Intelligent dispensing control method and system based on machine vision and storage medium

Abstract

The invention relates to the technical field of electronic packaging, and discloses an intelligent dispensing control method and system based on machine vision and a storage medium. The method comprises the following steps: acquiring a dispensing part image, and performing feature extraction on the dispensing part image to obtain feature information; according to the feature information, carrying out dispensing position area calculation to obtain the area of a dispensing area; calculating the surface roughness of the dispensing part according to the feature information of the image to obtain roughness information; according to the feature information, the dispensing position and the roughness information, the dispensing amount is calculated, and dispensing parameters are obtained; according to the dispensing parameters and the roughness information, the dispensing amount is adjusted, and the effective dispensing amount is obtained; according to the dispensing parameters, the effective dispensing amount and the dispensing area, dispensing operation is carried out, and a dispensing result is obtained; and according to the dispensing result, the dispensing quality is monitored and adjusted based on visual feedback control. According to the method, the dispensing precision of the dispensing machine can be improved, parameters are optimized, and efficient dispensing control is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of glue dispensing machines, and in particular to an intelligent glue dispensing control method, system and storage medium based on machine vision. Background Art

[0002] At present, the dispensing process in electronic packaging equipment is one of the important processes widely used in various integrated circuit packaging. As electronic products develop towards high density, miniaturization, lightweight and low cost, the precision and automation requirements of the dispensing process are also increasing. A dispensing machine is a device that can automatically spray glue, ink and other substances on the surface of a product. It is often used in the processing of electronic components such as BGA, FPC, and PCB. However, in the current dispensing machine system, parameters such as dispensing position and dispensing amount are usually controlled by PLC programming, and cannot automatically adapt to changes in different product sizes, colors and surface features. This requires manual setting, which is time-consuming and error-prone.

[0003] In addition, the dispensing program usually calculates the dispensing amount based on a fixed tape distance, but since the positions of parts cannot be completely consistent during the transmission process, there are differences in the dispensing amount, which affects the accuracy and consistency of dispensing. At the same time, for opaque parts, the existing dispensing machine cannot monitor the dispensing process in real time, thus reducing the overall dispensing quality.

[0004] With the development of machine vision technology, more and more electronic packaging equipment has begun to apply visual inspection mechanisms. Through image processing technology, the size, color and surface roughness of different products can be automatically identified, thereby improving the intelligent level of dispensing. Although some patented technologies have introduced control methods based on machine vision, these technologies are mainly aimed at printing processes and cannot be directly applied to dispensing processes. In particular, there are still significant limitations in adaptability and real-time control of different products.

[0005] The problem in the prior art is that the dispensing machine has low precision and cannot automatically adapt to changes in size, color, etc. of different products. In addition, for opaque parts, it is difficult to monitor the dispensing process in real time, resulting in unstable overall dispensing quality. Summary of the invention

[0006] The present invention provides an intelligent dispensing control method, system and storage medium based on machine vision. The method can improve the accuracy of a dispensing machine and optimize parameters to achieve efficient dispensing control of the dispensing machine.

[0007] In the first aspect, in order to solve the above technical problems, the present invention provides an intelligent dispensing control based on machine vision, comprising: Acquire a dispensing part image, and perform feature extraction on the dispensing part image to obtain feature information; Calculate the dispensing position area according to the characteristic information to obtain the dispensing area; Calculate the surface roughness of the dispensing part according to the characteristic information to obtain roughness information; Calculate the glue dispensing amount according to the characteristic information, the area of ​​the glue dispensing region and the roughness information to obtain glue dispensing parameters; According to the dispensing parameters and the roughness information, the dispensing amount is adjusted to obtain an effective dispensing amount; Perform a dispensing operation according to the dispensing parameters, the effective dispensing amount and the area of ​​the dispensing region to obtain a dispensing result; According to the dispensing results, the dispensing quality is monitored and adjusted based on visual feedback control.

[0008] Preferably, the calculation formula for the area of ​​the glue dispensing area is:

[0009] In the formula, Indicates the area of ​​the circular dispensing area; Indicates the radius of the circular dispensing area.

[0010]

[0011] In the formula, Represents the area of ​​the rectangular dispensing area; Indicates the length of the rectangular dispensing area; is the width of the rectangular dispensing area.

[0012] Preferably, the calculation formula for the surface roughness of the dispensing part is:

[0013] In the formula, Indicates surface roughness; Indicates the standard surface, that is, the standard dispensing amount when there is no roughness; Represents the actual surface roughness calculation function.

[0014] Preferably, the calculation formula for calculating the dispensing amount is:

[0015]

[0016] In the formula, Indicates the amount of glue dispensed in a circular area; Indicates the area of ​​the circular dispensing area; Indicates the amount of glue dispensed in a rectangular area; Represents the area of ​​the rectangular dispensing area; Indicates the dispensing depth; Indicates the density of the dispensing material; Indicates the dispensing radius; Indicates the dispensing height (thickness of the dispensing material); Indicates the diameter of the dispensing nozzle; Indicates the dispensing angle (the angle between the dispensing head and the surface); Indicates the dispensing cross-sectional area.

[0017] Preferably, the calculation formula for the effective dispensing amount is:

[0018] In the formula, Indicates the effective dispensing amount; Indicates the original dispensing amount; Indicates surface roughness; It is a coefficient usually used to consider other correction factors in the actual dispensing process (such as the characteristics of the dispensing material and equipment errors).

[0019] Preferably, performing the dispensing operation according to the dispensing parameters, the effective dispensing amount and the area of ​​the dispensing region to obtain the dispensing result includes: Determine the dispensing pressure and dispensing time according to the dispensing parameters, the effective dispensing amount and the area of ​​the dispensing region, and obtain the dispensing parameter settings that meet the process requirements; The dispensing parameter settings are input into the dispensing machine so that the dispensing machine performs a dispensing operation to obtain a dispensing result.

[0020] Preferably, the monitoring and adjusting of the dispensing quality based on visual feedback control according to the dispensing result includes: According to the dispensing results, quality assessment is performed based on the image grayscale mean, variance and dispensing area to obtain a dispensing model; wherein the expression of the dispensing model is:

[0021] In the formula, Indicates dispensing quality; Represents the grayscale mean of the dispensing area image; Represents the variance of grayscale values; Indicates the dispensing area.

[0022] According to the dispensing model, the dispensing parameters are evaluated by comparing the theoretical value and the actual value to obtain the dispensing amount deviation value, wherein the calculation formula of the dispensing amount deviation value is:

[0023] In the formula, Indicates the deviation of dispensing amount; Indicates the actual dispensing amount; Indicates the theoretical dispensing amount; According to the dispensing amount deviation value and the dispensing model, the dispensing model and dispensing parameters are optimized to obtain a high-precision dispensing result; wherein the calculation formula of the high-precision dispensing result is:

[0024] In the formula, Indicates high-precision dispensing results; Indicates dispensing quality; Represents the feedback regulation coefficient, which is used to control the regulation intensity; Indicates the dispensing deviation value.

[0025] In a second aspect, the present invention provides an intelligent dispensing control system based on machine vision, comprising: An image acquisition module acquires an image of a dispensing part and performs feature extraction on the image of the dispensing part to obtain feature information; An area calculation module is used to calculate the dispensing position area according to the characteristic information to obtain the area of ​​the dispensing area; A roughness calculation module is used to calculate the surface roughness of the dispensing part according to the characteristic information to obtain roughness information; A glue dispensing amount calculation module, used to calculate the glue dispensing amount according to the characteristic information, the glue dispensing position and the roughness information to obtain glue dispensing parameters; A glue dispensing amount adjustment module, used to adjust the glue dispensing amount according to the glue dispensing parameters and the roughness information to obtain an effective glue dispensing amount; A glue dispensing module, used to perform a glue dispensing operation according to the glue dispensing parameters, the effective glue dispensing amount and the glue dispensing area to obtain a glue dispensing result; The control module is used to monitor and adjust the dispensing quality based on the visual feedback control according to the dispensing result. In a third aspect, the present invention also provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements any one of the above-mentioned intelligent dispensing control methods based on machine vision.

[0026] In a fourth aspect, the present invention further provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned intelligent dispensing control methods based on machine vision.

[0027] The present invention discloses an intelligent dispensing control method based on machine vision. The method comprises acquiring an image of a dispensing part, and extracting features of the dispensing part image to obtain feature information; calculating the dispensing position area according to the feature information to obtain the dispensing area area; calculating the surface roughness of the dispensing part according to the feature information of the image to obtain the roughness information; calculating the dispensing amount according to the feature information, the dispensing position and the roughness information to obtain the dispensing parameters; adjusting the dispensing amount according to the dispensing parameters and the roughness information to obtain the effective dispensing amount; performing dispensing operations according to the dispensing parameters, the effective dispensing amount and the dispensing area area to obtain the dispensing results; and monitoring and adjusting the dispensing quality based on the visual feedback control according to the dispensing results. The method can develop dispensing machines that adapt to different parameters, improve accuracy, monitoring and visual inspection, optimize parameters, and achieve efficient dispensing control.

[0028] The present invention adopts an intelligent dispensing control method based on machine vision, which can effectively adapt to parts of different sizes, colors and surface features and realize high-precision dispensing control; based on the characteristic information of the parts, the dispensing parameters are automatically adjusted to ensure the accuracy and consistency of the dispensing amount; the application of visual detection and feedback control technology enables the equipment to automatically monitor and adjust during operation to optimize the dispensing quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic flow chart of an intelligent dispensing control method based on machine vision provided by the first embodiment of the present invention; Figure 2 It is a schematic diagram of the structure of an intelligent dispensing control system based on machine vision provided in the second embodiment of the present invention. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] Reference Figure 1 The first embodiment of the present invention provides an intelligent dispensing control method based on machine vision, comprising the following steps: S11, acquiring an image of a dispensing part, and performing feature extraction on the image of the dispensing part to obtain feature information; S12, calculating the dispensing position area according to the characteristic information to obtain the dispensing area; S13, calculating the surface roughness of the dispensing part according to the feature information of the image to obtain roughness information; S14, calculating the dispensing amount according to the characteristic information, the dispensing position and the roughness information to obtain the dispensing parameters; S15, adjusting the dispensing amount according to the dispensing parameters and the roughness information to obtain an effective dispensing amount; S16, performing a dispensing operation according to the dispensing parameters, the effective dispensing amount and the dispensing area to obtain a dispensing result; S17, monitors and adjusts the dispensing quality based on visual feedback control according to the dispensing result.

[0032] In step S11, an image of a dispensing part is acquired, and feature extraction is performed on the image of the dispensing part to obtain feature information.

[0033] In step S11, a camera is first set at the dispensing position of the dispensing machine, and the image of the part is obtained through the camera, including image information before and after dispensing. The camera can be a machine vision camera, CCD camera or scanning camera that comes with the dispensing machine or is installed externally. The specific installation method and position can be flexibly set according to the process requirements to ensure that the camera's field of view covers both the dispensing machine and the part.

[0034] Through the images acquired by the camera, the system can extract features of the parts, including the size of the parts, surface color changes, and surface roughness. First, the image processing algorithm is used to identify the boundaries of the parts and calculate the size of the parts. This information is used to determine the diameter and center position of the dispensing area, thereby accurately controlling the dispensing position. Secondly, the color recognition technology is used to analyze the color changes on the surface of the parts. The colors may include a variety of combinations such as white, gray, black or color, or different changes in a single color, such as black, white, and gray. These color information helps the system optimize the dispensing amount and dispensing parameter settings to ensure the accuracy of the dispensing effect under different color surface conditions.

[0035] In step S12, it is necessary to calculate the glue spotting position area based on the extracted part feature information to determine the area of ​​the glue spotting area.

[0036] First, in the previous step (i.e., step S11), the image information of the part is obtained through the camera, and the key feature information of the part is extracted. This information includes the size of the part, surface color, etc. This feature information provides basic data for the calculation of the dispensing area. In this step, the system uses these features, especially the size and surface features of the part, to determine the boundary and position of the dispensing area.

[0037] Next, the system uses image processing technology to first identify and analyze the contour of the part. Using contour detection algorithms, such as edge detection or Hough transform, the system can accurately identify the boundaries and center points of the part. Then, combined with the size information of the part, the system calculates the diameter and shape of the area where glue is needed. Usually, the glue area will correspond to the specific processing area or the area to be bonded of the part, so the system needs to combine the geometric features of the part to determine the center of the glue location.

[0038] Specifically, the calculation of the area of ​​the glue spotting area is achieved through the following steps. In a specific embodiment, if the part is a regular shape (such as a circle or rectangle), the calculation of the glue spotting area will be relatively simple, and the system only needs to accurately set the boundary of the glue spotting area according to the size of the part and the position of the area to be processed. For example, for a circular part, the system will calculate the radius of the circle and determine its center to determine the area of ​​the circular glue spotting area; for a rectangular part, the system will set the rectangular glue spotting area according to the length and width information.

[0039] Exemplarily, the calculation formula for the area of ​​the dispensing area is:

[0040] In the formula, Indicates the area of ​​the circular dispensing area; Indicates the radius of the circular dispensing area.

[0041]

[0042] In the formula, Represents the area of ​​the rectangular dispensing area; Indicates the length of the rectangular dispensing area; is the width of the rectangular dispensing area.

[0043] It is worth noting that when calculating the area of ​​the dispensing area, it is also necessary to correct the dispensing area in combination with the characteristic information of the part (such as color and roughness). If the surface roughness is high, the system may expand the dispensing area to ensure that the glue can effectively cover the rough surface. If the color of the part surface changes significantly, the dispensing area also needs to be adjusted to cope with the changes in glue adsorption performance caused by different colors.

[0044] For example, for areas with darker colors or rougher surfaces, the dispensing area may need to be slightly larger to ensure the coverage of the dispensing. For areas with lighter colors and smoother surfaces, the dispensing area can be slightly reduced to avoid waste or overflow caused by excessive glue.

[0045] Once the area and location of the dispensing area are calculated, the system will pass this information as input to the dispensing control system for subsequent dispensing operations. The dispensing gun will accurately apply glue to the specified location based on these calculated area parameters to ensure accuracy and consistency of dispensing.

[0046] In step S12 of the present invention, the area and position of the dispensing area are accurately calculated by analyzing the feature information of the parts, combining geometric calculation and image processing technology. This process ensures the efficiency and accuracy of the dispensing operation, and optimizes the dispensing process by combining the surface feature information to adapt it to the diverse needs of different parts.

[0047] In step S13, based on the image feature information extracted in step S12, the surface roughness of the dispensing part is calculated to obtain accurate roughness information. The specific implementation process is as follows: First of all, surface roughness is a key parameter to measure the surface quality of parts, which directly affects the adhesion of glue and the uniformity of dispensing. The level of surface roughness will affect the distribution, fluidity and uniformity of glue after curing in the dispensing process. Therefore, accurate calculation of roughness is crucial to determine the dispensing amount, dispensing pressure and the adjustment of glue fluidity.

[0048] Then, in step S12, the image of the part has been acquired and the feature extraction and calculation of the dispensing area have been completed. Next, the system uses the detailed information in these images to analyze the surface texture of the part through a high-precision image processing algorithm. The surface flatness or waviness is determined by comparing the brightness, texture changes, contrast and other information of different areas in the image. These data can reflect the slight undulations and irregular shapes of the surface of the part. If there are burrs on the surface of the part, the average height difference between the protruding part of the burr and the root of the burr is further calculated through edge detection or texture analysis algorithms to obtain the burr roughness.

[0049] It is worth noting that the system can select different algorithms according to the specific application scenarios of the parts. Common processing methods include Fourier transform for detecting surface frequency texture, or grayscale co-occurrence matrix for evaluating the surface roughness level. Based on the processed image data, the system calculates the surface roughness level, which usually includes flatness level, waviness level and smoothness level. For parts with complex surface structures (such as burrs or special textures), the system will further analyze the roughness distribution of local areas to ensure that the surface characteristics can be accurately captured on complex parts.

[0050] Exemplarily, the calculation formula for the surface roughness of the dispensing part is:

[0051] In the formula, Indicates surface roughness; Indicates the standard surface, that is, the standard dispensing amount when there is no roughness; Represents the actual surface roughness calculation function.

[0052] These calculation results will be output in a quantitative form, such as measuring the surface roughness with micrometer (μm) or nanometer (nm) accuracy. These values ​​will serve as the key reference for the next step S14 (adjustment of dispensing amount and dispensing time); It is worth noting that after obtaining the roughness information of the part surface, the system will combine this information with parameters such as the area of ​​the dispensing area and the dispensing position to accurately control the dispensing process. For example: For areas with higher roughness, the system will increase the amount of glue dispensed accordingly to ensure that the glue can fill the uneven surface areas and avoid glue leakage.

[0053] For areas with smooth surfaces, the system can reduce the amount of glue dispensed to ensure even glue distribution and prevent excessive use that may cause glue overflow or unevenness.

[0054] Through the surface roughness calculation in step S13, the system can accurately grasp the surface state of the part before dispensing, ensuring that the dispensing process can adapt to different surface characteristics. This step greatly improves the accuracy and stability of the dispensing process by combining image processing technology and algorithm analysis, providing key data support for dispensing amount adjustment and quality monitoring in subsequent steps.

[0055] This process ensures that even when faced with parts with complex surface structures, the system can automatically adjust the dispensing parameters, making the dispensing process more adaptable and flexible.

[0056] In step S14, the glue dispensing amount is calculated based on the characteristic information (including the size of the part, surface color change, etc.) and the surface roughness information obtained in the previous step S13, so as to obtain specific glue dispensing parameters.

[0057] First, in step S14, the system receives the part surface roughness information and other image feature information from the previous step S13. The surface roughness information of the part is obtained by the image processing algorithm in step S13, reflecting the surface texture, flatness, waviness and smoothness of the part. The feature information includes the size, shape, color change and location of the dispensing area of ​​the part. This information provides the basis for accurately calculating the dispensing amount.

[0058] It is worth mentioning that the calculation of the amount of glue dispensed is mainly based on three key factors. The first is the surface roughness of the part. The rougher the surface, the more glue is usually needed to fill the unevenness of the surface, so as to ensure that the glue is evenly attached to the surface. Conversely, a smoother surface requires less glue. Therefore, the surface roughness information directly affects the setting of the amount of glue dispensed; the second is the dispensing position and area. According to the part size and dispensing position area information extracted in the previous step, the system can determine the area of ​​the dispensing area that needs to be covered. The larger the dispensing area, the more dispensing amount is required; if the dispensing area is smaller, the dispensing amount is reduced accordingly; the third is the process parameters and part characteristics. The color change of the part surface will also affect the visibility and final effect of the glue, especially the different color surfaces may affect the drying speed and adhesion effect of the glue. The system will optimize the dispensing amount according to these characteristics to ensure the consistent dispensing quality of different color surfaces.

[0059] Specifically, the system calculates the dispensing amount by comprehensively analyzing the characteristic information of the parts (such as part size, dispensing position, color) and surface roughness, and using a preset algorithm or empirical model. The dispensing parameters include the following.

[0060] Glue dispensing volume: the specific glue volume or mass of each dispensing is obtained based on the calculation results; dispensing time: to ensure uniform distribution of glue, the system will set the dispensing time based on the dispensing volume and the surface characteristics of the parts. The length of time depends on the fluidity and viscosity of the glue. Dispensing pressure: the pressure applied during the dispensing process will determine the flow rate of the glue. The pressure is proportional to the dispensing volume. Too much or too little pressure will affect the dispensing effect; dispensing thickness: the system will make fine adjustments according to the required glue thickness (i.e. the height of the glue on the surface of the part) to ensure that the glue layer is evenly distributed.

[0061] Exemplarily, the dispensing amount calculation formula is:

[0062]

[0063] In the formula, Indicates the amount of glue dispensed in a circular area; Indicates the area of ​​the circular dispensing area; Indicates the amount of glue dispensed in a rectangular area; Represents the area of ​​the rectangular dispensing area; Indicates the dispensing depth; Indicates the density of the dispensing material; Indicates the dispensing radius; Indicates the dispensing height (thickness of the dispensing material); Indicates the diameter of the dispensing nozzle; Indicates the dispensing angle (the angle between the dispensing head and the surface); Indicates the dispensing cross-sectional area.

[0064] It is worth noting that according to the dispensing parameters calculated in step S14, the system can automatically adjust the operation of the dispensing equipment. At this time, the dispensing machine performs operations according to the dispensing parameters to ensure accurate control of the glue. In order to further optimize the dispensing effect, the system will also introduce a visual feedback mechanism to monitor the actual dispensing amount and quality in real time during the dispensing process. If the system detects that the actual dispensing is different from the expected effect, it will adjust the parameters immediately based on the feedback information to ensure the accuracy and consistency of the dispensing amount.

[0065] Step S14 of the present invention accurately calculates the dispensing amount and related parameters by integrating the characteristic information of the parts, the dispensing position and the surface roughness information. These parameters play an important role in the actual dispensing process, ensuring that the system automatically adjusts the dispensing amount and pressure according to the characteristics of different parts, improving the accuracy and consistency of the dispensing process, thereby improving the automation level and quality control capability of the entire dispensing process.

[0066] In step S15, based on the dispensing parameters and roughness information obtained in the previous step S14, the dispensing amount is further adjusted to obtain a final effective dispensing amount.

[0067] First, the system calculates the preliminary dispensing amount parameters by analyzing the feature information, dispensing position and surface roughness of the part. These parameters include dispensing thickness, dispensing time, dispensing pressure, etc. However, due to the diversity of part surface features and errors in actual operation, the preliminary calculated dispensing amount may not directly guarantee the best dispensing effect, so further adjustment is required in step S15.

[0068] Specifically, the following steps are mainly considered for adjusting the amount of glue dispensed. Adjustment is made according to the surface roughness. The surface roughness has a great influence on the adhesion of the glue. Generally, for the surface of parts with greater roughness (such as surfaces with burrs or obvious textures), the amount of glue dispensed needs to be increased to ensure that the glue can evenly cover and fill the uneven parts of the surface. For smooth surfaces, the amount of glue dispensed can be appropriately reduced because the glue can adhere more evenly to the smooth surface. The system analyzes the surface roughness information, adjusts the preliminary calculated glue dispensing parameters, and determines the final glue dispensing amount required for each glue dispensing position. Combined with the glue dispensing position information, the accuracy of the glue dispensing position also has a direct impact on the amount of glue dispensed. On some complex parts, the glue dispensing position may contain different surface features or spatial limitations, such as recessed areas or edges. The system will further analyze the relationship between the glue dispensing position and the roughness to adjust the glue spraying method and amount to avoid excessive or insufficient glue due to inconsistent positions or surface complexity. Automatic feedback adjustment, in addition to adjusting according to known parameters, the present invention also utilizes a visual feedback system. By monitoring the dispensing results in real time, the system can dynamically adjust the initially set dispensing parameters. For example, if the system detects that there is too much or too little glue in certain areas, the amount of glue dispensed can be adjusted in real time in subsequent operations to ensure the consistency and accuracy of dispensing. This feedback mechanism further improves the stability and accuracy of the dispensing process.

[0069] Then, through the above multi-level analysis and adjustment, the system finally obtains the effective dispensing amount that adapts to the surface characteristics of the parts. This effective dispensing amount not only integrates the characteristic information of the parts such as roughness, color change, size, etc., but also takes into account the particularity of the dispensing position. The final dispensing amount will be transmitted to the control system of the dispensing machine to ensure that the amount of glue sprayed is the most appropriate in each dispensing operation, avoiding wasting glue or affecting product quality.

[0070] Exemplarily, the calculation formula for the effective dispensing amount is:

[0071] In the formula, Indicates the effective dispensing amount; Indicates the original dispensing amount; Indicates surface roughness; It is a coefficient usually used to consider other correction factors in the actual dispensing process (such as the characteristics of the dispensing material and equipment errors).

[0072] Through the adjustment of step S15, the present invention significantly improves the dispensing accuracy and ensures that a consistent dispensing effect can be achieved on various part surfaces and operating conditions. In particular, the surface roughness information and feedback mechanism are utilized to make the dispensing process more adaptable and flexible in different application scenarios, effectively improving the dispensing quality and production efficiency.

[0073] In step S16, the dispensing operation is performed based on the effective dispensing amount, dispensing parameters, and the area of ​​the dispensing region obtained in the previous step S15.

[0074] First, the dispensing parameters, effective dispensing amount and dispensing area are obtained through steps S11 to S15.

[0075] Specifically, dispensing parameters refer to various settings that affect the dispensing process, including dispensing depth, dispensing speed, nozzle diameter, dispensing angle, etc. The setting of dispensing parameters determines how the dispensing material is distributed on the work surface. The effective dispensing volume refers to the amount of dispensing material actually used to complete a specific dispensing operation. This is calculated based on the material requirements and the area of ​​the dispensing area, and is the final volume of material that needs to be applied. The area of ​​the dispensing area refers to the area covered by the dispensing material, which depends on the shape and size of the designed product or dispensing object. By measuring the area of ​​a circle, rectangle or other geometric shape, it can provide a basis for calculating the dispensing volume.

[0076] Next, determine the dispensing pressure and dispensing time. Specifically, the dispensing pressure is the pressure inside the dispensing machine that acts on the dispensing material, which determines the rate at which the material flows out. Too much pressure may cause the material to overflow, while too little pressure may result in insufficient material and an inability to fully cover the required area. When determining the dispensing pressure, it is necessary to consider factors such as the flow characteristics and viscosity of the dispensing material. The dispensing time is the time that the dispensing head continues to dispense glue over the area. The dispensing time is used in conjunction with the dispensing pressure to ensure that the dispensing material is evenly distributed on the surface according to the predetermined amount. Too much time may result in too much material, while insufficient time may result in insufficient material coverage.

[0077] Next, set the dispensing parameters. This step is to input the determined dispensing pressure, dispensing time and other parameter settings into the dispensing machine based on the previous calculations and adjustments, so that the dispensing machine can perform dispensing operations according to these set parameters. The dispensing machine performs dispensing operations according to the set parameters (including dispensing position, dispensing amount, dispensing area, etc.). The dispensing gun will gradually complete the spraying or distribution of glue according to the working path and dispensing area set by the system.

[0078] In a specific embodiment, the glue gun will accurately move to the position where glue dispensing is required based on the center position and diameter of the glue dispensing area calculated previously. Combined with machine vision technology, the system will correct the position of the glue dispensing gun in real time to ensure that the glue is accurately sprayed to the target area. According to the effective glue dispensing amount adjusted in step S15, the glue dispensing gun will dispense glue with an optimized amount of glue based on the surface roughness and color characteristics of the part. The system will adjust the spraying speed and spraying time of the glue dispensing gun based on real-time feedback to ensure that the right amount of glue is distributed in each required area. During the glue dispensing process, the glue dispensing gun will ensure that the glue accurately covers the entire area based on the area information of the glue dispensing area, while avoiding waste or overspraying of glue. The movement path and speed of the glue dispensing gun will also be dynamically adjusted according to the area of ​​the glue dispensing area to ensure uniform glue distribution.

[0079] Finally, after the dispensing operation is completed, the system will generate dispensing feedback data based on the execution results to preliminarily evaluate the dispensing effect. This dispensing result includes whether the dispensing area is completely covered, whether the thickness of the glue is uniform, and whether it meets the previously set process standards.

[0080] In a specific embodiment, the system will collect images through the installed camera, obtain the image of the part after dispensing, and compare it with the previous image before dispensing. Through image analysis, the distribution and thickness of the glue, as well as whether there is leakage or overflow of glue, can be detected. If an abnormality occurs, the system will adjust the dispensing parameters or re-dispense the glue through the feedback mechanism to ensure that the final dispensing quality meets the requirements. If the visual inspection system finds that there is insufficient or uneven glue in the dispensing area, the system will automatically adjust the dispensing amount or operation path, and perform re-dispensing until the result meets the expectations. This closed-loop control mechanism greatly improves the accuracy and stability of dispensing.

[0081] Through step S16, the dispensing operation can not only be automatically and accurately executed according to specific parameters and real-time feedback, but also be monitored and adjusted in real time through the visual feedback system. This not only improves production efficiency and reduces manual intervention, but also ensures the quality and consistency of each dispensing operation, avoiding the errors and quality instability problems that occur in traditional manual dispensing.

[0082] In step S17, the system monitors and adjusts the dispensing results in real time based on visual feedback control to ensure the stability and accuracy of the dispensing quality. This process is a continuation of the previous step S16, that is, after the dispensing operation is completed in S16 and the preliminary dispensing results are obtained, the visual feedback system begins to play a role and further detects and optimizes the dispensing results.

[0083] First, after the dispensing operation in step S16 is completed, the camera installed on the dispensing machine or the external camera will immediately capture the image of the part after the dispensing is completed. This image records the distribution of glue after dispensing, including information such as the thickness, coverage area, and uniformity of the dispensing. Compared with the image before dispensing in step S16, the image after dispensing is the key to the quality evaluation of the entire dispensing process.

[0084] Next, the system uses visual inspection technology to analyze the acquired dispensing images, mainly checking the actual coverage area of ​​the dispensing area, the dispensing thickness, the uniformity of the glue distribution, and whether there are leaks, overflows, etc. By comparing with the preset dispensing parameters, such as the diameter, center position, dispensing amount and dispensing thickness of the dispensing area, the system can accurately determine whether the dispensing operation meets the set standards.

[0085] Based on the results of image processing, the vision system will evaluate the dispensing quality. If insufficient dispensing, uneven coverage or other quality issues are detected, the system will start the feedback control mechanism to automatically adjust the parameters of the dispenser. These parameters may include dispensing pressure, dispensing time, fine-tuning of the needle position, etc. to compensate for errors in operation. For example, if the amount of dispensing in a certain area is insufficient, the system will increase the amount of dispensing in that area or reposition the needle to ensure uniform distribution of dispensing.

[0086] Exemplarily, according to the dispensing result, quality assessment is performed based on the image grayscale mean, variance and dispensing area to obtain a dispensing model; wherein the expression of the dispensing model is:

[0087] In the formula, Indicates dispensing quality; Represents the grayscale mean of the dispensing area image; Represents the variance of grayscale values; Indicates the dispensing area.

[0088] According to the dispensing model, the dispensing parameters are evaluated by comparing the theoretical value and the actual value to obtain the dispensing amount deviation value, wherein the calculation formula of the dispensing amount deviation value is:

[0089] In the formula, Indicates the deviation of dispensing amount; Indicates the actual dispensing amount; Indicates the theoretical dispensing amount; According to the dispensing amount deviation value and the dispensing model, the dispensing model and dispensing parameters are optimized to obtain a high-precision dispensing result; wherein the calculation formula of the high-precision dispensing result is:

[0090] In the formula, Indicates high-precision dispensing results; Indicates dispensing quality; Represents the feedback regulation coefficient, which is used to control the regulation intensity; Indicates the dispensing deviation value.

[0091] In a specific embodiment, the system can make timely adjustments based on detected anomalies, and this process is dynamic and continuous. In some cases, if the problem is minor, such as a slight deviation in glue thickness, the system can automatically correct the parameters in subsequent dispensing operations to ensure that the accuracy of the next dispensing is more in line with the requirements; if the problem is more serious, such as a significant deviation in the dispensing area or excessive glue overflow, the system will sound an alarm and stop the operation to prevent more parts from being affected.

[0092] In addition to real-time monitoring and adjustment of each dispensing operation, the system also records the detailed data of each dispensing result and its feedback adjustment. These data can be used to optimize the parameter settings of subsequent dispensing operations, so that the system can gradually optimize itself, adapt to different process conditions and product characteristics, and improve overall production efficiency and consistency of dispensing quality.

[0093] Through monitoring and adjustment based on visual feedback control, the system of the present invention can accurately and dynamically adjust the dispensing results to ensure that the dispensing process meets the requirements of high precision. Visual feedback can not only detect and correct problems in the dispensing process in real time, but also make adaptive adjustments according to different part characteristics, significantly improving the reliability and consistency of dispensing. In addition, the system records and analyzes data during the dispensing process, further improving the intelligent level of the process and ensuring that the dispensing process is always in the optimal state.

[0094] The following describes the working process of the present invention by taking a relatively common scenario as an example. The working process is as follows: In the manufacturing process of smart phones, the assembly of camera modules is one of the key links. The glue dispensing process plays an important role in ensuring that the camera module is firmly bonded to the mobile phone body and improving the waterproof performance. The camera module has a complex shape and a small size, and there may be different materials on its surface, such as glass and metal. Therefore, the glue dispensing accuracy and the uniformity of glue distribution are very high. Through the method of the present invention, high-precision control and real-time quality monitoring of the camera module glue dispensing process can be achieved.

[0095] Step S11: Obtain camera module image and perform feature extraction A camera is installed at the dispensing position of the dispensing machine to obtain the image of the camera module before dispensing. Through image processing technology, the system extracts the characteristic information of the camera module, including the size of the module, surface color change and surface roughness. The system calculates the geometric dimensions of the camera module and identifies the location area where dispensing is required, such as the bonding area around the lens. By analyzing the color information of the module surface, the system can optimize the adhesion effect of the glue to the module surface and ensure accurate dispensing.

[0096] Step S12: Calculate the dispensing position area Based on the module feature information extracted in step S11, the system uses geometric calculation methods to determine the boundaries of the area on the camera module where glue is required, and calculates the area of ​​the glue area. For example, for the circular area around the camera module lens, the system calculates the area of ​​the glue area based on the lens diameter to ensure that the glue can evenly cover the entire bonding area.

[0097] Step S13: Calculate the surface roughness of the camera module By analyzing the texture information of the camera module surface material, the system identifies the difference in surface roughness between the glass and metal areas. The glass surface is relatively smooth, so the system reduces the amount of glue dispensed in this area; while the metal frame surface is relatively rough, the system increases the amount of glue dispensed accordingly to ensure good adhesion of the glue on the rough surface.

[0098] Step S14: Calculate the amount of glue dispensed Combining the camera module's size, surface color variation, and surface roughness information, the system calculates the specific glue dispensing amount and dispensing time required for each dispensing location. For example, in the rough metal frame area, the system increases the glue dispensing amount and sets a slightly longer dispensing time to ensure that the glue evenly covers the entire area. On the smooth glass surface, the glue dispensing amount is relatively small and the dispensing time is shorter to avoid excessive glue overflow.

[0099] Step S15: Adjust the amount of glue dispensed By analyzing the surface roughness information of the camera module, the system fine-tunes the initially calculated glue amount. For the high-roughness areas of the metal frame, the system increases the amount of glue to ensure that it can fully fill the tiny grooves on the surface; for the smooth glass area, the system further reduces the amount of glue to prevent glue overflow.

[0100] Step S16: Execute dispensing operation In the camera module dispensing operation, the dispensing gun accurately dispenses glue around the camera lens according to the set parameters. The dispensing gun moves to the specified position, and according to the effective dispensing amount calculated previously, the system controls the spraying of glue to ensure that the glue evenly covers the required bonding area. During the entire operation, the system monitors the dispensing process in real time to ensure correct operation.

[0101] Step S17: Quality monitoring and adjustment based on visual feedback After the dispensing operation is completed, the system uses the camera to obtain the module image after dispensing and analyzes the distribution of glue. The system detects the coverage, glue thickness and uniformity of the dispensing area. If it is found that there is insufficient or excessive glue in certain areas, the system automatically adjusts the dispensing parameters through the feedback mechanism and re-dispenses the glue if necessary. For example, if the system detects that there is a slight lack of glue in a certain area of ​​the camera frame, it will immediately increase the amount of glue in that area to ensure that the glue completely covers the edge of the module.

[0102] Through the method of the present invention, the camera module dispensing process achieves high-precision control. The glue is evenly distributed and the dispensing amount is accurate, which effectively improves the bonding quality and waterproof performance. The real-time visual feedback control mechanism ensures the consistency and reliability of the dispensing process of each camera module, reduces human intervention and defective rate, and improves production efficiency.

[0103] It can be seen from the above application scenarios that the present invention can provide precise and flexible dispensing control in complex parts dispensing processes, adapt to the needs of different surface characteristics, and ensure an efficient and high-quality production process.

[0104] In summary, the present invention discloses an intelligent dispensing control method based on machine vision. The method includes acquiring an image of a dispensing part, and extracting features of the dispensing part image to obtain feature information; calculating the dispensing position area according to the feature information to obtain the dispensing area area; calculating the surface roughness of the dispensing part according to the feature information of the image to obtain the roughness information; calculating the dispensing amount according to the feature information, the dispensing position and the roughness information to obtain the dispensing parameters; adjusting the dispensing amount according to the dispensing parameters and the roughness information to obtain the effective dispensing amount; performing dispensing operations according to the dispensing parameters, the effective dispensing amount and the dispensing area area to obtain the dispensing results; and monitoring and adjusting the dispensing quality based on the visual feedback control according to the dispensing results. The method can improve the dispensing accuracy of the dispensing machine, optimize the parameters, and achieve efficient dispensing control.

[0105] The present invention adopts an intelligent dispensing control method based on machine vision, which can effectively adapt to parts of different sizes, colors and surface features and realize high-precision dispensing control; based on the characteristic information of the parts, the dispensing parameters are automatically adjusted to ensure the accuracy and consistency of the dispensing amount; the application of visual detection and feedback control technology enables the equipment to automatically monitor and adjust during operation to optimize the dispensing quality.

[0106] Reference Figure 2 The second embodiment of the present invention provides an intelligent dispensing control system based on machine vision, comprising: An image acquisition module acquires an image of a dispensing part and performs feature extraction on the image of the dispensing part to obtain feature information; An area calculation module is used to calculate the dispensing position area according to the characteristic information to obtain the area of ​​the dispensing area; A roughness calculation module is used to calculate the surface roughness of the dispensing part according to the characteristic information to obtain roughness information; A glue dispensing amount calculation module, used to calculate the glue dispensing amount according to the characteristic information, the glue dispensing position and the roughness information to obtain glue dispensing parameters; A glue dispensing amount adjustment module, used to adjust the glue dispensing amount according to the glue dispensing parameters and the roughness information to obtain an effective glue dispensing amount; A glue dispensing module, used to perform a glue dispensing operation according to the glue dispensing parameters, the effective glue dispensing amount and the glue dispensing area to obtain a glue dispensing result; The control module is used to monitor and adjust the dispensing quality based on the visual feedback control according to the dispensing result. Preferably, the area calculation module is specifically used for: Calculate the area of ​​the glue dispensing area, wherein the calculation formula of the glue dispensing area is:

[0107] In the formula, Indicates the area of ​​the circular dispensing area; Indicates the radius of the circular dispensing area.

[0108]

[0109] In the formula, Represents the area of ​​the rectangular dispensing area; Indicates the length of the rectangular dispensing area; is the width of the rectangular dispensing area.

[0110] Preferably, the roughness calculation module is specifically used for: Calculate the surface roughness of the dispensing part, wherein the calculation formula of the surface roughness of the dispensing part is:

[0111] In the formula, Indicates surface roughness; Indicates the standard surface, that is, the standard dispensing amount when there is no roughness; Represents the actual surface roughness calculation function.

[0112] Preferably, the dispensing amount calculation module is specifically used for: Calculate the glue dispensing amount, wherein the calculation formula for the glue dispensing amount is:

[0113]

[0114] In the formula, Indicates the amount of glue dispensed in a circular area; Indicates the area of ​​the circular dispensing area; Indicates the amount of glue dispensed in a rectangular area; Represents the area of ​​the rectangular dispensing area; Indicates the dispensing depth; Indicates the density of the dispensing material; Indicates the dispensing radius; Indicates the dispensing height (thickness of the dispensing material); Indicates the diameter of the dispensing nozzle; Indicates the dispensing angle (the angle between the dispensing head and the surface); Indicates the dispensing cross-sectional area.

[0115] Preferably, the dispensing amount adjustment module is specifically used for: Calculate the effective glue dispensing amount, wherein the calculation formula of the effective glue dispensing amount is:

[0116] In the formula, Indicates the effective dispensing amount; Indicates the original dispensing amount; Indicates surface roughness; It is a coefficient usually used to consider other correction factors in the actual dispensing process (such as the characteristics of the dispensing material and equipment errors).

[0117] Preferably, the dispensing module is specifically used to perform a dispensing operation according to the dispensing parameters, the effective dispensing amount and the area of ​​the dispensing region to obtain a dispensing result, including: Determine the dispensing pressure and dispensing time according to the dispensing parameters, the effective dispensing amount and the area of ​​the dispensing region, and obtain the dispensing parameter settings that meet the process requirements; The dispensing parameter settings are input into the dispensing machine so that the dispensing machine performs a dispensing operation to obtain a dispensing result.

[0118] Preferably, the control module is specifically used to monitor and adjust the dispensing quality based on visual feedback control according to the dispensing result, including: According to the dispensing results, quality assessment is performed based on the image grayscale mean, variance and dispensing area to obtain a dispensing model; wherein the expression of the dispensing model is:

[0119] In the formula, Indicates dispensing quality; Represents the grayscale mean of the dispensing area image; Represents the variance of grayscale values; Indicates the dispensing area.

[0120] According to the dispensing model, the dispensing parameters are evaluated by comparing the theoretical value and the actual value to obtain the dispensing amount deviation value, wherein the calculation formula of the dispensing amount deviation value is:

[0121] In the formula, Indicates the deviation of dispensing amount; Indicates the actual dispensing amount; Indicates the theoretical dispensing amount; According to the dispensing amount deviation value and the dispensing model, the dispensing model and the dispensing parameters are optimized to obtain a high-precision dispensing result; wherein the calculation formula of the high-precision dispensing result is:

[0122] In the formula, Indicates high-precision dispensing results; Indicates dispensing quality; Represents the feedback regulation coefficient, which is used to control the regulation intensity; Indicates the dispensing deviation value.

[0123] It should be noted that the intelligent dispensing control system based on machine vision provided in an embodiment of the present invention is used to execute all the process steps of the intelligent dispensing control method based on machine vision in the above embodiment. The working principles and beneficial effects of the two correspond one to one, so they will not be repeated here.

[0124] The embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a program for a smart dispensing control method based on machine vision. When the processor executes the computer program, the steps in the above-mentioned embodiments of the smart dispensing control method based on machine vision are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as the area calculation module.

[0125] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0126] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0127] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0128] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0129] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0130] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0131] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. An intelligent dispensing control method based on machine vision, characterized in that: The following steps are involved: Acquire a dispensing part image, and perform feature extraction on the dispensing part image to obtain feature information; Calculate the dispensing position area according to the characteristic information to obtain the dispensing area; Calculate the surface roughness of the dispensing part according to the characteristic information to obtain roughness information; Calculate the glue dispensing amount according to the characteristic information, the area of ​​the glue dispensing region and the roughness information to obtain glue dispensing parameters; According to the dispensing parameters and the roughness information, the dispensing amount is adjusted to obtain an effective dispensing amount; Perform a dispensing operation according to the dispensing parameters, the effective dispensing amount and the area of ​​the dispensing region to obtain a dispensing result; According to the dispensing results, the dispensing quality is monitored and adjusted based on visual feedback control.

2. According to the machine vision-based intelligent dispensing control method of claim 1, it is characterized in that: The calculation formula of the dispensing area is: In the formula, Indicates the area of ​​the circular dispensing area; Indicates the radius of the circular dispensing area; In the formula, Represents the area of ​​the rectangular dispensing area; Indicates the length of the rectangular dispensing area; is the width of the rectangular dispensing area.

3. The intelligent dispensing control method based on machine vision according to claim 1 is characterized in that: The calculation formula for the surface roughness of the dispensing parts is: In the formula, Indicates surface roughness; Indicates the standard surface, that is, the standard dispensing amount when there is no roughness; Represents the actual surface roughness calculation function.

4. The intelligent dispensing control method based on machine vision according to claim 1 is characterized in that: The calculation formula for the dispensing amount is: In the formula, Indicates the amount of glue dispensed in a circular area; Indicates the area of ​​the circular dispensing area; Indicates the amount of glue dispensed in a rectangular area; Represents the area of ​​the rectangular dispensing area; Indicates the dispensing depth; Indicates the density of the dispensing material; Indicates the dispensing radius; Indicates the dispensing height (thickness of the dispensing material); Indicates the diameter of the dispensing nozzle; Indicates the dispensing angle (the angle between the dispensing head and the surface); Indicates the dispensing cross-sectional area.

5. The intelligent dispensing control method based on machine vision according to claim 1 is characterized in that: The calculation formula of the effective dispensing amount is: In the formula, Indicates the effective dispensing amount; Indicates the original dispensing amount; Indicates surface roughness; It is a coefficient usually used to consider other correction factors in the actual dispensing process (such as the characteristics of the dispensing material and equipment errors).

6. The intelligent dispensing control method based on machine vision according to claim 1 is characterized in that: The step of performing a dispensing operation according to the dispensing parameters, the effective dispensing amount and the area of ​​the dispensing region to obtain a dispensing result includes: Determine the dispensing pressure and dispensing time according to the dispensing parameters, the effective dispensing amount and the area of ​​the dispensing region, and obtain the dispensing parameter settings that meet the process requirements; The dispensing parameter settings are input into the dispensing machine so that the dispensing machine performs a dispensing operation to obtain a dispensing result.

7. The intelligent dispensing control method based on machine vision according to claim 1 is characterized in that: The monitoring and adjustment of the dispensing quality based on the visual feedback control according to the dispensing result includes: According to the dispensing results, quality assessment is performed based on the image grayscale mean, variance and dispensing area to obtain a dispensing model; wherein the expression of the dispensing model is: In the formula, Indicates dispensing quality; Represents the grayscale mean of the dispensing area image; Represents the variance of grayscale values; Indicates the area of ​​the dispensing area; According to the dispensing model, the dispensing parameters are evaluated by comparing the theoretical value and the actual value to obtain the dispensing amount deviation value, wherein the calculation formula of the dispensing amount deviation value is: In the formula, Indicates the deviation of dispensing amount; Indicates the actual dispensing amount; Indicates the theoretical dispensing amount; According to the dispensing amount deviation value and the dispensing model, the dispensing model and the dispensing parameters are optimized to obtain a high-precision dispensing result; wherein the calculation formula of the high-precision dispensing result is: In the formula, Indicates high-precision dispensing results; Indicates dispensing quality; Represents the feedback regulation coefficient, which is used to control the regulation intensity; Indicates the dispensing deviation value.

8. An intelligent dispensing control system based on machine vision, characterized in that: include: An image acquisition module acquires an image of a dispensing part and performs feature extraction on the image of the dispensing part to obtain feature information; An area calculation module is used to calculate the dispensing position area according to the characteristic information to obtain the area of ​​the dispensing area; A roughness calculation module is used to calculate the surface roughness of the dispensing part according to the characteristic information to obtain roughness information; A glue dispensing amount calculation module, used to calculate the glue dispensing amount according to the characteristic information, the glue dispensing position and the roughness information to obtain glue dispensing parameters; A glue dispensing amount adjustment module, used to adjust the glue dispensing amount according to the glue dispensing parameters and the roughness information to obtain an effective glue dispensing amount; A glue dispensing module, used to perform a glue dispensing operation according to the glue dispensing parameters, the effective glue dispensing amount and the glue dispensing area to obtain a glue dispensing result; The control module is used to monitor and adjust the dispensing quality based on the visual feedback control according to the dispensing result.

9. An electronic device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a machine vision-based intelligent dispensing control method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute a machine vision-based intelligent dispensing control method as described in any one of claims 1 to 7.

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