Modular Fast Intelligent Installation Method for Micro-Electro-Polymerization Dust Collector Based on Visual Recognition

Abstract

The present invention discloses a modular rapid intelligent installation method for a microelectro-polymerization dust collector based on visual recognition, which relates to the technical field of equipment installation. In the present invention, corresponding tags are assigned to each module component of the microelectro-polymerization dust collector, the tags of each module component are identified and parsed, the installation positions of each module component are determined based on the parsed results, and each module component is assembled using automated equipment in a pre-divided assembly area. After assembly, the welding positions of each module component of the microelectro-polymerization dust collector are welded, the welding positions of each module component are marked as target welding areas, after welding is completed, image information of different target welding areas is collected and evaluated, and the initial welding parameters of different target welding areas are adjusted according to the comparison result between the evaluation result and the preset quality standard, which improves the installation speed and installation quality while realizing installation intelligence.

Description

Technical Field

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

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

[0003] However, the existing modular, rapid, and intelligent installation methods for micro-electric polymerization dust collectors still have the following deficiencies in the actual application process:

[0004] When assembling and welding each module of the micro-electric polymerization dust collector, the evaluation of welding quality is ignored, resulting in the inability to guarantee the welding quality of each micro-electric polymerization dust collector during the entire construction process, the installation quality cannot be ensured, and there are potential safety hazards;

[0005] In addition, there is a lack of an effective welding parameter adjustment mechanism during the welding quality evaluation, and it is impossible to optimize the welding parameters of the subsequent welding process based on the real-time welding quality evaluation results of each welding position, with a relatively low degree of intelligence.

[0006] Therefore, a modular, rapid, and intelligent installation method for a micro-electric polymerization dust collector based on visual recognition is introduced. Summary of the Invention

[0007] The purpose of the present invention is to solve the problems pointed out in the background technique and propose a modular, rapid, and intelligent installation method for a micro-electric polymerization dust collector based on visual recognition.

[0008] The purpose of the present invention can be achieved through the following technical solutions: A modular, rapid, and intelligent installation method for a micro-electric polymerization dust collector based on visual recognition, including:

[0009] Visual recognition installation: Assemble each module component of the micro-electric polymerization dust collector using automated equipment in a pre-divided assembly area;

[0010] Assembly quality evaluation: Extract the initial welding parameters between each module component from a preset welding parameter database; and weld each module component of the assembled micro-electric polymerization dust collector, mark the welding positions of each module component as target welding areas, collect image information of different target welding areas after welding and evaluate it, adjust the initial welding parameters of different target welding areas according to the comparison result between the evaluation result and the preset quality standard, and input the adjusted welding parameters of the target welding areas into the welding parameter database for update;

[0011] Installation data recording: After welding is completed, the microelectrostatic precipitator is installed by a hoisting device; the assembly process and the number of module assemblies, module welds, and equipment hoists during the installation process are tracked and recorded in real time;

[0012] Installation progress evaluation: 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 a corresponding qualified number of installed precipitators is set for each divided evaluation time window. If the number of installed precipitators within a certain evaluation time window does not reach the corresponding set qualified number of installed precipitators, then the corresponding steps are executed to generate a progress anomaly report for the corresponding evaluation time window and send it to the management personnel.

[0013] As a preferred embodiment of the present invention, after welding is completed, image information of different target welding areas is collected and evaluated. Specifically:

[0014] Separate the pore areas in the target welding area image information, and obtain the circularity corresponding to each group of separated pore areas;

[0015] Set the reference threshold range corresponding to the circularity, match the circularity corresponding to each group of pore areas with the reference threshold range, count the pixel points of each pore area within the reference threshold range, and perform the conversion of the actual area according to the resolution of the image. After the conversion is completed, accumulate the actual areas of each pore area, denoted as the pore product; calculate the proportion of the pore product of the target welding area in the total area of the target welding area, denoted as the pore defect proportion;

[0016] Separate the crack areas in the target welding area image information, and obtain the aspect ratio of the contours corresponding to each group of separated crack areas;

[0017] Set the reference threshold corresponding to the length ratio, compare the aspect ratio corresponding to each group of crack areas with the reference threshold, count the pixel points of each crack area higher than the reference threshold, and perform the conversion of the actual area according to the resolution of the image. After the conversion is completed, accumulate the actual areas of each crack area, denoted as the crack product; calculate the proportion of the crack product of the target welding area in the total area of the target welding area, denoted as the crack defect proportion;

[0018] Identify the lack of fusion defect areas in the welding area image information, calculate the gray mean value of the lack of fusion defect areas, and compare it with the preset normal gray mean value. Calculate the difference between the two mean values, denoted as the gray difference. Count the pixel points of the lack of fusion defect areas with a gray difference higher than the preset gray threshold difference, and perform the conversion of the actual area according to the resolution of the image. After the conversion is completed, accumulate them, denoted as the lack of fusion product; calculate the proportion of the lack of fusion product in the total area of the target welding area, denoted as the fusion defect proportion.

[0019] As a preferred embodiment of the present invention, the evaluation results are compared with the preset quality standards, specifically as follows:

[0020] The preset quality standards include the allowable error ratios corresponding to the gas defect ratio, crack defect ratio, and fusion defect ratio in different target welding areas, denoted as ; the gas defect ratio, crack defect ratio, and fusion defect ratio in different target welding areas are respectively denoted as ;

[0021] According to the formula perform weighted calculation to determine the welding quality index Y of different target welding areas; are the influence weight factors corresponding to the gas defect ratio, crack defect ratio, and fusion defect ratio respectively.

[0022] As a preferred embodiment of the present invention, adjust the initial welding parameters of different target welding areas, specifically as follows:

[0023] Compare the welding quality index Y of different target welding areas with the corresponding preset welding qualification index. If the welding quality index Y of a certain target welding area is higher than the preset welding qualification index, trigger an adjustment signal and send it to the management personnel. After the management personnel determine and receive the adjustment signal, extract the welding quality index Y of the target welding area, the image information of the target welding area, and the initial welding parameters, and adjust the initial welding parameters.

[0024] As a preferred embodiment of the present invention, if the management personnel refuse to receive the adjustment signal, then specifically execute:

[0025] Extract the welding quality index Y of the target welding area and input it into the pre-constructed defect evaluation database. The defect evaluation database pre-stores each group of welding parameter adjustment schemes, and each group of welding parameter adjustment schemes includes the welding parameter adjustment ranges corresponding to the gas defect ratio, crack defect ratio, and fusion defect ratio;

[0026] Extract the gas defect ratio, crack defect ratio, and fusion defect ratio corresponding to the welding quality index Y of the target welding area;

[0027] Use the Euclidean distance to calculate the gas defect ratio, crack defect ratio, and fusion defect ratio of the target welding area and the gas defect ratio, crack defect ratio, and fusion defect ratio of each group of welding parameter adjustment schemes in the defect evaluation database, and denote the calculation result as d;

[0028] Sort the welding parameter adjustment plans for each group according to the calculation result d, select the welding parameter adjustment plan with the smallest calculation result d as the target plan, and adjust it based on the initial welding parameters according to the welding parameter adjustment range in the target plan. After the adjustment is completed, send it to the management personnel. After the management personnel confirm, adjust the welding equipment according to the adjusted welding parameters, and use it as the initial welding parameters for the target welding area of the next group of micro-electric polymerization dust collectors.

[0029] As a preferred implementation manner of the present invention, execute the corresponding steps to generate a progress exception report within the corresponding evaluation time window and send it to the management personnel. Specifically:

[0030] Extract the distance-to-qualified quantity, construction status model within the corresponding evaluation time window, and the final qualified installed dust collector quantity in the next evaluation time window and fill them into a pre-set report template, so as to generate a progress exception report within the corresponding evaluation time window and send it to the management personnel.

[0031] As a preferred implementation manner of the present invention, the specific process of obtaining the construction status model is as follows:

[0032] Preset that different dust collector quantities respectively correspond to a theoretical module assembly times, theoretical module welding times, and theoretical equipment hoisting times;

[0033] Extract the module assembly times, module welding times, and equipment hoisting times within the corresponding evaluation time window, denoted as a1, b1, c1; extract the theoretical module assembly times, theoretical module welding times, and theoretical equipment hoisting times corresponding to the qualified installed dust collector quantity, denoted as a2, b2, c2, and through Calculation, obtain the assembly evaluation ratio, welding evaluation ratio, and hoisting evaluation ratio within the corresponding evaluation time window;

[0034] Take the assembly evaluation ratio, welding evaluation ratio, and hoisting evaluation ratio as the length value, width value, and height value respectively, and construct a three-dimensional rectangular model, and use the constructed three-dimensional rectangular model as the construction status model within the corresponding evaluation time window.

[0035] As a preferred implementation manner of the present invention, the specific process of obtaining the distance-to-qualified quantity and the final qualified installed dust collector quantity in the next evaluation time window is as follows:

[0036] Extract the actual dust collector quantity within the corresponding evaluation time window of the unqualified installed dust collector, and perform a difference calculation with the qualified installed dust collector quantity to obtain the distance-to-qualified quantity of the corresponding evaluation time window. Add the distance-to-qualified quantity to the qualified installed dust collector quantity in the next evaluation time window as the final qualified installed dust collector quantity in the next evaluation time window.

[0037] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0038] In the present invention, corresponding tags are assigned to each module component of the microelectro-polymerization dust collector, the tags of each module component are identified and parsed, the installation positions of each module component are determined based on the parsed results, and each module component is assembled in a pre-divided assembly area by using automated equipment. After the assembly, the module components of the microelectro-polymerization dust collector are welded, the welding positions of each module component are marked as target welding areas, after the welding is completed, the image information of different target welding areas is collected and evaluated, and the initial welding parameters of different target welding areas are adjusted according to the comparison result between the evaluation result and the preset quality standard. While realizing intelligent installation, the installation speed and installation quality are improved;

[0039] In the present invention, according to the real-time welding quality evaluation results of each welding position, the welding quality index is compared with the preset qualified index to trigger an adjustment signaling, and the management personnel adjust the parameters accordingly. If the management personnel refuse, an adaptation scheme is selected from the defect evaluation database for adjustment. After the adjustment, a closed-loop feedback is formed through detection to continuously improve the welding quality and improve the degree of installation intelligence;

[0040] In the present invention, by dividing the evaluation time window and comparing 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, which is convenient for the management personnel to find abnormal progress. At the same time, the qualified number of installed dust collectors in the next evaluation time window is adjusted to make the installation plan adapt to the actual situation and ensure that the project is promoted according to the plan, thereby improving the installation speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] For the convenience of those skilled in the art to understand, the present invention will be further described below with reference to the accompanying drawings.

[0042] Figure 1 is the flow chart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative work belong to the scope of protection of the present invention.

[0044] Please refer to Figure 1 shown, the modular rapid intelligent installation method of the microelectro-polymerization dust collector based on visual recognition includes:

[0045] Visual recognition installation: When manufacturing each modular component of the micro - electric polymerization dust collector in the factory, corresponding labels are assigned to each modular component of the micro - electric polymerization dust collector; the labels include the type, number, size, and installation position of each modular component; multiple groups of high - definition cameras are pre - deployed at the installation site to collect image data of each modular component from different angles 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 recognized and parsed, and based on the parsed results, the installation positions of each modular component are determined, and each modular component is assembled using automated equipment in the pre - divided assembly area; that is, robotic arms, etc.

[0046] It should be noted that a unique QR code label is marked on the prominent and non - interfering position for subsequent use of the middle box body (such as the outside of the side plate) through a laser marking machine. This QR code label stores rich information, including not only the type of the middle box body as "dust removal middle box body", assuming the number "MXD - 003", size information (length 5 meters, width 3 meters, height 2 meters), but also its installation position in the overall structure of the dust collector, for example, above the bottom steel structure frame and adjacent to the middle box bodies on the left and right sides; each modular component is like having a special instruction, providing key information for accurate identification and installation at the installation site later.

[0047] Assembly quality assessment: After assembly, the initial welding parameters between each modular component are extracted from the preset welding parameter database; the initial welding parameters include welding current, voltage, welding speed, wire feeding speed, etc.; and each modular component of the assembled micro - electric polymerization dust collector is welded, marking the welding positions of each modular component as the target welding areas. After welding is completed, image information of different target welding areas is collected and evaluated. According to the comparison result between the evaluation result and the preset quality standard, 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 update.

[0048] It should be noted that after welding is completed, high - definition industrial cameras installed on the automatic welding robot or at specific positions immediately collect images of the welds. These cameras have high resolution, high frame rate, and good low - light performance, and can clearly capture the details of the welds under different ambient light conditions. To ensure full coverage of the welds, multiple cameras are arranged to take pictures from different angles to obtain multi - perspective images of the welds. Ensure complete imaging of the circumferential welds of the entire target welding area.

[0049] Specifically:

[0050] Step 1: Use the threshold segmentation algorithm to separate the pore regions in the image information of the target welding area, and obtain the circularity corresponding to each group of separated pore regions; that is, calculate using the circularity calculation formula, and the formula is expressed as , where A is the area of the pore region and P is the perimeter of the pore region;

[0051] Set the reference threshold range corresponding to the circularity, match the circularity corresponding to each group of pore regions with the reference threshold range, count the pixel points of each pore region within the reference threshold range, and convert the actual area according to the resolution of the image. After the conversion is completed, accumulate the actual areas of each pore region, denoted as the pore product; calculate the proportion of the pore product of the target welding region in the total area of the target welding region, denoted as the pore defect proportion;

[0052] Step 2: Use the Canny edge detection algorithm to separate the crack regions in the image information of the target welding region, and obtain the aspect ratio of the length to width of the contours corresponding to each separated crack region; the calculation method of the length ratio is to divide the length of the circumscribed rectangle of the contour by the width;

[0053] Set the reference threshold corresponding to the length ratio, compare the aspect ratio corresponding to each group of crack regions with the reference threshold, count the pixel points of each crack region higher than the reference threshold, and convert the actual area according to the resolution of the image. After the conversion is completed, accumulate the actual areas of each crack region, denoted as the crack product; calculate the proportion of the crack product of the target welding region in the total area of the target welding region, denoted as the crack defect proportion;

[0054] Step 3: Use the region growing algorithm to identify the lack of fusion defect regions in the image information of the welding region, calculate the gray mean value of the lack of fusion defect regions, and compare it with the preset normal gray mean value. Calculate the difference between the two mean values, denoted as the gray difference. Count the pixel points of the lack of fusion defect regions with a gray difference higher than the preset gray threshold difference, and convert the actual area according to the resolution of the image. After the conversion is completed, accumulate them, denoted as the lack of fusion product; calculate the proportion of the lack of fusion product in the total area of the target welding region, denoted as the fusion defect proportion;

[0055] It should be noted that before analyzing the image information of the target welding region, a filtering algorithm is used to remove the noise in the image, such as Gaussian filtering, median filtering, etc. Gaussian filtering can smooth the image and reduce the influence of random noise; median filtering has a good effect on removing salt-and-pepper noise. Then, histogram equalization processing is performed to enhance the contrast of the image and make the features of the weld more obvious.

[0056] Step 4: The preset quality standard includes the allowable error ratios corresponding to the gas defect proportion, crack defect proportion, and lack of fusion defect proportion of different target welding regions, denoted as ; Denote the gas defect proportion, crack defect proportion, and lack of fusion defect proportion of different target welding regions as ;

[0057] According to the formula Perform weighted calculation to determine the welding quality index Y of different target welding areas; They are the influence weight factors corresponding to the gas defect ratio, crack defect ratio, and fusion defect ratio respectively;

[0058] Step Five: Compare the welding quality index Y of different target welding areas with the corresponding preset welding qualification index. If the welding quality index Y of a certain target welding area is higher than the preset welding qualification index, trigger an adjustment signal and send it to the management personnel. After the management personnel determine and receive the adjustment signal, extract the welding quality index Y of the target welding area, the image information of the target welding area, and the initial welding parameters, and adjust the initial welding parameters;

[0059] If the management personnel refuse to receive the adjustment signal, extract the welding quality index Y of the target welding area and input it into the pre-constructed defect assessment database. The defect assessment database stores in advance various groups of welding parameter adjustment schemes. Each group of welding parameter adjustment schemes includes the adjustment ranges of welding parameters corresponding to the gas defect ratio, crack defect ratio, and fusion defect ratio; the adjustment ranges of welding parameters include the adjustment ranges of welding parameters such as welding current, voltage, welding speed, and wire feeding speed;

[0060] Extract the gas defect ratio, crack defect ratio, and fusion defect ratio corresponding to the welding quality index Y of the target welding area;

[0061] Use the Euclidean distance to calculate the gas defect ratio, crack defect ratio, and fusion defect ratio of the target welding area and the gas defect ratio, crack defect ratio, and fusion defect ratio of each group of welding parameter adjustment schemes in the defect assessment database, and represent the calculation result by d;

[0062] Sort each group of welding parameter adjustment schemes according to the calculation result d, select the welding parameter adjustment scheme with the smallest calculation result d as the target scheme, and adjust based on the welding parameter adjustment range in the target scheme on the basis of the initial welding parameters. After the adjustment is completed, send it to the management personnel. After the management personnel confirm, adjust the welding equipment according to the adjusted welding parameters, and use it as the initial welding parameters for the next group of target welding areas of the micro-electro polymerization dust collector;

[0063] It should be noted that the welding parameter adjustment scheme is confirmed by relevant technical personnel in the welding field and is adjusted and updated in real time according to historical data;

[0064] For example, when the gas defect ratio is too high, increasing the welding current within a certain range can promote gas escape and reduce gas defects. However, if the current is too large, other problems will be caused, such as increased spatter and burn-through of the welded parts. After comprehensively considering these factors, the adjustment range of the welding current corresponding to the gas defect ratio is determined. These bases can help the management personnel better understand the scientificity and rationality of parameter adjustment and enhance their trust in the adjustment scheme;

[0065] After the initial welding parameters are adjusted according to the target plan and sent to the management personnel, upon receiving the adjusted welding parameters, the management personnel need to apply these parameters in the actual welding operation and conduct a quality inspection on the target welding area again after welding to obtain the new welding quality index Y and data on the proportion of gas defects, crack defects, and fusion defects. If the new welding quality index meets the qualified standard, it indicates that the parameter adjustment is effective this time; if it still fails to meet the requirements, the new data should be fed back to the management personnel or the defect evaluation database, and re-analyzed and calculated to further optimize the adjustment plan, and the parameter adjustment and verification should be carried out again until the welding quality meets the requirements. This closed-loop feedback mechanism can ensure the continuous improvement of welding quality, improve production efficiency and product quality;

[0066] Installation data recording: Install the microelectro-polymerization dust collector after welding through a hoisting device; track and record in real time the number of module assemblies, the number of module welds, and the number of equipment hoists during the assembly process and the installation process;

[0067] It should be noted that sensors are installed on each device, such as a counter installed on the robotic arm to record the number of completed module splices.

[0068] Installation progress evaluation: According to the time plan of the installation project, divide the total duration in the time plan into X evaluation time windows; where X > 3, which is specifically set according to the number of microelectro-polymerization dust collectors in the installation project and the installation difficulty; and set the corresponding qualified number of installed dust collectors for each divided evaluation time window. If the number of installed dust collectors within a certain evaluation time window does not reach the corresponding set qualified number of installed dust collectors, then perform the corresponding steps to generate a progress exception report for the corresponding evaluation time window and send it to the management personnel;

[0069] Specifically:

[0070] Preset that different numbers of dust collectors respectively correspond to a theoretical number of module assemblies, a theoretical number of module welds, and a theoretical number of equipment hoists;

[0071] Extract the number of module assemblies, the number of module welds, and the number of equipment hoists within the corresponding evaluation time window, denoted as a1, b1, c1; extract the theoretical number of module assemblies, the theoretical number of module welds, and the theoretical number of equipment hoists corresponding to the qualified number of installed dust collectors, denoted as a2, b2, c2, and through Calculation, obtain the assembly evaluation ratio, welding evaluation ratio, and hoisting evaluation ratio within the corresponding evaluation time window;

[0072] Take the assembly evaluation ratio, welding evaluation ratio, and hoisting evaluation ratio as the length value, width value, and height value respectively, construct a three-dimensional rectangular model, and use the constructed three-dimensional rectangular model as the construction status model within the corresponding evaluation time window;

[0073] It should be noted that taking the assembly evaluation ratio, welding evaluation ratio, and hoisting evaluation ratio as the length, width, and height of the three-dimensional rectangle respectively to construct a three-dimensional rectangular model visually shows the relative relationship between the progress of each link. Managers can quickly understand which link has a relatively faster or slower progress and the balance degree between each link by observing the shape of the three-dimensional rectangle and the ratio of each dimension; for example, if a certain dimension of the three-dimensional rectangle is significantly longer or shorter, it indicates that there is a large difference in the progress of the corresponding construction link compared with other links, which is convenient for timely discovering potential progress problems;

[0074] Extract the actual number of dust collectors within the corresponding evaluation time window for the unqualified installation of dust collectors, calculate the difference with the number of qualified installed dust collectors, obtain the number of qualified distances within the corresponding evaluation time window, and add the number of qualified distances to the number of qualified installed dust collectors in the next evaluation time window as the final number of qualified installed dust collectors in the next evaluation time window;

[0075] Extract the number of qualified distances, construction status model within the corresponding evaluation time window, and the final number of qualified installed dust collectors in the next evaluation time window and fill them into a pre-set report template, so as to generate a progress anomaly report within the corresponding evaluation time window and send it to the manager;

[0076] 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 limit the invention to the specific implementation manners. Obviously, according to the content of this specification, many modifications and changes can be made. These embodiments are selected and specifically described in this specification to better explain the principle and practical application of the present invention, so that those skilled in the art can understand and utilize the present invention well. The present invention is only limited 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 time 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, the corresponding steps are executed to generate a progress exception report within the corresponding evaluation time window and send it to the management personnel; Execute the corresponding steps to generate a progress exception report within the corresponding evaluation time window and send it to the management personnel, specifically: 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 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, and the constructed three-dimensional rectangular model is used as the construction status model within the corresponding assessment time window; 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.

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.

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