Micro-electro-polymerization dust remover modularized rapid intelligent installation method based on visual identification

Abstract

The invention discloses a micro-electro-polymerization dust remover modularized rapid intelligent installation method based on visual identification, and relates to the technical field of equipment installation. According to the method, the corresponding labels are given to all the module components of the micro-electro-polymerization dust remover, the labels of all the module components are recognized and analyzed, the installation positions of all the module components are determined based on the analysis result, and all the module components are assembled in the pre-divided assembly areas through the automatic equipment, so that the assembly efficiency of the micro-electro-polymerization dust remover is improved. All module components of the assembled micro-electro-polymerization dust remover are welded, the welding positions of all the module components are marked as target welding areas, and after welding is completed, image information of different target welding areas is collected and evaluated; the initial welding parameters of the different target welding areas are adjusted according to the comparison result of the evaluation result and the preset quality standard, and the installation speed and the installation quality are improved while intelligent installation is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment installation, and in particular to a modular, rapid and intelligent installation method for a micro-electropolymerization dust collector based on visual recognition. Background Art

[0002] In the metallurgical industry, micro-electropolymerization dust collector, as a key equipment, is crucial to ensuring the production environment and improving production efficiency.

[0003] However, the modular rapid intelligent installation method of micro-electropolymerization dust collector in the prior art still has the following shortcomings in practical application: When assembling and welding the modules of the micro-electropolymerization dust collector, the assessment of welding quality was ignored, resulting in the welding quality of each micro-electropolymerization dust collector not being guaranteed during the entire construction process, and the installation quality not being guaranteed, posing a safety hazard; In addition, there is a lack of effective welding parameter adjustment mechanism when conducting welding quality assessment, and the welding parameters of the subsequent welding process cannot be optimized based on the real-time welding quality assessment results of each welding position, and the degree of intelligence is low.

[0004] To this end, a modular, rapid and intelligent installation method for micro-electropolymerization dust collector based on visual recognition is introduced. Summary of the invention

[0005] The purpose of the present invention is to solve the problems pointed out in the background technology and to propose a modular, rapid and intelligent installation method for micro-electropolymerization dust collector based on visual recognition.

[0006] The purpose of the present invention can be achieved by the following technical solution: A modular rapid intelligent installation method for a micro-electropolymerization dust collector based on visual recognition, comprising: Visual identification installation: assemble the various module components of the micro-electropolymerization dust collector using automated equipment in the pre-divided assembly area; Assembly quality assessment: extract the initial welding parameters between each module component from the preset welding parameter database; weld each module component of the assembled micro-electropolymerization dust collector, mark the welding position of each module component as the target welding area, collect image information of different target welding areas after welding is completed and evaluate them, adjust the initial welding parameters of different target welding areas according to the comparison between the evaluation results and the preset quality standards, and input the adjusted welding parameters of the target welding areas into the welding parameter database for updating; Installation data recording: Install the micro-electropolymerization dust collector after welding through hoisting equipment; track and record the number of module assembly, module welding and equipment hoisting during the assembly and installation process in real time; Installation progress assessment: According to the time plan of the installation project, the total duration in the time plan is divided into X evaluation time windows; where X>3; and the corresponding number of qualified installed dust collectors is set for each divided evaluation time window. If the number of dust collectors installed in a certain evaluation time window does not reach the corresponding set number of qualified installed dust collectors, execute the corresponding steps to generate a progress exception report within the corresponding evaluation time window and send it to the management personnel.

[0007] As a preferred embodiment of the present invention, after welding is completed, image information of different target welding areas is collected and evaluated, specifically: Separating the pore areas in the target welding area image information, and obtaining the circularity corresponding to each group of separated pore areas; The reference threshold range corresponding to the circularity is set, the circularity corresponding to each group of pore areas is matched with the reference threshold range, the pixel points of each pore area within the reference threshold range are counted, and the actual area is converted according to the resolution of the image. After the conversion is completed, the actual area of ​​each pore area is accumulated and recorded as the pore area; the proportion of the pore area of ​​the target welding area in the total area of ​​the target welding area is calculated, which is recorded as the pore defect proportion; Separating the crack regions in the target welding region image information, and obtaining the aspect ratios of the contours corresponding to each group of separated crack regions; Set a reference threshold corresponding to the length ratio, compare the length-width ratio corresponding to each group of crack areas with the reference threshold, count the pixel points of each crack area that is higher than the reference threshold, and convert the actual area according to the resolution of the image. After the conversion, add up the actual areas of each crack area and record them as crack products; calculate the proportion of the crack product of the target welding area in the total area of ​​the target welding area, and record them as crack proportion; Identify the unfused defect area in the welding area image information, calculate the grayscale mean of the unfused defect area, and compare it with the preset normal grayscale mean, calculate the difference between the two groups of means, record it as the grayscale difference, count the pixels of the unfused defect area whose grayscale difference is higher than the preset grayscale threshold difference, and convert the actual area according to the resolution of the image. After the conversion is completed, add them up and record them as the unfused area; calculate the proportion of the unfused area in the total area of ​​the target welding area, record it as the fusion ratio.

[0008] As a preferred embodiment of the present invention, the evaluation result is compared with the preset quality standard, specifically: The preset quality standard includes the allowable error ratios corresponding to the gas defect ratio, crack defect ratio and melt defect ratio in different target welding areas, which are recorded as ; The gas defect ratio, crack defect ratio and melt defect ratio of different target welding areas are recorded as ; According to the formula Perform weighted calculation to determine the welding quality index Y of different target welding areas; They are the influencing weight factors corresponding to the gas shortage ratio, crack shortage ratio and melting shortage ratio respectively.

[0009] As a preferred embodiment of the present invention, the initial welding parameters of different target welding areas are adjusted, specifically: The welding quality index Y of different target welding areas is compared with the corresponding preset welding qualification index. If the welding quality index Y of a target welding area is higher than the preset welding qualification index, the adjustment signal is triggered and sent to the management personnel. After the management personnel confirms and receives the adjustment signal, the welding quality index Y of the target welding area, the image information of the target welding area and the initial welding parameters are extracted, and the initial welding parameters are adjusted.

[0010] As a preferred implementation of the present invention, if the administrator refuses to receive the adjustment signaling, the following steps are specifically performed: Extract the welding quality index Y of the target welding area and input it into a pre-built defect assessment database, in which various groups of welding parameter adjustment schemes are pre-stored, and each group of welding parameter adjustment schemes includes welding parameter adjustment ranges corresponding to gas defect ratio, crack defect ratio and molten defect ratio; Extract the gas defect ratio, crack defect ratio and melt defect ratio corresponding to the welding quality index Y of the target welding area; The Euclidean distance is used to calculate the gas defect ratio, crack defect ratio and molten defect ratio of the target welding area and the gas defect ratio, crack defect ratio and molten defect ratio of each group of welding parameter adjustment schemes in the defect assessment database, and the calculation result is represented by d; Sort each group of welding parameter adjustment plans according to the calculated result d, select the welding parameter adjustment plan with the smallest calculated result d as the target plan, and adjust the initial welding parameters according to the welding parameter adjustment range within the target plan. After the adjustment is completed, send it to the management personnel. After confirmation, the management personnel adjust the welding equipment according to the adjusted welding parameters and use them as the initial welding parameters for the next group of micro-electropolymerization dust collector target welding areas.

[0011] As a preferred implementation of the present invention, corresponding steps are executed to generate a progress exception report within the corresponding evaluation time window and send it to the management personnel, specifically: The qualified distance quantity, construction status model and final qualified installed dust collector quantity of the corresponding evaluation time window are extracted and filled into the pre-set report template, thereby generating a progress exception report within the corresponding evaluation time window and sending it to the management personnel.

[0012] As a preferred implementation of the present invention, the specific process of obtaining the construction status model is as follows: The preset number of different dust collectors corresponds to a theoretical module assembly number, a theoretical module welding number, and a theoretical equipment hoisting number; Extract the module assembly times, module welding times, and equipment hoisting times within the corresponding evaluation time window, recorded as a1, b1, and c1; extract the theoretical module assembly times, theoretical module welding times, and theoretical equipment hoisting times corresponding to the number of qualified installed dust collectors, recorded as a2, b2, and c2. Calculate and obtain the assembly assessment ratio, welding assessment ratio and hoisting assessment ratio within the corresponding assessment time window; The assembly assessment ratio, welding assessment ratio and hoisting assessment ratio are used as the length value, width value and height value respectively, and a three-dimensional rectangular model is constructed. The constructed three-dimensional rectangular model is used as the construction status model within the corresponding evaluation time window.

[0013] As a preferred embodiment of the present invention, the specific process of obtaining the qualified number of distances and the final qualified number of installed dust collectors in the next evaluation time window is as follows: Extract the actual number of dust collectors that have not reached the qualified installation within the corresponding evaluation time window, and calculate the difference between the actual number and the number of qualified installed dust collectors to obtain the qualified number of distance in the corresponding evaluation time window; add the qualified number of distance and the number of qualified installed dust collectors in the next evaluation time window to obtain the final qualified number of installed dust collectors in the next evaluation time window.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The present invention assigns corresponding labels to each module component of the micro-electropolymerization dust collector, identifies and analyzes the labels of each module component, determines the installation position of each module component based on the analysis result, assembles each module component in a pre-divided assembly area using automated equipment, welds each module component of the assembled micro-electropolymerization dust collector, marks the welding position of each module component as a target welding area, collects and evaluates image information of different target welding areas after welding, adjusts the initial welding parameters of different target welding areas according to the comparison result between the evaluation result and the preset quality standard, and improves the installation speed and installation quality while realizing intelligent installation; The present invention compares the welding quality index with the preset qualified index according to the real-time welding quality evaluation results of each welding position, triggers the adjustment signaling, and the manager adjusts the parameters accordingly. If the manager refuses, an adaptation scheme is selected from the defect evaluation database for adjustment. After the adjustment, a closed-loop feedback is formed through detection, so as to continuously improve the welding quality and enhance the intelligent degree of installation. The present invention divides the evaluation time window and compares the actual number of installed dust collectors with the qualified number. If the standard is not met, a three-dimensional rectangular model is constructed to display the progress relationship of each link, so that management personnel can find abnormal progress. At the same time, the number of qualified installed dust collectors in the next evaluation time window is adjusted to adapt the installation plan to the actual situation, ensure that the project is advanced as planned, and improve the installation speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0016] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0017] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than 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.

[0018] See also Figure 1 As shown, the modular rapid intelligent installation method of micro-electropolymerization dust collector based on visual recognition includes: Visual identification installation: When manufacturing the modular components of the micro-electropolymerization dust collector in the factory, assign corresponding labels to the modular components of the micro-electropolymerization dust collector; the labels include the type, number, size and installation location of each modular component; multiple groups of high-definition cameras are pre-deployed at the installation site to collect image data corresponding to different angles of each modular component and perform pre-processing; the pre-processing includes denoising and enhancement, etc.; after the pre-processing is completed, the labels of each modular component are identified and analyzed, and the installation location of each modular component is determined based on the results of the analysis, and the modular components are assembled in the pre-divided assembly area using automated equipment, i.e., robotic arms, etc.; It should be noted that a unique QR code label is marked on the middle box body in a conspicuous position (such as the outside of the side panel) that does not affect subsequent use by using a laser marking machine. This QR code label stores a wealth of information, including not only the type of the middle box body, which is "dust collection middle box body", the assumed number "MXD-003", the size information (5 meters long, 3 meters wide, and 2 meters high), but also its installation position in the overall structure of the dust collector, such as being located above the bottom steel structure frame and adjacent to the middle boxes on the left and right sides; each module component is like having an exclusive description, providing key information for subsequent accurate identification and installation at the installation site.

[0019] Assembly quality assessment: After the assembly is completed, the initial welding parameters between the module components are extracted from the preset welding parameter database; the initial welding parameters include welding current, voltage, welding speed, wire feeding speed, etc.; and the module components of the assembled micro-electropolymerization dust collector are welded, and the welding position of each module component is marked as the target welding area. After the welding is completed, the image information of different target welding areas is collected and evaluated. According to the comparison between the evaluation results and the preset quality standards, the initial welding parameters of different target welding areas are adjusted, and the adjusted welding parameters of the target welding areas are input into the welding parameter database for updating; It should be noted that after welding is completed, high-definition industrial cameras installed on automatic welding robots or specific locations will immediately capture images of the weld. These cameras have high resolution, high frame rate, and good low-light performance, and can clearly capture the details of the weld under different ambient light conditions. In order to ensure full coverage of the weld, multiple cameras will be arranged to shoot from different angles to obtain multi-view images of the weld, ensuring complete imaging of the circumferential weld of the entire target welding area.

[0020] Specifically: Step 1: Use the threshold segmentation algorithm to separate the pore areas in the target welding area image information, and obtain the circularity corresponding to each group of separated pore areas; that is, use the circularity calculation formula to calculate, the formula is expressed as , where A is the area of ​​the stomatal region and P is the perimeter of the stomatal region; The reference threshold range corresponding to the circularity is set, the circularity corresponding to each group of pore areas is matched with the reference threshold range, the pixel points of each pore area within the reference threshold range are counted, and the actual area is converted according to the resolution of the image. After the conversion is completed, the actual area of ​​each pore area is accumulated and recorded as the pore area; the proportion of the pore area of ​​the target welding area in the total area of ​​the target welding area is calculated, which is recorded as the pore defect proportion; Step 2: Use the Canny edge detection algorithm to separate the crack areas in the target welding area image information, and obtain the length-width ratio of the contours corresponding to each group of separated crack areas; the length ratio is calculated by dividing the length of the circumscribed rectangle of the contour by the width; Set a reference threshold corresponding to the length ratio, compare the length-width ratio corresponding to each group of crack areas with the reference threshold, count the pixel points of each crack area that is higher than the reference threshold, and convert the actual area according to the resolution of the image. After the conversion, add up the actual areas of each crack area and record them as crack products; calculate the proportion of the crack product of the target welding area in the total area of ​​the target welding area, and record them as crack proportion; Step 3: Use the region growing algorithm to identify the unfused defect area in the welding area image information, calculate the grayscale mean of the unfused defect area, and compare it with the preset normal grayscale mean, calculate the difference between the two groups of means, record it as the grayscale difference, count the pixels of the unfused defect area whose grayscale difference is higher than the preset grayscale threshold difference, and convert the actual area according to the resolution of the image. After the conversion is completed, add them up and record them as the unfused product; calculate the proportion of the unfused product in the total area of ​​the target welding area, record it as the fusion ratio; It should be noted that before analyzing the image information of the target welding area, a filtering algorithm is used to remove the noise in the image, such as Gaussian filtering and median filtering. Gaussian filtering can smooth the image and reduce the impact of random noise; median filtering has a good removal effect on salt and pepper noise. Then, histogram equalization processing is performed to enhance the contrast of the image and make the characteristics of the weld more obvious.

[0021] Step 4: The preset quality standards include the allowable error ratios corresponding to the gas defect ratio, crack defect ratio, and melt defect ratio in different target welding areas, recorded as ; The gas defect ratio, crack defect ratio and melt defect ratio of different target welding areas are recorded as ; According to the formula Perform weighted calculation to determine the welding quality index Y of different target welding areas; They are the influencing weight factors corresponding to the gas shortage ratio, crack shortage ratio and melting shortage ratio respectively; Step 5: Compare the welding quality index Y of different target welding areas with the corresponding preset welding qualified index. If the welding quality index Y of a certain target welding area is higher than the preset welding qualified index, the adjustment signaling is triggered and sent to the management personnel. After the management personnel confirms and receives the adjustment signaling, the welding quality index Y of the target welding area, the image information of the target welding area and the initial welding parameters are extracted, and the initial welding parameters are adjusted. If the manager refuses to receive the adjustment signal, the welding quality index Y of the target welding area is extracted and input into a pre-built defect assessment database, in which various groups of welding parameter adjustment schemes are pre-stored. Each group of welding parameter adjustment schemes includes welding parameter adjustment ranges corresponding to gas defect ratio, crack defect ratio and molten defect ratio; the welding parameter adjustment range includes the adjustment ranges of welding parameters such as welding current, voltage, welding speed and wire feeding speed; Extract the gas defect ratio, crack defect ratio and melt defect ratio corresponding to the welding quality index Y of the target welding area; The Euclidean distance is used to calculate the gas defect ratio, crack defect ratio and molten defect ratio of the target welding area and the gas defect ratio, crack defect ratio and molten defect ratio of each group of welding parameter adjustment schemes in the defect assessment database, and the calculation result is represented by d; Sort each group of welding parameter adjustment plans according to the calculation result d, select the welding parameter adjustment plan with the smallest calculation result d as the target plan, and adjust the initial welding parameters according to the welding parameter adjustment range within the target plan. After the adjustment is completed, send it to the management personnel. After the management personnel confirms it, adjust the welding equipment according to the adjusted welding parameters and use them as the initial welding parameters for the next group of micro-electropolymerization dust collector target welding areas; It should be noted that the welding parameter adjustment plan is confirmed by relevant technical personnel in the welding field and is adjusted and updated in real time based on historical data; For example, when the gas shortage ratio is too high, increasing the welding current within a certain range can promote gas escape and reduce gas shortage, but too large a current will cause other problems, such as increased spatter and burn-through of welds. After comprehensively considering these factors, the adjustment range of the welding current corresponding to the gas shortage ratio is determined. These bases can help managers better understand the scientificity and rationality of parameter adjustment and enhance their trust in the adjustment plan; After the initial welding parameters are adjusted according to the target plan and sent to the management personnel; after receiving the adjusted welding parameters, the management personnel need to apply these parameters in the actual welding operation, and re-test the target welding area after welding to obtain the new welding quality index Y and the gas defect ratio, crack defect ratio and melt defect ratio data. If the new welding quality index meets the qualified standard, it means that the parameter adjustment is effective; if it still does not meet the requirements, the new data should be fed back to the management personnel or the defect assessment database, and re-analyzed and calculated to further optimize the adjustment plan, and the parameters should be adjusted and verified again until the welding quality meets the requirements. This closed-loop feedback mechanism can ensure continuous improvement of welding quality, improve production efficiency and product quality; Installation data recording: Install the micro-electropolymerization dust collector after welding through hoisting equipment; track and record the number of module assembly, module welding and equipment hoisting during the assembly and installation process in real time; It should be noted that sensors are installed on various devices, such as counters installed on robotic arms, to record the number of completed module splicing.

[0022] Installation progress assessment: According to the time plan of the installation project, the total duration in the time plan is divided into X evaluation time windows; where X>3, which is set according to the number of micro-electropolymerization dust collectors and the installation difficulty of the installation project; and the corresponding number of qualified installed dust collectors is set for each divided evaluation time window. If the number of dust collectors installed in a certain evaluation time window does not reach the corresponding set number of qualified installed dust collectors, the corresponding steps are executed to generate a progress exception report within the corresponding evaluation time window and send it to the management personnel; Specifically: The preset number of different dust collectors corresponds to a theoretical module assembly number, a theoretical module welding number, and a theoretical equipment hoisting number; Extract the module assembly times, module welding times, and equipment hoisting times within the corresponding evaluation time window, recorded as a1, b1, and c1; extract the theoretical module assembly times, theoretical module welding times, and theoretical equipment hoisting times corresponding to the number of qualified installed dust collectors, recorded as a2, b2, and c2. Calculate and obtain the assembly assessment ratio, welding assessment ratio and hoisting assessment ratio within the corresponding assessment time window; The assembly assessment ratio, welding assessment ratio and hoisting assessment ratio are used as the length value, width value and height value respectively, and a three-dimensional rectangular model is constructed, and the constructed three-dimensional rectangular model is used as the construction status model within the corresponding assessment time window; It should be noted that the three-dimensional rectangular model is constructed by taking the assembly assessment ratio, welding assessment ratio and hoisting assessment ratio as the length, width and height of the three-dimensional rectangle respectively, which intuitively shows the relative relationship between the progress of each link in a visual way. Managers can quickly understand which link is progressing relatively faster or slower, and the degree of balance between the links by observing the shape of the three-dimensional rectangle and the proportion of each dimension; for example, if one dimension of the three-dimensional rectangle is obviously longer or shorter, it means that the progress of the corresponding construction link is significantly different from that of other links, which is convenient for timely discovery of potential progress problems; Extract the actual number of dust collectors that have not reached the qualified installation time window corresponding to the evaluation time window, and calculate the difference between the actual number and the number of qualified installation dust collectors to obtain the qualified number of distance in the corresponding evaluation time window, and add the qualified number of distance to the number of qualified installation dust collectors in the next evaluation time window as the final qualified number of installation dust collectors in the next evaluation time window; Extract the qualified distance quantity, construction status model and the final qualified installed dust collector quantity of the next evaluation time window in the corresponding evaluation time window and fill them into the pre-set report template, so as to generate the progress exception report in the corresponding evaluation time window and send it to the management personnel; The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A modular rapid intelligent installation method for micro-electropolymerization dust collector based on visual recognition, characterized in that: include: Visual identification installation: assemble the various module components of the micro-electropolymerization dust collector using automated equipment in the pre-divided assembly area; Assembly quality assessment: extract the initial welding parameters between each module component from the preset welding parameter database; weld each module component of the assembled micro-electropolymerization dust collector, mark the welding position of each module component as the target welding area, collect image information of different target welding areas after welding is completed and evaluate them, adjust the initial welding parameters of different target welding areas according to the comparison between the evaluation results and the preset quality standards, and input the adjusted welding parameters of the target welding areas into the welding parameter database for updating; Installation data recording: Install the micro-electropolymerization dust collector after welding through hoisting equipment; track and record the number of module assembly, module welding and equipment hoisting during the assembly and installation process in real time; Installation progress assessment: According to the time plan of the installation project, the total duration in the time plan is divided into X evaluation time windows; where X>3; and the corresponding number of qualified installed dust collectors is set for each divided evaluation time window. If the number of dust collectors installed in a certain evaluation time window does not reach the corresponding set number of qualified installed dust collectors, execute the corresponding steps to generate a progress exception report within the corresponding evaluation time window and send it to the management personnel.

2. According to the method for modular rapid intelligent installation of micro-electropolymerization dust collector based on visual recognition according to claim 1, it is characterized in that: After welding is completed, image information of different target welding areas is collected and evaluated, specifically: Separating the pore areas in the target welding area image information, and obtaining the circularity corresponding to each group of separated pore areas; The reference threshold range corresponding to the circularity is set, the circularity corresponding to each group of pore areas is matched with the reference threshold range, the pixel points of each pore area within the reference threshold range are counted, and the actual area is converted according to the resolution of the image. After the conversion is completed, the actual area of ​​each pore area is accumulated and recorded as the pore area; the proportion of the pore area of ​​the target welding area in the total area of ​​the target welding area is calculated, which is recorded as the pore defect proportion; Separating the crack regions in the target welding region image information, and obtaining the aspect ratios of the contours corresponding to each group of separated crack regions; Set a reference threshold corresponding to the length ratio, compare the length-width ratio corresponding to each group of crack areas with the reference threshold, count the pixel points of each crack area that is higher than the reference threshold, and convert the actual area according to the resolution of the image. After the conversion, add up the actual areas of each crack area and record them as crack products; calculate the proportion of the crack product of the target welding area in the total area of ​​the target welding area, and record them as crack proportion; Identify the unfused defect area in the welding area image information, calculate the grayscale mean of the unfused defect area, and compare it with the preset normal grayscale mean, calculate the difference between the two groups of means, record it as the grayscale difference, count the pixels of the unfused defect area whose grayscale difference is higher than the preset grayscale threshold difference, and convert the actual area according to the resolution of the image. After the conversion is completed, accumulate it and record it as the unfused product; Calculate the proportion of the unfused area in the total area of ​​the target welding area and record it as the lack of fusion ratio.

3. The modular rapid intelligent installation method of micro-electropolymerization dust collector based on visual recognition according to claim 2 is characterized in that: Compare the assessment results with pre-set quality standards, specifically: The preset quality standard includes the allowable error ratios corresponding to the gas defect ratio, crack defect ratio and melt defect ratio in different target welding areas, which are recorded as ; The gas defect ratio, crack defect ratio and melt defect ratio of different target welding areas are recorded as ; According to the formula Perform weighted calculation to determine the welding quality index Y of different target welding areas; They are the influencing weight factors corresponding to the gas shortage ratio, crack shortage ratio and melting shortage ratio respectively.

4. The modular rapid intelligent installation method of micro-electropolymerization dust collector based on visual recognition according to claim 3 is characterized in that: Adjust the initial welding parameters of different target welding areas, specifically: The welding quality index Y of different target welding areas is compared with the corresponding preset welding qualification index. If the welding quality index Y of a target welding area is higher than the preset welding qualification index, the adjustment signal is triggered and sent to the management personnel. After the management personnel confirms and receives the adjustment signal, the welding quality index Y of the target welding area, the image information of the target welding area and the initial welding parameters are extracted, and the initial welding parameters are adjusted.

5. The modular rapid intelligent installation method of micro-electropolymerization dust collector based on visual recognition according to claim 4 is characterized in that: If the administrator refuses to receive the adjustment signaling, the following steps will be performed: Extract the welding quality index Y of the target welding area and input it into a pre-built defect assessment database, in which various groups of welding parameter adjustment schemes are pre-stored, and each group of welding parameter adjustment schemes includes welding parameter adjustment ranges corresponding to gas defect ratio, crack defect ratio and molten defect ratio; Extract the gas defect ratio, crack defect ratio and melt defect ratio corresponding to the welding quality index Y of the target welding area; The Euclidean distance is used to calculate the gas defect ratio, crack defect ratio and molten defect ratio of the target welding area and the gas defect ratio, crack defect ratio and molten defect ratio of each group of welding parameter adjustment schemes in the defect assessment database, and the calculation result is represented by d; Sort each group of welding parameter adjustment plans according to the calculated result d, select the welding parameter adjustment plan with the smallest calculated result d as the target plan, and adjust the initial welding parameters according to the welding parameter adjustment range within the target plan. After the adjustment is completed, send it to the management personnel. After confirmation, the management personnel adjust the welding equipment according to the adjusted welding parameters and use them as the initial welding parameters for the next group of micro-electropolymerization dust collector target welding areas.

6. The modular rapid intelligent installation method of micro-electropolymerization dust collector based on visual recognition according to claim 5 is characterized in that: Execute the corresponding steps to generate a progress exception report within the corresponding evaluation time window and send it to the management personnel, specifically: The qualified distance quantity, construction status model and final qualified installed dust collector quantity of the corresponding evaluation time window are extracted and filled into the pre-set report template, thereby generating a progress exception report within the corresponding evaluation time window and sending it to the management personnel.

7. The modular rapid intelligent installation method of micro-electropolymerization dust collector based on visual recognition according to claim 6 is characterized in that: The specific process of obtaining the construction status model is as follows: The preset number of different dust collectors corresponds to a theoretical module assembly number, a theoretical module welding number, and a theoretical equipment hoisting number; Extract the module assembly times, module welding times, and equipment hoisting times within the corresponding evaluation time window, recorded as a1, b1, and c1; extract the theoretical module assembly times, theoretical module welding times, and theoretical equipment hoisting times corresponding to the number of qualified installed dust collectors, recorded as a2, b2, and c2. Calculate and obtain the assembly assessment ratio, welding assessment ratio and hoisting assessment ratio within the corresponding assessment time window; The assembly assessment ratio, welding assessment ratio and hoisting assessment ratio are used as the length value, width value and height value respectively, and a three-dimensional rectangular model is constructed. The constructed three-dimensional rectangular model is used as the construction status model within the corresponding evaluation time window.

8. The modular rapid intelligent installation method of micro-electropolymerization dust collector based on visual recognition according to claim 7 is characterized in that: The specific process of obtaining the qualified number of distance and the final qualified number of installed dust collectors in the next evaluation time window is as follows: Extract the actual number of dust collectors that have not reached the qualified installation within the corresponding evaluation time window, and calculate the difference between the actual number and the number of qualified installed dust collectors to obtain the qualified number of distance in the corresponding evaluation time window; add the qualified number of distance and the number of qualified installed dust collectors in the next evaluation time window to obtain the final qualified number of installed dust collectors in the next evaluation time window.

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