Cleaning robot path planning method and system for photovoltaic panel

Abstract

The invention provides a cleaning robot path planning method and system for a photovoltaic panel. The method comprises the following steps: acquiring three-dimensional elevation data and temperature distribution data of a photovoltaic array and current and voltage data of a photovoltaic string; according to the three-dimensional elevation data and the temperature distribution data, identifying a height mutation region and a shadow boundary region on the surface of the photovoltaic panel; generating a current and voltage characteristic curve according to the current and voltage data, and positioning a hot spot region associated with the shadow boundary region according to an abnormal peak point in the current and voltage characteristic curve; based on the spatial coordinate information of the height mutation region, the shadow boundary region and the hot spot region, generating a cleaning path plan which is used for defining cleaning priorities of the path avoidance region and the hot spot region; according to the invention, accurate perception of the complex environment on the surface of the photovoltaic panel is realized, intelligent path planning considering obstacle avoidance and hot spot preferential cleaning is generated, and the cleaning efficiency and the operation stability of the photovoltaic system are improved.

Description

Technical Field

[0001] The present invention relates to the field of robotics technology, and in particular to a path planning method and system for a cleaning robot for photovoltaic panels. Background Art

[0002] During the long-term operation of photovoltaic power plants, pollutants such as dust, fallen leaves, and bird droppings easily accumulate on the surfaces of photovoltaic panels, reducing power generation efficiency and even causing localized hot spot effects, affecting equipment safety and lifespan. Consequently, the demand for automated cleaning of photovoltaic panels is becoming increasingly urgent, especially in large-scale photovoltaic arrays. Cleaning robots must possess efficient path planning capabilities to prioritize cleaning of key areas and effectively avoid obstacles, thereby improving cleaning efficiency and the level of intelligent power plant operation and maintenance.

[0003] Current research has proposed a path planning scheme for cleaning robots based on a combination of two-dimensional image recognition and a fixed path. This scheme uses a camera to capture images of the photovoltaic panel surface, employs image processing techniques to identify visible contaminated areas, and then cleans row by row or column by column along a pre-set gridded path. However, this scheme has some significant drawbacks. For example, its reliance on two-dimensional visual information makes it difficult to accurately identify highly variable areas and complex shadow boundaries, resulting in a lack of dynamic adjustment capabilities for the cleaning path. Furthermore, due to its inability to fully integrate the actual operating parameters of the photovoltaic panels, cleaning priorities are set irrationally, leading to the risk of missing critical areas, which in turn affects cleaning effectiveness and the recovery of power generation performance. Summary of the Invention

[0004] The present invention provides a path planning method and system for a cleaning robot for photovoltaic panels, which are used to solve the problems in the prior art such as the lack of dynamic adjustment capability of the cleaning path, unreasonable setting of cleaning priorities, and the risk of missing key areas.

[0005] In a first aspect, the present invention provides a path planning method for a cleaning robot for photovoltaic panels, comprising: Obtain three-dimensional elevation data and temperature distribution data of the photovoltaic array, as well as current and voltage data of the photovoltaic strings; Identifying, based on the three-dimensional elevation data and temperature distribution data, height mutation areas and shadow boundary areas on the surface of the photovoltaic panel; generating a current-voltage characteristic curve according to the current-voltage data, and locating a hot spot region associated with the shadow boundary region according to an abnormal peak point in the current-voltage characteristic curve; Based on the spatial coordinate information of the height mutation area, the shadow boundary area and the hot spot area, a cleaning path plan is generated, and the cleaning path plan is used to define the cleaning priority of the path avoidance area and the hot spot area.

[0006] Optionally, identifying a height mutation area and a shadow boundary area on the surface of the photovoltaic panel based on the three-dimensional elevation data and the temperature distribution data includes: Comparing the height values ​​of adjacent points in the three-dimensional elevation data, when the height difference between any two adjacent points exceeds a set height difference, marking the point with the larger height value as an elevation mutation point, connecting all adjacent elevation mutation points to form a mutation boundary line, and defining the area surrounded by the mutation boundary line as the elevation mutation area on the photovoltaic panel surface; Associating the temperature value of each measurement point in the temperature distribution data with the corresponding position on the surface of the photovoltaic panel, and calculating the temperature change rate of each measurement point and adjacent measurement points within a preset range; Marking the measurement points corresponding to the temperature change rate exceeding the set temperature critical value as shadow boundary points, connecting all adjacent shadow boundary points to form a shadow boundary line, and defining the area surrounded by the shadow boundary line as a preliminary shadow boundary area; The shadow boundary points in the preliminary shadow boundary area that are within the highly sudden change area are removed to obtain a shadow boundary area.

[0007] Optionally, generating a current-voltage characteristic curve according to the current-voltage data, and locating a hot spot area associated with the shadow boundary area according to an abnormal peak point in the current-voltage characteristic curve, includes: Arranging the current measurement values ​​and voltage measurement values ​​of the photovoltaic strings in the current and voltage data according to the measurement time sequence to generate a data sequence; sorting the voltage measurement values ​​in the data sequence in ascending order to generate a sorted data sequence, and connecting the voltage measurement values ​​and corresponding current measurement values ​​in the sorted data sequence to generate a current-voltage characteristic curve; Identifying a target section in the current-voltage characteristic curve where a current measurement value monotonically increases with a voltage measurement value and a current step decrease occurs, and selecting a data point with a maximum current measurement value from the target section as an abnormal peak point; Determine the physical location of the photovoltaic string to which the abnormal peak point belongs based on the voltage measurement value and the current measurement value corresponding to the abnormal peak point and the preset position mapping relationship of the photovoltaic string; Calculate the shortest spatial distance between the physical location and the boundary line of the shadow boundary area. When the shortest spatial distance is less than a set distance threshold, mark the photovoltaic panel area covered by the photovoltaic string to which the physical location belongs as a hot spot area associated with the shadow boundary area.

[0008] Optionally, identifying a target section in the current-voltage characteristic curve where a step-down current value occurs during a process in which the current measurement value monotonically increases with the voltage measurement value, and selecting a data point with a maximum current measurement value from the target section as an abnormal peak point includes: Traversing the data points of the current-voltage characteristic curve in ascending order of voltage, when detecting that the absolute current difference between the first two data points of three consecutive data points in the first cycle is less than the first tolerance value, and the absolute current difference between the last two data points is greater than the second tolerance value, marking the third data point in the first cycle as a descending starting point; Taking the decline starting point as the starting point, traversing the data points of the current-voltage characteristic curve, and when detecting that the absolute current differences of three consecutive data points in the second period are all less than a third tolerance value, marking the first data point in the second period as the decline ending point; The section between the descent starting point and the descent ending point is defined as a target section; The current measurement value of each data point in the target section is compared, and the data point with the largest current measurement value is selected as the abnormal peak point.

[0009] Optionally, determining the physical position of the photovoltaic string to which the abnormal peak point belongs based on the voltage measurement value and the current measurement value corresponding to the abnormal peak point and a preset position mapping relationship of the photovoltaic string includes: The voltage measurement value corresponding to the abnormal peak point is used as the query voltage value, and the corresponding current measurement value is used as the query current value; According to the registration table in the preset position mapping relationship of the photovoltaic strings, the registration table records the number, rated voltage range, rated current range and corresponding physical position coordinates of each photovoltaic string; Grouping all photovoltaic strings whose query voltage values ​​are within the rated voltage range of each photovoltaic string in the registration table into a voltage matching string set; Combining all photovoltaic strings whose query current values ​​are within the rated current range of each photovoltaic string in the voltage matching string set into a fully matching string set; A target photovoltaic string with the smallest boundary difference in the rated voltage range is selected from the set of completely matched photovoltaic strings, and the physical position coordinates corresponding to the target photovoltaic string are used as the physical position of the photovoltaic string to which the abnormal peak point belongs.

[0010] Optionally, generating a cleaning path plan based on the spatial coordinate information of the height mutation area, the shadow boundary area, and the hot spot area includes: Marking the mutation boundary line of the highly mutation area as the path avoidance area boundary line; Calculate the minimum distance between the center points of each hot spot area, use the reciprocal of the minimum distance as a density reference value, and sort the hot spot areas according to the density reference value to generate a priority traversal sequence; calculating a direction distribution of shadow boundary points of the shadow boundary area to determine a main traveling direction according to the direction distribution; According to the priority traversal sequence, a segmented straight line path connecting the center points of all hot spot areas is generated, and the segmented straight line path and the priority traversal sequence are combined into a cleaning path planning, and the segmented straight line path detours the path avoidance area boundary line.

[0011] Optionally, according to the priority traversal sequence, a segmented straight line path connecting the center points of all hot spot areas is generated, and the segmented straight line path and the priority traversal sequence are combined into a cleaning path plan, wherein the segmented straight line path detours the path avoidance area boundary line, including: Connecting the center points of adjacent hot spot areas in the order of the priority traversal sequence to form an initial segmented straight line path; Calculating the shortest spatial distance between each straight line segment in the initial segmented straight line path and the boundary line of the path avoidance area; Marking a straight line segment whose shortest spatial distance is less than a safety threshold as an intermediate straight line segment, and determining a critical point on the intermediate straight line segment that is closest to the boundary line of the path avoidance area; According to the set avoidance distance, the critical point is translated along the normal direction of the path avoidance area boundary line at the critical point to generate an adjustment point; Splitting the intermediate straight line segment into two alternative straight line segments, wherein the first alternative straight line segment connects the starting point of the intermediate straight line segment to the adjustment point, and the second alternative straight line segment connects the adjustment point to the end point of the intermediate straight line segment; In the order of the priority traversal sequence, the two alternative straight line segments and the straight line segment whose shortest spatial distance is greater than or equal to the safety threshold are recombined into a segmented straight line path of the detour path avoidance area boundary line.

[0012] In a second aspect, the present invention provides a path planning system for a cleaning robot for photovoltaic panels, comprising: An acquisition module is used to obtain the three-dimensional elevation data and temperature distribution data of the photovoltaic array, as well as the current and voltage data of the photovoltaic strings; an identification module, configured to identify height mutation areas and shadow boundary areas on the surface of the photovoltaic panel based on the three-dimensional elevation data and temperature distribution data; a positioning module, configured to generate a current-voltage characteristic curve according to the current-voltage data, and locate a hot spot region associated with the shadow boundary region according to an abnormal peak point in the current-voltage characteristic curve; A generation module is used to generate a cleaning path plan based on the spatial coordinate information of the highly sudden change area, the shadow boundary area and the hot spot area, and the cleaning path plan is used to define the cleaning priority of the path avoidance area and the hot spot area.

[0013] In a third aspect, the present invention provides a computing device comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a path planning method for a cleaning robot for photovoltaic panels as described in the first aspect above.

[0014] In a fourth aspect, the present invention provides a computer storage medium storing a computer program, which, when executed by a computer, implements a path planning method for a cleaning robot for photovoltaic panels as described in the first aspect.

[0015] In the present invention, the three-dimensional elevation data and temperature distribution data of the photovoltaic array, and the current and voltage data of the photovoltaic string are obtained; based on the three-dimensional elevation data and temperature distribution data, the height mutation area and the shadow boundary area on the surface of the photovoltaic panel are identified; based on the current and voltage data, a current-voltage characteristic curve is generated, and based on the abnormal peak points in the current-voltage characteristic curve, the hot spot area associated with the shadow boundary area is located; based on the spatial coordinate information of the height mutation area, the shadow boundary area and the hot spot area, a cleaning path planning is generated, and the cleaning path planning is used to define the cleaning priority of the path avoidance area and the hot spot area. The technical solution provided by the present invention breaks through the limitations of the existing technology that relies on two-dimensional images. By integrating three-dimensional spatial elevation data (solving blind spots in height change identification), surface temperature field distribution (capturing dynamic boundaries of shadows) and real-time electrical parameters (perceiving abnormal operating status), a multi-dimensional data foundation is constructed to provide data support for the accurate identification of physical obstacles and shadow hot spots; by identifying areas of height mutation, the outlines of physical obstacles such as rooftop equipment and brackets are accurately located to avoid robot collisions; by identifying shadow boundary areas, changes in projection boundaries of trees, buildings, etc. are dynamically captured to overcome the misjudgment of shadows by two-dimensional vision; battery cell failures caused by shadows are captured based on the step characteristics of the current-voltage characteristic curve to avoid missing invisible hot spots; by coupling the hot spot position with the distance of the shadow boundary, interference from non-shadow factors is eliminated to improve the reliability of fault location; by defining the cleaning priority of path avoidance areas and hot spot areas, dynamic avoidance of three-dimensional obstacles is achieved to ensure priority processing of high-loss areas. Furthermore, by arranging the current and voltage measurements of the photovoltaic strings in sequence to generate a characteristic curve, the target current step-down section is identified in the voltage ascending section and the maximum current point is extracted as the abnormal peak point. The physical position of the photovoltaic string to which the abnormal peak point belongs is determined by combining the preset position mapping relationship of the photovoltaic string. The spatial correlation is verified by calculating the shortest distance between this position and the shadow boundary. Finally, the photovoltaic panel area that meets the distance threshold is marked as a hot spot area associated with the shadow boundary area. It can capture cell failures caused by shadows and overcome the blind spot detection of internal faults by two-dimensional vision. By verifying the spatial distance between the hot spot position and the shadow boundary, it eliminates interference from non-shadow factors such as equipment aging and improves the accuracy of fault identification. It provides hot spot area data that has been double-verified in electrical and spatial terms for path planning, ensuring that areas with high power generation losses receive the highest scanning priority. This breaks through the limitation of existing technologies that rely solely on visual judgment of surface stains and achieves intelligent decision-making based on actual power generation losses.

[0016] These and other aspects of the present invention will become more readily apparent from the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 A flow chart of a path planning method for a cleaning robot for photovoltaic panels provided by the present invention; Figure 2 A schematic structural diagram of a path planning system for a cleaning robot for photovoltaic panels provided by the present invention; Figure 3 A schematic structural diagram of a computing device provided by the present invention. DETAILED DESCRIPTION

[0019] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0020] In some of the processes described in the specification and claims of the present invention and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., and do not represent the order of precedence, nor do they limit "first" and "second" to be different types.

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0022] In response to the problems of decreased power generation efficiency and hot spot effects caused by dust, fallen leaves, and bird droppings on the surface of photovoltaic panels in large-scale photovoltaic power stations, existing solutions based on two-dimensional image recognition and fixed-path cleaning have obvious shortcomings in practical applications. It is difficult to accurately identify highly mutated areas and complex shadow boundaries, and it is impossible to locate potential hot spot areas in combination with the operating status of photovoltaic modules, resulting in a lack of dynamic adjustment capabilities for cleaning paths and unreasonable priority division. Based on this, the present invention obtains the three-dimensional elevation, temperature distribution, and current and voltage data of the photovoltaic array, comprehensively identifies highly mutated areas, shadow boundaries, and associated hot spot areas, and generates a cleaning path plan with avoidance function and cleaning priority distinction based on its spatial coordinate information, thereby improving cleaning efficiency and the level of intelligent operation and maintenance, and making up for the limitations of traditional visual recognition and fixed-path cleaning methods in complex scenarios. Figure 1 A flow chart of a path planning method for a cleaning robot for photovoltaic panels is provided in an embodiment of the present invention, such as Figure 1 As shown, the method includes: Step 101: Acquire three-dimensional elevation data and temperature distribution data of the photovoltaic array, and current and voltage data of the photovoltaic strings; In this step, the photovoltaic array refers to a power generation unit composed of multiple photovoltaic modules arranged in rows and columns, including a support structure and electrical connection system. Three-dimensional elevation data refers to the three-dimensional spatial coordinates (X, Y, Z) of each location point on the photovoltaic panel surface, acquired through lidar scanning, and is used to characterize elevation changes. Temperature distribution data refers to the collection of photovoltaic panel surface temperature values ​​and corresponding location coordinates collected by infrared sensors, reflecting the thermal field distribution characteristics. A photovoltaic string refers to an electrical unit formed by multiple photovoltaic modules connected in series, serving as the basic unit for current and voltage monitoring. Current and voltage data refers to the time series of current and voltage measurements output by the photovoltaic string in real time during operation.

[0023] In this embodiment of the present invention, a laser radar scan is used to generate three-dimensional elevation data (including the X, Y, and Z coordinates of each location point) on the surface of the photovoltaic array. Simultaneously, an infrared thermal imager is used to collect temperature distribution data on the photovoltaic panel surface (including the temperature value and coordinates of each measurement point). Simultaneously, current and voltage data (including a time series of string output current and voltage measurements) are acquired in real time from the photovoltaic string monitoring module. The three-dimensional elevation data is used to construct spatial terrain, the temperature distribution data maps thermal field changes, and the current and voltage data reflect the electrical operating status. Together, these three data elements form the data foundation for path planning.

[0024] Step 102: Identifying height mutation areas and shadow boundary areas on the photovoltaic panel surface based on the three-dimensional elevation data and temperature distribution data; In an embodiment of the present invention, the height values ​​of adjacent position points in the three-dimensional elevation data are compared. If the height difference exceeds the set height difference, the position point with the larger height value is marked as an elevation mutation point, and the adjacent elevation mutation points are connected to form a mutation boundary line. The area within the mutation boundary line is defined as a height mutation area. At the same time, the temperature change rate of each measurement point in the temperature distribution data and the adjacent measurement points within a preset range is calculated, and the measurement point corresponding to the temperature change rate exceeding the set temperature critical value is marked as a shadow boundary point. The adjacent shadow boundary points are aggregated to form a preliminary shadow boundary area, and then the overlapping part with the height mutation area is excluded, and finally a pure shadow boundary area is output.

[0025] Step 103: generating a current-voltage characteristic curve according to the current-voltage data, and locating a hot spot region associated with the shadow boundary region according to an abnormal peak point in the current-voltage characteristic curve; In an embodiment of the present invention, after the current and voltage data are arranged in order of measurement time, they are then sorted in ascending order according to the voltage measurement values ​​to obtain a sorted data sequence, and the voltage measurement values ​​and the corresponding current measurement values ​​therein are connected to generate a current-voltage characteristic curve; a target section in the curve where the current suddenly drops when the voltage increases is identified, and the data point with the largest current measurement value in the section is selected as the abnormal peak point; based on the voltage measurement value and the current measurement value corresponding to the abnormal peak point, combined with the preset position mapping relationship of the photovoltaic string, the physical position of the photovoltaic string to which the abnormal peak point belongs is determined; the shortest spatial distance between the physical position and the boundary line of the shadow boundary area is calculated, and when the shortest spatial distance is less than the set distance threshold, the photovoltaic panel area covered by the photovoltaic string to which the physical position belongs is marked as a hot spot area.

[0026] Step 104: generating a cleaning path plan based on the spatial coordinate information of the height mutation area, the shadow boundary area, and the hot spot area, wherein the cleaning path plan is used to define cleaning priorities of the path avoidance area and the hot spot area; In an embodiment of the present invention, the mutation boundary line of the highly mutation area is marked as the path avoidance area boundary line; the minimum distance between the center points of each hot spot area is calculated, and the reciprocal of the minimum distance is used as the density reference value to sort the hot spot areas and generate a priority traversal sequence; the directional distribution of the shadow boundary points of the shadow boundary area is calculated to determine the main direction of travel; according to the priority traversal sequence, a segmented straight line path connecting the center points of all hot spot areas is generated, and it is combined with the priority traversal sequence into a cleaning path planning, wherein the segmented straight line path bypasses the path avoidance area boundary line.

[0027] The embodiments of the present invention use three-dimensional elevation data to accurately identify areas with sudden height changes, such as rooftop equipment (to avoid collisions), and dynamically capture shadow boundaries in combination with temperature distribution, thereby overcoming the defects of existing technologies in misjudging three-dimensional obstacles and shadows; locate the real hot spot area based on the step characteristics of the current-voltage characteristic curve, and eliminate equipment aging interference through spatial correlation verification with the shadow boundary, solving the problem of missed hot spot detection due to ignoring electrical parameters; generate a cleaning priority sequence based on the hot spot density distribution, and plan a low-energy turning path in combination with the shadow direction, so as to achieve priority coverage and three-dimensional avoidance of high-loss areas, thereby improving cleaning efficiency and power generation recovery effects.

[0028] The present invention provides a specific embodiment, step 102, identifying the height mutation area and the shadow boundary area on the surface of the photovoltaic panel based on the three-dimensional elevation data and the temperature distribution data, specifically includes the following steps: Step 201: comparing the height values ​​of adjacent points in the three-dimensional elevation data. When the height difference between any two adjacent points exceeds a set height difference, the point with the larger height value is marked as an elevation mutation point. All adjacent elevation mutation points are connected to form a mutation boundary line. The area surrounded by the mutation boundary line is defined as the elevation mutation area on the photovoltaic panel surface. In this step, adjacent position points refer to sampling points with adjacent spatial coordinates in the three-dimensional elevation data, and the spacing is determined by the lidar resolution. The height difference refers to the absolute value of the difference between the Z coordinates of two adjacent position points, reflecting the amplitude of the height change. Setting the height difference refers to the threshold for determining whether it is an elevation mutation point, which is set based on the typical height of the photovoltaic bracket (5-20cm). The elevation mutation point refers to the position point where the height difference exceeds the threshold, representing the edge of the equipment or the protrusion of the bracket. The mutation boundary line refers to the closed polyline formed by connecting all adjacent elevation mutation points, which is used to define the obstacle outline. The height mutation area refers to the continuous surface area surrounded by the mutation boundary line, which serves as the robot avoidance target.

[0029] In an embodiment of the present invention, all adjacent position points in the three-dimensional elevation data are traversed, and the height difference between every two adjacent position points is calculated (obtained by subtracting the lower point value from the higher point value). When the height difference exceeds a set height difference (such as 10 cm), the higher point (i.e., the position point with a larger height value) is marked as an elevation mutation point; adjacent elevation mutation points are connected by straight lines to form a closed mutation boundary line, and the continuous area enclosed by the boundary line is defined as the height mutation area (corresponding to the rooftop equipment outline).

[0030] Step 202: Associating the temperature value of each measurement point in the temperature distribution data with the corresponding position on the surface of the photovoltaic panel, and calculating the temperature change rate of each measurement point and adjacent measurement points within a preset range; In this step, the measurement point refers to the temperature data unit collected by the infrared thermal imager, including the temperature value and location coordinates. The preset range refers to a fixed neighborhood (such as a circular area with a radius of 5 cm) centered on the current measurement point. The temperature change rate refers to the maximum temperature change per unit distance.

[0031] In an embodiment of the present invention, the temperature value of each measurement point in the temperature distribution data is bound to its XY coordinates on the surface of the photovoltaic panel. With each measurement point as the center, adjacent measurement points within a preset range (such as a 3×3 grid) are taken around it, and the maximum temperature difference between the center point and the adjacent measurement points is calculated and divided by the grid spacing to obtain the temperature change rate (unit: ℃ / cm).

[0032] Step 203: Mark the measurement points corresponding to the temperature change rates exceeding the set temperature threshold as shadow boundary points, connect all adjacent shadow boundary points to form a shadow boundary line, and define the area surrounded by the shadow boundary line as a preliminary shadow boundary area; In this step, setting the temperature threshold refers to the threshold for determining the shadow boundary, which is set based on the typical temperature drop gradient caused by sudden changes in illumination. Shadow boundary points are measurement points where the temperature change rate exceeds the threshold and correspond to the shadow edge. Shadow boundary lines are continuous lines connecting adjacent shadow boundary points and are used to define the shadow outline. The preliminary shadow boundary area refers to the initial area enclosed by the shadow boundary line, which includes both the actual shadow and the device projection.

[0033] In an embodiment of the present invention, when the temperature change rate of a measurement point exceeds a set temperature critical value (e.g., 2°C / cm), the point is marked as a shadow boundary point; spatially adjacent shadow boundary points are connected by straight lines to form a shadow boundary line, and the area enclosed by the boundary line is defined as a preliminary shadow boundary area (including the shadow interference of the device itself).

[0034] Step 204: removing the shadow boundary points in the preliminary shadow boundary region that are within the highly sudden change region to obtain a shadow boundary region; In this step, the shadow boundary area refers to the dynamic shadow area after removing the interference points in the highly mutated area, which is used for hot spot association analysis.

[0035] In an embodiment of the present invention, all shadow boundary points in the preliminary shadow boundary area that are located in a highly mutated area (such as device projection points) are deleted, and the remaining shadow boundary points are reconnected to form a pure shadow boundary line. The area surrounded by the boundary line is output as the final shadow boundary area (containing only external shadows such as trees and buildings).

[0036] The embodiments of the present invention accurately identify the outlines of physical obstacles such as rooftop equipment and brackets (i.e., height mutation areas) through height mutation point detection of three-dimensional elevation data, thereby solving the problem that existing two-dimensional vision solutions cannot distinguish three-dimensional obstacles; based on the temperature gradient, shadow boundary point positioning and interference point removal, the real external shadow range (i.e., shadow boundary area) is dynamically captured, overcoming the defect of fixed cameras in misjudging complex projections.

[0037] The present invention provides a specific embodiment, step 103, generating a current-voltage characteristic curve based on the current-voltage data, and locating a hot spot area associated with the shadow boundary area based on an abnormal peak point in the current-voltage characteristic curve, specifically includes the following steps: Step 301: Arranging the current measurement values ​​and voltage measurement values ​​of the photovoltaic strings in the current and voltage data according to the measurement time sequence to generate a data sequence; In this step, the data sequence refers to a set of current and voltage data pairs arranged in time sequence, and each data pair includes a voltage measurement value and a current measurement value at the same moment.

[0038] In the embodiment of the present invention, the current measurement values ​​and voltage measurement values ​​of the same photovoltaic string are paired and arranged in the order of the measurement time of the current and voltage data to form a data sequence with increasing timestamps, for example: [t1: V1, I1], [t2: V2, I2], ...).

[0039] Step 302: sorting the voltage measurement values ​​in the data sequence in ascending order to generate a sorted data sequence, and connecting the voltage measurement values ​​and corresponding current measurement values ​​in the sorted data sequence to generate a current-voltage characteristic curve; In this step, the sorted data sequence refers to the set of current and voltage data pairs after the voltage values ​​are arranged in ascending order. The current-voltage characteristic curve refers to a line graph of the relationship between voltage and current, reflecting the electrical characteristics of the string.

[0040] In an embodiment of the present invention, the voltage measurement values ​​in the data sequence are sorted from small to large, and the current measurement value corresponding to each voltage measurement value is kept unchanged to generate a sorted data sequence; all current measurement value points are connected in ascending order of voltage to form a broken line, which is the current-voltage characteristic curve, wherein the horizontal axis is voltage and the vertical axis is current.

[0041] Step 303: Identify a target section in the current-voltage characteristic curve where a step-down current value occurs during a process in which the current measurement value monotonically increases with the voltage measurement value, and select a data point with a maximum current measurement value from the target section as an abnormal peak point; In this step, the target segment refers to the continuous segment of the characteristic curve where the current step decreases, defined by the starting and ending points of the decrease. The abnormal peak point is the data point with the maximum current measurement value within the target segment, indicating a cell breakdown failure caused by shadowing.

[0042] In an embodiment of the present invention, the characteristic curve is scanned in the ascending direction of voltage. When three consecutive data points are detected that meet the following conditions: the current difference between the first two data points is less than a first tolerance value, and the current difference between the last two data points is greater than a second tolerance value, the third data point is marked as a descent starting point. The scan is continued from this point to a current variation stabilization section (i.e., the absolute current differences of the three consecutive data points are all less than the third tolerance value), and the first data point of the stabilization section is marked as a descent ending point. The section between the descent starting point and the descent ending point is defined as a target section, and the data point with the largest current measurement value in the section is selected as the abnormal peak point.

[0043] Step 304: determining the physical location of the photovoltaic string to which the abnormal peak point belongs based on the voltage measurement value and the current measurement value corresponding to the abnormal peak point and the preset position mapping relationship of the photovoltaic string; In this step, the preset position mapping refers to a pre-stored table of correspondence between PV string electrical parameters and spatial locations, including the PV string number, rated voltage range, rated current range, and corresponding physical location coordinates. The physical location refers to the (X, Y) coordinates of the center point of the PV string in the array to which the abnormal peak point belongs.

[0044] In an embodiment of the present invention, the voltage measurement value of the abnormal peak point is extracted as the query voltage value, and the corresponding current measurement value is used as the query current value; the registration table in the preset position mapping relationship is queried, and photovoltaic strings whose query voltage values ​​are within their rated voltage range and whose current measurement values ​​are within their rated current range are screened out to obtain a voltage matching string set, and the target photovoltaic string with the smallest boundary difference of the rated voltage range is selected from it, and its installation position coordinates are used as the physical position of the photovoltaic string to which the abnormal peak point belongs.

[0045] Step 305: Calculate the shortest spatial distance between the physical location and the boundary line of the shadow boundary area. When the shortest spatial distance is less than a set distance threshold, mark the photovoltaic panel area covered by the photovoltaic string to which the physical location belongs as a hot spot area associated with the shadow boundary area. In this step, the shortest spatial distance is the minimum vertical distance between the physical location of the PV string to which the abnormal peak point belongs and all line segments of the boundary line of the shadow boundary area. The distance threshold is set to the maximum distance for determining whether a hot spot is associated with a shadow, typically half the string size. The hot spot area is the rectangular area of ​​the PV panel marked as a shadow-related fault, coinciding with the string installation location.

[0046] In this embodiment of the present invention, the shortest spatial distance between the physical location of the photovoltaic string to which the abnormal peak point belongs and the boundary line of the shadow boundary area (the minimum distance from the point to the broken line) is calculated; if the distance is less than a set distance threshold (such as half the width of the photovoltaic string), the rectangular area of ​​the photovoltaic panel covered by the string is marked as a shadow boundary-associated hot spot area.

[0047] The embodiment of the present invention accurately locates the breakdown point (abnormal peak point) of the battery cell caused by shadows through step-down section detection of the current-voltage characteristic curve, thereby solving the problem of missed detection of internal electrical faults in existing visual solutions; combining string position mapping with shadow boundary space verification, it eliminates interference from non-shadow factors such as equipment aging, and improves the reliability of hot spot identification; provides hot spot area data with dual electrical and spatial verification for path planning, ensuring that areas with high power generation losses are cleared first; compared with traditional image recognition solutions, it improves the accuracy of hot spot positioning and the coverage of key areas.

[0048] The present invention provides a specific embodiment, step 303, identifying a target section in the current-voltage characteristic curve where a current measurement value monotonically increases with a voltage measurement value and a current step-down occurs, and selecting a data point with a maximum current measurement value from the target section as an abnormal peak point, specifically comprising the following steps: Step 311: Traversing the data points of the current-voltage characteristic curve in ascending order of voltage, when it is detected that the absolute current difference between the first two data points of three consecutive data points in the first cycle is less than the first tolerance value, and the absolute current difference between the last two data points is greater than the second tolerance value, marking the third data point in the first cycle as a descending starting point; In this step, the first cycle refers to the detection window of three consecutive data points that first meet the conditions, reflecting the initial step position. The first tolerance value is the upper limit of the current fluctuation in the flat period, set based on the percentage of the PV string's short-circuit current. The second tolerance value is the minimum amplitude threshold for the current step decrease, set based on the typical value of a current sag caused by shadowing. The starting point of the decrease is the starting point of the current sag, corresponding to the inflection point of the characteristic curve.

[0049] In an embodiment of the present invention, the data points of the characteristic curve are scanned sequentially in the ascending direction of voltage. When three consecutive data points (such as the first data point P1, the second data point P2, and the third data point P3) are detected for the first time and meet the following conditions: the absolute difference in current between the first data point P1 and the second data point P2 is less than a first tolerance value (such as 0.5% of the short-circuit current), and the absolute difference in current between the second data point P2 and the third data point P3 is greater than a second tolerance value (such as 5% of the short-circuit current), the third data point P3 is marked as the starting point of the decline.

[0050] Step 312: Starting from the falling starting point, traverse the data points of the current-voltage characteristic curve, and when it is detected that the absolute current differences of three consecutive data points in the second cycle are all less than a third tolerance value, mark the first data point in the second cycle as the falling end point; In this step, the second cycle refers to the detection window of three consecutive data points that first meet the steady-state condition after the starting point of the current drop. The third tolerance value refers to the upper limit of the new current plateau fluctuation, which should be less than the first tolerance value to ensure stability. The end point of the current drop refers to the point where the current stops falling and enters a stable plateau.

[0051] In an embodiment of the present invention, sequential scanning is continued starting from the starting point of the descent. When three consecutive data points (the first data point Q1, the second data point Q2, and the third data point Q3) are detected again and meet the following conditions: the absolute current differences between the first data point Q1 and the second data point 2, and between the second data point Q2 and the third data point Q3 are all less than a third tolerance value (such as 1% short-circuit current), the first data point Q1 is marked as the descent end point.

[0052] Step 313: defining the section between the descent start point and the descent end point as a target section; In the embodiment of the present invention, all continuous data points between the drop start point and the drop end point are connected to form a target section (ie, a complete section of the current step drop).

[0053] Step 314: comparing the current measurement value of each data point in the target section, and selecting the data point with the largest current measurement value as the abnormal peak point; In the embodiment of the present invention, the current value of each data point in the target section is traversed, the values ​​are directly compared, and the data point with the largest current measurement value is selected as the abnormal peak point.

[0054] The embodiment of the present invention locks the current step segment through a triple tolerance mechanism, eliminates interference such as light fluctuations, and solves the problem of traditional solutions missing detection of internal electrical faults; directly compares and selects abnormal peak points based on current measurement values, avoids errors introduced by complex algorithms, and improves the reliability of hot spot positioning; the accuracy of step drop event detection is improved and the time consumption of hot spot positioning is reduced.

[0055] The present invention provides a specific embodiment, step 304, determining the physical location of the photovoltaic string to which the abnormal peak point belongs based on the voltage measurement value and the current measurement value corresponding to the abnormal peak point, combined with the preset position mapping relationship of the photovoltaic string, specifically includes the following steps: Step 321: using the voltage measurement value corresponding to the abnormal peak point as the query voltage value, and using the corresponding current measurement value as the query current value; In this step, the voltage value queried refers to the voltage measurement value corresponding to the abnormal peak point on the current-voltage characteristic curve, which is used to match the rated voltage range of the string. The current value queried refers to the current measurement value corresponding to the abnormal peak point on the current-voltage characteristic curve, which is used to match the rated current range of the string.

[0056] In the embodiment of the present invention, the voltage measurement value corresponding to the abnormal peak point is extracted as the query voltage value, and the current measurement value corresponding to the abnormal peak point is extracted as the query current value, and the two together constitute the electrical query condition for string matching.

[0057] Step 322: According to the registration table in the preset position mapping relationship of the photovoltaic strings, the registration table records the number, rated voltage range, rated current range and corresponding physical location coordinates of each photovoltaic string; In this step, the registration table refers to a predefined PV string parameter database, which includes a mapping relationship between electrical parameters and spatial positions.

[0058] In an embodiment of the present invention, a string registration table is called from the preset position mapping relationship of the photovoltaic strings. The table stores the serial number, rated voltage range (such as [Vmin, Vmax]), rated current range (such as [Imin, Imax]) and installation center point coordinates (X, Y) of each photovoltaic string.

[0059] Step 323: combining all photovoltaic strings whose query voltage values ​​are within the rated voltage range of each photovoltaic string in the registration table into a voltage matching string set; In this step, the voltage matching string set refers to a subset of photovoltaic strings whose query voltage values ​​are within the rated voltage range.

[0060] In an embodiment of the present invention, all photovoltaic strings in the registration table are traversed to determine whether the query voltage value falls within the rated voltage range of a certain string (i.e., Vmin≤query voltage value≤Vmax), and all photovoltaic strings that meet the conditions are grouped into a voltage matching string set.

[0061] Step 324: combining all photovoltaic strings whose query current values ​​are within the rated current range of each photovoltaic string in the voltage matching string set into a fully matching string set; In this step, the fully matched string set refers to a subset of photovoltaic strings that simultaneously meet the matching of the rated voltage range and the rated current range.

[0062] In the embodiment of the present invention, a secondary screening is performed in the voltage-matching string set to determine whether the query current value falls within the rated current range of the PV strings in the voltage-matching string set (i.e., Imin≤query current value≤Imax), and all strings that meet the conditions are combined into a fully matched string set.

[0063] Step 325: Select a target photovoltaic string with the smallest boundary difference in the rated voltage range from the set of completely matched photovoltaic strings, and use the physical position coordinates corresponding to the target photovoltaic string as the physical position of the photovoltaic string to which the abnormal peak point belongs; In this step, the target PV string refers to the PV string with the narrowest rated voltage range in the fully matched string set, that is, the PV string with the most accurate positioning.

[0064] In this embodiment of the present invention, the boundary difference (Vmax-Vmin) of the rated voltage range of each PV string in the fully matched string set is calculated, and the string with the smallest boundary difference is selected as the target PV string. The corresponding physical position coordinate (X, Y) is used as the physical position of the PV string to which the abnormal peak point belongs.

[0065] The embodiment of the present invention accurately locates the spatial position of the faulty string through dual matching of electrical parameters and the registration table in the preset position mapping relationship, solving the positioning deviation problem caused by traditional image recognition's insensitivity to the electrical status of the components. It also selects the photovoltaic string with the narrowest rated voltage range to improve the positioning accuracy to the single-string level.

[0066] For example, in a rooftop distributed photovoltaic scenario, to determine the physical location of the PV string to which an abnormal peak point belongs, the voltage measurement value of 31.5V corresponding to the abnormal peak point is first used as the query voltage value, and the current measurement value of 7.2A is used as the query current value. This information is then combined with the rated voltage and current ranges of each PV string recorded in the registration table, along with their corresponding physical location coordinates, for a matching analysis. The registration table information includes: PV string 1 has a rated voltage range of [30V–33V], a rated current range of [6.5A–7.5A], and a location coordinate of (10.2, 5.3); PV string 2 has a rated voltage range of [31V–34V], a rated current range of [7.0A–8.0A], and a location coordinate of (8.7, 6.1); and PV string 3 has a rated voltage range of [29V–32V], a rated current range of [7.1A–7.8A], and a location coordinate of (12.5, 4.9). The system first matches the query voltage value with the rated voltage range of each PV string, determining that the rated voltage ranges of PV string 1 and PV string 2 both include 31.5V, thus forming a voltage-matched string set. Based on this set, the system then compares the query current value of 7.2A with the rated current ranges of these two PV strings, finding that both meet the conditions, thus forming a fully matched string set {PV string 1, PV string 2}. The system then selects the target PV string with the smallest rated voltage range boundary difference from the fully matched string set. The system calculates that the rated voltage range widths of PV string 1 and PV string 2 are both 3V. Given the same boundary difference, PV string 1, the first matched, is selected by default, and its corresponding physical location coordinates (10.2, 5.3) are used as the location of the PV string to which the abnormal peak point belongs.

[0067] The present invention provides a specific embodiment, step 104, generating a cleaning path plan based on the spatial coordinate information of the height mutation area, the shadow boundary area, and the hot spot area, specifically includes the following steps: Step 401: Marking the sudden change boundary line of the highly sudden change area as the path avoidance area boundary line; In this step, the path avoidance area boundary line refers to the closed contour polyline of the highly sudden change area, which is composed of the sudden change boundary line, and the robot path must not cross this boundary.

[0068] In the embodiment of the present invention, the sudden change boundary line of the highly sudden change area is marked as the path avoidance area boundary line, and the boundary line serves as an absolute restricted area for the robot to avoid collision.

[0069] Step 402: Calculate the minimum distance between the center points of each hot spot area, use the reciprocal of the minimum distance as a density reference value, and sort the hot spot areas according to the density reference value to generate a priority traversal sequence; In this step, the density reference value refers to the reciprocal of the minimum distance between the center points of the hot spot regions, and the larger the value, the more concentrated the hot spots are. The priority traversal sequence refers to the access order list of the hot spot regions arranged in descending order of the density reference value.

[0070] In the embodiment of the present application, the Euclidean distance between each center point of the hot spot region and its nearest neighbor center point is calculated, and the minimum distance is taken. The density reference value = 1 / minimum distance (reflecting the local hot spot concentration) is calculated. All hot spot regions are sorted in descending order of the density reference value to generate the priority traversal sequence, wherein the region with the highest density reference value is ranked first.

[0071] Step 403: Calculate the direction distribution of the shadow boundary points of the shadow boundary region to determine the main travel direction according to the direction distribution; In this step, the direction distribution refers to the statistical histogram of the tangent angle of the shadow boundary contour points, reflecting the boundary trend distribution. The main travel direction refers to the angle with the highest frequency in the direction distribution, which is used for path orientation.

[0072] In the embodiment of the present application, the coordinates of all shadow boundary points in the shadow boundary region are extracted, the tangent direction of each point (i.e. the angle of the adjacent point connecting line) is calculated, and the angle histogram of all tangent directions is counted. The angle corresponding to the peak value of the histogram is determined as the main travel direction (e.g. 30° represents northeast direction).

[0073] Step 404: According to the priority traversal sequence, a segmented straight line path connecting the center points of all hot spot regions is generated, and the segmented straight line path and the priority traversal sequence are combined as a cleaning path planning, and the segmented straight line path circumvents the path avoidance region boundary line; In this step, the segmented straight line path refers to a polyline path composed of multiple straight lines, each straight line connecting two hot spot center points. The cleaning path planning refers to a decision instruction set containing the segmented straight line path and the priority traversal sequence, guiding the robot movement.

[0074] In the embodiment of the present application, the center points of adjacent hot spot regions are connected in the order of the priority traversal sequence to form an initial segmented straight line path. The shortest spatial distance between each straight line segment in the path and the path avoidance region boundary line is calculated. The straight line segment with the shortest spatial distance less than the safety threshold is marked as an intermediate straight line segment, and the critical point closest to the path avoidance region boundary line on the intermediate straight line segment is determined. According to the set avoidance distance, the critical point is translated along the normal direction of the path avoidance region boundary line at the critical point to generate an adjustment point. The intermediate straight line segment is split into two alternative straight line segments, and the two alternative straight line segments and the straight line segment with the shortest spatial distance greater than or equal to the safety threshold are recombined into a segmented straight line path circumventing the path avoidance region boundary line in the order of the priority traversal sequence.

[0075] The embodiments of the present invention achieve precise avoidance of three-dimensional obstacles through the path avoidance area boundary line, thereby reducing the risk of collision; dynamically generate a priority sequence based on the density of hot spots to ensure that high-loss areas are covered first; plan the path along the main direction of the shadow to reduce repeated cleaning and reduce the energy consumption of the robot.

[0076] The present invention provides a specific embodiment, step 404, generating a segmented straight line path connecting the center points of all hot spot areas according to the priority traversal sequence, combining the segmented straight line path with the priority traversal sequence into a cleaning path plan, wherein the segmented straight line path detours the path avoidance area boundary line, specifically comprising the following steps: Step 411: Connecting the center points of adjacent hot spot areas in the order of the priority traversal sequence to form an initial segmented straight line path; In this step, the initial segmented straight line path refers to the broken line path formed by directly connecting the center points of the hot spots according to the priority traversal sequence, without considering obstacle avoidance.

[0077] In an embodiment of the present invention, the center points of adjacent hot spot areas are sequentially connected with straight line segments, such as [BA, AC], strictly in the order of the priority traversal sequence (such as [B, A, C]), to generate an initial segmented straight line path without avoidance processing.

[0078] Step 412: Calculate the shortest spatial distance between each straight line segment in the initial segmented straight line path and the path avoidance area boundary line; In this step, the shortest spatial distance refers to the shortest vertical distance from the straight line segment to the boundary line of the path avoidance area, reflecting the collision risk level.

[0079] In the embodiment of the present invention, for each straight line segment in the initial segmented straight line path, the shortest spatial distance between the straight line segment and the boundary line of the path avoidance area is calculated (ie, the minimum value of the shortest vertical distances from all points on the straight line segment to the boundary line).

[0080] Step 413: Mark the straight line segment whose shortest spatial distance is less than the safety threshold as an intermediate straight line segment, and determine the critical point on the intermediate straight line segment that is closest to the boundary line of the path avoidance area; In this step, the middle straight segment refers to the path segment that needs to be adjusted to avoid the shortest spatial distance, which satisfies the safety threshold. The critical point refers to the point on the middle straight segment closest to the avoidance boundary line.

[0081] In this embodiment of the present invention, if the shortest spatial distance of a straight line segment is less than a safety threshold (eg, 10 cm), it is marked as an intermediate straight line segment; and the critical point (perpendicular point) closest to the boundary line is located on the segment.

[0082] Step 414: according to the set avoidance distance, the critical point is translated along the normal direction of the path avoidance area boundary line at the critical point to generate an adjustment point; In this step, the avoidance distance is the minimum distance for the robot to safely operate, which is set based on the physical dimensions. The adjustment point is the new path turning point generated by shifting the critical point.

[0083] In the embodiment of the present invention, the critical point is translated along the normal direction of the boundary line at the critical point (pointing to the outside of the obstacle) by a set avoidance distance (eg, 15 cm) to generate an adjustment point.

[0084] Step 415: Split the middle straight line segment into two replacement straight line segments, wherein the first replacement straight line segment connects the starting point of the middle straight line segment to the adjustment point, and the second replacement straight line segment connects the adjustment point to the end point of the middle straight line segment; In this step, the replacement straight line segments refer to two new paths that bypass the obstacles and replace the original dangerous path segments.

[0085] In the embodiment of the present invention, the middle straight line segment is split from the critical point into two replacement straight line segments: the first connects the original starting point to the adjustment point, and the second connects the adjustment point to the original end point, forming a detour path.

[0086] Step 416: Recombining the two alternative straight line segments and the straight line segment whose shortest spatial distance is greater than or equal to the safety threshold into a segmented straight line path of the detour path avoidance area boundary line in the order of the priority traversal sequence; In the embodiment of the present invention, the replacement straight line segment and the unadjusted straight line segment (shortest spatial distance ≥ safety threshold) are reconnected strictly in the order of priority traversal sequence to form a segmented straight line path for safe detour.

[0087] The embodiment of the present invention generates adjustment points by translating the normal direction of the critical point, accurately avoiding three-dimensional obstacles and reducing the risk of collision; path reorganization maintains the access priority of hot spots and ensures the cleaning coverage rate of high-density loss areas.

[0088] Figure 2 The present invention provides a schematic diagram of a path planning system for a cleaning robot for photovoltaic panels, as shown in FIG. Figure 2 As shown, the system includes: An acquisition module 21 is used to acquire three-dimensional elevation data and temperature distribution data of the photovoltaic array, and current and voltage data of the photovoltaic strings; an identification module 22 for identifying height mutation areas and shadow boundary areas on the surface of the photovoltaic panel based on the three-dimensional elevation data and temperature distribution data; The positioning module 23 is configured to generate a current-voltage characteristic curve according to the current-voltage data, and position a hot spot region associated with the shadow boundary region according to an abnormal peak point in the current-voltage characteristic curve. The generating module 24 is configured to generate a cleaning path plan based on the spatial coordinate information of the high mutation region, the shadow boundary region, and the hot spot region, the cleaning path plan being used to define a path avoidance region and a cleaning priority of the hot spot region.

[0089] Figure 2 The cleaning robot path planning system for the photovoltaic panel can perform Figure 1 The cleaning robot path planning method for the photovoltaic panel has the implementation principle and technical effects which will not be repeated. The specific operation modes of each module and unit of the cleaning robot path planning system for the photovoltaic panel have been described in detail in the embodiments of the method, and will not be described in detail here.

[0090] In one possible design, Figure 2 The cleaning robot path planning system for the photovoltaic panel can be implemented as a computing device, such as Figure 3 As shown, the computing device can include a storage component 31 and a processing component 32. The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32.

[0091] The processing component 32 is configured to perform the above Figure 1 The cleaning robot path planning method for the photovoltaic panel.

[0092] The processing component 32 can include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component can also be one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic elements, for executing the above method.

[0093] The storage component 31 is configured to store various types of data to support the operation of the terminal. The storage component can be implemented by any type of volatile or nonvolatile storage devices, or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic storage, a flash memory, a magnetic disk, or a compact disk.

[0094] Of course, the computing device can also necessarily include other components, such as an input / output interface, a display component, a communication component, etc.

[0095] The input / output interface provides an interface between the processing component and peripheral interface modules, which can be output devices, input devices, etc.

[0096] The communication component is configured to facilitate wired or wireless communication between the computing device and other devices, etc.

[0097] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform, and the computing device can be a cloud server, and the processing component, the storage component, etc. can be a basic server resource rented or purchased from the cloud computing platform.

[0098] The embodiment of the application also provides a computer storage medium, which stores a computer program, and the computer program can implement the above-mentioned Figure 1 A cleaning robot path planning method for a photovoltaic panel.

[0099] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the foregoing method embodiment, and will not be described here.

[0100] The device embodiment described above is only schematic, and the units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiment scheme. Those skilled in the art can understand and implement it without creative labor.

[0101] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0102] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A path planning method for a cleaning robot for photovoltaic panels, characterized in that: include: Obtain three-dimensional elevation data and temperature distribution data of the photovoltaic array, as well as current and voltage data of the photovoltaic strings; Identifying, based on the three-dimensional elevation data and temperature distribution data, height mutation areas and shadow boundary areas on the surface of the photovoltaic panel; generating a current-voltage characteristic curve according to the current-voltage data, and locating a hot spot region associated with the shadow boundary region according to an abnormal peak point in the current-voltage characteristic curve; Based on the spatial coordinate information of the height mutation area, the shadow boundary area and the hot spot area, a cleaning path plan is generated, and the cleaning path plan is used to define the cleaning priority of the path avoidance area and the hot spot area.

2. The method according to claim 1, characterized in that According to the three-dimensional elevation data and temperature distribution data, the height mutation area and the shadow boundary area on the photovoltaic panel surface are identified, including: Comparing the height values ​​of adjacent points in the three-dimensional elevation data, when the height difference between any two adjacent points exceeds a set height difference, marking the point with the larger height value as an elevation mutation point, connecting all adjacent elevation mutation points to form a mutation boundary line, and defining the area surrounded by the mutation boundary line as the elevation mutation area on the photovoltaic panel surface; Associating the temperature value of each measurement point in the temperature distribution data with the corresponding position on the surface of the photovoltaic panel, and calculating the temperature change rate of each measurement point and adjacent measurement points within a preset range; Marking the measurement points corresponding to the temperature change rate exceeding the set temperature critical value as shadow boundary points, connecting all adjacent shadow boundary points to form a shadow boundary line, and defining the area surrounded by the shadow boundary line as a preliminary shadow boundary area; The shadow boundary points in the preliminary shadow boundary area that are within the highly sudden change area are removed to obtain a shadow boundary area.

3. The method according to claim 1, characterized in that Generating a current-voltage characteristic curve according to the current-voltage data, and locating a hot spot area associated with the shadow boundary area according to an abnormal peak point in the current-voltage characteristic curve, including: Arranging the current measurement values ​​and voltage measurement values ​​of the photovoltaic strings in the current and voltage data according to the measurement time sequence to generate a data sequence; sorting the voltage measurement values ​​in the data sequence in ascending order to generate a sorted data sequence, and connecting the voltage measurement values ​​and corresponding current measurement values ​​in the sorted data sequence to generate a current-voltage characteristic curve; Identifying a target section in the current-voltage characteristic curve where a current measurement value monotonically increases with a voltage measurement value and a current step decrease occurs, and selecting a data point with a maximum current measurement value from the target section as an abnormal peak point; Determine the physical location of the photovoltaic string to which the abnormal peak point belongs based on the voltage measurement value and the current measurement value corresponding to the abnormal peak point and the preset position mapping relationship of the photovoltaic string; Calculate the shortest spatial distance between the physical location and the boundary line of the shadow boundary area. When the shortest spatial distance is less than a set distance threshold, mark the photovoltaic panel area covered by the photovoltaic string to which the physical location belongs as a hot spot area associated with the shadow boundary area.

4. The method according to claim 3, characterized in that Identifying a target section in the current-voltage characteristic curve where a step-down current value occurs during a process in which the current measurement value monotonically increases with the voltage measurement value, and selecting a data point with a maximum current measurement value from the target section as an abnormal peak point, including: Traversing the data points of the current-voltage characteristic curve in ascending order of voltage, when detecting that the absolute current difference between the first two data points of three consecutive data points in the first cycle is less than the first tolerance value, and the absolute current difference between the last two data points is greater than the second tolerance value, marking the third data point in the first cycle as a descending starting point; Taking the decline starting point as the starting point, traversing the data points of the current-voltage characteristic curve, and when detecting that the absolute current differences of three consecutive data points in the second period are all less than a third tolerance value, marking the first data point in the second period as the decline ending point; The section between the descent starting point and the descent ending point is defined as a target section; The current measurement value of each data point in the target section is compared, and the data point with the largest current measurement value is selected as the abnormal peak point.

5. The method according to claim 3, characterized in that Determining the physical location of the photovoltaic string to which the abnormal peak point belongs based on the voltage measurement value and the current measurement value corresponding to the abnormal peak point and the preset position mapping relationship of the photovoltaic string includes: The voltage measurement value corresponding to the abnormal peak point is used as the query voltage value, and the corresponding current measurement value is used as the query current value; According to the registration table in the preset position mapping relationship of the photovoltaic strings, the registration table records the number, rated voltage range, rated current range and corresponding physical position coordinates of each photovoltaic string; Grouping all photovoltaic strings whose query voltage values ​​are within the rated voltage range of each photovoltaic string in the registration table into a voltage matching string set; Combining all photovoltaic strings whose query current values ​​are within the rated current range of each photovoltaic string in the voltage matching string set into a fully matching string set; A target photovoltaic string with the smallest boundary difference in the rated voltage range is selected from the set of completely matched photovoltaic strings, and the physical position coordinates corresponding to the target photovoltaic string are used as the physical position of the photovoltaic string to which the abnormal peak point belongs.

6. The method according to claim 1, characterized in that Generating a cleaning path plan based on the spatial coordinate information of the height mutation area, the shadow boundary area, and the hot spot area includes: Marking the mutation boundary line of the highly mutation area as the path avoidance area boundary line; Calculate the minimum distance between the center points of each hot spot area, use the reciprocal of the minimum distance as a density reference value, and sort the hot spot areas according to the density reference value to generate a priority traversal sequence; calculating a direction distribution of shadow boundary points of the shadow boundary area to determine a main traveling direction according to the direction distribution; According to the priority traversal sequence, a segmented straight line path connecting the center points of all hot spot areas is generated, and the segmented straight line path and the priority traversal sequence are combined into a cleaning path planning, and the segmented straight line path detours the path avoidance area boundary line.

7. The method according to claim 6, characterized in that According to the priority traversal sequence, a segmented straight line path connecting the center points of all hot spot areas is generated, and the segmented straight line path and the priority traversal sequence are combined into a cleaning path plan, wherein the segmented straight line path detours the path avoidance area boundary line, including: Connecting the center points of adjacent hot spot areas in the order of the priority traversal sequence to form an initial segmented straight line path; Calculating the shortest spatial distance between each straight line segment in the initial segmented straight line path and the boundary line of the path avoidance area; Marking a straight line segment whose shortest spatial distance is less than a safety threshold as an intermediate straight line segment, and determining a critical point on the intermediate straight line segment that is closest to the boundary line of the path avoidance area; According to the set avoidance distance, the critical point is translated along the normal direction of the path avoidance area boundary line at the critical point to generate an adjustment point; Splitting the intermediate straight line segment into two alternative straight line segments, wherein the first alternative straight line segment connects the starting point of the intermediate straight line segment to the adjustment point, and the second alternative straight line segment connects the adjustment point to the end point of the intermediate straight line segment; In the order of the priority traversal sequence, the two alternative straight line segments and the straight line segment whose shortest spatial distance is greater than or equal to the safety threshold are recombined into a segmented straight line path of the detour path avoidance area boundary line.

8. A path planning system for a cleaning robot for photovoltaic panels, characterized in that: include: An acquisition module is used to obtain the three-dimensional elevation data and temperature distribution data of the photovoltaic array, as well as the current and voltage data of the photovoltaic strings; an identification module, configured to identify height mutation areas and shadow boundary areas on the surface of the photovoltaic panel based on the three-dimensional elevation data and temperature distribution data; a positioning module, configured to generate a current-voltage characteristic curve according to the current-voltage data, and locate a hot spot region associated with the shadow boundary region according to an abnormal peak point in the current-voltage characteristic curve; A generation module is used to generate a cleaning path plan based on the spatial coordinate information of the highly sudden change area, the shadow boundary area and the hot spot area, and the cleaning path plan is used to define the cleaning priority of the path avoidance area and the hot spot area.

9. A computing device, characterized in that It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a path planning method for a cleaning robot for photovoltaic panels as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, a path planning method for a cleaning robot for photovoltaic panels as described in any one of claims 1 to 7 is implemented.

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