Valve body tapping calibration method and system based on machine vision

Abstract

The invention relates to the technical field of valve body machining, in particular to a valve body tapping calibration method and system based on machine vision, and the method comprises the steps that before valve body tapping, the defect degree and the defect distribution condition of the surface of a to-be-tapped hole site in a valve body are obtained, and then gray level statistical analysis is conducted to obtain a gray level deviation coefficient and a gray level uniformity coefficient; a tapping defect slight symptom coefficient is obtained through output based on the gray offset coefficient and the gray uniformity coefficient, the quality of the to-be-tapped hole site is evaluated, and a slight defect signal is generated based on a quality evaluation result; the defect type of the surface of a to-be-tapped hole site in the valve body is obtained, meanwhile, fusion analysis is conducted on the defect type, the slight defect signal and the defect type to obtain a regulation and control coefficient, and tapping parameters are dynamically calibrated; through a comprehensive and multi-dimensional evaluation mode, the defect degree and the distribution condition of the surface of the to-be-tapped hole site are accurately grasped, the machining parameters are dynamically regulated and controlled according to different defect types, the defect influence is effectively relieved, and the tapping success rate is increased.

Description

Technical Field

[0001] The present invention relates to the technical field of valve body processing, and in particular to a valve body tapping calibration method and system based on machine vision. Background Art

[0002] In the valve body processing, tapping is a key process. The tapping quality directly affects the overall performance and service life of the valve body. However, the holes to be tapped on the valve body may have various defects before processing, such as scratches, rust, etc. These defects will lead to tapping failure or unqualified products.

[0003] At present, traditional valve body tapping methods often lack effective evaluation of the quality of the tapped holes, and are unable to accurately grasp the degree and distribution of defects on the hole surface. During the tapping process, it is also difficult to dynamically adjust the processing parameters according to the actual defects of the holes, thereby failing to effectively reduce the impact of defects on the tapping quality, resulting in a low tapping success rate and unstable product quality.

[0004] Therefore, a valve body tapping calibration method is needed that can accurately evaluate the quality of the hole to be tapped and dynamically adjust the processing parameters according to the hole defects to improve the tapping success rate and product quality. Summary of the invention

[0005] The object of the present invention is to provide a valve body tapping calibration method and system based on machine vision to solve at least one of the above-mentioned prior art problems.

[0006] In a first aspect, the present invention provides a valve body tapping calibration method based on machine vision, comprising the following steps: Before tapping the valve body, obtain the defect degree and defect distribution of the surface of the hole to be tapped on the valve body, and then perform grayscale statistical analysis to obtain the grayscale deviation coefficient and grayscale uniformity coefficient; Then, based on the grayscale deviation coefficient and grayscale uniformity coefficient, the tapping defect lightness coefficient is output, the quality of the hole to be tapped is evaluated, and based on the quality evaluation result, a slight defect signal is generated; Then, the defect type on the surface of the hole to be tapped on the valve body is obtained, and the control coefficient is obtained by fusion analysis with the slight defect signal and the defect type to dynamically calibrate the tapping parameters.

[0007] In a second aspect, the present invention provides a valve body tapping calibration system based on machine vision, the system comprising: Quality analysis module: Before tapping the valve body, obtain the defect degree and defect distribution of the surface of the hole to be tapped on the valve body, and then perform grayscale statistical analysis to obtain the grayscale deviation coefficient and grayscale uniformity coefficient; Then, based on the grayscale deviation coefficient and grayscale uniformity coefficient, the tapping defect lightness coefficient is output, the quality of the hole to be tapped is evaluated, and based on the quality evaluation result, a slight defect signal is generated; Parameter control module: obtains the defect type on the surface of the hole to be tapped on the valve body, and simultaneously integrates and analyzes it with the slight defect signal and the defect type to obtain the control coefficient, and dynamically calibrates the tapping parameters.

[0008] Beneficial effects of the present invention: (1) The present invention combines grayscale histogram analysis, surface sub-region division, and annular sub-region division to quantitatively evaluate the hole to be tapped from multiple dimensions. It not only considers the grayscale deviation of the entire inner surface of the hole, but also goes deep into the annular sub-regions at different levels to comprehensively and meticulously grasp the degree and distribution of defects. Compared with the traditional single-dimensional detection method, the evaluation result is more accurate and reliable, providing a more targeted basis for subsequent tapping operations. (2) The present invention can accurately distinguish between qualified and defective sub-areas, and quantitatively classify defects by calculating multiple coefficients, such as grayscale deviation coefficient, grayscale uniformity coefficient and tapping defect lightness coefficient, so that workers can clearly understand the specific quality status of each hole to be tapped, and take corresponding treatment measures for defects of different degrees, thereby avoiding blind processing and improving production efficiency and product quality stability; (3) The present invention identifies the defect type based on the slight defect signal generated by quality assessment, and calculates the control coefficient in combination with the regional offset coefficient to achieve dynamic control of the processing parameters. The method of flexibly adjusting the processing parameters according to the actual defect situation effectively reduces the impact of defects on the tapping quality and greatly improves the tapping success rate. For example, for scratch defects, by increasing the cutting speed or reducing the feed rate, the scratches can be covered or reduced to a certain extent, ensuring the smooth progress of the tapping process. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in 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.

[0010] Figure 1 is a flow chart of a valve body tapping calibration method based on machine vision provided in Embodiment 1 of the present invention; Figure 2 It is a structural schematic diagram of a valve body tapping calibration system based on machine vision provided in the second embodiment of the present invention; Figure 3It is a structural schematic diagram of a valve body tapping calibration device based on machine vision provided in Example 3 of the present invention. DETAILED DESCRIPTION

[0011] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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 should fall within the scope of protection of the present invention.

[0012] Embodiment 1 Figure 1 A flowchart of a valve tapping calibration method based on machine vision is provided for the first embodiment of the present invention. The embodiment of the present invention can be applied to a valve tapping calibration method based on machine vision. The valve tapping calibration method based on machine vision can be performed by a valve tapping calibration system based on machine vision. The valve tapping calibration system based on machine vision can be implemented by software and / or hardware. The valve tapping calibration system based on machine vision can be configured in a valve tapping calibration device based on machine vision. Optionally, a valve tapping calibration device based on machine vision can be an electronic device, which can be a notebook, a desktop computer, a smart tablet, etc. The embodiment of the present invention does not limit this.

[0013] An embodiment of the present invention provides a valve body tapping calibration method based on machine vision, which specifically includes the following steps: Step 1: Before the valve body tapping process, the defect degree and defect distribution of the surface of the hole to be tapped on the valve body are analyzed to evaluate the quality of the hole to be tapped, and a slight defect signal is generated based on the quality evaluation result; In some embodiments, before the valve body tapping process, an image of the hole to be tapped on the valve body is acquired by image recognition technology and converted into a grayscale histogram; Based on the gray value of the gray histogram, the inner surface of the tapped hole is divided into regions, and the regions with the same gray value are divided into the same region to obtain surface sub-regions; It should be explained that the region division of the inner surface of the hole to be tapped can be performed by using methods such as threshold segmentation or region growing in image processing technology, combined with the grayscale value in the grayscale histogram, to divide the inner surface of the hole to be tapped into multiple surface sub-regions, so that the grayscale value in each surface sub-region is consistent. The image processing technology here is all existing technology and will not be repeated here; Based on any surface sub-region, the gray value of the surface sub-region is extracted, the gray value is calculated to be different from the standard gray value, and the absolute value is taken to obtain the gray offset value, and then the gray offset value is calculated to be a ratio of the standard gray value to obtain the gray offset ratio; It should be explained that the standard grayscale value represents the grayscale characteristics of the inner surface of the hole to be tapped under an ideal state, that is, the standard grayscale value is the grayscale value preset by the process before the tapping process of the hole to be tapped; A grayscale offset ratio threshold is preset, and the grayscale offset ratio of each surface sub-region is compared and analyzed with the grayscale offset ratio threshold; If the grayscale offset ratio is less than or equal to the grayscale offset ratio threshold, it means that the grayscale value of the surface sub-region corresponding to the grayscale offset ratio has a small deviation from the standard grayscale value, that is, the surface sub-region corresponding to the grayscale offset ratio is marked as a qualified surface sub-region; If the grayscale offset ratio is greater than the grayscale offset ratio threshold, it means that the grayscale value of the surface sub-region corresponding to the grayscale offset ratio deviates greatly from the standard grayscale value, that is, the surface sub-region corresponding to the grayscale offset ratio is marked as a surface defect sub-region; Extract the grayscale deviation ratios of all qualified sub-regions on the surface, calculate the mean, and obtain the qualified deviation mean; At the same time, the grayscale offset ratios of all surface defect sub-regions are extracted and marked as defect offset ratios; The area of ​​the surface defect sub-region is extracted and the ratio is calculated with the total area of ​​the inner surface of the hole to be tapped to obtain the defect influence coefficient; By formula: , calculate and obtain the defect offset weighted ratio QB, where is the defect offset ratio B, and Y is the defect influence coefficient; The defect offset weighted ratios of all surface defect sub-areas are averaged to obtain the defect offset mean; Then, the qualified offset mean and the defective offset mean are weightedly summed to obtain the grayscale offset coefficient; It should be explained that the weighted basis for weighting the average of qualified offsets and the average of defective offsets is: the weighted summation is calculated based on the proportion of all qualified surface sub-areas and all defective surface sub-areas in the total area of ​​the inner surface of the hole to be tapped; Then, based on the thread rotation direction and thread width during the tapping process, the inner surface of the hole to be tapped is divided into regions to obtain multiple annular sub-regions; It should be explained that the annular sub-areas represent different levels along the hole depth direction, and each layer corresponds to a specific thread feature; Based on any annular sub-region, the grayscale offset ratios of all qualified surface sub-regions in the annular sub-region and the defect offset weighted ratios of all surface defect sub-regions on the surface of the annular sub-region are extracted, and the weighted sum is performed according to the area proportions of each qualified surface sub-region and the surface defect sub-region in the annular sub-region to obtain the regional offset coefficient; Extract the regional shift coefficients of all annular sub-regions and obtain the regional shift coefficient sequence: , where N represents the total number of all annular sub-regions, and the value of N is 1, 2, ..., N; The wavelet basis function is used to perform wavelet decomposition on the regional migration coefficient sequence to obtain the detail coefficient; It should be explained that the wavelet basis functions include but are not limited to: Haar wavelet, Daubechies wavelet (db series), Symlets wavelet; It should be explained that wavelet decomposition is to decompose the signal at different scales. Each layer of decomposition will obtain a set of detail coefficients (representing the high-frequency part of the signal and reflecting the local changes of the signal). Wavelet decomposition is based on discrete wavelet transform (DWT), and its core is to convolve and downsample the signal through a high-pass filter g. Exemplarily, the calculation process of the detail coefficient is: By formula: , calculate the detail coefficient ; Where L is the length of the high-pass filter, k is the summation index, and its value range is from 0 to L-1. is the coefficient of the high-pass filter in the wavelet decomposition process; Then, based on the detail coefficient, the gray uniformity coefficient is calculated; Exemplarily, the calculation process of the gray uniformity coefficient is: By formula: , calculate and obtain the gray uniformity coefficient E; Then the grayscale deviation coefficient and the grayscale uniformity coefficient are summed and calculated to obtain the tapping defect lightness coefficient; A tapping defect light coefficient threshold is preset, and the tapping defect light coefficient is compared and analyzed with the tapping defect light coefficient threshold, so as to evaluate the quality of the hole to be tapped; If the tapping defect light coefficient is less than or equal to the tapping defect light coefficient threshold, it means that the quality of the hole to be tapped is good and its defect is relatively slight, that is, a slight defect signal is generated; If the tapping defect light coefficient is less than or equal to the tapping defect light coefficient threshold, it means that the quality of the hole to be tapped is poor and its defect is serious, that is, a serious defect signal is generated, thereby prompting the staff to re-process the hole to be tapped to avoid tapping failure or unqualified products; Step 2: Based on the slight defect signal, the control coefficient is calculated according to the defect type and degree, so as to dynamically control the processing parameters; Among them, the processing parameters include but are not limited to: cutting speed, feed rate; In some embodiments, based on the slight defect signal, the defect type of any annular sub-region is identified by image recognition technology, and at the same time, the regional offset coefficient of the annular sub-region is obtained, so as to calculate the control coefficient; Among them, defect types include but are not limited to: scratches, rust; Based on the defect type and the control coefficient of the annular sub-region, when the annular sub-region is in the tapping process, the processing parameters are controlled; Exemplarily, based on the defect type and control coefficient of the annular sub-region, when the annular sub-region is performing a tapping process, the process of regulating the processing parameters is as follows: Taking the defect type of slight scratch as an example, the cutting speed can be increased based on the control coefficient to speed up the material removal rate, thereby covering up or reducing the impact of the scratch to a certain extent; the feed rate can also be reduced based on the control coefficient to reduce the contact pressure between the tool and the workpiece during each cutting, which helps to reduce the extension and depth of the scratch; Among them, the process of obtaining the control coefficient is: By formula: , calculate and obtain the control coefficient K, where: is the regional deviation coefficient, E is the gray uniformity coefficient, Q is the tapping defect lightness coefficient, is the coefficient weight factor, the value range is [0,1], is the overall impact factor, ranging from [0,1], and ; It needs to be explained that Indicates the contribution of the regional offset of each annular sub-region to the control coefficient. The larger the regional offset coefficient S is, the more serious the grayscale offset in the annular sub-region is, and the greater the positive impact on the control coefficient is. Indicates the impact of overall grayscale uniformity and overall quality on the current annular sub-region, It is a proportional coefficient that links the overall grayscale uniformity with the overall quality assessment. When E is large and Q is small, it means that the overall grayscale is uneven and the quality is poor, which will amplify the influence of the regional offset coefficient S of the current annular sub-region on the control coefficient; otherwise, it will reduce the influence; The technical solution of the embodiment of the present invention is mainly as follows: before tapping the valve body, the inner surface of the hole to be tapped is divided into multiple surface sub-areas by using image recognition technology combined with grayscale histogram analysis; by calculating the grayscale offset ratio and comparing it with a preset threshold, the qualified and defective sub-areas are accurately distinguished; not only that, the qualified offset mean and the defective offset mean are further calculated, and the grayscale offset coefficient is obtained by weighted summation based on the sub-area area ratio; at the same time, the annular sub-areas are divided according to the thread characteristics, the regional offset coefficient sequence is calculated and wavelet decomposition is performed to obtain the detail coefficient and then the grayscale uniformity coefficient is calculated, and finally the tapping defect mild coefficient is comprehensively obtained; the present invention accurately grasps the defect degree and distribution of the surface of the hole to be tapped through a comprehensive and multi-dimensional evaluation method, providing a reliable basis for subsequent tapping operations; then based on the slight defect signal generated by the quality evaluation, the defect type is identified and the control coefficient is calculated in combination with the regional offset coefficient, and the processing parameters are dynamically adjusted according to different defect types, effectively reducing the impact of defects and improving the tapping success rate, and the control coefficient takes into account multiple factors and can be flexibly adapted according to the actual hole position.

[0014] Embodiment 2 Based on Example 1, please refer to Figure 2 As shown, a valve body tapping calibration system based on machine vision according to an embodiment of the present invention includes: Quality analysis module: Before the valve body tapping process, the defect degree and defect distribution of the surface of the hole to be tapped on the valve body are analyzed to obtain the grayscale deviation coefficient and grayscale uniformity coefficient respectively, and then the tapping defect lightness coefficient is calculated based on the grayscale deviation coefficient and grayscale uniformity coefficient, and the quality of the hole to be tapped is evaluated. Based on the quality evaluation results, a slight defect signal is generated; Parameter control module: Based on slight defect signals, the control coefficient is calculated according to the defect type and degree, so as to dynamically control the processing parameters.

[0015] Embodiment 3 Reference Figure 3 The embodiment of the present invention further provides a computer device 3, comprising: a memory 302 and a processor 301 and a computer program 303 stored in the memory 302. When the computer program 303 is executed on the processor 301, a valve body tapping calibration method based on machine vision as described in any one of the above methods is implemented.

[0016] The computer device 3 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 3 may include, but is not limited to, a processor 301 and a memory 302. Those skilled in the art will appreciate that Figure 3It is only an example of computer device 3 and does not constitute a limitation on computer device 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components, for example, it may also include input and output devices, network access devices, etc.

[0017] The processor 301 may be a central processing unit (CPU), or 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.

[0018] In some embodiments, the memory 302 may be an internal storage unit of the computer device 3, such as a hard disk or memory of the computer device 3. In other embodiments, the memory 302 may also be an external storage device of the computer device 3, such as a plug-in hard disk, a smart memory card (SmartMediaCard, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device 3. Further, the memory 302 may also include both an internal storage unit of the computer device 3 and an external storage device. The memory 302 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program, etc. The memory 302 may also be used to temporarily store data that has been output or is to be output.

[0019] Embodiment 4 An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, a valve body tapping calibration method based on machine vision as described in any one of the above methods is implemented.

[0020] In this embodiment, if the integrated unit 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 this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, 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 can at least include: any entity or device that can carry the computer program code to the camera / terminal device, recording medium, 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. For example, USB flash drive, mobile hard disk, disk or optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0021] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0022] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0023] In the embodiments disclosed in the present application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0024] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0025] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0026] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A valve body tapping calibration method based on machine vision, characterized in that: The following steps are involved: Before tapping the valve body, obtain the defect degree and defect distribution of the surface of the hole to be tapped on the valve body, and then perform grayscale statistical analysis to obtain the grayscale deviation coefficient and grayscale uniformity coefficient; Then, based on the grayscale deviation coefficient and grayscale uniformity coefficient, the tapping defect lightness coefficient is output, the quality of the hole to be tapped is evaluated, and based on the quality evaluation result, a slight defect signal is generated; Then, the defect type on the surface of the hole to be tapped on the valve body is obtained, and the control coefficient is obtained by fusion analysis with the slight defect signal and the defect type to dynamically calibrate the tapping parameters.

2. A valve body tapping calibration method based on machine vision according to claim 1, characterized in that: The process of obtaining the grayscale deviation coefficient is: Before tapping the valve body, the image of the hole to be tapped on the valve body is obtained through image recognition technology and converted into a grayscale histogram; Based on the gray value of the gray histogram, the inner surface of the tapped hole is divided into regions, and the regions with the same gray value are divided into the same region to obtain surface sub-regions; Extract the deviation ratio between the gray value of each surface sub-region and the standard gray value, which is recorded as gray deviation ratio; If the grayscale offset ratio is less than or equal to the grayscale offset ratio threshold, the corresponding surface sub-region is marked as a qualified surface sub-region; If the grayscale offset ratio is greater than the grayscale offset ratio threshold, the corresponding surface sub-region is marked as a surface defect sub-region; Calculate the grayscale offset ratios of all qualified surface sub-regions and defective surface sub-regions respectively, and obtain the qualified offset mean and the defect offset mean; Then the qualified offset mean and the defective offset mean are weighted to obtain the grayscale offset coefficient.

3. A valve body tapping calibration method based on machine vision according to claim 2, characterized in that: The qualified offset mean is obtained by averaging the grayscale offset ratios of all qualified sub-regions on the surface.

4. The valve body tapping calibration method based on machine vision according to claim 2, characterized in that: The process of defect shifting the mean is: By formula: , the defect offset weighted ratio QB is calculated, where B is the grayscale offset ratio of the surface defect sub-region, and Y is the defect influence coefficient, which is the ratio of the area of ​​the surface defect sub-region to the total area of ​​the inner surface of the hole to be tapped; The defect offset mean is obtained by processing the weighted average of the defect offsets of all surface defect sub-areas.

5. The valve body tapping calibration method based on machine vision according to claim 1, characterized in that: The process of obtaining the gray uniformity coefficient is: Based on the thread rotation direction and thread width during the tapping process, the inner surface of the hole to be tapped is divided into regions to obtain multiple annular sub-regions; The grayscale offset ratio of all qualified sub-regions on the surface of the annular sub-region is weighted and the defect offset weighted ratio of all defective sub-regions on the surface of the annular sub-region is weighted to obtain the region offset coefficient; Extracting the regional shift coefficients of all annular sub-regions to obtain a regional shift coefficient sequence; The wavelet basis function is used to perform wavelet decomposition on the regional migration coefficient sequence to obtain the detail coefficient ; Based on the detail coefficient, the output grayscale uniformity coefficient E is calculated using the wavelet coefficient energy formula.

6. The valve body tapping calibration method based on machine vision according to claim 1, characterized in that: The process of obtaining the tapping defect lightness coefficient is as follows: The grayscale deviation coefficient and the grayscale uniformity coefficient are summed up to obtain the tapping defect lightness coefficient.

7. A valve body tapping calibration method based on machine vision according to claim 6, characterized in that: The process of evaluating the quality of the hole to be tapped and generating a minor defect signal based on the quality evaluation results is as follows: If the tapping defect light symptomatic coefficient is ≤ the tapping defect light symptomatic coefficient threshold, a light defect signal is generated.

8. The valve body tapping calibration method based on machine vision according to claim 1, characterized in that: The process of dynamically calibrating tapping parameters is: Identify the defect type of any annular sub-region, calculate it with the regional offset coefficient of the annular sub-region, and obtain the control coefficient; Based on the defect type and the control coefficient of the annular sub-region, the processing parameters are controlled when the annular sub-region is undergoing a tapping process.

9. The valve body tapping calibration method based on machine vision according to claim 1, characterized in that: The process of obtaining the control coefficient is: By formula: , calculate and obtain the control coefficient K, where: is the regional deviation coefficient, E is the gray uniformity coefficient, Q is the tapping defect lightness coefficient, is the coefficient weight factor, is the overall impact factor.

10. A valve body tapping calibration system based on machine vision, characterized in that: The system is used to execute the method described in any one of claims 1 to 9, and the system comprises: Quality analysis module: Before tapping the valve body, obtain the defect degree and defect distribution of the surface of the hole to be tapped on the valve body, and then perform grayscale statistical analysis to obtain the grayscale deviation coefficient and grayscale uniformity coefficient; Then, based on the grayscale deviation coefficient and grayscale uniformity coefficient, the tapping defect lightness coefficient is output, the quality of the hole to be tapped is evaluated, and based on the quality evaluation result, a slight defect signal is generated; Parameter control module: obtains the defect type on the surface of the hole to be tapped on the valve body, and simultaneously integrates and analyzes it with the slight defect signal and the defect type to obtain the control coefficient, and dynamically calibrates the tapping parameters.

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