Automatic positioning method for cutting screen of cutting machine based on visual analysis

Abstract

The invention discloses a cutting machine cutting screen automatic positioning method and system based on visual analysis, and relates to the technical field of visual image processing, and the method comprises the steps: monitoring the cutter position and angle of a cutting machine in real time, and generating cutter state data; the surface texture, the edge shape and existing flaws of the cut screen are analyzed, and corresponding surface feature data are generated; data fusion is carried out, dynamic correction is carried out on visual analysis, a path correction instruction of the cutter is generated, and real-time feedback is carried out on the obtained path correction instruction; and the contact state of the cutter and the screen is continuously monitored, if it is monitored that the cutter deviates from the preset path or abnormal conditions exist, the working state of the cutting machine is adjusted in time, and fault-tolerant processing is executed. According to the method, the recognition capability of the cutting machine on the surface flaws, textures and edges of the screen is improved, the cutter deviation can be corrected in real time in the cutting process, and the cutting precision and quality are ensured.

Description

Technical Field

[0001] The invention relates to the technical field of visual image processing, in particular to an automatic positioning method for a cutting machine cutting screen based on visual analysis. Background Art

[0002] With the rapid development of industrial automation and intelligent manufacturing technology, cutting machines have played an important role in various production processes. Especially in the production of liquid crystal displays (LCD) and OLED displays, cutting technology has become a crucial process. Traditional cutting machines mainly rely on precise control of mechanical parts and manual operation to ensure cutting accuracy, but the limitations of this method are gradually emerging, especially when dealing with high-precision and high-quality screen materials, it is easy to have problems such as tool deviation, uneven cutting, and material damage during cutting.

[0003] In addition, as the market's requirements for cutting accuracy and production efficiency continue to increase, traditional cutting methods can no longer meet the high standards of modern production lines. Therefore, combining intelligent sensors, visual analysis and data fusion technology to improve the automation and accuracy of cutting machines has become an important development direction. Summary of the invention

[0004] In view of the problems existing in the above-mentioned background technology, the present invention proposes an automatic positioning method for cutting screens by a cutting machine based on visual analysis.

[0005] Therefore, the problem to be solved by the present invention is how to improve the recognition capability of the cutting machine to the defects, textures and edges on the screen surface.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: In the first aspect, the present invention provides a method for automatic positioning of a cutting machine cutting a screen based on visual analysis, which includes: using embedded sensors to collect key parameters of the tool in real time and generate tool status data; using a multi-angle camera and an image processing algorithm to visually analyze the surface texture, edge shape and existing defects of the cutting screen, and generate corresponding surface feature data; using a Kalman filter algorithm to fuse the acquired tool status data and the surface feature data, dynamically correct the visual analysis, generate a path correction instruction for the tool, and provide real-time feedback on the obtained path correction instruction; during the cutting process, continuously monitor the contact state of the tool and the screen. If it is detected that the tool deviates from the predetermined path or there is an abnormal situation, the working state of the cutting machine is adjusted in time through visual analysis and embedded sensor data, and fault-tolerant processing is performed to avoid cutting failure or damage.

[0007] As a preferred solution of the method for automatic positioning of a cutting machine cutting screen based on visual analysis described in the present invention, the key parameters of the tool include tool position, tool angle, cutting speed and cutting pressure.

[0008] As a preferred solution of the method for automatic positioning of a cutting machine cutting a screen based on visual analysis described in the present invention, the image processing algorithm includes: using an edge detection algorithm and frequency domain filtering technology to process the image data collected by each camera to highlight small flaws, cracks or irregular changes on the screen surface, and ensure that the surface features in the image are not interfered with by background noise or lighting changes.

[0009] As a preferred solution of the automatic positioning method for cutting screens by a cutting machine based on visual analysis of the present invention, the visual analysis of the surface texture of the cutting screen includes the following steps: extracting and quantifying texture features in the image using a local binary pattern and a Gabor filter method; the calculation method of the local binary pattern is: in, For location The LBP value at is the number of neighborhood pixels, is the pixel value in the neighborhood P, is the pixel value of the center pixel, is a symbolic function, when hour, ,otherwise ; Gabor filter extracts the texture features of the image in the frequency domain, the formula is: in, For the Gabor filter at position The output value at is the direction of the filter, is the wavelength of the filter, which determines the frequency of the filter; is the standard deviation of the filter, which controls the spatial scale of the filter.

[0010] As a preferred solution of the automatic positioning method for cutting screens by a cutting machine based on visual analysis of the present invention, the dynamic correction of visual analysis includes: using an improved dynamic path correction algorithm to adjust the tool position of the cutting machine through real-time feedback to ensure that the tool path is accurately aligned with the surface features of the screen; assuming that the current tool position is , the correction path is expressed by the following formula: in, is the correction value of tool position, is the comprehensive error metric obtained through data fusion, and The error metric is and Directional gradient; is the angle of the current tool; is the correction factor. As a preferred solution of the method for automatic positioning of the cutting machine cutting screen based on visual analysis described in the present invention, the continuous monitoring of the contact status between the tool and the screen includes: real-time monitoring of the contact status between the tool and the screen, real-time collection of key tool parameters through the embedded sensor; monitoring the relative position, surface features and cutting progress of the tool and the screen through image recognition technology.

[0011] As a preferred solution of the method for automatic positioning of a cutting machine cutting a screen based on visual analysis described in the present invention, the adjustment of the working state of the cutting machine includes: when an abnormal situation is detected, a comprehensive analysis of the collected and monitored data is performed, including: by identifying the texture changes on the screen surface and the relative position of the tool, analyzing whether the tool has angle deviation or misoperation; the real-time pressure information provided by the embedded sensor data feeds back whether the tool is damaged or the cutting quality is affected due to excessive pressure; if the abnormal situation is confirmed, the working state of the cutting machine is adjusted immediately, and the adjustment measures include slowing down, pausing or restarting the cutting path.

[0012] In a second aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the method for automatic positioning of a cutting machine cutting screen based on visual analysis as described in the first aspect of the present invention are implemented.

[0013] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the method for automatic positioning of a cutting machine cutting screen based on visual analysis as described in the first aspect of the present invention are implemented.

[0014] The beneficial effects of the present invention are as follows: the present invention uses embedded sensors to monitor the status data of the tool in real time, and combines a multi-angle camera and a dedicated image processing algorithm to dynamically analyze the surface features of the cutting screen, thereby realizing real-time correction of the tool path. By integrating the tool status data and the visual analysis results, the present invention not only improves the cutting machine's ability to identify screen surface defects, textures and edges, but also can correct the tool deviation in real time during the cutting process, ensuring cutting accuracy and quality. At the same time, the present invention enhances the adaptability and reliability of the cutting machine under complex working conditions by introducing a fault-tolerant processing mechanism, avoiding cutting failures or damage caused by tool deviation or abnormal conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 Flowchart of the method for automatic positioning of cutting screen for cutting machine based on visual analysis. DETAILED DESCRIPTION

[0017] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0018] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0019] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0020] Example 1 Reference Figure 1 , which is the first embodiment of the present invention, provides a method for automatically positioning a cutting screen by a cutting machine based on visual analysis, comprising: S1: Use embedded sensors (such as angle sensors and position sensors) to monitor the cutting machine's tools in real time, collect key tool parameters, and generate tool status data.

[0021] The tool status data includes parameters such as tool position, tool angle, cutting speed and pressure. The data is transmitted to the visual system in real time through the data processing module for subsequent processing.

[0022] First, a series of embedded sensors are installed on the tool part of the cutting machine, including but not limited to angle sensors (such as gyroscopes or fiber optic sensors) and position sensors (such as encoders or laser sensors). These sensors will be precisely configured at the joints of the tool, the tool drive shaft and other key parts.

[0023] The following key parameters of the tool are continuously collected in real time by sensors: Tool position refers to the position coordinates of the tool in space. The position sensor will output the position data of the tool relative to the cutting platform, usually in millimeters or microns.

[0024] Tool angle refers to the rotation angle of the tool relative to the cutting object or cutting platform. The angle sensor will output real-time angle changes, usually in degrees (°) or radians (rad).

[0025] Cutting speed refers to the speed at which the tool moves during the cutting process, and can be expressed in meters per second (m / s) or millimeters per second (mm / s).

[0026] Cutting pressure refers to the pressure applied by the tool to the material during the cutting process, which is usually detected by a pressure sensor.

[0027] The data collected by each sensor will be converted into digital signals and summarized by the data acquisition module.

[0028] Furthermore, the original sensor signal is decoded and converted into useful numerical data, and a filtering algorithm is applied to remove noise to ensure the accuracy and stability of the signal. The collected data is calibrated and synchronized to generate tool status data.

[0029] S2: Uses multi-angle cameras and dedicated image processing algorithms to analyze the surface texture, edge shape, and possible defects of the cut screen and generate corresponding surface feature data.

[0030] S2.1: By arranging multiple low-cost industrial cameras at different angles of the cutting platform, the surface image data of the cutting screen is captured in real time and synchronously.

[0031] The image data obtained by each camera will cover different perspectives of the cut screen to avoid information loss caused by a single perspective.

[0032] The camera angle and shooting position will be optimized according to the screen size and shape to ensure that the surface details of each area can be captured.

[0033] S2.2: De-noise, enhance and adjust the contrast of the image data collected by each camera to extract surface texture and detail information.

[0034] This processing step uses edge detection algorithms (such as the Canny algorithm) and frequency domain filtering technology to highlight small flaws, cracks or irregular changes on the screen surface, ensuring that the surface features in the image are not interfered with by background noise or lighting changes.

[0035] Specifically, by high-pass filtering the image, the surface details of the screen can be enhanced and unnecessary noise can be removed; Through the improved Canny edge detection, the mutation points of the screen surface edge in the image can be identified to help extract the surface contour information. The edge detection formula is: in, For the location The edge strength at and are the gradient values ​​of the image in the horizontal and vertical directions, respectively. For the original image at position The pixel value at .

[0036] It can be seen that the edge detection formula combines the local gradient changes of the image to help extract tiny features such as screen edges and cracks, enhance the important details in the image, and reduce the interference of environmental noise.

[0037] S2.3: Analyze the processed image through the texture analysis algorithm to extract the surface texture features of the cut screen, and combine with pattern recognition technology to identify the geometric shape, edge contour and possible surface defects (such as cracks, bubbles, dents, etc.) of the screen.

[0038] Based on these texture features, surface feature data is generated, which includes the geometric information of the screen, the location of defects, and the surface material characteristics.

[0039] Preferably, the key to texture analysis is to extract and quantify texture features in the image. To this end, the local binary pattern (LBP) and Gabor filtering methods are used to enhance detail recognition capabilities. The calculation method of the local binary pattern (LBP) is: in, For location The LBP value at is the number of neighborhood pixels (usually 8 neighborhoods are selected), is the pixel value in the neighborhood P, is the pixel value of the center pixel, is a symbolic function, when hour, ,otherwise .

[0040] It should be noted that by extracting local texture features through local binary patterns (LBP), the algorithm can effectively describe the texture features of local areas in the image in a binarized manner, which helps to detect surface details or small defects and is particularly suitable for screen texture analysis.

[0041] Furthermore, the Gabor filter can effectively extract the texture features of the image in the frequency domain, and the formula is: in, For the Gabor filter at position The output value at is the direction of the filter, is the wavelength of the filter, which determines the frequency of the filter; is the standard deviation of the filter, which controls the spatial scale of the filter.

[0042] It should be noted that the Gabor filter can extract texture features of images at multiple scales and directions. Compared with traditional texture analysis methods, it can more comprehensively capture the multi-dimensional texture information of the cutting screen surface and adapt to changes in different types of materials on the screen.

[0043] Furthermore, texture changes (e.g., sudden changes or irregularities in texture) are used to detect cracks. The presence of cracks can usually be determined by texture deviations in texture analysis (e.g., changes in texture consistency); bubbles or depressions usually appear as abnormal changes in reflectivity in local areas, and texture irregularities can be used to identify these local abnormal areas.

[0044] Based on the results of texture analysis and pattern recognition, surface feature data will be generated.

[0045] S3: Fusing the tool status data obtained in step S1 with the visual system analysis results in step S2. Dynamically correct visual analysis to generate accurate tool path correction instructions, and provide real-time feedback on the instructions.

[0046] It should be noted that real-time feedback is provided to the cutting machine control system to ensure accurate alignment of the cutter with the edge of the screen and optimal adjustment of the cutting trajectory.

[0047] S3.1: The tool status data (such as tool position, angle, speed, pressure, etc.) acquired in real time in step S1 is combined with the surface feature data acquired in step S2 through a data fusion algorithm.

[0048] To this end, a Kalman filter algorithm is used to fuse these data to eliminate noise and optimize the accuracy of the tool path.

[0049] It should be noted that the Kalman filter can dynamically estimate the current position and posture of the tool, and combine it with the surface feature data to correct the deviation of the tool path. Specifically, the formula for correcting the deviation of the tool path is: in, For the moment state estimation (correction values ​​of tool position and angle), is the Kalman gain, The surface feature data (such as texture, edge, etc.) extracted in step S2; is the observation matrix.

[0050] S3.2: According to the fused tool status data and surface feature data, the cutting path of the tool is corrected in real time.

[0051] The correction process uses an improved dynamic path correction algorithm to adjust the cutting machine's tool position through real-time feedback to ensure that the tool path is accurately aligned with the screen surface features (such as edges, defects, textures, etc.). Assume that the current tool position is , the correction path is expressed by the following formula: in, is the correction value of tool position, is the comprehensive error metric obtained through data fusion, and The error metric is and Directional gradient; is the angle of the current tool; is the correction factor. Specifically, the tool state data obtained in step S1 and the surface feature data obtained in step S2 are integrated through the data fusion algorithm to obtain a comprehensive error metric (position error, angle error, etc.). All errors are combined through weighted calculation to obtain a comprehensive error metric .

[0052] S3.3: After correcting the tool path, the corresponding path correction instructions are generated and the instructions are fed back to the cutting machine in real time to ensure that the tool can accurately align with the edge of the screen according to the new path.

[0053] S3.4: The generated tool path correction instruction is transmitted to the cutting machine control system in real time. The control system dynamically adjusts the position, angle, speed and other parameters of the cutting machine to ensure the accurate docking of the tool with the edge of the screen.

[0054] Furthermore, during the cutting process, the cutting path is continuously optimized based on real-time feedback data (including visual data and sensor data), and the tool angle and cutting progress are automatically adjusted. Especially when the screen surface material is uneven or an error occurs, the cutting path is automatically modified to ensure that the cutting accuracy and quality are not affected.

[0055] Among them, during the cutting process, the angle adjustment of the tool is an important factor in optimizing the cutting path, especially when the screen surface is uneven or has defects.

[0056] In order to ensure that the tool can adapt to changes in real time during the cutting process, a dynamic adjustment method based on the path angle correction model is used to automatically adjust the tool angle. The formula is as follows: in, For the moment The adjustment amount of the tool angle, is the angle adjustment coefficient, which controls the amplitude of the correction. is the sensitivity of the path error to the tool angle, and are the error data of vision and sensor feedback respectively, is the tool angle correction.

[0057] It can be seen that the present invention uses Kalman filtering, dynamic path correction algorithm and real-time feedback mechanism to ensure that the tool path can always accurately align with the edge of the screen and optimize the adjustment during the complex cutting process. Through these innovative calculation formulas and algorithms, it can dynamically respond to changes in the screen surface, improve cutting accuracy and efficiency, and avoid error accumulation and fixed path problems in traditional methods.

[0058] S5: During the cutting process, the contact status between the tool and the screen is continuously monitored. If the tool is detected to deviate from the predetermined path or there is an abnormal situation such as excessive pressure, the working status of the cutting machine is adjusted in time through visual analysis and embedded sensor data, and fault-tolerant processing (such as deceleration, pause, etc.) is performed to avoid cutting failure or damage.

[0059] Specifically, during the cutting process, the contact status between the tool and the screen is monitored in real time through embedded sensors and a visual system. The embedded sensors continuously collect data on the tool pressure, position, angle, and speed, while the visual system uses image recognition technology to monitor the relative position of the tool and the screen, surface features, and cutting progress.

[0060] Based on this real-time data, it is analyzed whether the tool has deviated from the predetermined path, whether there is excessive contact pressure, or whether there are other abnormal conditions (such as uneven contact surfaces between the tool and the screen or vibrations).

[0061] When it is detected that the tool deviates from the path or the contact pressure is too high, an abnormal warning is triggered according to the preset tolerance threshold. Specifically, the deviation between the current tool motion state and the preset trajectory is compared, and the pressure data is evaluated in real time to see if it exceeds the safety range. For example, when the pressure sensor detects that the pressure of the tool contacting the screen exceeds the set value, it is immediately judged as a potential danger and a warning signal is sent to the control system.

[0062] In addition, when an abnormal situation is detected, the data from the visual system and the sensor are comprehensively analyzed. For example, the visual system can analyze whether the tool has angle deviation or misoperation by identifying the texture changes on the screen surface and the relative position of the tool; at the same time, the real-time pressure information provided by the embedded sensor data can accurately feedback whether the tool is damaged or the cutting quality is affected due to excessive pressure. By fusing the data of the two, the cause of the abnormality can be identified more accurately and corresponding measures can be taken quickly.

[0063] Once the abnormal situation is confirmed, immediately adjust the working state of the cutting machine. The adjustment measures include slowing down, pausing or restarting the cutting path, etc.

[0064] For possible cutting deviations or unexpected errors, an effective fault-tolerant processing mechanism needs to be designed. Through data feedback and path correction mechanisms, the cutting path is replanned according to the current tool status and screen surface features. This can not only correct the errors caused by deviations from the path, but also quickly adjust the cutting method when the screen surface changes (such as hardness differences, defects, etc.), thereby avoiding cutting failures.

[0065] By executing an error-tolerant processing mechanism, the path is corrected in real time, allowing the tool to better adapt to changes in screen material.

[0066] This embodiment also provides a computer device, which is suitable for the case of an automatic positioning method for cutting a screen by a cutting machine based on visual analysis, and includes a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the automatic positioning method for cutting a screen by a cutting machine based on visual analysis as proposed in the above embodiment. The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0067] This embodiment further provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for automatically positioning a cutting screen by a cutting machine based on visual analysis as proposed in the above embodiment is implemented. In summary, the present invention uses embedded sensors to monitor the status data of the tool in real time, and combines multi-angle cameras and dedicated image processing algorithms to dynamically analyze the surface features of the cutting screen, thereby realizing real-time correction of the tool path. By integrating the tool status data and visual analysis results, the present invention not only improves the cutting machine's ability to identify screen surface defects, textures and edges, but also can correct tool deviations in real time during the cutting process, ensuring cutting accuracy and quality. At the same time, the present invention enhances the adaptability and reliability of the cutting machine under complex working conditions by introducing a fault-tolerant processing mechanism, avoiding cutting failures or damage caused by tool deviation or abnormal conditions.

[0068] Example 2 Referring to Table 1, which is the second embodiment of the present invention, this embodiment provides an automatic positioning method for cutting a screen by a cutting machine based on visual analysis. In order to verify the beneficial effects of the present invention, a scientific demonstration is carried out through simulation experiments.

[0069] In this embodiment, in order to verify the effectiveness of the automatic positioning method of the cutting machine based on visual analysis, a simulated cutting process is set up, including cutting machine hardware, embedded sensors, visual system, and data processing module, etc. During the test, the cutting machine tool collects key parameters in real time through embedded sensors (including angle sensors, position sensors, and pressure sensors), and combines the data collected by the sensors with the visual analysis data to perform real-time correction of the cutting path.

[0070] The test hardware layout includes: The cutting machine is equipped with multiple angle sensors (gyroscopes) and position sensors (laser encoders) installed at key locations on the tool.

[0071] Four low-cost industrial cameras are arranged above the cutting platform, located at different angles to ensure full capture of the screen surface information.

[0072] The cutting machine control system and data processing module (using embedded system) receive and process data from sensors and vision systems in real time.

[0073] The real-time position, angle, speed and pressure data of the tool are collected through angle sensors and position sensors.

[0074] The camera system collects image data of the screen surface in real time, removes noise and performs enhancement processing to extract the edge shape and defects of the screen (such as cracks, bubbles, dents, etc.).

[0075] After image processing, pattern recognition technology is used to extract surface texture features and surface geometry to generate surface feature data.

[0076] The obtained tool status data and the extracted surface feature data are fused through the Kalman filter algorithm to correct the deviation of the tool path; based on the fused data, the control system adjusts the tool position and cutting angle through a dynamic path correction algorithm to ensure that the tool is accurately aligned with the edge of the screen.

[0077] During the cutting process, the contact status between the cutter and the screen is continuously monitored to detect whether there is deviation from the predetermined path or abnormal pressure. If a problem is found, fault tolerance processing is immediately triggered, such as deceleration, pause, etc., to ensure that the cutting process is not interrupted or the screen is not damaged. Some experimental data are as follows: Table 1 Experimental data table According to the data in the above table, we can know that: The tool position, angle, cutting speed and pressure data showed stable changes throughout the cutting process, which was consistent with the expected cutting parameter range. In most experiments, the tool path correction was very small (for example, the path correction in the fourth experiment was 0.00 mm), which showed that the cutting method had high accuracy under normal cutting conditions.

[0078] When there are defects on the cutting surface (such as cracks, bubbles, dents, etc.), the tool path can be detected and corrected in real time. For example, in the first test, a crack was detected when the tool cut the screen, and the path correction amount was 0.02 mm; in the third test, due to the dent, the tool path correction amount was 0.03 mm, showing the flexibility and accuracy of the system when facing surface defects. Defect detection and path correction show that this method can adapt to irregular changes in the screen surface and avoid cutting errors caused by defects in traditional methods.

[0079] In addition, the cutting pressure data shows that under normal circumstances, the pressure applied by the tool (such as 155N in the fourth test) is within the safe range, ensuring the cutting quality. When excessive pressure is detected (such as when the pressure in the fifth test reaches 170N), the system adjusts the tool path in time through real-time data feedback to avoid damage to the screen.

[0080] Compared with the prior art, the automatic positioning method of the present invention has significant advantages. The prior art usually relies on a static path and a single sensor, which is easily affected by defects or irregular surfaces, resulting in reduced cutting accuracy. In contrast, the present invention uses multiple sensors and a visual system for data fusion, which can correct path deviations in real time, especially when facing complex surfaces, and can automatically adjust cutting parameters and avoid damage caused by path errors.

[0081] In addition, the fault-tolerant processing mechanism of the present invention effectively avoids excessive contact pressure between the cutter and the screen, significantly reduces the risk of cutting failure, and improves the stability of the cutting machine.

[0082] 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 preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for automatically positioning a cutting machine cutting screen based on visual analysis, characterized in that: include: Use embedded sensors to collect key tool parameters in real time and generate tool status data; Use multi-angle cameras and image processing algorithms to visually analyze the surface texture, edge shape, and existing defects of the cut screen and generate corresponding surface feature data; The obtained tool state data and the surface feature data are fused using a Kalman filter algorithm, the visual analysis is dynamically corrected, a path correction instruction of the tool is generated, and the obtained path correction instruction is fed back in real time; During the cutting process, the contact status between the tool and the screen is continuously monitored. If the tool is detected to deviate from the predetermined path or there is an abnormal situation, the working status of the cutting machine is adjusted in time through visual analysis and embedded sensor data, and fault-tolerant processing is performed to avoid cutting failure or damage.

2. The automatic positioning method for cutting screen by a cutting machine based on visual analysis as claimed in claim 1, characterized in that: The key parameters of the tool include tool position, tool angle, cutting speed and cutting pressure.

3. The automatic positioning method for cutting screen by a cutting machine based on visual analysis as claimed in claim 2, characterized in that: The image processing algorithm includes: The image data collected by each camera is passed through edge detection algorithms and frequency domain filtering technology to highlight small flaws, cracks or irregular changes on the screen surface, ensuring that the surface features in the image are not disturbed by background noise or lighting changes.

4. The automatic positioning method for cutting screen by a cutting machine based on visual analysis as claimed in claim 3, characterized in that: The visual analysis of the surface texture of the cut screen comprises the following steps: Local binary pattern and Gabor filtering methods are used to extract and quantify texture features in images; The local binary pattern is calculated as follows: in, For location The LBP value at is the number of neighborhood pixels, is the pixel value in the neighborhood P, is the pixel value of the center pixel, is a symbolic function, when hour, ,otherwise ; The Gabor filter extracts the texture features of the image in the frequency domain. The formula is: in, For the Gabor filter at position The output value at is the direction of the filter, is the wavelength of the filter, which determines the frequency of the filter; is the standard deviation of the filter, which controls the spatial scale of the filter.

5. The automatic positioning method for cutting screen by a cutting machine based on visual analysis as claimed in claim 4, characterized in that: The dynamic correction of visual analysis includes: Adopting an improved dynamic path correction algorithm, the tool position of the cutting machine is adjusted through real-time feedback to ensure that the tool path is accurately aligned with the surface features of the screen; Set the current tool position to , the correction path is expressed by the following formula: in, is the correction value of tool position, is the comprehensive error metric obtained through data fusion, and The error metric is and Directional gradient; is the angle of the current tool; is the correction factor.

6. The automatic positioning method for cutting screen by a cutting machine based on visual analysis as claimed in claim 5, characterized in that: The continuous monitoring of the contact status between the tool and the screen includes: The contact state between the tool and the screen is monitored in real time, and key parameters of the tool are collected in real time through the embedded sensor; the relative position, surface features and cutting progress of the tool and the screen are monitored through image recognition technology.

7. The automatic positioning method for cutting screen by a cutting machine based on visual analysis according to claim 6, characterized in that: The adjusting the working state of the cutting machine comprises: When an abnormal situation is detected, comprehensive analysis of the collected and monitored data is performed, including: By identifying the texture changes on the screen surface and the relative position of the tool, it can analyze whether the tool has angle deviation or misoperation; Real-time pressure information provided by embedded sensor data provides feedback on whether the tool is damaged or the cutting quality is affected due to excessive pressure; If an abnormal situation is confirmed, the working state of the cutting machine is adjusted immediately, and the adjustment measures include slowing down, pausing or restarting the cutting path.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for automatically positioning a cutting machine cutting screen based on visual analysis as described in any one of claims 1 to 7 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for automatically positioning a cutting screen by a cutting machine based on visual analysis according to any one of claims 1 to 7 are implemented.

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