, the crack change safety value under standard tension and the crack change abnormality safety value are obtained by experiments by technicians in this field. The obtaining method is: obtaining historical operation data, obtaining cracks and deformations that will not cause the pulling output end to fail from the historical operation data, importing the deformation abnormality value calculation formula to calculate the deformation abnormality, importing the crack change abnormality value calculation formula to calculate the crack change abnormality, obtaining the calculated maximum deformation abnormality value and the maximum crack change abnormality value, respectively setting them as the deformation abnormality safety value and the crack change abnormality safety value; and the specific obtaining method of the deformation change safety value under standard tension and the crack change safety value under standard tension refers to the deformation abnormality safety value and the crack change abnormality safety value, that is, obtaining historical operation data, using standard tension (preferably the maximum tension that the equipment can withstand) to pull, evaluating crack changes and deformation changes, obtaining the maximum crack changes and deformation changes that will not cause the pulling output end to fail, and calculating the deformation change safety value under standard tension and the crack change safety value under standard tension; In this step, the deformation data of the output end of the electric pulling device is first analyzed to accurately analyze the deformation anomaly, and then the tiny cracks on the image data of the output end of the electric pulling device are obtained, and the characteristics of the cracks are analyzed, so as to accurately analyze the anomaly of the output end of the electric pulling device. In the calculation formula of the abnormal value of the pulling output end, the abnormal change speed and the overall abnormal situation are comprehensively considered to make a comprehensive judgment on the abnormality of the pulling output end; Step 4: Performing operation process abnormality analysis based on the obtained abnormality analysis results of the tension equipment and the abnormality analysis results of the output end during the operation process; In one specific embodiment, the abnormality analysis of the operation process performed by obtaining the abnormality analysis results of the tension equipment during operation and the abnormality analysis results of the output end during operation includes the following specific contents: Obtain the calculated pulling process abnormal value and the calculated pulling output end abnormal value, and substitute them into the running process abnormal value calculation formula to calculate the running process abnormal value, wherein the running process abnormal value calculation formula is: ,in, is the abnormal proportion coefficient of the tension process; Step 5: Analyze and output the abnormal situation according to the abnormal analysis results of the operation process; In one specific embodiment, the analysis and output of abnormal conditions according to the obtained abnormal analysis results of the operation process include the following specific contents: The calculated operation process abnormal value is compared with the set operation process abnormal threshold. If the obtained operation process abnormal value is greater than or equal to the set operation process abnormal threshold, an operation abnormality warning is issued. If the obtained operation process abnormal value is less than the set operation process abnormal threshold, the operation is displayed as normal.

[0019] What needs to be specifically explained in this embodiment is that the speed abnormality ratio coefficient, the deformation abnormality ratio coefficient, the tension process abnormality ratio coefficient and the operation process abnormality threshold are determined in the following manner: 500 sets of electric pulling equipment operation data and electric pulling equipment output end image change data during the working process are obtained, and the operation process abnormality values ​​are substituted into the operation process abnormality value calculation formula to calculate the operation process abnormality value, and the judgment result of whether the equipment is damaged after use is obtained. The judgment result of whether the equipment is damaged after use and the calculated operation process abnormality value are imported into the fitting software, and the speed abnormality ratio coefficient, the deformation abnormality ratio coefficient, the tension process abnormality ratio coefficient and the operation process abnormality threshold are outputted in accordance with the maximum judgment accuracy.

[0020] The advantages of this embodiment over the prior art are as follows: the workpiece to be detected is installed at the working end of the electric tension equipment, the operation data of the electric tension equipment and the image change data of the output end of the electric tension equipment during the working process are obtained, the data of the electric tension equipment during the working process are imported into the abnormal analysis model of the tension equipment to perform abnormal analysis on the tension equipment, the image change data of the output end of the electric tension equipment obtained are imported into the abnormal analysis model of the output end change to perform abnormal analysis on the output end, the abnormal analysis of the operation process is performed by the abnormal analysis results of the tension equipment during the operation process and the abnormal analysis results of the output end obtained, and the abnormal situation is analyzed and output according to the abnormal analysis results of the operation process obtained. This application improves the safety of the electric tension equipment during the operation process by comprehensively analyzing the operation data and the output end connection data of the tension equipment during the operation process, and at the same time, by analyzing the changes in the output end of the tension equipment during the operation process, the characteristics that affect the safe operation of the output end are obtained, and then the obtained characteristics are evaluated and analyzed for comprehensive data, thereby improving the monitoring efficiency and accuracy of the output end of the tension equipment.

[0021] Example 2

[0022] like Figure 3As shown, the electric pulling equipment operation monitoring system based on machine vision is implemented based on the above-mentioned electric pulling equipment operation monitoring method based on machine vision, and specifically includes a data acquisition module, a pulling equipment abnormality analysis module, an output end abnormality analysis module, an operation process abnormality analysis module, an abnormal situation output module and a control module; wherein the data acquisition module is used to install the workpiece to be detected on the working end of the electric pulling equipment, and obtain the operation data of the electric pulling equipment and the image change data of the output end of the electric pulling equipment during the working process; the pulling equipment abnormality analysis module is used to obtain the data of the electric pulling equipment during the working process and import it into the pulling equipment abnormality analysis model to perform the pulling equipment abnormality analysis; the output end abnormality analysis module is used to obtain the image change data of the output end of the electric pulling equipment and import it into the output end change abnormality analysis model to perform Output end abnormality analysis; an operation process abnormality analysis module, used for performing operation process abnormality analysis through the obtained tensile equipment abnormality analysis results during operation and the output end abnormality analysis results; an abnormal situation output module, used for analyzing and outputting abnormal situations according to the obtained operation process abnormality analysis results; a control module, used for controlling the operation of the data acquisition module, the tensile equipment abnormality analysis module, the output end abnormality analysis module, the operation process abnormality analysis module and the abnormal situation output module. It should be noted here that the output end of the data acquisition module is respectively connected to the tensile equipment abnormality analysis module and the output end abnormality analysis module, the output end of the tensile equipment abnormality analysis module and the output end of the output end abnormality analysis module are both connected to the operation process abnormality analysis module, and the output end of the operation process abnormality analysis module is connected to the abnormal situation output module.

[0023] Example 3

[0024] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the above-mentioned method for monitoring the operation of electric pulling equipment based on machine vision by calling the computer program stored in the memory.

[0025] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement the electric pulling equipment operation monitoring method based on machine vision provided by the above method embodiment. The electronic device may also include other components for implementing the functions of the device, for example, the electronic device may also have components such as a wired or wireless network interface and an input and output interface, so as to input and output data. This embodiment will not be described in detail here.

[0026] Example 4

[0027] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon; When the computer program is executed on a computer device, the computer device is enabled to execute the above-mentioned method for monitoring the operation of electric pulling equipment based on machine vision.

[0028] For example, the computer readable storage medium can be a read-only memory, a random access memory, a read-only CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0029] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center through a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

Claims

1. The method for monitoring the operation of electric pulling equipment based on machine vision is characterized in that: It includes the following specific steps: Step 1: Install the workpiece to be inspected on the working end of the electric tension equipment, and obtain the operation data of the electric tension equipment during the working process and the image change data of the output end of the electric tension equipment; Step 2: Obtain the data of the electric pulling equipment during the working process and import it into the pulling equipment abnormality analysis model to perform pulling equipment abnormality analysis; Step 3: Import the acquired output end image change data of the electric pulling equipment into the output end change abnormality analysis model to perform output end abnormality analysis; Step 4: Performing operation process abnormality analysis based on the obtained abnormality analysis results of the tension equipment and the abnormality analysis results of the output end during the operation process; Step 5: Analyze and output the abnormal situation based on the abnormal analysis results of the operation process.

2. The method for monitoring the operation of electric pulling equipment based on machine vision according to claim 1, characterized in that: The method of obtaining the electric pulling equipment data during the working process and importing it into the pulling equipment abnormality analysis model to perform pulling equipment abnormality analysis includes the following specific steps: S21, obtaining speed, tension and deformation data of the output end of the electric pulling device during operation; S22, obtaining the speed and pulling force of the electric pulling device during operation, and importing the obtained speed and pulling force into the pulling process abnormal value calculation formula to calculate the pulling process abnormal value, wherein the pulling process abnormal value calculation formula is: , where T is the test time, a is the speed anomaly ratio, vt is the running speed data of the pulling equipment at time t, vm is the set standard pulling speed, ft is the running tension data of the pulling equipment at time t, fm is the set standard pulling tension, and dt is the time integral.

3. The method for monitoring the operation of electric pulling equipment based on machine vision according to claim 2, characterized in that: The obtained output end image change data of the electric pulling device is imported into the output end change abnormality analysis model to perform output end abnormality analysis, which includes the following specific steps: S31, obtaining deformation data and image data of the output end of the electric pulling device, and obtaining the initial image of the output end of the electric pulling device at the same time, importing the obtained real-time deformation image data of the output end of the electric pulling device and the initial image data of the output end of the electric pulling device into the deformation abnormal value calculation formula to calculate the deformation abnormal value, wherein the deformation abnormal value calculation formula is: , where m is the number of cameras that acquire images, s() is the area of ​​the image, pi is the starting image of the output end of the device shot at the i-th angle, Mi is the real-time deformation image of the output end of the electric pulling device shot at the i-th angle, is the intersection of two images, is the union of two images, where i is any item from 1 to m; S32, obtaining image data of the output end of the electric pulling device, obtaining crack length and maximum width data on the image, and importing the obtained crack length and maximum width data on the image into a crack change abnormal value calculation formula to calculate the crack change abnormal value, wherein the crack change abnormal value calculation formula is: , where cj is the distance between the jth crack and the contact point between the output end and the workpiece, n is the number of cracks, mj is the length of the jth crack, zj is the width of the jth crack, mc is the safety value of the crack length, zc is the safety value of the crack width, and j is any item from 1 to n.

4. The method for monitoring the operation of electric pulling equipment based on machine vision according to claim 3, characterized in that: The step of importing the acquired output end image change data of the electric pulling equipment into the output end change abnormality analysis model to perform output end abnormality analysis also includes the following specific steps: S33, obtaining the calculated deformation abnormal value and crack change abnormal value, and at the same time obtaining the deformation abnormal value and crack change abnormal value at the start time of this pulling process, and importing them into the pulling output end abnormal value calculation formula to calculate the pulling output end abnormal value, wherein the pulling output end abnormal value calculation formula is: , where kxm is the safety value of abnormal deformation, kxzm is the safety value of deformation change under standard tension, kxz is the abnormal deformation value at the beginning of this pulling process, F is the standard tension, Fm is the average tension during the pulling process, klm is the safety value of abnormal crack change, and klzm is the safety value of crack change under standard tension. is the deformation anomaly ratio.

5. The method for monitoring the operation of electric pulling equipment based on machine vision according to claim 4, characterized in that: The abnormality analysis of the operation process performed by obtaining the abnormality analysis results of the tension equipment and the abnormality analysis results of the output end during the operation process includes the following specific contents: Obtain the calculated pulling process abnormal value and the calculated pulling output end abnormal value, and substitute them into the running process abnormal value calculation formula to calculate the running process abnormal value, wherein the running process abnormal value calculation formula is: ,in, is the abnormal proportion coefficient of the tension process.

6. The method for monitoring the operation of electric pulling equipment based on machine vision according to claim 5, characterized in that: The analysis and output of abnormal situations according to the abnormal analysis results of the operation process obtained include the following specific contents: The calculated operation process abnormal value is compared with the set operation process abnormal threshold. If the obtained operation process abnormal value is greater than or equal to the set operation process abnormal threshold, an operation abnormality warning is issued. If the obtained operation process abnormal value is less than the set operation process abnormal threshold, the operation is displayed as normal.

7. An electric pulling equipment operation monitoring system based on machine vision, which is implemented based on the electric pulling equipment operation monitoring method based on machine vision according to any one of claims 1 to 6, characterized in that: It specifically includes a data acquisition module, a tension equipment abnormality analysis module, an output end abnormality analysis module, an operation process abnormality analysis module, an abnormal situation output module and a control module; The data acquisition module is used to install the workpiece to be detected on the working end of the electric pulling device, and acquire the operation data of the electric pulling device and the image change data of the output end of the electric pulling device during the working process; The tension equipment abnormality analysis module is used to obtain the electric tension equipment data during the working process and import it into the tension equipment abnormality analysis model to perform tension equipment abnormality analysis; The output end abnormality analysis module is used to obtain the image change data of the output end of the electric pulling equipment and import it into the output end change abnormality analysis model to perform output end abnormality analysis; The operation process abnormality analysis module is used to perform operation process abnormality analysis based on the obtained tensile equipment abnormality analysis results and output end abnormality analysis results during the operation process; The abnormal situation output module is used to analyze and output the abnormal situation according to the obtained abnormal analysis result of the operation process; The control module is used for controlling the operation of the data acquisition module, the tension equipment abnormality analysis module, the output end abnormality analysis module, the operation process abnormality analysis module and the abnormal situation output module.

8. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; It is characterized in that the processor executes the method for monitoring the operation of electric pulling equipment based on machine vision as described in any one of claims 1 to 6 by calling the computer program stored in the memory.

9. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer executes the method for monitoring the operation of electric pulling equipment based on machine vision as described in any one of claims 1 to 6.

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