Automatic replacement system for colored photovoltaic adhesive film

Abstract

The application discloses an automatic replacement system of color photovoltaic adhesive film, and particularly relates to the photovoltaic power station operation and maintenance technical field, comprising a sensing and detecting module, a decision control module, an execution operation module, a material processing and supply module and a safety auxiliary module; the sensing and detecting module is used for acquiring the position, contour and surface state data of the photovoltaic module; the application constructs a three-dimensional grid model of the module through the decision control module, realizes intelligent scoring and global path optimization of the work unit based on a rule base, and generates an optimal work sequence; through adaptive process parameter adjustment in the execution operation module, the hot air power and the winding tension are dynamically adjusted according to the surface color, temperature and defect characteristics of the module, so that the old adhesive film peeling quality and the new adhesive film laying precision are significantly improved; the whole-process automation from detection, decision, execution to quality review is realized, the replacement efficiency and quality consistency are effectively improved, and the work safety is ensured.

Description

TECHNICAL FIELD

[0001] The application discloses an automatic replacement system for color photovoltaic adhesive film, and particularly relates to the photovoltaic power station operation and maintenance technical field. BACKGROUND

[0002] In the rapid development of modern building integrated photovoltaics, the aesthetic and durability requirements of color photovoltaic modules are increasingly improved; with the continuous expansion of the BIPV market, color photovoltaic adhesive film as a key material to realize the colorization of modules, the defects such as discoloration, aging and cracking caused by long-term outdoor exposure are increasingly prominent, which directly affects the power generation efficiency and architectural aesthetics.

[0003] The traditional scheme mainly relies on manual replacement operation: one way is for the operation and maintenance personnel to climb to the roof or facade, identify the defect area by naked eye, use a handheld hot air gun to locally heat the old adhesive film, and then peel off and clean it with a scraper or manually, then wipe the surface with a cleaning agent, and finally align and paste the new adhesive film according to experience; another way is to use a mobile lifting platform to assist the work, and a team to complete the removal and laying of the adhesive film.

[0004] However, the traditional manual replacement method has significant problems in efficiency and quality: on the one hand, the manual operation process is tedious, relying on individual experience from defect identification, old film peeling to new film laying, and the flatness and dust-free level of the adhesive film laying are difficult to guarantee, which may cause bubbles and wrinkles, affecting the performance and appearance of the module; on the other hand, manual work in high altitude and electrical environment has safety risks, and improper force control during peeling may damage the fragile photovoltaic cells, causing hidden faults; in addition, the entire replacement process lacks digital recording and quality traceability, which is not conducive to operation and maintenance management optimization; therefore, it is crucial to develop an automatic color photovoltaic adhesive film replacement system that can realize fully automated and intelligent operation, with precise perception, autonomous decision-making and high-quality execution capabilities. SUMMARY

[0005] In view of this, in order to solve the problems raised in the background art, an automatic replacement system for color photovoltaic adhesive film is proposed.

[0006] The purpose of the application can be achieved by the following technical solutions: the application provides an automatic replacement system for color photovoltaic adhesive film, comprising: a perception and detection module, a decision control module, an execution operation module, a material processing and supply module, and a safety auxiliary module;

[0007] The specific steps are as follows:

[0008] S1, obtaining the position, contour and surface state data set of the target photovoltaic module through the perception and detection module;

[0009] S2, the decision control module processes the position, contour and surface state data set, and plans the optimal operation path and sequence of old adhesive film removal and new adhesive film laying;

[0010] S3, the execution operation module drives the hot air and winding mechanism to soften and peel off the old adhesive film according to the optimal operation path and sequence, so as to obtain a clean component working surface;

[0011] S4, the cleaning unit in the execution operation module purifies and activates the clean component working surface, so as to achieve a pretreated surface on which the new adhesive film can be installed;

[0012] S5, the material processing and supply module transports the new colored photovoltaic adhesive film to the specified position according to the operation instruction, and the execution and operation module performs accurate positioning and initial pressing;

[0013] S6, the execution operation module uniformly lays the new colored photovoltaic adhesive film which has completed the initial pressing at a constant pressure and completes the cutting, forming a component with new adhesive film;

[0014] S7, the perception and detection module automatically scans and detects the component with new adhesive film, and generates a replacement quality review report;

[0015] S8, the decision control module integrates the replacement quality review report and the whole process data of this operation to generate a system operation report, and transmits it to the operation and maintenance center, and the system resets or switches to the next operation task.

[0016] The technical effects and advantages of the present application are as follows:

[0017] 1, through the cooperative operation of the perception and detection module and the execution operation module, the present application realizes the full-process automation from old adhesive film peeling, surface cleaning to new adhesive film laying, greatly reduces the demand for manual intervention and operation time; at the same time, based on the accurate positioning and constant pressure laying control of three-dimensional vision, the high flatness and bubble-free adhesion of adhesive film replacement are ensured, and the replacement efficiency and quality consistency are significantly improved;

[0018] 2, the three-dimensional grid model and rule base of the component built by the decision control module realize intelligent planning of operation path and self-adaptive adjustment of process parameters; the system can dynamically adjust the hot air power and winding tension according to the defect type, surface color and temperature distribution, which can effectively avoid damage to photovoltaic cells while ensuring the peeling effect, and significantly improve the intelligent level and reliability of the operation process;

[0019] 3、The application realizes automatic detection and whole-process safety monitoring of replacement quality by integrating quality review and safety auxiliary modules; the system can automatically generate a replacement quality review report, and in combination with environmental sensors and anti-collision devices, real-time safety of operation is ensured, forming traceable digital operation and maintenance records, and providing effective support for intelligent operation and maintenance management of photovoltaic power stations. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the application, and for those skilled in the basic field, other drawings can also be obtained without creative labor.

[0021] Figure 1 The system overall architecture and data flow diagram.

[0022] Figure 2 The core decision and planning flowchart.

[0023] Figure 3 The automatic execution workflow diagram. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the application will be described clearly and completely in the following with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only some embodiments of the application, not all embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the application.

[0025] Referring to Figure 1 The overall architecture of the automatic replacement system of the colored photovoltaic adhesive film described in the application includes five core modules: a perception and detection module, a decision control module, an execution operation module, a material processing and supply module, and a safety auxiliary module. The perception and detection module sends the collected position, contour and surface state data set to the decision control module; the decision control module plans the optimal operation path and sequence accordingly, and respectively issues control instructions and operation instructions to the execution operation module and the material processing and supply module; the safety auxiliary module provides safety state monitoring and protection for the whole system; the execution operation module and the material processing and supply module interact with materials, and jointly complete the replacement operation; finally, the decision control module transmits the generated system operation report to the operation and maintenance center.

[0026] The system is composed of five functional modules: the perception and detection module is used to obtain the position, contour and surface state data set of the target photovoltaic module. Specifically, it includes visual sensors, laser radars, GPS modules, infrared thermographs and the like; the decision control module is the core calculation and decision unit, which has a built-in rule base and optimization algorithm, and is used to process the perception data, plan the optimal operation path and sequence; the execution operation module is the mechanical execution unit, including the mechanical arm, the end effector (integrating the hot air gun, the winding mechanism, the cleaning unit, the pressing roller and the cutting knife), responsible for the removal of old film, surface treatment and new film laying; the material handling and supply module includes new adhesive film spool and conveying mechanism, waste adhesive film recycling bin, and protective bin, responsible for the automatic supply and recovery of materials; the safety auxiliary module includes environmental sensors (wind speed, rainfall), emergency stop button, anti-collision sensors (laser radar, ultrasonic) and anti-static and grounding devices, to ensure the safety of the system under various working conditions.

[0027] S1, obtaining the position, contour and surface state data set of the target photovoltaic module through the perception and detection module;

[0028] In this embodiment, the position data includes: global GPS coordinates, used for the initial positioning of the mobile platform; relative distance, indicating the nearest straight line distance between the base of the mechanical arm and the surface of the target module; inclination and orientation angle, used to determine the spatial attitude of the module plane; and gap with adjacent objects, used to evaluate the operation space to avoid collision.

[0029] The contour data includes: two-dimensional boundary pixel coordinates, collected by a high-resolution industrial camera, used to preliminarily identify the range of the module in the image; three-dimensional physical dimensions, i.e. length, width and diagonal length of the module, measured by a three-dimensional laser scanner; and three-dimensional coordinates of the frame, indicating the precise position of the module frame in space, which is a key boundary for planning a safe motion path.

[0030] The surface state data includes: color value, measured by a spectral sensor, used to identify the color model of the old adhesive film and provide a basis for heating parameters; defect type, defect area and defect position, obtained by analyzing the collected high-definition images by a visual recognition system, such as identifying scratches, bubbles, yellowing and their specific area and coordinates; surface flatness error, calculated by analyzing three-dimensional point cloud data, used to evaluate the overall flatness of the surface; local deformation depth, used to identify the specific degree of surface concave or convex; and infrared temperature distribution map, collected by an infrared thermal imager, used to indirectly evaluate the adhesive state of the film and the internal battery piece health status.

[0031] S2, the decision control module processes the position, contour and surface state data set, and plans the optimal operation path and sequence for the removal of old adhesive film and the laying of new adhesive film;

[0032] Referring to Figure 2As shown, the core process of the decision control module in the application for planning the optimal work path and sequence. The process starts from the component three-dimensional grid model constructed by the perception data; first, step S21 is performed to convert all position and contour data to the three-dimensional world coordinate system with the robot base as the origin; then, step S22 is performed to score each work unit through the rule base and output a work unit sequence; based on this sequence, step S23 is performed to obtain the fine path point sequence and dynamic operation parameter set corresponding to each work unit; finally, step S24 is performed to sequence optimize and collision detect all path point sequences, and verify through the judgment node, if the collision detection passes, the optimal work path and sequence are successfully output, if not, return to re-optimization and detection until the verification passes.

[0033] The steps of the optimal work path and sequence are as follows:

[0034] S21, convert all the position data and the contour data to the three-dimensional world coordinate system with the robot base as the origin, map the surface state data to the corresponding position of the component three-dimensional model to obtain the component three-dimensional grid model;

[0035] This embodiment specifically illustrates the implementation of this step: first, the global GPS coordinates of the target photovoltaic component are obtained through differential GPS technology, and the inclination and orientation angles measured by the inertial measurement unit are combined to convert them into position and attitude in the three-dimensional world coordinate system relative to the robot base through the coordinate transformation matrix. At the same time, the component and adjacent object gap, two-dimensional boundary pixel coordinates and frame three-dimensional coordinates obtained by laser radar scanning are unified to this world coordinate system through point cloud registration and coordinate solution, so as to determine the accurate three-dimensional physical size and spatial contour of the component.

[0036] Then, the color value, defect type, defect area and defect position information in the two-dimensional image collected by the high-resolution camera are inversely projected to the constructed component three-dimensional contour surface through the internal and external parameters determined by camera calibration, realizing texture mapping of surface state data on the three-dimensional model. In addition, the surface flatness error and local deformation depth data measured by the structured light sensor are directly assigned to the corresponding vertices of the three-dimensional grid as vertex displacement, and the deformation is corrected. The infrared temperature distribution map generated by the infrared thermal imager is also mapped through coordinates to be superimposed on the three-dimensional grid model as a temperature attribute layer.

[0037] Finally, all data are integrated to obtain a component three-dimensional grid model with multi-dimensional attribute information, in which the vertex contains three-dimensional world coordinates, color, temperature, deformation depth, and the face piece is associated with defect type and defect area attributes.

[0038] The center point coordinate of the component is (116.391234°E, 39.913456°N, 55.2m) and the coordinate of the base of the robot arm is (116.391245°E, 39.913445°N, 55.0m) by the differential GPS acquisition in the embodiment. The inclination of the component is 25° and the orientation angle (azimuth angle) is 180° (due south) by the IMU. About 1 million three-dimensional points of the surface of the component are obtained by laser radar scanning.

[0039] First, the GPS coordinates (longitude, latitude and height) are converted into plane rectangular coordinates by UTM projection. After conversion, the center point coordinate of the component is (500120.500, 4420125.750, 55.2) and the coordinate of the base of the robot arm is (500120.488, 4420125.735, 55.0). Obviously, the offset of the component center relative to the base of the robot arm is calculated: ΔX = 0.012m, ΔY = 0.015m, ΔZ = 0.2m.

[0040] Considering the inclination of the component 25° and the orientation angle 180°, a rotation matrix R is established. The original point cloud collected by the laser radar is converted by the hand-eye calibration matrix H and the global pose matrix R. The coordinate of a point in the sensor coordinate system is (1.5, 0.2, 2.1). The coordinate of the point converted to the base coordinate system of the robot arm is The calculation is as follows:

[0041] ;

[0042] Where is the translation vector. After conversion, the coordinate of the point is (1.512, 0.215, 2.305).

[0043] After all the point clouds are converted, the three-dimensional physical dimensions of the component are obtained by calculating the boundary of the point cloud: length 1756mm, width 1038mm, and frame thickness 35mm. At the same time, the three-dimensional coordinates of the frame are extracted, for example, a frame corner point coordinate is (0.015, 0.020, 0.305).

[0044] Further, by analyzing the point cloud, the gap between the component and the adjacent component on the north side is calculated to be 102mm.

[0045] A preliminary three-dimensional mesh model composed of triangular facets is generated by using a triangulation algorithm (such as Delaunay triangulation) to process the converted point cloud The number of vertices of this mesh is about 500,000 and the number of facets is about 1 million, and the spatial range is consistent with the calculated physical dimensions.

[0046] ​A defect of type "crack" is identified by the vision sensor at pixel coordinates (1250, 800) in the image. The area of the defect region is 245 pixels.

[0047] Through camera calibration, the intrinsic matrix K and extrinsic matrix R of the camera are known . For a vertex on the initial mesh with coordinates (0.855, 0.412, 0.318), its corresponding image pixel coordinates are calculated by the projection formula

[0048] ;

[0049] Through calculation, we get , which falls within the crack defect region. Therefore, we assign the color value (R = 45, G = 45, B = 45) and the defect type "crack" label to the vertex .

[0050] The calculation of the defect area is to count the total area of all triangles marked as "crack" on the three-dimensional mesh. After calculation, the defect area of the defect on the actual three-dimensional component is 28.5mm 2 .

[0051] The structured light sensor measures that there is a depression at the vertex relative to the ideal plane, with a local deformation depth of 0.5mm. The surface flatness error in this area is 1.2mm / m.

[0052] According to the measured deformation depth vector D and the vertex normal vector , the vertex position is corrected so that it accurately reflects the surface topography:

[0053] ;

[0054] This fine-tuning causes the mesh to produce a depression at this point.

[0055] The infrared thermal imager measures the temperature at vertex as 48.5°C. By the same coordinate projection, the temperature value is assigned as an attribute to the vertex .

[0056] After all the above steps of calculation and mapping, we finally get a high-precision three-dimensional mesh model of the component with multi-dimensional attributes . Each vertex of the model not only contains its accurate three-dimensional coordinates in the base coordinate system of the robot arm, but also has additional attributes such as color, defect label, temperature, etc.

[0057] S22, input the component three-dimensional grid model into the working unit, score each working unit through the rule base, and output a working unit sequence according to the score;

[0058] This embodiment specifically illustrates the specific implementation of the intelligent decision and sequencing process. The process is based on an accurate three-dimensional grid model, and quantitative evaluation and global optimization are performed through the rule base.

[0059] The component three-dimensional grid model received by the system All defect information has been included. The working unit divides the component surface into several working units according to the defect type, defect area, defect position, and local deformation depth.

[0060] For example, three main working areas are identified on

[0061] Unit A: the defect type is "crack", the defect area is 28.5mm 2 , the local deformation depth is 0.5mm, and the three-dimensional coordinates of the center point are (0.855, 0.412, 0.318).

[0062] Unit B: the defect type is "contamination", the defect area is 150.0mm 2 , the local deformation depth is 0.1mm, and the center point coordinates are (0.300, 0.700, 0.310).

[0063] Unit C: the defect type is "structural crack", the defect area is 15.0mm 2 , the local deformation depth is 1.5mm, and the center point coordinates are (1.200, 0.300, 0.315).

[0064] The rule base includes: working unit priority determination rules, safe operation boundary rules, process parameter self-adaptive rules, and global sequence optimization rules.

[0065] The working unit priority determination rule provides that when the defect type is identified as a structural risk or the local deformation depth exceeds the first preset threshold, the highest processing priority is given; and under the same priority, the working unit is sorted according to the spatial coordinates from top to bottom and from left to right according to the spatial proximity principle.

[0066] When the defect type is identified as "structural risk" or the local deformation depth exceeds the first preset threshold (set to 1.0mm), the highest basic score is given.

[0067] ​​Under the same basis, according to the spatial coordinates of the work unit, from top to bottom (Y axis negative direction), from left to right (X axis positive direction), calculate the space closest to the additional points .

[0068] For example: unit A (crack, deformation 0.5mm): .

[0069] Unit B (contamination, deformation 0.1mm): .

[0070] Unit C (structural crack, deformation 1.5mm): due to "structural crack" and deformation >1.0mm, .

[0071] The safety operation boundary rule stipulates that the motion trajectory of the end effector of the robot arm must maintain a distance greater than the first safety distance from the three-dimensional coordinates of the component frame; and stipulates that when the gap with the adjacent object is less than the second safety distance, the movement range of the robot arm in that direction is limited or a specific avoidance posture is triggered.

[0072] The motion trajectory of the end effector of the robot arm must maintain a distance greater than the first safety distance from the three-dimensional coordinates of the component frame (set as 30mm). Calculate the distance from the center point of each unit to the nearest frame . If , then a penalty point is given .

[0073] After calculation, the of units A, B and C are all greater than 30mm, so .、

[0074] The rule also stipulates that when the gap with the adjacent object is less than the second safety distance (set as 80mm), the movement is limited. In this example, the gap is 102mm>80mm, so it does not affect the scoring.

[0075] The process parameter adaptive rule stipulates that the power of the heater and the wind speed set value are functions of the color value and the real-time infrared temperature distribution map. For dark colors or low temperature areas, automatically adjust the heating parameters; and stipulates that the tension set value of the winding mechanism is negatively related to the defect type and the local deformation depth. When there are crack defects or the deformation depth increases, automatically adjust the upper limit of the tension.

[0076] This rule is mainly used to set specific parameters in the subsequent execution stage, but can be used as a verification item in the planning stage and does not participate in this scoring, so its additional points .

[0077] The comprehensive score of each unit is calculated as follows:

[0078] ,

[0079] Unit A: ;

[0080] Unit B: ;

[0081] Unit C: .

[0082] S23, input multi-dimensional data of single job unit, after processing, get fine path point sequence and dynamic operation parameter set corresponding to each job unit;

[0083] According to the comprehensive score, the initial job sequence is [unit C, unit A, unit B]. Because unit A and B are the same, according to the "spatial proximity principle", assuming that the starting point of the mechanical arm is (0, 0, 0), the Euclidean distance to unit A and B is calculated, unit A is closer, so it is arranged before unit B.

[0084] The global sequence optimization rule stipulates that under the premise of meeting all priority and safety rules, the total moving path of the mechanical arm should follow the traveling salesman problem optimization algorithm to achieve the goal of the shortest total travel time; And stipulate that sequence optimization must pass through collision detection virtual simulation verification based on three-dimensional contour data.

[0085] Specifically, the system abstracts the initial sequence [C, A, B] as a traveling salesman problem, the goal is to find the shortest total moving path of visiting the three points in sequence, then the system will calculate the total path length under different sequences:

[0086] Sequence [C, A, B] path: C (1.200, 0.300) - A (0.855, 0.412) - B (0.300, 0.700); The total path length is about 1.82 meters.

[0087] Sequence [C, B, A] path: C (1.200, 0.300) - B (0.300, 0.700) - A (0.855, 0.412); The total path length is about 1.95 meters.

[0088] The optimization algorithm finds that under the premise of meeting the highest priority of unit C, the sequence [C, A, B] is the shortest sequence of total travel.

[0089] S24, input the fine path point sequence of all job units, after sequence optimization and collision detection, output the optimal job path and sequence.

[0090] Finally, the sequence must be verified by collision detection virtual simulation based on three-dimensional contour data. The simulation confirms that the mechanical arm has no collision risk when moving along the path, with the distance to the component frame and adjacent objects being greater than the first and second safety distances.

[0091] The decision control module outputs the optimal work path and sequence as [unit C, unit A, unit B]. This sequence ensures that high-priority tasks are executed first, while minimizing the total movement path of the mechanical arm and fully complying with all safety constraints.

[0092] S3, the execution operation module drives the hot air and winding mechanism to soften and peel the old adhesive film according to the optimal work path and sequence, obtaining a clean component working surface;

[0093] This embodiment specifically illustrates the specific implementation of old adhesive film removal. The execution operation module controls the mechanical arm to carry the end effector to the first work unit C (center coordinates (1.200, 0.300, 0.315)) in the sequence. The process parameter adaptive rule is activated:

[0094] According to the color value (dark gray, RGB about (50, 50, 50)) and the infrared temperature distribution map (current surface temperature 22°C) of unit C, the rule library function is queried, and the hot air gun power is automatically set to 1500W, the outlet air speed is 8m / s, and the temperature is set to 280°C. The mechanical arm moves along the planned path at a speed of 50mm / s to uniformly soften the old adhesive film.

[0095] According to the defect type (structural crack) and the local deformation depth (1.5mm) of unit C, the rule library determines that the mechanical strength here is low, and the tension set value is negatively related to these parameters. Therefore, the system automatically lowers the upper limit of the tension, controls the servo motor to apply a smaller constant tension F1=8N, and immediately and smoothly peels the old adhesive film after hot air softening, avoiding secondary damage to the fragile area. The peeled old adhesive film is sent to the recycling bin of the material processing and supply module.

[0096] S4, the cleaning unit in the execution operation module purifies and activates the clean component working surface to achieve a pretreated surface that can install new adhesive film;

[0097] The surface treatment process is specifically illustrated in this embodiment. After the old film is removed, the cleaning unit is immediately started. First, the ion wind rod integrated on the end effector is powered on to generate positive and negative ion flow, neutralizing the static charge (voltage from ±5 kV to within ±50 V) generated on the work surface due to stripping. At the same time, a high-speed clean airflow (flow rate 0.3 m³ / min) blows away surface dust particles. Subsequently, a set of non-contact plasma spray guns is started to generate low-temperature plasma in an inert gas atmosphere, and the work surface is scanned for 3 seconds. This process can effectively remove organic contaminants and increase the surface energy from 35 mN / m to more than 72 mN / m, greatly enhancing the spreading and adhesion of the new film, forming a pretreated surface that can install the new film.

[0098] S5, the material processing and supply module transports the new colored photovoltaic film to the specified position according to the operation instruction, and the execution and operation module performs precise positioning and initial pressing;

[0099] The new film supply and positioning process is specifically illustrated in this embodiment. After receiving the instruction from the decision control module, the material processing and supply module starts to act on the reel and conveying mechanism to pull out the new colored photovoltaic film (specification: 1756 mm x 1038 mm x 0.3 mm) from the warehouse and transport it to the specified position (such as the starting point coordinates (0.000, 0.000, 0.350)) at the edge of the module at a constant speed of 0.5 m / s. The vision system (auxiliary positioning camera) of the execution and operation module captures the marker points of the new film edge in real time and compares them with the three-dimensional coordinates of the module frame. Through a closed-loop control algorithm, the mechanical arm fine-tunes the position of the new film until the positioning error is less than ±0.5 mm. Subsequently, the pressing roller on the end effector applies a constant pressure of 150 N to the starting end of the film to perform initial pressing with a length of 50 mm, ensuring that the new film is firmly fixed in the starting position.

[0100] S6, the execution and operation module lays the new colored photovoltaic film that has completed the initial pressing at a constant pressure and at a uniform speed, and completes the cutting, forming a module with a replaced new film;

[0101] The new film laying and cutting process is specifically illustrated in this embodiment. The pressing roller maintains a constant pressure of 150 N, and the mechanical arm drags the new film at a uniform speed of 80 mm / s from the starting point to the end point. In this process, the reel mechanism of the material processing and supply module provides a constant reverse tension (such as 5 N) to ensure that the film surface is always taut and flat without wrinkles. When the laying reaches the end point, the sensor detects that the film has covered the entire module area, and the ultrasonic cutting knife built into the end effector is started to move at a speed of 20 mm / s along the inside of the module frame (2 mm away from the frame), completing the cutting. After cutting is completed, a module with a replaced new film is formed.

[0102] S7, the sensing and detection module automatically scans and detects the component with the replaced new film, and generates a replacement quality review report;

[0103] This embodiment specifically illustrates the quality review process. After laying, the sensing and detection module scans the component again. The 3D vision sensor detects the surface flatness error of the new film, and requires that the full-field error is less than 0.5 mm / m; the infrared thermal imager re-shoots the infrared temperature distribution map to check whether there are bubbles caused by poor bonding (bubble areas will show low temperature anomalies); the high-resolution camera checks the edge cutting quality and compares it with the standard color value in the database to check whether there is color difference.

[0104] All detection data are processed in real time to generate a replacement quality review report. The report content is, for example: "Unit C area laying flatness: 0.3 mm / m, qualified; no bubble alarm in the whole field; edge cutting burr <0.1 mm, qualified; average color difference ΔE <1.5, qualified."

[0105] S8, the decision control module integrates the replacement quality review report and the whole process data of this operation to generate a system operation report, and transmits it to the operation and maintenance center, while the system resets or switches to the next operation task.

[0106] This embodiment specifically illustrates the data integration and task management process. The decision control module integrates the replacement quality review report of S7 with the global GPS coordinates of this operation, the operation time (such as a total of 15 minutes), the length of the consumed materials, the process parameters used by each unit (such as hot air temperature, tension value), path planning log, and environmental data recorded by the safety auxiliary module (such as the maximum wind speed of 3.5 m / s, no rainfall), etc. to generate a system operation report.

[0107] The report is transmitted to the data platform of the operation and maintenance center through the 4G / 5G network. Then, the system control robot returns to the safe standby position, the recycling bin of the material processing and supply module is sealed, and is ready for transfer processing. The system resets and waits for instructions. If there is a next component to be replaced in the task queue, the system immediately switches to the next operation task and starts a new cycle from step S1.

[0108] The material processing and supply module includes a reel and conveying mechanism for carrying and releasing new film, a recycling bin for storing waste film, and a material bin for protecting new film.

[0109] Specifically, the steps S5 and S6 are specifically implemented; the reel and conveying mechanism are responsible for the conveying of the new film; the recycling bin stores the peeled off waste film after step S3; and the material bin provides dustproof and moistureproof protection for the whole new film before operation.

[0110] The safety auxiliary module includes environmental sensors for monitoring wind speed and rainfall, emergency stop buttons distributed throughout the system, laser radar and ultrasonic sensors for collision avoidance, and anti-static and grounding devices for protecting photovoltaic cells.

[0111] Specifically, the environmental sensors monitor wind speed and rainfall in real time, and if the wind speed exceeds 10 m / s or rainfall is detected, the system suspends operation; the emergency stop buttons are distributed on the robot base and the control box for emergency intervention by personnel; the laser radar and ultrasonic sensors continuously monitor for collision avoidance to ensure a safe distance between the robot and the surrounding environment; and the anti-static and grounding devices work in conjunction with the ion wind rod in step S4 to ensure that the photovoltaic cells are not damaged by static electricity during operation.

[0112] In summary, referring to the complete work flow of the color photovoltaic adhesive film automatic replacement system according to the present application shown in Figure 3 The system starts from step S1, acquires the data set of the target photovoltaic module through the sensing and detection module; then executes step S2, processes the data and plans the optimal operation path and sequence through the decision control module; then, the operation module executes step S3 (drives the hot air and winding mechanism to soften and peel off the old adhesive film), step S4 (purifies and activates the work surface through the cleaning unit); at the same time, the material handling and supply module transports the new adhesive film and performs initial pressing according to the instructions in step S5; thereafter, the operation module continues to execute step S6 (uniformly lays and cuts the new adhesive film at a constant pressure); after the operation is completed, the system enters the quality review stage, executes step S7 (the sensing and detection module automatically scans and detects the replaced module) and generates a replacement quality review report; this report serves as a key decision node, if the quality is qualified, step S8 (generates a system operation report and transmits it to the operation and maintenance center) is executed, if the quality is not qualified, the information is fed back to the decision control module, and step S2 is re-executed to plan a new path and parameters, forming a closed-loop quality control process.

[0113] Secondly: the drawings of the disclosed embodiments of the present application only involve the structures involved in the disclosed embodiments of the present application, other structures can be referred to the usual design, and in the case of no conflict, the same embodiments and different embodiments of the present application can be combined with each other;

[0114] Finally: the above only describes the preferred embodiments of the present application and is not used to limit the present application, any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. Automatic replacement system of colored photovoltaic adhesive film, characterized in that, The system comprises a perception and detection module, a decision control module, an execution operation module, a material processing and supply module, and a safety assistance module. The specific steps are as follows: S1, obtaining the position, contour and surface state data set of the target photovoltaic module through the perception and detection module; S2, the decision control module processes the position, contour and surface state data set, and plans the optimal operation path and sequence for removing the old adhesive film and laying the new adhesive film; S3, the execution operation module drives the hot air and winding mechanism to soften and peel off the old adhesive film according to the optimal operation path and sequence, and obtains a clean module working surface; S4, the cleaning unit in the execution operation module purifies and activates the clean module working surface to achieve a pretreated surface that can install new adhesive film; S5, the material processing and supply module transports the new colored photovoltaic adhesive film to the designated position according to the operation instruction, and performs precise positioning and initial pressing by the execution and operation module; S6, the execution operation module uniformly lays and cuts the new colored photovoltaic adhesive film that has completed the initial pressing at a constant pressure to form a module with new adhesive film; S7, the perception and detection module automatically scans and detects the module with new adhesive film, and generates a replacement quality review report; S8, the decision control module integrates the replacement quality review report and the whole process data of this operation to generate a system operation report, and transmits it to the operation and maintenance center, while the system resets or switches to the next operation task.

2. The automatic replacement system of colored photovoltaic adhesive film according to claim 1, characterized in that: The position data, contour data and surface state data of the target photovoltaic module are as follows: the position data includes global GPS coordinates, relative distance, inclination angle, orientation angle and gap with adjacent objects; the contour data includes two-dimensional boundary pixel coordinates, three-dimensional physical dimensions and three-dimensional coordinates of the frame; and the surface state data includes color value, defect type, defect area, defect position, surface flatness error, local deformation depth and infrared temperature distribution map.

3. The automatic replacement system of colored photovoltaic adhesive film according to claim 1, characterized in that: The steps of the optimal operation path and sequence are as follows: S21, converting the position data and contour data into a three-dimensional world coordinate system with the base of the mechanical arm as the origin, and mapping the surface state data to the corresponding position of the module three-dimensional model to obtain a module three-dimensional mesh model; S22, inputting the module three-dimensional mesh model into a work unit, scoring each work unit through a rule library, and outputting a work unit sequence according to the score; S23, inputting the multi-dimensional data of a single work unit, and obtaining the corresponding fine path point sequence and dynamic operation parameter set of each work unit after processing; S24, inputting the fine path point sequence of all work units, and outputting the optimal operation path and sequence after sequence optimization and collision detection.

4. The automatic replacement system of colored photovoltaic adhesive film according to claim 3, characterized in that: The work unit divides the module surface into different work blocks according to the defect type, defect area, defect position and local deformation depth; and the rule library includes work unit priority determination rules, safety operation boundary rules, process parameter self-adaptive rules and global sequence optimization rules.

5. The automatic replacement system of colored photovoltaic adhesive film according to claim 4, characterized in that: The work unit priority determination rules provide that when the defect type is identified as a structural risk or the local deformation depth exceeds a first preset threshold, the highest processing priority is given. And it is provided that, under the same priority, the operation units are sorted according to the spatial coordinates from top to bottom and from left to right.

6. The automatic replacement system of colored photovoltaic adhesive film according to claim 4, characterized in that: The safety operation boundary rule stipulates that the motion trajectory of the end effector of the mechanical arm must maintain a distance greater than the first safety distance from the three-dimensional coordinates of the component frame; and when the gap with the adjacent object is less than the second safety distance, the movement range of the mechanical arm in that direction is limited or a specific avoidance posture is triggered.

7. The automatic replacement system of colored photovoltaic adhesive film according to claim 4, characterized in that: The process parameter adaptive rule stipulates that the power of the heater and the wind speed setting value are functions of the color value and the real-time infrared temperature distribution map; for dark colors or low temperature areas, the heating parameters are automatically adjusted higher; and the tension setting value of the winding mechanism is negatively correlated with the defect type and the local deformation depth; when there are crack defects or the deformation depth increases, the upper limit of the tension is automatically adjusted lower.

8. The automatic replacement system of colored photovoltaic adhesive film according to claim 4, characterized in that: The global sequence optimization rule stipulates that, under the premise of meeting all priority and safety rules, the total movement path of the mechanical arm should follow the traveling salesman problem optimization algorithm to achieve the goal of the shortest total travel time; and the sequence optimization must be verified by collision detection virtual simulation based on three-dimensional contour data.

9. The automatic replacement system of colored photovoltaic adhesive film according to claim 1, characterized in that: The material processing and supply module includes a reel and conveying mechanism for carrying and releasing new adhesive film, a recycling bin for storing waste adhesive film, and a material bin for protecting the new adhesive film.

10. The automatic replacement system of colored photovoltaic adhesive film according to claim 1, characterized in that: The safety auxiliary module includes environmental sensors for monitoring wind speed and rainfall, emergency stop buttons distributed throughout the system, laser radar and ultrasonic sensors for collision avoidance, and anti-static and grounding devices for protecting photovoltaic cells.

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