Intelligent control method for full-automatic pipe scraping device of air cooler

Abstract

The present application relates to the technical field of data processing, more particularly, the present application relates to an intelligent control method of a full-automatic tube-scraping device of an air cooler, the method comprising: obtaining the three-dimensional coordinates and the over-difference of each tube port in the tube-scraping sequence of the air cooler; determining the tube-scraping difficulty coefficient of the tube port based on the material hardness coefficient of the tube port, the deviation degree of the actual diameter from the standard diameter, and the deviation degree of the actual tube wall thickness from the standard tube wall thickness; obtaining the estimated working time of the tube port according to the tube-scraping difficulty coefficient, the over-difference, and the preset tube-scraping parameters; performing weighted summation on the density of the tube port, the estimated working time, and the over-difference to determine the priority weight of the tube port; sorting the tube ports in the tube-scraping sequence according to the priority weight to obtain the final tube-scraping path; and controlling the tube-scraping device to automatically scrape the finned tube of the tube port in the tube-scraping sequence along the final tube-scraping path, thereby effectively improving the working efficiency of the tube-scraping device of the air cooler.

Description

Technical Field

[0001] This invention relates to the field of data processing technology. More specifically, this invention relates to an intelligent control method for a fully automatic scraping device for air coolers. Background Technology

[0002] Air coolers are core heat exchange equipment that uses finned tubes for heat exchange. During manufacturing, the actual length of the finned tubes extending beyond the tube box often exceeds design requirements. Therefore, a tube-scraping process is necessary to cut off the excess length, ensuring it meets the standards for subsequent welding and equipment use. The quality and efficiency of the tube-scraping process directly affect the overall performance and production cycle of the air cooler.

[0003] Currently, most methods rely on operators' personal experience to judge and control the scraper's rotation speed, feed rate, and scraping distance. This method has significant technical problems. First, the inconsistency of manual operation leads to inconsistent final lengths and surface qualities at each tube end, making it difficult to guarantee uniform processing accuracy. Second, production efficiency is low, heavily dependent on the operator's skill level. In contrast, fully automated tube scraping equipment for air coolers uses path planning algorithms to optimize the robotic arm's movement trajectory, thus replacing manual labor and improving efficiency.

[0004] However, due to differences in the degree of deviation, material, and size of each pipe opening, the actual processing time varies greatly. Therefore, a path that only pursues the shortest travel distance may lead to increased equipment waiting time due to the concentrated processing of multiple time-consuming and difficult pipe openings, and its overall processing efficiency may not be optimal, failing to achieve a true balance between time and efficiency.

[0005] Therefore, how to improve the efficiency of fully automatic scraping control of air coolers is an urgent problem to be solved. Summary of the Invention

[0006] To address the aforementioned technical problem of improving the efficiency of fully automatic scraping tube control in air coolers, this invention proposes an intelligent control method for a fully automatic scraping tube device in air coolers, comprising the following steps:

[0007] The process involves: acquiring the three-dimensional coordinates and deviation of each tube opening in the tube-to-scraping sequence of the air cooler; determining the tube-scraping difficulty coefficient based on the tube opening's material hardness coefficient, the deviation between the actual diameter and the standard diameter, and the deviation between the actual tube wall thickness and the standard tube wall thickness; obtaining the estimated working time of the tube opening based on the tube-scraping difficulty coefficient, deviation, and preset tube-scraping parameters; determining the priority weight of the tube openings by weighted summation of the tube opening density, estimated working time, and deviation; sorting the tube openings in the tube-to-scraping sequence according to their priority weights to obtain the final tube-scraping path; and controlling the tube-scraping device to automatically scrape the finned tubes of the tube openings in the tube-to-scraping sequence along the final tube-scraping path.

[0008] This invention provides a method for determining the scraping sequence of a scraping device, which can improve work efficiency and reduce wasted time. In determining the scraping sequence, this invention considers that relying solely on the shortest movement path may not account for the scraping length of individual pipe openings, thus increasing unnecessary time consumption. Therefore, this invention determines the processing difficulty of the pipe openings by considering material and dimensional deviations, and comprehensively considers the processing difficulty, required time, deviation amount, and spatial distribution of each pipe opening to determine the priority weight and scraping sequence. In this way, this invention can intelligently balance the actual processing time and the robotic arm movement time, not just relying on the shortest spatial path. This effectively reduces the possibility of long equipment waiting times and efficiency bottlenecks caused by continuously processing high-difficulty, time-consuming pipe openings, thereby optimizing the overall scraping operation process and significantly improving the overall efficiency and equipment utilization of the fully automatic scraping system for air coolers.

[0009] According to the intelligent control method of the fully automatic tube scraping device for an air cooler provided by the present invention, the method for obtaining the three-dimensional coordinates and deviation of each tube opening in the tube-to-scraping sequence of the air cooler further includes: determining the scraping parameters of the initial tube opening based on the material of the initial tube opening, the scraping parameters including scraper rotation speed, feed speed, and maximum feed distance; capturing an image of the tube sheet of the air cooler, and using the center point of the initial tube opening in the tube sheet image as the image coordinates of the initial tube opening; converting the image coordinates into machine coordinates through the conversion relationship between the camera pixel coordinate system and the robot arm base coordinate system to obtain the three-dimensional coordinates of each initial tube opening, wherein the vertical coordinate in the three-dimensional coordinates of the initial tube opening is the surface reference height of the tube opening; and measuring the actual extension length of each finned tube through a distance measuring sensor.

[0010] This invention combines machine vision and ranging sensors to quickly and accurately obtain the precise position of each initial pipe opening in three-dimensional space and the precise length to be scraped, laying the foundation for subsequent path planning.

[0011] According to the present invention, an intelligent control method for a fully automatic scraping tube device for an air cooler includes a method for obtaining the deviation of the tube opening, comprising: obtaining the actual extension length of the finned tube directly above the tube opening; and determining the deviation of the tube opening based on the difference between the actual extension length and the extension length threshold.

[0012] According to the present invention, an intelligent control method for a fully automatic tube scraping device for an air cooler is provided. The method for obtaining the tube sequence to be scraped in the air cooler includes: adding an initial tube port with an out-of-tolerance value greater than 0 to the tube sequence to be scraped, thereby obtaining each tube port in the tube sequence to be scraped.

[0013] This invention uses the deviation measurement to determine the pipe ends that need to be processed, forming a list of pipe ends to be processed. It can automatically filter out pipe ends with the correct length, avoiding unnecessary processing actions and wasting time, and improving the targeting and efficiency of the operation.

[0014] According to the present invention, an intelligent control method for a fully automatic tube scraping device for an air cooler includes determining the tube scraping difficulty coefficient of the tube opening by: weighting the deviation between the actual diameter and the standard diameter of the tube opening according to a preset diameter deviation coefficient to obtain a diameter index; weighting the deviation between the actual tube wall thickness and the standard tube wall thickness of the tube opening according to a preset tube wall thickness deviation coefficient to obtain a thickness index; obtaining the sum of 1 and the diameter index and the thickness index, and multiplying the sum by the material hardness coefficient of the tube opening as the tube scraping difficulty coefficient of the tube opening.

[0015] According to the intelligent control method of the fully automatic tube scraping device for an air cooler provided by the present invention, obtaining the estimated working time of the tube inlet includes:

[0016] ;

[0017] , , These represent the estimated working time, scraping difficulty coefficient, and deviation amount for the i-th pipe opening, respectively. , , The parameters for scraping tubes are the maximum feed distance, feed rate, and scraper rotation speed. For correction factor, This is the floor symbol.

[0018] This invention provides a precise method for calculating the estimated working time of pipe openings. By comprehensively considering the length to be scraped and the processing difficulty, and by correcting it through the scraper rotation speed, the working time of each pipe opening can be accurately predicted, providing a key data foundation for subsequent optimization of the pipe scraping sequence.

[0019] According to the present invention, an intelligent control method for a fully automatic tube scraping device for an air cooler includes a method for obtaining the density of tube openings, comprising: taking the average Euclidean distance between the image coordinates of all tube openings in the tube-to-scraping sequence as the average spacing; and calculating the ratio of the number of tube openings in a neighborhood range with the image coordinates of one tube opening in the tube-to-scraping sequence as the center and the average spacing as a preset multiple as the radius to the total number of tube openings, thereby obtaining the density of the tube openings.

[0020] According to the intelligent control method of the fully automatic tube scraping device for air coolers provided by the present invention, the determination of the priority weight of the tube openings further includes: dynamically adjusting the weight coefficients of density, estimated working time and deviation amount; increasing the weight coefficient of deviation amount when the total number of tube openings in the tube scraping sequence is less than or equal to a first threshold; increasing the weight coefficient of density when the total number of tube openings in the tube scraping sequence is greater than the first threshold and less than a second threshold; and increasing the weight coefficients of density and estimated working time when the total number of tube openings in the tube scraping sequence is greater than or equal to the second threshold.

[0021] According to the present invention, an intelligent control method for a fully automatic tube scraping device for an air cooler includes sorting the tube openings in the tube scraping sequence according to priority weights to obtain the final tube scraping path. This includes: dividing the tube openings in the tube scraping sequence into high, medium, and low priority sets according to priority weights; clustering the tube openings in the high priority set and determining the tube scraping order of the tube openings in the high priority set according to the principle of minimum intra-class distance and maximum inter-class distance; and sequentially inserting the tube openings in the medium and low priority sets into the path gaps of the high priority tube openings' scraping order to obtain the final tube scraping path.

[0022] According to the present invention, an intelligent control method for a fully automatic tube scraping device for an air cooler is provided, wherein the control device automatically scrapes the finned tubes at the tube openings in the tube-to-scraping sequence along the final scraping path, and further includes: monitoring the scraping resistance in real time during the automatic scraping process; and adjusting the scraper feed speed according to the cutting resistance when the scraping resistance deviates from the preset normal range.

[0023] This invention effectively reduces the possibility of tool or workpiece damage due to overload by monitoring the cutting resistance in real time during the scraping process and dynamically adjusting the feed rate according to the resistance changes, thereby improving the robustness of the equipment while ensuring the processing quality.

[0024] The present invention has the following beneficial effects:

[0025] Based on the above technical solution, this invention provides an intelligent control method for a fully automatic tube scraping device for air coolers. In determining the scraping sequence, this invention considers that relying solely on the shortest movement path might not account for the scraping length of individual tube openings, thus increasing unnecessary time consumption. Therefore, this invention determines the processing difficulty of the tube openings through material and dimensional deviations, comprehensively considering the processing difficulty, required time, deviation amount, and spatial distribution of each tube opening to determine the priority weight and scraping sequence. In this way, this invention can intelligently balance the actual processing time and the robotic arm movement time, not just relying on the shortest spatial path. This effectively reduces the possibility of long equipment waiting times and efficiency bottlenecks caused by continuously processing high-difficulty, time-consuming tube openings, thereby optimizing the overall tube scraping operation process and significantly improving the overall efficiency and equipment utilization of the fully automatic tube scraping device for air coolers. Attached Figure Description

[0026] Figure 1 This is a flowchart illustrating the steps of an intelligent control method for a fully automatic scraping device for an air cooler according to an embodiment of the present invention. Detailed Implementation

[0027] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0028] In the air cooler manufacturing process, finned tubes need to be inserted into the tube sheets of the front and rear tube boxes. The design requires the finned tubes to extend 5-8mm beyond the tube sheet surface. However, due to assembly errors, the actual total length often exceeds the vertical distance between the front and rear tube sheets by 20-25mm. Therefore, it is necessary to scrape the finned tubes that extend 5-10mm beyond the design distance on the tube sheet.

[0029] Please see Figure 1 , Figure 1 This is a flowchart illustrating the steps of an intelligent control method for a fully automatic scraping tube device for an air cooler according to an embodiment of the present invention. The method includes the following steps:

[0030] S1: Obtain the three-dimensional coordinates of each initial port in the air cooler.

[0031] For example, in an embodiment of the present invention, the scraping parameters of the initial pipe opening are determined according to the material of the initial pipe opening. The scraping parameters include scraper rotation speed, feed speed, and maximum feed distance.

[0032] Specifically, the scraper rotation speed range can be set to 50-200. The feed rate range can be set to 0.5-2. The maximum feed distance can be set to 2. The specific settings can be configured according to actual needs.

[0033] For example, in an embodiment of the present invention, obtaining the three-dimensional coordinates of each initial port in the air cooler includes: taking an image of the tube sheet of the air cooler, using the center point of the initial port in the tube sheet image as the image coordinates of the initial port; converting the image coordinates into machine coordinates through the conversion relationship between the camera pixel coordinate system and the robot arm base coordinate system to obtain the three-dimensional coordinates of each initial port, wherein the vertical coordinate in the three-dimensional coordinates of the initial port is the surface reference height of the port.

[0034] Specifically, the fully automated tube scraping device is calibrated, and the industrial camera's intrinsic and extrinsic parameters are calibrated. A unified coordinate mapping transformation relationship is established between the vision system and the robotic arm, and the calibration error is controlled within ±0.1mm. The tube box parameters of the air cooler are input, including the tube sheet size and the tube nozzle arrangement matrix. An extension length threshold is set according to actual process requirements. As an example, the extension length threshold can be set to... The final number of initial nozzles in the air cooler is: .

[0035] The tube sheet image of the air cooler is captured using a calibrated industrial camera. The center point of each initial tube opening in the tube sheet image is identified using a deep learning algorithm and used as the coordinate of the initial tube opening. The initial tube openings in the tube sheet image are segmented into instances, and the image coordinates of each initial tube opening in the image coordinate system are identified and output. The coordinates of each initial tube opening are then transformed to obtain the three-dimensional coordinates of each initial tube opening, which include the horizontal coordinate, the vertical coordinate, and the vertical axis.

[0036] For example, the actual protrusion length of the finned tube is measured using a distance sensor.

[0037] Specifically, the robotic arm of the scraping device is moved so that the laser rangefinder at its end moves to a predetermined position directly above the center of each initial tube opening. In this embodiment of the invention, a position 10mm away from the tube opening surface can be used as the measurement position. After reaching the measurement position, the laser rangefinder is triggered to measure the actual distance from the scraping device to the end face of the finned tube, thereby obtaining the actual extension length of the finned tube in each initial tube opening.

[0038] The three-dimensional coordinates of each initial tube opening in the air cooler tube sheet can be obtained by following the steps above.

[0039] S2: Obtain each tube opening in the tube-to-scraping sequence of the air cooler, and determine the tube-scraping difficulty coefficient based on the tube opening's material hardness coefficient, the deviation between the actual diameter and the standard diameter, and the deviation between the actual tube wall thickness and the standard tube wall thickness.

[0040] It should be noted that, since the finned tubes at the tube openings on the tube sheet may exceed the design distance, these finned tubes exceeding the design distance need to be scraped. The scraping length at the tube opening is determined by the actual extension length of the finned tube at the tube opening. Therefore, in this embodiment of the invention, the actual extension length of the finned tube can be obtained and compared with an extension length threshold to determine its deviation amount, and the tube openings that need to be scraped can be determined based on the deviation amount.

[0041] For example, in an embodiment of the present invention, the method for obtaining the deviation of the pipe opening includes: obtaining the actual extension length of the finned tube directly above the pipe opening; and determining the deviation of the pipe opening based on the difference between the actual extension length and the extension length threshold.

[0042] Understandably, the deviation of the pipe opening reflects the basic length of the pipe that needs to be scraped at each pipe opening. The larger the deviation, the longer the basic length of the pipe that needs to be scraped at the pipe opening.

[0043] For example, in an embodiment of the present invention, the method for obtaining the tube sequence to be scraped in an air cooler includes: adding an initial tube port with an out-of-tolerance value greater than 0 as a tube port to be processed into the tube sequence to be scraped, thereby obtaining each tube port in the tube sequence to be scraped; conversely, if the out-of-tolerance value is less than or equal to 0, then the initial tube port does not need to be scraped.

[0044] It is understood that in the embodiments of the present invention, the initial pipe port is a collective term for all pipe ports in the air cooler. The initial pipe port includes the pipe ports in the pipe-to-scrape sequence and other initial pipe ports not included in the pipe-to-scrape sequence. Since other initial pipe ports not included in the pipe-to-scrape sequence do not need to be processed or subsequent steps need to be performed, the pipe ports mentioned in the embodiments of the present invention are all pipe ports in the pipe-to-scrape sequence.

[0045] By following the steps above, all the pipe openings that need to be scraped can be obtained. By combining all the pipe openings that need to be scraped, the sequence of pipes to be scraped in the air cooler can be obtained.

[0046] It should be further explained that the conventional method of directly planning the scraping path using the shortest distance does not consider the distribution of processing time. If high-difficulty pipe openings appear consecutively along the shortest path, the robotic arm needs to stay in operation for a longer period, increasing the total processing time. During the scraping process of finned tubes at the pipe openings, the scraping difficulty is determined by the characteristics of the workpiece itself. Material hardness is the core source of scraping resistance; the higher the hardness, the greater the friction and cutting force during scraping, and the greater the processing difficulty. Deviations between the actual and standard values ​​of the pipe diameter and wall thickness will increase positioning accuracy and difficulty. Therefore, the scraping time is directly related to the processing difficulty; the higher the difficulty, the slower the feed rate and the more precise the parameter control, resulting in a longer processing time.

[0047] Based on this, embodiments of the present invention can determine the tube-scraping difficulty coefficient of the tube opening by the material hardness coefficient, the deviation between the actual diameter and the standard diameter, and the deviation between the actual tube wall thickness and the standard tube wall thickness in the tube-scraping sequence. This is to assess the influence of the tube opening's own characteristics on the tube-scraping difficulty. The higher the degree of influence, the more it needs to be given priority when planning the tube-scraping route.

[0048] It is understandable that diameter deviation has a magnifying effect on the difficulty of tube scraping. When the actual diameter deviates significantly from the standard diameter, it indicates that the tube opening may be too thick or too thin, requiring the robotic arm's vision positioning system to achieve higher precision alignment. If the alignment deviation exceeds 0.5mm, it may lead to contact deviation between the scraper and the tube edge, resulting in uneven scraping or, in severe cases, scraper jamming or scratching the tube plate. The positioning error tolerance is low. Therefore, the impact of diameter deviation on the overall difficulty of tube scraping is more significant than a linear relationship. In this embodiment of the invention, a large diameter deviation coefficient can be set as a weighting factor for the degree of deviation between the actual and standard diameters of the tube opening. This diameter deviation coefficient reflects the degree of influence of the deviation between the actual and standard diameters of the tube opening on the difficulty coefficient of tube scraping.

[0049] The effect of wall thickness deviation on the difficulty of scraping is nearly linear. When the actual wall thickness is greater than the standard wall thickness, the scraping resistance increases, but the scraper can adjust the feed pressure within the reserved pressure adjustment range in the process parameters. Conversely, when the actual wall thickness is less than the standard wall thickness, the resistance decreases, but the scraping depth needs to be controlled to prevent the pipe wall from becoming too thin. Compared to the rigid constraint of diameter deviation on positioning accuracy, the impact of wall thickness deviation can be dynamically compensated through process parameters, and its contribution to the overall difficulty is lower than that of diameter deviation. Therefore, in this embodiment of the invention, a small pipe wall thickness deviation coefficient is set as a weighting coefficient for the degree of deviation. This pipe wall thickness deviation coefficient is used to reflect the degree of influence of the deviation between the actual pipe wall thickness and the standard pipe wall thickness on the difficulty coefficient of scraping the pipe end.

[0050] As an example, the diameter deviation coefficient can be set to 1.2 and the pipe wall thickness deviation coefficient can be set to 0.8; the specific settings can be adjusted according to actual needs.

[0051] For example, in an embodiment of the present invention, determining the scraping difficulty coefficient of the pipe opening includes: weighting the deviation between the actual diameter and the standard diameter of the pipe opening according to a preset diameter deviation coefficient to obtain a diameter index; weighting the deviation between the actual pipe wall thickness and the standard pipe wall thickness of the pipe opening according to a preset pipe wall thickness deviation coefficient to obtain a thickness index; obtaining the sum of 1 and the diameter index and the thickness index, and multiplying the sum by the material hardness coefficient of the pipe opening as the scraping difficulty coefficient of the pipe opening.

[0052] For ease of understanding, embodiments of the present invention provide a formula for the difficulty coefficient of scraping the pipe at the pipe opening, as detailed in the following formula:

[0053] ;

[0054] Let be the difficulty coefficient of scraping the i-th pipe opening. The value is the hardness coefficient of the material at the pipe opening. To preset the diameter deviation coefficient, Let be the actual diameter of the i-th pipe opening. The standard diameter of the tube opening on the tube sheet. This is the preset pipe wall thickness deviation coefficient. Let be the actual pipe wall thickness of the i-th pipe opening. The standard wall thickness at the tube opening on the tube sheet. It is the absolute value symbol.

[0055] In the formula for the difficulty coefficient of scraping pipe, It refers to the degree of deviation between the actual diameter of the pipe opening and the standard diameter. This refers to the diameter. The greater the deviation between the actual diameter of the pipe opening and the standard diameter, the more difficult it will be to align the scraper center with the center of the pipe opening.

[0056] It refers to the degree of deviation between the actual pipe wall thickness and the standard pipe wall thickness. This refers to the thickness. The greater the deviation between the actual pipe wall thickness and the standard pipe wall thickness, the greater the resistance the scraper center will encounter during the scraping process at the pipe end. At this time, there is no deviation between the actual pipe wall thickness and the standard pipe wall thickness. ;when When the actual pipe wall thickness is greater than the standard pipe wall thickness, the actual pipe wall is too thick; when At that time, the actual pipe wall thickness was less than the standard pipe wall thickness, and the actual pipe wall was too thin.

[0057] Material hardness, diameter deviation, and wall thickness deviation collectively affect the processing difficulty. Based on the above steps, the scraping difficulty coefficient of each pipe opening can be obtained, which is used to characterize the influence of pipe opening material and size on the scraping difficulty of the pipe opening.

[0058] Understandably, the higher the difficulty level of pipe scraping, the slower the feed rate and the more precise the parameter control need to be, thus requiring a longer scraping time for that pipe opening. When determining the scraping sequence of the pipe scraping device, the path can be interleaved based on the estimated working time of the pipe opening to improve scraping efficiency.

[0059] S3: Based on the scraping difficulty coefficient, deviation amount, and preset scraping parameters, obtain the estimated working time of the pipe opening.

[0060] It should be noted that the maximum feed distance per cycle is 2mm. The core objective of scraping the tube is to eliminate the deviation at the tube end and make the remaining length meet the design requirements. However, due to the equipment process constraints of the maximum feed distance per cycle, when the deviation is large, it is necessary to scrape multiple times to complete the removal of the deviation at the tube end. If the maximum feed distance per cycle is too deep, it may cause damage to the tube wall or overload of the scraper.

[0061] Based on this, embodiments of the present invention can determine the number of scraping operations required for the pipe opening to compensate for the deviation amount based on the deviation amount and the maximum feed distance, and determine the total feed distance based on the number of scraping operations and the maximum feed distance, so as to determine the feed time required for the pipe opening. Finally, by combining the scraper rotation speed, the estimated working time of the pipe opening can be accurately obtained.

[0062] For example, in an embodiment of the present invention, the estimated working time of the nozzle can be obtained by referring to the following formula:

[0063] ;

[0064] Let i be the estimated working time for the i-th pipe opening. Let be the difficulty coefficient of scraping the i-th pipe opening. The deviation is the amount of the i-th nozzle. This refers to the maximum feed distance in the scraper parameters. The feed rate in the scraper parameters. For correction factor, The scraper speed is the parameter in the scraper tube. This is the floor symbol.

[0065] The correction factor can be set to 50; the specific setting can be adjusted according to actual needs.

[0066] In the method of calculating estimated working hours It refers to the feed time; the slower the feed rate, the longer the feed time.

[0067] This is a speed correction term. During the tube scraping process, the scraper speed determines the scraping amount per unit time. The lower the speed, the fewer the number of cuts per unit time, the lower the actual scraping efficiency, the longer the time consumed, and the greater the estimated working time of the tube opening; conversely, the higher the speed, the higher the efficiency, and the smaller the estimated working time of the tube opening.

[0068] Thus, in determining the estimated working time of the nozzle, the present invention combines the feed time and the speed correction term. This ensures that the basic time corresponding to the total scraping volume is sufficient through the feed time, and also reflects the actual efficiency difference at different speeds through the speed correction term, thereby accurately obtaining the estimated working time required for the nozzle.

[0069] S4: The priority weight of the pipe openings is determined by weighted summation of the pipe opening density, estimated working time, and deviation amount.

[0070] Understandably, the estimated working time and deviation of the pipe openings can be obtained from the above steps. The larger the estimated working time and deviation, the more priority should be given when determining the pipe scraping sequence. However, the density distribution of each pipe opening in the pipe scraping sequence directly affects the movement efficiency. Therefore, when determining the processing sequence, it is also necessary to obtain the spatial distribution of the pipe openings. Pipe openings with higher density should be processed first to improve work efficiency.

[0071] Based on this, when determining the priority order of scraping each pipe opening in the pipe-to-scraping sequence, the embodiments of the present invention can obtain the pipe opening density, estimated working time and deviation amount respectively, and comprehensively evaluate the priority of the pipe opening in the pipe-to-scraping sequence from these three aspects.

[0072] For example, in an embodiment of the present invention, the method for obtaining the density of the tube openings includes: taking the average Euclidean distance between the image coordinates of all tube openings in the tube-to-scrape sequence as the average spacing; and calculating the ratio of the number of tube openings in a neighborhood range with the image coordinates of one tube opening in the tube-to-scrape sequence as the center and the average spacing as a preset multiple as the radius to the total number of tube openings, to obtain the density of the tube openings.

[0073] The average spacing can be set to a multiple of 1.5; the specific setting can be adjusted according to actual needs. The greater the density of the pipe openings, the higher the probability that the pipe opening is located in the core area of ​​the area to be scraped. Prioritizing the processing of pipe openings during scraping can reduce the repeated movement of the robotic arm in dense areas, thereby effectively reducing the scraping time.

[0074] It should be noted that when determining the priority weight of pipe openings by weighted summation based on the density of pipe openings, estimated working time, and deviation, the weight coefficient for density is... Weighting coefficient for estimated working hours Weighting coefficients for deviation The sum of these values ​​is 1. The total number of tube openings in the tube-to-scraping sequence determines the scale of the tube-scraping task. When setting specific weighting coefficients, the weighting coefficients need to be adjusted according to the total number of tube openings in the tube-to-scraping sequence.

[0075] For example, in an embodiment of the present invention, determining the priority weight of the pipe openings further includes: dynamically adjusting the weight coefficients of density, estimated working time, and deviation; increasing the weight coefficient of deviation when the total number of pipe openings in the pipe-to-scraping sequence is less than or equal to a first threshold; increasing the weight coefficient of density when the total number of pipe openings in the pipe-to-scraping sequence is greater than the first threshold and less than a second threshold; and increasing the weight coefficients of density and estimated working time when the total number of pipe openings in the pipe-to-scraping sequence is greater than or equal to the second threshold, wherein the first threshold is less than the second threshold.

[0076] The first threshold can be set as the number of nozzles in the air cooler. 0.4 times; the second threshold can be set to the number of nozzles in the air cooler. It is 0.6 times the actual value; the specific value can be set according to actual needs.

[0077] Specifically, when the total number of pipe openings in the pipe-to-scraping sequence is less than or equal to the first threshold, the total number of pipe openings is relatively small, the task scale is small, and the proportion of robotic arm movement time is low. In this case, even if processing is done in a decentralized manner, it will not significantly increase the total working time. However, the risk of missing high-risk pipe openings with large deviations will also be high. Therefore, it is necessary to prioritize the processing of pipe openings with large deviations, increase the priority of such pipe openings, and increase the corresponding weight coefficient of deviations.

[0078] As an example, it can be set to , , The specific details can be adjusted according to actual needs.

[0079] When the total number of pipe openings in the pipe-to-scraping sequence is greater than the first threshold but less than the second threshold, the total number of pipe openings is moderate, the task scale is moderate, and the ratio of movement time to processing time is balanced. Reducing ineffective movement to optimize efficiency is the core requirement. In this case, if scattered pipe openings are processed first, frequent long-distance movements of the robotic arm may increase the total working time. Therefore, it is necessary to prioritize the processing of densely populated pipe openings, and the corresponding density weight coefficient needs to be increased.

[0080] As an example, it can be set to , , The specific details can be adjusted according to actual needs.

[0081] When the total number of pipe openings in the pipe-to-scraping sequence is greater than or equal to the second threshold, the total number of pipe openings is large, the task scale is large, the processing time accounts for a high proportion, and the movement time loss is amplified. In this case, it is necessary to prioritize processing dense areas and pipe openings with long processing times to reduce movement and the possibility of project backlog. Therefore, the weighting coefficients for the corresponding density and estimated working time need to be increased.

[0082] As an example, it can be set to , , The specific details can be adjusted according to actual needs.

[0083] Thus, by dynamically adjusting the weight coefficients of density, estimated working time, and excess, the embodiments of the present invention can ensure that the final priority weights can focus on the scale requirements of the current tube-scraping sequence, thereby improving resource utilization efficiency.

[0084] To facilitate understanding, this embodiment of the invention provides a weighted summation of the pipe density, estimated working time, and deviation amount to obtain a relationship between the priority weights of the pipes, as detailed below:

[0085] ;

[0086] Let i be the priority weight of the i-th port. The weighting coefficients for density. Let represent the density of the i-th pipe opening. The number of tube openings in the tube-to-scraping sequence. Let represent the density of the j-th orifice in the sequence to be scraped. The weighting factor for estimating working hours, Let i be the estimated working time for the i-th pipe opening. Let j be the estimated working time for the j-th pipe opening in the pipe scraping sequence. The weighting coefficient for the excess amount. The deviation is the amount of the i-th nozzle. The deviation is the value of the j-th orifice in the tube to be scraped sequence.

[0087] In this relation, It represents the density ratio of the i-th pipe opening. This reflects the necessity of prioritizing densely populated areas. The larger the weighting coefficient, the more necessary it is to prioritize clustered areas in order to reduce travel time.

[0088] It represents the percentage of the estimated working time for the i-th pipe opening. This reflects the impact of the estimated working time of a single pipe opening on the total working time. The larger the weighting coefficient, the more necessary it is to plan for long-working-time pipe openings in advance to avoid later accumulation.

[0089] It represents the percentage of out-of-tolerance quantity at the i-th pipe opening. It reflects the urgency of the pipe end needing scraping. The higher the weighting coefficient, the more priority is given to processing pipe ends with serious deviations, thus reducing the risk of secondary processing.

[0090] Thus, by combining density, estimated working time, and deviation, the embodiments of the present invention can accurately assess the priority of each pipe opening. That is, the higher the priority weight, the higher the priority. Based on this, the order of pipe opening scraping in the pipe scraping sequence can be planned, which can effectively improve the work efficiency.

[0091] S5: Sort the tube openings in the tube-to-scraping sequence according to priority weight to obtain the final tube-to-scraping path; control the tube-to-scraping device to automatically scrape the finned tubes in the tube openings of the tube-to-scraping sequence along the final tube-to-scraping path.

[0092] It should be noted that, based on the priority weight, the embodiments of the present invention can achieve the synergy of high-priority priority processing and shortest movement path by further integrating the spatial position of the pipe opening. Thus, while ensuring that pipe openings with serious defects and high processing difficulty are processed first, the efficiency of the scraping device is optimized by reducing the idle movement time of the robotic arm through spatial clustering and path optimization.

[0093] For example, in an embodiment of the present invention, sorting the tube openings in the tube-to-scraping sequence according to priority weights to obtain the final tube-scraping path includes: dividing the tube openings in the tube-to-scraping sequence into three priority sets: high, medium, and low, according to priority weights; clustering the tube openings in the high priority set and determining the tube-scraping order of the tube openings in the high priority set according to the principle of minimum intra-class distance and maximum inter-class distance; and sequentially inserting the tube openings in the medium priority set and the low priority set into the path gaps of the tube-scraping order of the high priority tube openings to obtain the final tube-scraping path.

[0094] For example, when dividing the tube openings in the tube-to-scraping sequence into three priority sets of high, medium, and low according to priority weights, the division can be made based on the comparison result between the priority weights and a preset threshold.

[0095] Specifically, the maximum priority weight of the tube openings in the tube-to-scrape sequence can be obtained first. According to the priority weight of the pipe opening The priority level of the pipe is determined by the maximum priority weight, specifically including the following three cases:

[0096] In one possible scenario, At this point, the i-th pipe opening is classified into a high-priority set. The characteristics of the pipe openings in the high-priority set are: large deviations, core locations in dense regions, and long processing times.

[0097] In another possible scenario, At this point, the i-th pipe opening is divided into a medium-priority set. The characteristics of the pipe openings in the medium-priority set fall between those of the high-priority set and the low-priority set.

[0098] In yet another possible scenario, At this point, the i-th pipe opening is divided into a low-priority set. The characteristics of the pipe openings in the low-priority set are small deviations, relatively scattered distribution, and short processing time.

[0099] Based on the above steps, the pipe openings in the high-priority set can be obtained. By setting the number of clusters in the K-means clustering algorithm to cluster the high-priority set, pipe openings that are spatially adjacent can be divided into the same cluster, and finally multiple clusters are obtained. The number of clusters can be set to 8. The step of determining the clusters according to K-means clustering can be implemented by existing technology, and will not be described in detail in this embodiment of the invention.

[0100] Specifically, when determining the scraping sequence of high-priority pipe openings based on the principle of minimizing intra-class distance and maximizing inter-class distance, the sorting is performed with the goal of minimizing the total moving distance between all pipe openings within a cluster. The access order of clusters is determined based on the principle of maximizing the distance between clusters. This allows clusters that are farther away from other cluster centers to be accessed first when determining the scraping sequence, thereby creating a larger path gap between clusters. This enables the insertion of medium and low-priority pipe openings along the way from near to far during the movement of the robotic arm, thus making full use of time for scraping and reducing wasted idle time.

[0101] For example, when controlling the scraping device to automatically scrape the finned tubes at the tube openings in the tube-to-scraping sequence along the final scraping path, the final scraping path and scraping parameters can be loaded first. After the robotic arm moves to the tube opening, the vision system accurately positions itself again to ensure alignment with the center of the tube opening. Scraping is then performed based on the deviation amount. After scraping is completed, the actual extension length of the finned tube at the tube opening is acquired to determine the deviation amount. In response to the deviation amount being 0, the tube opening is determined to be qualified for scraping, and the process continues to acquire the next tube opening until the scraping operation of all tube openings in the tube-to-scraping sequence is completed.

[0102] For example, in an embodiment of the present invention, controlling the scraping device to automatically scrape the finned tubes at the tube openings in the tube-to-scraping sequence along the final scraping path further includes: monitoring the scraping resistance in real time during the automatic scraping process; and adjusting the scraper feed speed according to the cutting resistance when the scraping resistance deviates from the preset normal range.

[0103] As can be seen, in this embodiment of the invention, when realizing the intelligent control of the air cooler tube scraping device, the three-dimensional coordinates and deviation of each tube opening in the tube-to-scraping sequence of the air cooler can be obtained; the tube scraping difficulty coefficient of the tube opening is determined based on the tube opening material hardness coefficient, the deviation between the actual diameter and the standard diameter, and the deviation between the actual tube wall thickness and the standard tube wall thickness; the estimated working time of the tube opening is obtained according to the tube scraping difficulty coefficient, the deviation, and the preset tube scraping parameters; the tube opening density, the estimated working time, and the deviation are weighted and summed to determine the priority weight of the tube opening; the tube openings in the tube-to-scraping sequence are sorted according to the priority weight to obtain the final tube scraping path; the tube scraping device is controlled to automatically scrape the finned tubes of the tube openings in the tube-to-scraping sequence along the final tube scraping path, effectively improving the working efficiency of the air cooler tube scraping device.

[0104] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An intelligent control method for a full-automatic air cooler pipe scraping device, characterized in that, include: The scraping parameters of the initial pipe opening are determined based on the material of the initial pipe opening. The scraping parameters include scraper rotation speed, feed speed, and maximum feed distance. The tube sheet of the air cooler is photographed, and the center point of the initial tube opening in the tube sheet image is used as the image coordinate of the initial tube opening. The image coordinates are converted into machine coordinates through the transformation relationship between the camera pixel coordinate system and the robot arm base coordinate system to obtain the three-dimensional coordinates of each initial tube opening. The vertical coordinate in the three-dimensional coordinates of the initial tube opening is the surface reference height of the tube opening. The actual extension length of each finned tube is measured by a distance measuring sensor. The three-dimensional coordinates and deviation of each tube opening in the tube-to-scrape sequence of the air cooler are obtained. The deviation of the tube opening is obtained by: obtaining the actual extension length of the finned tube directly above the tube opening; and determining the deviation of the tube opening based on the difference between the actual extension length and the extension length threshold. Based on the material hardness coefficient of the pipe opening, the degree of deviation between the actual diameter and the standard diameter, and the degree of deviation between the actual pipe wall thickness and the standard pipe wall thickness, the pipe opening scraping difficulty coefficient is determined, including: weighting the degree of deviation between the actual diameter and the standard diameter of the pipe opening according to a preset diameter deviation coefficient to obtain a diameter index; weighting the degree of deviation between the actual pipe wall thickness and the standard pipe wall thickness of the pipe opening according to a preset pipe wall thickness deviation coefficient to obtain a thickness index; obtaining the sum of 1 and the diameter index and the thickness index, and multiplying the sum by the material hardness coefficient of the pipe opening as the pipe opening scraping difficulty coefficient; Based on the scraping difficulty coefficient, deviation amount, and preset scraping parameters, the estimated working time at the pipe inlet is obtained, including: ； , , are respectively the estimated working time, the scraping difficulty coefficient, the overage of the i-th nozzle, , , are respectively the maximum feed distance, the feed speed, the scraper rotation speed in the scraping parameters, is a correction coefficient, is a ceiling symbol; The priority weight of the pipe openings is determined by weighting and summing the density of the pipe openings, the estimated working time, and the deviation. The tube openings in the tube-to-scraping sequence are sorted according to their priority weights to obtain the final tube-scraping path; The scraping device automatically scrapes the finned tubes at the tube openings in the tube-to-scraping sequence along the final scraping path.

2. The intelligent control method of the full-automatic pipe-scraping device of an air cooler according to claim 1, characterized in that, The methods for obtaining the sequence of tubes to be scraped in an air cooler include: The initial pipe openings with an out-of-tolerance value greater than 0 are added to the pipe opening sequence to be scraped, thus obtaining each pipe opening in the pipe opening sequence to be scraped.

3. The intelligent control method of the full-automatic tube scraping device of the air cooler according to claim 1, characterized in that, Methods for obtaining the density of pipe openings include: The average Euclidean distance between the image coordinates of all tube openings in the tube-to-scraping sequence is taken as the average spacing. The density of tube openings is obtained by calculating the ratio of the number of tube openings in the neighborhood range with the image coordinates of a tube opening in the tube-to-scraping sequence as the center and the average spacing as a preset multiple as the radius.

4. The intelligent control method of the full-automatic tube scraping device of the air cooler according to claim 1, characterized in that, The determination of the priority weight of the pipe opening also includes: The weighting coefficients for density, estimated working time, and deviation are dynamically adjusted. When the total number of tube openings in the tube-to-scraping sequence is less than or equal to the first threshold, the weighting coefficient for deviation is increased. When the total number of tube openings in the tube-to-scraping sequence is greater than the first threshold but less than the second threshold, the weighting coefficient for density is increased. When the total number of tube openings in the tube-to-scraping sequence is greater than or equal to the second threshold, the weighting coefficients for density and estimated working time are increased.

5. The intelligent control method for a fully automatic scraping tube device for an air cooler according to claim 1, characterized in that, The step of sorting the tube openings in the tube-to-scraping sequence according to priority weights to obtain the final tube-scraping path includes: The tube openings in the tube-to-scraping sequence are divided into three priority sets: high, medium, and low, based on their priority weights. The tube openings in the high-priority set are clustered, and the tube-scraping order of the tube openings in the high-priority set is determined according to the principle of minimum intra-class distance and maximum inter-class distance. The tube openings in the medium-priority set and the low-priority set are inserted into the path gaps of the tube-scraping order of the high-priority tube openings in sequence to obtain the final tube-scraping path.

6. The intelligent control method for a fully automatic scraping device for an air cooler according to claim 1, characterized in that, The control scraping device automatically scrapes the finned tubes at the tube openings in the tube-to-scraping sequence along the final scraping path, and also includes: During the automatic tube scraping process, the scraping resistance is monitored in real time; when the scraping resistance deviates from the preset normal range, the feed speed of the scraper is adjusted according to the cutting resistance.

Images