A path planning method and system for a cleaning robot for photovoltaic panels

Abstract

This invention provides a path planning method and system for a cleaning robot used on photovoltaic panels. It acquires three-dimensional elevation and temperature distribution data of the photovoltaic array, as well as current and voltage data of the photovoltaic strings. Based on the three-dimensional elevation and temperature distribution data, it identifies height abrupt change regions and shadow boundary regions on the photovoltaic panel surface. Based on the current and voltage data, it generates current and voltage characteristic curves and locates hot spot regions associated with shadow boundary regions based on abnormal peak points in the current and voltage characteristic curves. Based on the spatial coordinate information of the height abrupt change regions, shadow boundary regions, and hot spot regions, it generates a cleaning path plan, which defines the cleaning priority of path avoidance areas and hot spot regions. This invention achieves accurate perception of the complex environment on the photovoltaic panel surface, generating intelligent path planning that balances obstacle avoidance and hot spot priority cleaning, thereby improving cleaning efficiency and the operational stability of the photovoltaic system.

Description

Technical Field

[0001] This invention relates to the field of robotics, and in particular to a path planning method and system for a cleaning robot used for photovoltaic panels. Background Technology

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

[0003] Current research has proposed a path planning scheme for cleaning robots based on a combination of 2D image recognition and fixed paths. This scheme uses cameras to capture images of the photovoltaic panel surface, employs image processing technology to identify visible soiled areas, and then performs row-by-row or column-by-column cleaning based on a pre-defined grid path. However, this scheme has some significant drawbacks. For example, relying on 2D visual information makes it difficult to accurately identify areas with varying heights and complex shadow boundaries, resulting in a lack of dynamic adjustment capabilities for the cleaning path. Furthermore, the lack of sufficient integration with the actual operating parameters of the photovoltaic modules leads to unreasonable cleaning priority settings, posing a risk of overlooking critical areas and consequently affecting cleaning effectiveness and the recovery of power generation performance. Summary of the Invention

[0004] This invention provides a path planning method and system for a cleaning robot used for photovoltaic panels, which solves the problems in the prior art such as lack of dynamic adjustment capability of cleaning path, unreasonable setting of cleaning priority, and 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:

[0006] Acquire three-dimensional elevation data and temperature distribution data of the photovoltaic array, as well as current and voltage data of the photovoltaic strings;

[0007] Based on the three-dimensional elevation data and temperature distribution data, regions of abrupt height changes and shadow boundary regions on the surface of the photovoltaic panel were identified.

[0008] Based on the current and voltage data, a current and voltage characteristic curve is generated, and based on the abnormal peak points in the current and voltage characteristic curve, the hot spot region associated with the shadow boundary region is located.

[0009] Based on the spatial coordinate information of the height abrupt change region, the shadow boundary region, and the hot spot region, a cleaning path plan is generated. The cleaning path plan is used to define the cleaning priority of the path avoidance region and the hot spot region.

[0010] Optionally, based on the three-dimensional elevation data and temperature distribution data, regions of abrupt height changes and shadow boundary regions on the surface of the photovoltaic panel are identified, including:

[0011] Compare 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, mark the point with the larger height value as an elevation change point. Connect all adjacent elevation change points to form a change boundary line. Define the area enclosed by the change boundary line as the height change area on the surface of the photovoltaic panel.

[0012] The temperature value of each measurement point in the temperature distribution data is correlated with the corresponding position on the surface of the photovoltaic panel, and the temperature change rate of each measurement point and its adjacent measurement points within a preset range is calculated.

[0013] The measurement points where the temperature change rate exceeds the set temperature threshold are marked as shadow boundary points. All adjacent shadow boundary points are connected to form a shadow boundary line. The area enclosed by the shadow boundary line is defined as the preliminary shadow boundary region.

[0014] The shadow boundary points within the height abrupt change region in the initial shadow boundary region are removed to obtain the shadow boundary region.

[0015] Optionally, 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 region associated with the shadow boundary region is located, including:

[0016] Based on the measurement time sequence, the current measurement values ​​and voltage measurement values ​​of the photovoltaic string in the current and voltage data are arranged to generate a data sequence;

[0017] The voltage measurement values ​​in the data sequence are sorted in ascending order to generate a sorted data sequence. The voltage measurement values ​​and corresponding current measurement values ​​in the sorted data sequence are connected to generate a current-voltage characteristic curve.

[0018] Identify the target segment in the current-voltage characteristic curve where the current measurement value increases monotonically with the voltage measurement value and the current value drops sharply; select the data point with the largest current measurement value from the target segment as the abnormal peak point.

[0019] Based on the voltage and current measurements corresponding to the abnormal peak points, and in conjunction with the preset position mapping relationship of the photovoltaic strings, the physical location of the photovoltaic string to which the abnormal peak points belong is determined.

[0020] Calculate the shortest spatial distance between the physical location and the boundary line of the shadow boundary region. 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 region.

[0021] Optionally, identify the target segment in the current-voltage characteristic curve where the current measurement value shows a step drop as the voltage measurement value monotonically increases, and select the data point with the largest current measurement value from the target segment as the abnormal peak point, including:

[0022] The data points of the current-voltage characteristic curve are traversed along the voltage ascending direction. When it is detected that the absolute difference of the current between the first two data points in the first cycle is less than the first tolerance value and the absolute difference of the current between the last two data points is greater than the second tolerance value, the third data point in the first cycle is marked as the starting point of the decline.

[0023] Starting from the point of descent, the data points of the current-voltage characteristic curve are traversed. When it is detected that the absolute difference of current of three consecutive data points in the second cycle is less than the third tolerance value, the first data point in the second cycle is marked as the point of descent termination.

[0024] The segment between the starting point of descent and the ending point of descent is defined as the target segment;

[0025] Compare the current measurement values ​​of each data point within the target section, and select the data point with the largest current measurement value as the abnormal peak point.

[0026] Optionally, based on the voltage and current measurements corresponding to the abnormal peak points, and in conjunction with a preset location mapping relationship for the photovoltaic strings, the physical location of the photovoltaic string to which the abnormal peak point belongs is determined, including:

[0027] Use the voltage measurement value corresponding to the abnormal peak point as the query voltage value, and the corresponding current measurement value as the query current value;

[0028] According to the registration table in the preset location mapping relationship of photovoltaic strings, the registration table records the number, rated voltage range, rated current range and corresponding physical location coordinates of each photovoltaic string;

[0029] All photovoltaic strings whose query voltage values ​​fall within the rated voltage range of each photovoltaic string in the registration table are grouped into a voltage-matched string set;

[0030] All photovoltaic strings whose query current values ​​fall within the rated current range of each photovoltaic string in the voltage matching string set are combined into a fully matched string set.

[0031] Select the target photovoltaic string with the smallest boundary difference in the rated voltage range from the set of perfectly matched strings, and use the physical location coordinates of the target photovoltaic string as the physical location of the photovoltaic string to which the abnormal peak point belongs.

[0032] Optionally, based on the spatial coordinate information of the height abrupt change region, the shadow boundary region, and the hot spot region, a cleaning path plan is generated, including:

[0033] Mark the mutation boundary line of the highly abrupt region as the path avoidance region boundary line;

[0034] Calculate the minimum distance between the center points of each hot spot region, use the reciprocal of the minimum distance as the density reference value, and sort the hot spot regions according to the density reference value to generate a priority traversal sequence;

[0035] Calculate the directional distribution of the shadow boundary points in the shadow boundary region to determine the main direction of travel based on the directional distribution;

[0036] Based on the priority traversal sequence, a segmented straight path connecting the center points of all hot spot regions is generated. The segmented straight path is combined with the priority traversal sequence to form a cleaning path plan. The segmented straight path bypasses the boundary line of the path avoidance area.

[0037] Optionally, based on the priority traversal sequence, segmented straight paths connecting the center points of all hot spot regions are generated. These segmented straight paths are combined with the priority traversal sequence to form a cleaning path plan. The segmented straight paths bypass the boundary lines of the path avoidance area, including:

[0038] Connect the center points of adjacent hot spot regions in the order of the priority traversal sequence to form an initial segmented straight path;

[0039] Calculate the shortest spatial distance between each straight segment in the initial segmented straight path and the boundary line of the path avoidance area;

[0040] Mark the straight line segment whose shortest spatial distance is less than the safety threshold as the middle straight line segment, and determine the critical point on the middle straight line segment that is closest to the boundary line of the path avoidance area;

[0041] Based on the set avoidance distance, the critical point is translated along the normal direction of the boundary line of the path avoidance area at the critical point to generate an adjustment point;

[0042] The intermediate straight line segment is divided 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 ending point of the intermediate straight line segment.

[0043] Following the order of the priority traversal sequence, 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 of the detour path avoidance area boundary line.

[0044] Secondly, the present invention provides a path planning system for a cleaning robot for photovoltaic panels, comprising:

[0045] The acquisition module is used to acquire three-dimensional elevation data and temperature distribution data of the photovoltaic array, as well as current and voltage data of the photovoltaic strings;

[0046] The identification module is used to identify regions of abrupt height changes and shadow boundary regions on the surface of the photovoltaic panel based on the three-dimensional elevation data and temperature distribution data.

[0047] The positioning module is used to generate a current-voltage characteristic curve based on the current-voltage data, and to locate the hot spot region associated with the shadow boundary region based on the abnormal peak points in the current-voltage characteristic curve.

[0048] The generation module is used to generate a cleaning path plan based on the spatial coordinate information of the height change region, the shadow boundary region, and the hot spot region. The cleaning path plan is used to define the cleaning priority of the path avoidance region and the hot spot region.

[0049] Thirdly, the present invention provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked 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.

[0050] Fourthly, 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.

[0051] In this invention, three-dimensional elevation data and temperature distribution data of the photovoltaic array, as well as current and voltage data of the photovoltaic strings, are acquired. Based on the three-dimensional elevation data and temperature distribution data, height abrupt change regions and shadow boundary regions on the surface of the photovoltaic panels are identified. Based on the current and voltage data, current and voltage characteristic curves are generated, and based on the abnormal peak points in the current and voltage characteristic curves, hot spot regions associated with the shadow boundary regions are located. Based on the spatial coordinate information of the height abrupt change regions, the shadow boundary regions, and the hot spot regions, a cleaning path plan is generated, which is used to define the cleaning priority of the path avoidance areas and the hot spot regions. The technical solution provided by this invention overcomes the limitations of existing technologies that rely on two-dimensional images. By integrating three-dimensional spatial elevation data (solving blind spots in height change recognition), surface temperature field distribution (capturing dynamic shadow boundaries), and real-time electrical parameters (sensing abnormal operating states), a multi-dimensional data foundation is constructed, providing data support for the accurate identification of physical obstacles and shadow hotspots. Through the identification of height abrupt change areas, the outlines of physical obstacles such as roof equipment and supports are accurately located, avoiding robot collisions. Through the identification of shadow boundary areas, changes in the projected boundaries of trees, buildings, etc., are dynamically captured, overcoming the misjudgment of shadows by two-dimensional vision. Based on the step characteristics of current and voltage characteristic curves, the failure of battery cells caused by shadows is captured, avoiding the omission of invisible hotspots. By coupling the distance between the hotspot position and the shadow boundary, interference from non-shadow factors is eliminated, improving the reliability of fault location. By defining the cleaning priority of path avoidance areas and hotspot areas, dynamic three-dimensional obstacle avoidance is achieved, ensuring that high-loss areas are treated first. Furthermore, characteristic curves are generated by sequentially arranging the current and voltage measurements of photovoltaic strings. In the voltage ascending sequence, target segments with step-drop current are identified, and the maximum current point is extracted as an abnormal peak point. Combining the preset position mapping relationship of the photovoltaic strings, the physical location of the photovoltaic string to which the abnormal peak point belongs is determined. Spatial correlation is verified by calculating the shortest distance between this location and the shadow boundary. Finally, photovoltaic panel areas meeting the distance threshold are marked as hotspot areas associated with the shadow boundary area. This method can capture cell failures caused by shadows, overcoming the blind spot of two-dimensional vision for internal fault detection. Verification of the spatial distance between the hotspot location and the shadow boundary eliminates interference from non-shadow factors such as equipment aging, improving fault identification accuracy. It provides hotspot area data with dual electrical and spatial verification for path planning, ensuring that high-power-loss areas receive the highest scanning priority. This overcomes the limitations of existing technologies that rely solely on visual judgment of surface stains, enabling intelligent decision-making based on actual power generation losses.

[0052] These or other aspects of the invention will become more apparent from the following description of the embodiments. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 A flowchart illustrating a path planning method for a cleaning robot used for photovoltaic panels, provided by the present invention;

[0055] Figure 2 This is a schematic diagram of the structure of a path planning system for a cleaning robot used for photovoltaic panels, provided by the present invention.

[0056] Figure 3 This is a schematic diagram of the structure of a computing device provided by the present invention. Detailed Implementation

[0057] To enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0058] In some of the processes described in the specification, claims, and accompanying drawings of this invention, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to different types.

[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] To address the issues of reduced power generation efficiency and hot spot effects on photovoltaic (PV) panel surfaces in large-scale PV power plants caused by dust accumulation, fallen leaves, and bird droppings, existing solutions based on two-dimensional image recognition and fixed-path cleaning have significant shortcomings in practical applications. They struggle to accurately identify areas of height abrupt changes and complex shadow boundaries, and cannot pinpoint potential hot spot areas in conjunction with the PV module's operating status. This results in a lack of dynamic adjustment capabilities for the cleaning path and unreasonable priority allocation. Therefore, this invention acquires three-dimensional elevation, temperature distribution, and current / voltage data of the PV array to comprehensively identify areas of height abrupt changes, shadow boundaries, and associated hot spot areas. Based on their spatial coordinate information, it generates a cleaning path plan with avoidance capabilities and priority differentiation, thereby improving cleaning efficiency and operational intelligence, and overcoming the limitations of traditional visual recognition and fixed-path cleaning methods in complex scenarios. Figure 1 A flowchart of a path planning method for a cleaning robot for photovoltaic panels is provided as an embodiment of the present invention, as follows: Figure 1 As shown, the method includes:

[0061] Step 101: 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;

[0062] In this step, a photovoltaic array refers to a power generation unit formed by arranging multiple photovoltaic modules in rows and columns, including the support structure and electrical connection system. Three-dimensional elevation data refers to the three-dimensional spatial coordinates (X, Y, Z) of various points on the surface of the photovoltaic panel obtained through lidar scanning, used to characterize height changes. Temperature distribution data refers to the set of surface temperature values ​​of the photovoltaic panel and their corresponding coordinates collected by infrared sensors, reflecting the characteristics of the thermal field distribution. 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 real-time current and voltage measurements output by the photovoltaic string during operation.

[0063] In this embodiment of the invention, three-dimensional elevation data (including the X, Y, and Z coordinates of each location point) is generated by scanning the surface of the photovoltaic array with a lidar. Simultaneously, temperature distribution data (temperature value and coordinates of each measurement point) on the surface of the photovoltaic panel is collected by an infrared thermal imager. At the same time, current and voltage data (including the time series of string output current and voltage measurements) are obtained in real time from the monitoring module of the photovoltaic string. The three-dimensional elevation data is used to construct the spatial terrain, the temperature distribution data maps the thermal field changes, and the current and voltage data reflects the electrical operating status. Together, these three elements constitute the data foundation for path planning.

[0064] Step 102: Based on the three-dimensional elevation data and temperature distribution data, identify the height abrupt change areas and shadow boundary areas on the surface of the photovoltaic panel;

[0065] In this embodiment of the invention, the height values ​​of adjacent points in the three-dimensional elevation data are compared. If the height difference exceeds a set height difference value, the point with the larger height value is marked as an elevation abrupt change point. Adjacent elevation abrupt change points are connected to form an abrupt change boundary line, and the area within the abrupt change boundary line is defined as an elevation abrupt change region. At the same time, the temperature change rate of each measurement point in the temperature distribution data and its adjacent measurement points within a preset range are calculated. Measurement points whose temperature change rate exceeds a set temperature threshold are marked as shadow boundary points. Adjacent shadow boundary points are aggregated to form a preliminary shadow boundary region. Then, the overlapping part with the elevation abrupt change region is excluded, and finally, a pure shadow boundary region is output.

[0066] Step 103: Generate a current-voltage characteristic curve based on the current-voltage data, and locate the hot spot region associated with the shadow boundary region based on the abnormal peak points in the current-voltage characteristic curve;

[0067] In this embodiment of the invention, the current and voltage data are arranged in chronological order of measurement time, and then sorted in ascending order by voltage measurement value to obtain a sorted data sequence. The voltage measurement value and the corresponding current measurement value are connected to generate a current-voltage characteristic curve. The target segment of the curve where the current drops sharply when the voltage increases is identified, and the data point with the largest current measurement value in the segment is selected as the abnormal peak point. Based on the voltage and current measurement values ​​corresponding to the abnormal peak point, combined with the preset position mapping relationship of the photovoltaic string, the physical location of the photovoltaic string to which the abnormal peak point belongs is determined. The shortest spatial distance between the physical location and the boundary line of the shadow boundary area is calculated. When the shortest spatial distance is less than a set distance threshold, the photovoltaic panel area covered by the photovoltaic string to which the physical location belongs is marked as the hot spot area.

[0068] Step 104: Based on the spatial coordinate information of the height change region, the shadow boundary region, and the hot spot region, generate a cleaning path plan. The cleaning path plan is used to define the cleaning priority of the path avoidance region and the hot spot region.

[0069] In this embodiment of the invention, the boundary line of the height abrupt change region is marked as the boundary line of the path avoidance region; the minimum distance between the center points of each hot spot region is calculated, and the reciprocal of the minimum distance is used as the density reference value to sort the hot spot regions and generate a priority traversal sequence; the directional distribution of the shadow boundary points of the shadow boundary region is calculated to determine the main travel direction; according to the priority traversal sequence, a segmented straight path connecting the center points of all hot spot regions is generated, and it is combined with the priority traversal sequence to form a sweeping path plan, wherein the segmented straight path detours around the boundary line of the path avoidance region.

[0070] This invention accurately identifies areas with abrupt height changes, such as rooftop equipment, using three-dimensional elevation data (to avoid collisions). It also dynamically captures shadow boundaries based on temperature distribution, overcoming the shortcomings of existing technologies in misjudging three-dimensional obstacles and shadows. It locates real hotspot areas based on the step characteristics of current-voltage characteristic curves and verifies the spatial correlation with shadow boundaries to eliminate interference from equipment aging, thus solving the problem of missed hotspot detection due to neglecting electrical parameters. Furthermore, it generates a cleaning priority sequence based on hotspot density distribution and plans low-energy turning paths based on shadow orientation, achieving priority coverage and three-dimensional avoidance of high-loss areas, improving cleaning efficiency and power generation recovery.

[0071] This invention provides a specific embodiment. Step 102 involves identifying height abrupt change regions and shadow boundary regions on the photovoltaic panel surface based on the three-dimensional elevation data and temperature distribution data. This specifically includes the following steps:

[0072] Step 201: Compare 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, mark the point with the larger height value as an elevation change point. Connect all adjacent elevation change points to form a change boundary line. Define the area enclosed by the change boundary line as the height change area on the surface of the photovoltaic panel.

[0073] In this step, adjacent location points refer to sampling points with adjacent spatial coordinates in the 3D elevation data, with the spacing determined by the LiDAR resolution. The height difference refers to the absolute value of the difference between the Z-coordinate values ​​of two adjacent location points, reflecting the magnitude of height change. The height difference setting is a threshold for determining whether a point represents an elevation abrupt change, based on the typical height of photovoltaic supports (5-20cm). An elevation abrupt change point is a location where the height difference exceeds the threshold, representing an edge of the equipment or a protrusion on the support. The abrupt change boundary line is a closed polyline connecting all adjacent elevation abrupt changes, used to define the obstacle outline. The height abrupt change region refers to the continuous surface area enclosed by the abrupt change boundary line, serving as a target for robot avoidance.

[0074] In this embodiment of the invention, all adjacent points in the three-dimensional elevation data are traversed, and the height difference between every two adjacent points is calculated (obtained by subtracting the lower point value from the higher point value). When the height difference exceeds a set height difference value (e.g., 10cm), the higher point (i.e., the point with the larger height value) is marked as an elevation change point. Adjacent elevation change points are connected by straight lines to form a closed change boundary line. The continuous area enclosed by this boundary line is defined as the height change area (corresponding to the outline of the roof equipment).

[0075] Step 202: Associate the temperature value of each measurement point in the temperature distribution data with the corresponding position on the photovoltaic panel surface, and calculate the temperature change rate of each measurement point with the adjacent measurement points within the preset range;

[0076] In this step, the measurement point refers to the temperature data unit acquired by the infrared thermal imager, including the temperature value and location coordinates. The preset range refers to a fixed neighborhood range centered on the current measurement point (e.g., a circular area with a radius of 5cm). The rate of temperature change refers to the maximum temperature change per unit distance.

[0077] In this embodiment of the invention, the temperature value of each measurement point of the temperature distribution data is bound to its XY coordinates on the surface of the photovoltaic panel; taking each measurement point as the center, adjacent measurement points within a preset range (such as a 3×3 grid) around it are taken, 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).

[0078] Step 203: Mark the measurement points where the temperature change rate exceeds the set temperature critical value as shadow boundary points, connect all adjacent shadow boundary points to form a shadow boundary line, and define the area enclosed by the shadow boundary line as the preliminary shadow boundary region;

[0079] In this step, the temperature threshold is set as the threshold for determining the shadow boundary, based on a typical temperature drop gradient caused by abrupt changes in illumination. Shadow boundary points are measurement points where the rate of temperature change exceeds the threshold, corresponding to the shadow edge location. The shadow boundary line is a continuous polyline connecting adjacent shadow boundary points, used to define the shadow outline. The initial shadow boundary region refers to the initial area enclosed by the shadow boundary line, containing both the actual shadow and the device projection.

[0080] In this embodiment of the invention, when the temperature change rate of the measurement point exceeds the set temperature critical value (e.g., 2℃ / cm), the point is marked as a shadow boundary point; 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 the preliminary shadow boundary area (including the device's own shadow interference).

[0081] Step 204: Remove the shadow boundary points within the height abrupt change region in the preliminary shadow boundary region to obtain the shadow boundary region;

[0082] In this step, the shaded boundary region refers to the dynamic shaded region after removing interfering points within the region of height abrupt change, which is used for hot spot correlation analysis.

[0083] In this embodiment of the invention, all shadow boundary points located in the height abrupt change region (such as device projection points) in the initial shadow boundary region are deleted, and the remaining shadow boundary points are reconnected to form a pure shadow boundary line. The area enclosed by this boundary line is output as the final shadow boundary region (containing only external shadows such as trees and buildings).

[0084] This invention, through the detection of height abrupt change points in three-dimensional elevation data, accurately identifies the outlines of physical obstacles such as roof equipment and supports (i.e., height abrupt change areas), solving the problem that existing two-dimensional vision solutions cannot distinguish three-dimensional obstacles; based on temperature gradient-based shadow boundary point positioning and interference point removal, it dynamically captures the true external shadow range (i.e., shadow boundary area), overcoming the defect of fixed cameras misjudging complex projections.

[0085] This invention provides a specific embodiment. Step 103 involves generating a current-voltage characteristic curve based on the current-voltage data, and locating the hot spot region associated with the shadow boundary region based on the abnormal peak points in the current-voltage characteristic curve. This specifically includes the following steps:

[0086] Step 301: Arrange the current measurement values ​​and voltage measurement values ​​of the photovoltaic string in the current and voltage data according to the measurement time sequence to generate a data sequence;

[0087] In this step, the data sequence refers to a set of current and voltage data pairs arranged in chronological order, with each data pair containing voltage and current measurements taken at the same time.

[0088] In this embodiment of the invention, the current and voltage measurements of the same photovoltaic string are paired and arranged according to the measurement time sequence of the current and voltage data to form a data sequence that increases in time stamp, for example: [t1: V1, I1], [t2: V2, I2], ...).

[0089] Step 302: Sort the voltage measurement values ​​in the data sequence in ascending order to generate a sorted data sequence. Connect the voltage measurement values ​​and the corresponding current measurement values ​​in the sorted data sequence to generate a current-voltage characteristic curve.

[0090] In this step, the sorted data sequence refers to the set of current and voltage data pairs arranged in ascending order of voltage values. The current-voltage characteristic curve refers to a line graph showing the voltage-current relationship, reflecting the string's electrical characteristics.

[0091] In this embodiment of the invention, the voltage measurement values ​​in the data sequence are sorted from smallest to largest, while keeping the current measurement value corresponding to each voltage measurement value 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, where the horizontal axis is voltage and the vertical axis is current.

[0092] Step 303: Identify the target segment in the current-voltage characteristic curve where the current measurement value increases monotonically with the voltage measurement value and the current value drops sharply; select the data point with the largest current measurement value from the target segment as the abnormal peak point.

[0093] In this step, the target segment refers to the continuous section on the characteristic curve where the current drops sharply, defined by the start and end points of the drop. The abnormal peak point refers to the data point with the largest current measurement within the target segment, characterizing cell breakdown faults caused by shading.

[0094] In this embodiment of the invention, the characteristic curve is scanned along the voltage ascending direction. When three consecutive data points are detected that satisfy the following conditions: the current difference between the first two data points is less than the first tolerance value, and the current difference between the last two data points is greater than the second tolerance value, the third data point is marked as the starting point of the decline. From this point, the scan continues until the current change stabilizes (i.e., the absolute current difference of the three consecutive data points is less than the third tolerance value), and the first data point in the stabilizing section is marked as the ending point of the decline. The section between the starting point of the decline and the ending point of the decline is defined as the target section, and the data point with the largest current measurement value in this section is selected as the abnormal peak point.

[0095] Step 304: Based on the voltage and current measurements corresponding to the abnormal peak points, and in conjunction with the preset position mapping relationship of the photovoltaic strings, determine the physical location of the photovoltaic string to which the abnormal peak point belongs;

[0096] In this step, the preset location mapping relationship refers to the pre-stored correspondence table between the electrical parameters of the photovoltaic strings and their spatial locations, including the photovoltaic string number, rated voltage range, rated current range, and corresponding physical location coordinates. The physical location refers to the center point coordinates (X, Y) of the photovoltaic string to which the abnormal peak point belongs in the array.

[0097] In this embodiment of the 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 location mapping relationship is queried to filter out photovoltaic strings whose query voltage value is within the rated voltage range and whose current measurement value is within the rated current range, so as to obtain a voltage matching string set. The target photovoltaic string with the smallest boundary difference of the rated voltage range is selected from them, and its installation location coordinates are used as the physical location of the photovoltaic string to which the abnormal peak point belongs.

[0098] Step 305: Calculate the shortest spatial distance between the physical location and the boundary line of the shadow boundary region. 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 region.

[0099] In this step, the shortest spatial distance refers to the minimum vertical distance between the physical location of the photovoltaic string to which the abnormal peak point belongs and all line segments of the boundary line of the shadow boundary region. The distance threshold is the maximum distance for determining the association between the hot spot and the shadow, typically half the string size. The hot spot region refers to the rectangular area of ​​the photovoltaic panel marked as having a shadow-associated fault, coinciding with the string installation location.

[0100] In this embodiment of the invention, the shortest spatial distance (minimum distance from point to line) between the physical location of the photovoltaic string to which the abnormal peak point belongs and the boundary line of the shadow boundary region 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 the shadow boundary associated hot spot region.

[0101] This invention, through the detection of the step drop segment of the current-voltage characteristic curve, accurately locates the cell breakdown point (abnormal peak point) caused by shadows, solving the problem of missed detection of internal electrical faults in existing vision solutions; combined with string position mapping and shadow boundary space verification, it eliminates interference from non-shadow factors such as equipment aging, improving the reliability of hot spot recognition; it provides hot spot area data with electrical and spatial dual verification for path planning, ensuring priority cleaning of high power generation loss areas; compared with traditional image recognition solutions, it improves the accuracy of hot spot positioning and the coverage of key areas.

[0102] This invention provides a specific embodiment, step 303, identifying the target segment in the current-voltage characteristic curve where the current measurement value shows a step drop in current value during the monotonically increasing process of the voltage measurement value, and selecting the data point with the largest current measurement value from the target segment as the abnormal peak point, specifically including the following steps:

[0103] Step 311: Traverse the data points of the current-voltage characteristic curve along the voltage ascending direction. When it is detected that the absolute difference of the current between the first two data points in the first cycle is less than the first tolerance value and the absolute difference of the current between the last two data points is greater than the second tolerance value, mark the third data point in the first cycle as the starting point of the decline.

[0104] In this step, the first period refers to the detection window of three consecutive data points that first meet the conditions, reflecting the initial step position. The first tolerance value refers to the upper limit of current fluctuation in the flat section, set based on the percentage of the short-circuit current of the photovoltaic string. The second tolerance value refers to the minimum amplitude threshold of the current step drop, set based on the typical value of the current drop caused by shading. The drop start point refers to the starting position of the current drop, corresponding to the inflection point of the characteristic curve.

[0105] In this embodiment of the invention, the data points of the characteristic curve are scanned sequentially along the voltage ascending direction. 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, the absolute difference of the current between the first data point P1 and the second data point P2 is less than the first tolerance value (such as 0.5% short-circuit current), and the absolute difference of the current between the second data point P2 and the third data point P3 is greater than the second tolerance value (such as 5% short-circuit current), then the third data point P3 is marked as the starting point of the decline.

[0106] Step 312: Starting from the starting point of the descent, traverse the data points of the current-voltage characteristic curve. When it is detected that the absolute difference of current of three consecutive data points in the second cycle is less than the third tolerance value, mark the first data point in the second cycle as the descent termination point.

[0107] 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 descent. The third tolerance value refers to the upper limit of the fluctuation of the new current plateau, which is less than the first tolerance value to ensure stability. The descent termination point refers to the position where the current ends its descent and enters a stable plateau.

[0108] In this embodiment of the invention, the sequential scanning continues 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, satisfying that the absolute difference of the current between the first data point Q1 and the second data point Q2, and between the second data point Q2 and the third data point Q3 are all less than the third tolerance value (such as 1% short-circuit current), the first data point Q1 is marked as the descent termination point.

[0109] Step 313: Define the segment between the starting point of descent and the ending point of descent as the target segment;

[0110] In this embodiment of the invention, all continuous data points from the starting point of the descent to the ending point of the descent are connected to form the target segment (i.e., the complete segment of the current step descent).

[0111] Step 314: Compare the current measurement values ​​of each data point within the target section, and select the data point with the largest current measurement value as the abnormal peak point;

[0112] In this embodiment of the invention, the current value of each data point within the target segment is traversed, the numerical values ​​are directly compared, and the data point with the largest current measurement value is selected as the abnormal peak point.

[0113] This invention employs a triple tolerance mechanism to lock the current step segment, eliminating interference from light fluctuations and other factors, thus solving the problem of missed detection of internal electrical faults in traditional solutions. It directly compares and selects abnormal peak points based on current measurements, avoiding errors introduced by complex algorithms and improving the reliability of hot spot location. The accuracy of step drop event detection is improved, and the time required for hot spot location is reduced.

[0114] This invention provides a specific embodiment. Step 304 involves determining the physical location of the photovoltaic string to which the abnormal peak point belongs based on the voltage and current measurement values ​​corresponding to the abnormal peak point, combined with a preset position mapping relationship of the photovoltaic string. This specifically includes the following steps:

[0115] Step 321: Use the voltage measurement value corresponding to the abnormal peak point as the query voltage value, and use the corresponding current measurement value as the query current value;

[0116] In this step, the voltage value refers to the measured voltage value corresponding to the abnormal peak point on the current-voltage characteristic curve, used to match the string's rated voltage range. The current value refers to the measured current value corresponding to the abnormal peak point on the current-voltage characteristic curve, used to match the string's rated current range.

[0117] In this embodiment of the 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. The two together constitute the electrical query conditions for string matching.

[0118] Step 322: According to the registration table in the preset location 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;

[0119] In this step, the registration form refers to a predefined database of photovoltaic string parameters, which contains the mapping relationship between electrical parameters and spatial location.

[0120] In this embodiment of the invention, the string registration table is called from the preset position mapping relationship of the photovoltaic string. The table stores the number, rated voltage range (e.g., [Vmin, Vmax]), rated current range (e.g., [Imin, Imax]), and installation center point coordinates (X, Y) of each photovoltaic string.

[0121] Step 323: Combine all photovoltaic strings whose query voltage values ​​are within the rated voltage range of each photovoltaic string in the registration table into a voltage-matched string set;

[0122] In this step, the voltage matching string set refers to the subset of photovoltaic strings whose voltage values ​​are within the rated voltage range.

[0123] In this embodiment of the invention, all photovoltaic strings in the registration table are traversed, and it is determined whether the query voltage value falls within the rated voltage range of a certain string (i.e., Vmin≤query voltage value≤Vmax). All photovoltaic strings that meet the conditions are then combined into a voltage matching string set.

[0124] Step 324: Combine 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 matched string set.

[0125] In this step, the fully matched string set refers to the subset of photovoltaic strings that simultaneously meet the requirements of matching the rated voltage range and the rated current range.

[0126] In this embodiment of the 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 photovoltaic 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.

[0127] Step 325: Select the target photovoltaic string with the smallest boundary difference in the rated voltage range from the set of perfectly matched strings, and use the physical location coordinates corresponding to the target photovoltaic string as the physical location of the photovoltaic string to which the abnormal peak point belongs;

[0128] In this step, the target photovoltaic string refers to the photovoltaic string with the narrowest rated voltage range in the set of perfectly matched strings, that is, the photovoltaic string with the most accurate positioning.

[0129] In this embodiment of the invention, the boundary difference (Vmax-Vmin) of the rated voltage range of each photovoltaic string in the perfectly matched string set is calculated, the string with the smallest boundary difference is selected as the target photovoltaic string, and its corresponding physical location coordinates (X,Y) are used as the physical location of the photovoltaic string to which the abnormal peak point belongs.

[0130] This invention achieves precise location of faulty strings by dual matching of electrical parameters with a registration table in a preset location mapping relationship, solving the positioning deviation problem caused by the insensitivity of traditional image recognition to the electrical state of components; and selects the photovoltaic string with the narrowest rated voltage range to improve positioning accuracy to the single string level.

[0131] For example, in a rooftop distributed photovoltaic (PV) scenario, to determine the physical location of the PV string to which the 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 is then matched with the rated voltage range, rated current range, and corresponding physical location coordinates of each PV string recorded in the registration table. 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 location coordinates 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 location coordinates 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 location coordinates of (12.5, 4.9). The system first matches the query voltage value with the rated voltage range of each photovoltaic string. It determines that the rated voltage range of both photovoltaic string 1 and photovoltaic string 2 includes 31.5V, thus forming a voltage-matched string set. Next, based on this set, it further compares the query current value of 7.2A with the rated current range of these two photovoltaic strings, finding that both meet the condition, thus forming a fully matched string set {PV string 1, PV string 2}. Subsequently, the system selects the target photovoltaic string with the smallest boundary difference of the rated voltage range from the fully matched string set. It calculates that the rated voltage range width of both photovoltaic string 1 and photovoltaic string 2 is 3V. Given the same boundary difference, the system defaults to selecting the first matched photovoltaic string 1, and its corresponding physical location coordinates (10.2, 5.3) are taken as the location of the photovoltaic string to which the abnormal peak point belongs.

[0132] This invention provides a specific embodiment. Step 104 involves generating a cleaning path plan based on the spatial coordinate information of the height abrupt change region, the shadow boundary region, and the hot spot region. This specifically includes the following steps:

[0133] Step 401: Mark the mutation boundary line of the highly abrupt region as the path avoidance region boundary line;

[0134] In this step, the path avoidance zone boundary line refers to the closed contour polyline of the region with abrupt changes in height, which is formed by the boundary line of the abrupt change. The robot path must not cross this boundary.

[0135] In this embodiment of the invention, the boundary line of the height abrupt change region is marked as the boundary line of the path avoidance region, which serves as the absolute no-go zone for robot collision avoidance.

[0136] Step 402: Calculate the minimum distance between the center points of each hot spot region, use the reciprocal of the minimum distance as the density reference value, and sort the hot spot regions according to the density reference value to generate a priority traversal sequence;

[0137] In this step, the density reference value refers to the reciprocal of the minimum spacing between the center points of the hotspot region; a larger value indicates a denser hotspot. The priority traversal sequence refers to the list of hotspot regions accessed in descending order of their density reference values.

[0138] In this embodiment of the invention, the Euclidean distance between the center point of each hot spot region and its nearest neighbor center point is calculated, and the minimum distance is taken; the density reference value is calculated as 1 / minimum distance (reflecting the local hot spot aggregation degree); all hot spot regions are sorted from largest to smallest according to the density reference value to generate a priority traversal sequence, wherein the region with the highest density reference value is ranked first.

[0139] Step 403: Calculate the directional distribution of the shadow boundary points in the shadow boundary region to determine the main direction of travel based on the directional distribution;

[0140] In this step, the direction distribution refers to the statistical histogram of the tangent angles of the shaded boundary contour points, reflecting the distribution of the boundary orientation. The main direction of travel refers to the angle that appears most frequently in the direction distribution and is used for path orientation.

[0141] In this embodiment of the invention, 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 line connecting adjacent points) is calculated, the angle histogram of all tangent directions is statistically analyzed, and the angle corresponding to the peak value of the histogram is determined as the main direction of travel (e.g., 30° represents the northeast direction).

[0142] Step 404: Based on the priority traversal sequence, generate segmented straight paths connecting the center points of all hot spot regions, and combine the segmented straight paths with the priority traversal sequence to form a cleaning path plan. The segmented straight paths bypass the boundary lines of the path avoidance area.

[0143] In this step, the segmented straight path refers to a polygonal path composed of multiple straight lines, each connecting two hot spot center points. The cleaning path planning refers to a set of decision instructions, including the segmented straight path and a priority traversal sequence, that guides the robot's movement.

[0144] In this embodiment of the invention, the center points of adjacent hot spot regions are connected in the order of the priority traversal sequence to form an initial segmented straight path; the shortest spatial distance between each straight segment in the path and the boundary line of the path avoidance area is calculated; the straight segments with a shortest spatial distance less than a safety threshold are marked as intermediate straight segments, and the critical point on the intermediate straight segment closest to the boundary line of the path avoidance area is determined; according to the set avoidance distance, the critical point is translated along the normal direction of the boundary line of the path avoidance area at the critical point to generate an adjustment point; the intermediate straight segment is split into two alternative straight segments, and in the order of the priority traversal sequence, the two alternative straight segments and the straight segments with a shortest spatial distance greater than or equal to the safety threshold are recombined to form a segmented straight path that bypasses the boundary line of the path avoidance area.

[0145] This invention achieves precise obstacle avoidance in three dimensions by using the boundary line of the path avoidance area, thus reducing the risk of collision; it dynamically generates a priority sequence based on hot spot density to ensure priority coverage of high-loss areas; and it plans paths along the main direction of shadow to reduce repeated cleaning and lower robot energy consumption.

[0146] This invention provides a specific embodiment, step 404, which involves generating segmented straight-line paths connecting the center points of all hot spot regions based on the priority traversal sequence, and combining the segmented straight-line paths with the priority traversal sequence to form a cleaning path plan. The segmented straight-line paths bypass the boundary lines of the path avoidance area, specifically including the following steps:

[0147] Step 411: Connect the center points of adjacent hot spot regions according to the priority traversal sequence to form an initial segmented straight path;

[0148] In this step, the initial segmented straight 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.

[0149] In this embodiment of the invention, the center points of adjacent hot spot regions are connected sequentially by straight line segments, such as [BA, AC], in strict accordance with the priority traversal sequence (e.g., [B, A, C]), to generate an initial segmented straight line path without any avoidance processing.

[0150] Step 412: Calculate the shortest spatial distance between each straight segment in the initial segmented straight path and the boundary line of the path avoidance area;

[0151] In this step, the shortest spatial distance refers to the shortest vertical distance from the straight segment to the boundary line of the path avoidance zone, reflecting the collision risk level.

[0152] In this embodiment of the invention, for each straight line segment in the initial segmented straight path, the shortest spatial distance between it and the boundary line of the path avoidance area is calculated (i.e., the minimum value of the shortest vertical distance from all points on the straight line segment to the boundary line).

[0153] Step 413: Mark the straight line segments with the shortest spatial distance less than the safety threshold as intermediate straight line segments, and determine the critical point on the intermediate straight line segment that is closest to the boundary line of the path avoidance area;

[0154] In this step, the middle straight segment refers to the path segment that needs to be avoided and adjusted, satisfying the condition that the shortest spatial distance is less than the safety threshold. The critical point refers to the point on the middle straight segment that is closest to the avoidance boundary line.

[0155] In this embodiment of the invention, if the shortest spatial distance of a straight line segment is less than a safety threshold (e.g., 10cm), it is marked as an intermediate straight line segment; and the critical point (perpendicular point) closest to the boundary line is located on this line segment.

[0156] Step 414: Based on the set avoidance distance, translate the critical point along the normal direction of the boundary line of the path avoidance area at the critical point to generate an adjustment point;

[0157] In this step, the avoidance distance is set as the minimum distance for safe robot operation, based on physical dimensions. The adjustment point refers to the turning point of the new path generated after the critical point is translated.

[0158] In this embodiment of the invention, the critical point is shifted by a set avoidance distance (e.g., 15cm) along the normal direction of the boundary line at the critical point (pointing outward from the obstacle) to generate an adjustment point.

[0159] Step 415: Divide 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 ending point of the intermediate straight line segment;

[0160] In this step, the alternative straight segments refer to two new paths that bypass the obstacles, replacing the original dangerous path segments.

[0161] In this embodiment of the invention, the middle straight line segment is split from the critical point into two alternative straight line segments: the first connects the original starting point to the adjustment point, and the second connects the adjustment point to the original ending point, forming a detour path.

[0162] Step 416: According to the order of the priority traversal sequence, recombine the two alternative straight line segments with the straight line segment whose shortest spatial distance is greater than or equal to the safety threshold to form a segmented straight line path of the detour path avoidance area boundary line.

[0163] In this embodiment of the invention, the replacement straight line segment and the unadjusted straight line segment (shortest spatial distance ≥ safety threshold) are strictly reconnected according to the priority traversal sequence to form a segmented straight line path for safe detour.

[0164] This invention generates adjustment points by translating the normal direction of critical points, accurately avoiding three-dimensional obstacles and reducing the risk of collision; path reorganization maintains the priority of hot spot access, ensuring cleaning coverage of high-density loss areas.

[0165] Figure 2 This invention provides a schematic diagram of a path planning system for a cleaning robot used for photovoltaic panels, as shown in the embodiment of the invention. Figure 2 As shown, the system includes:

[0166] The acquisition module 21 is used to acquire the three-dimensional elevation data and temperature distribution data of the photovoltaic array, as well as the current and voltage data of the photovoltaic string;

[0167] The identification module 22 is used to identify the height abrupt change areas and shadow boundary areas on the surface of the photovoltaic panel based on the three-dimensional elevation data and temperature distribution data.

[0168] The positioning module 23 is used to generate a current-voltage characteristic curve based on the current-voltage data, and to locate the hot spot region associated with the shadow boundary region based on the abnormal peak points in the current-voltage characteristic curve.

[0169] The generation module 24 is used to generate a cleaning path plan based on the spatial coordinate information of the height change region, the shadow boundary region and the hot spot region. The cleaning path plan is used to define the cleaning priority of the path avoidance region and the hot spot region.

[0170] Figure 2 The aforementioned path planning system for a cleaning robot used for photovoltaic panels can perform... Figure 1 The implementation principle and technical effects of the path planning method for a cleaning robot for photovoltaic panels described in the illustrated embodiment will not be repeated here. The specific methods by which each module and unit of the path planning system for a cleaning robot for photovoltaic panels in the above embodiments perform their operations have been described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0171] In one possible design, Figure 2 The path planning system for a cleaning robot for photovoltaic panels shown in the embodiment can be implemented as a computing device, such as... Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0172] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 32.

[0173] The processing component 32 is used for the above Figure 1 The embodiment describes a path planning method for a cleaning robot used for photovoltaic panels.

[0174] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as 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 components to perform the above-described method.

[0175] Storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0176] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0177] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0178] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0179] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0180] This invention also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The illustrated embodiment presents a path planning method for a cleaning robot used for photovoltaic panels.

[0181] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0182] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0183] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part 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, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0184] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A path planning method for a cleaning robot used for photovoltaic panels, characterized in that, include: Acquire three-dimensional elevation data and temperature distribution data of the photovoltaic array, as well as current and voltage data of the photovoltaic strings; Based on the three-dimensional elevation data and temperature distribution data, regions of abrupt height changes and shadow boundary regions on the surface of the photovoltaic panel were identified. Based on the current and voltage data, a current and voltage characteristic curve is generated, and based on the abnormal peak points in the current and voltage characteristic curve, the hot spot region associated with the shadow boundary region is located. Based on the spatial coordinate information of the height abrupt change region, the shadow boundary region, and the hot spot region, a cleaning path plan is generated. The cleaning path plan is used to define the cleaning priority of the path avoidance region and the hot spot region. Based on the aforementioned three-dimensional elevation data and temperature distribution data, regions of abrupt height changes and shadow boundary regions on the surface of the photovoltaic panel are identified, including: Compare 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, mark the point with the larger height value as an elevation change point. Connect all adjacent elevation change points to form a change boundary line. Define the area enclosed by the change boundary line as the height change area on the surface of the photovoltaic panel. The temperature value of each measurement point in the temperature distribution data is correlated with the corresponding position on the surface of the photovoltaic panel, and the temperature change rate of each measurement point and its adjacent measurement points within a preset range is calculated. The measurement points where the temperature change rate exceeds the set temperature threshold are marked as shadow boundary points. All adjacent shadow boundary points are connected to form a shadow boundary line. The area enclosed by the shadow boundary line is defined as the preliminary shadow boundary region. Remove the shadow boundary points within the height abrupt change region from the initial shadow boundary region to obtain the shadow boundary region; 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 region associated with the shadow boundary region is located, including: Based on the measurement time sequence, the current measurement values ​​and voltage measurement values ​​of the photovoltaic string in the current and voltage data are arranged to generate a data sequence; The voltage measurement values ​​in the data sequence are sorted in ascending order to generate a sorted data sequence. The voltage measurement values ​​and corresponding current measurement values ​​in the sorted data sequence are connected to generate a current-voltage characteristic curve. Identify the target segment in the current-voltage characteristic curve where the current measurement value increases monotonically with the voltage measurement value and the current value drops sharply; select the data point with the largest current measurement value from the target segment as the abnormal peak point. Based on the voltage and current measurements corresponding to the abnormal peak points, and in conjunction with the preset position mapping relationship of the photovoltaic strings, the physical location of the photovoltaic string to which the abnormal peak points belong is determined. Calculate the shortest spatial distance between the physical location and the boundary line of the shadow boundary region. 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 region. Based on the spatial coordinate information of the height abrupt change region, the shadow boundary region, and the hot spot region, a cleaning path plan is generated, including: Mark the mutation boundary line of the highly abrupt region as the path avoidance region boundary line; Calculate the minimum distance between the center points of each hot spot region, use the reciprocal of the minimum distance as the density reference value, and sort the hot spot regions according to the density reference value to generate a priority traversal sequence; Calculate the directional distribution of the shadow boundary points in the shadow boundary region to determine the main direction of travel based on the directional distribution; Based on the priority traversal sequence, a segmented straight path connecting the center points of all hot spot regions is generated. The segmented straight path is combined with the priority traversal sequence to form a cleaning path plan. The segmented straight path bypasses the boundary line of the path avoidance area.

2. The method according to claim 1, characterized in that, Identify the target segment in the current-voltage characteristic curve where the current measurement value shows a step drop as the voltage measurement value monotonically increases. Select the data point with the largest current measurement value from the target segment as the abnormal peak point, including: The data points of the current-voltage characteristic curve are traversed along the voltage ascending direction. When it is detected that the absolute difference of the current between the first two data points in the first cycle is less than the first tolerance value and the absolute difference of the current between the last two data points is greater than the second tolerance value, the third data point in the first cycle is marked as the starting point of the decline. Starting from the point of descent, the data points of the current-voltage characteristic curve are traversed. When it is detected that the absolute difference of current of three consecutive data points in the second cycle is less than the third tolerance value, the first data point in the second cycle is marked as the point of descent termination. The segment between the starting point of descent and the ending point of descent is defined as the target segment; Compare the current measurement values ​​of each data point within the target section, and select the data point with the largest current measurement value as the abnormal peak point.

3. The method according to claim 1, characterized in that, Based on the voltage and current measurements corresponding to the abnormal peak points, and in conjunction with the preset position mapping relationship of the photovoltaic strings, the physical location of the photovoltaic string to which the abnormal peak point belongs is determined, including: Use the voltage measurement value corresponding to the abnormal peak point as the query voltage value, and the corresponding current measurement value as the query current value; According to the registration table in the preset location mapping relationship of photovoltaic strings, the registration table records the number, rated voltage range, rated current range and corresponding physical location coordinates of each photovoltaic string; All photovoltaic strings whose query voltage values ​​fall within the rated voltage range of each photovoltaic string in the registration table are grouped into a voltage-matched string set; All photovoltaic strings whose query current values ​​fall within the rated current range of each photovoltaic string in the voltage matching string set are combined into a fully matched string set. Select the target photovoltaic string with the smallest boundary difference in the rated voltage range from the set of perfectly matched strings, and use the physical location coordinates of the target photovoltaic string as the physical location of the photovoltaic string to which the abnormal peak point belongs.

4. The method according to claim 1, characterized in that, Based on the priority traversal sequence, segmented straight paths connecting the center points of all hot spot regions are generated. These segmented straight paths are combined with the priority traversal sequence to form a cleaning path plan. The segmented straight paths bypass the boundary lines of the path avoidance area, including: Connect the center points of adjacent hot spot regions in the order of the priority traversal sequence to form an initial segmented straight path; Calculate the shortest spatial distance between each straight segment in the initial segmented straight path and the boundary line of the path avoidance area; Mark the straight line segment whose shortest spatial distance is less than the safety threshold as the middle straight line segment, and determine the critical point on the middle straight line segment that is closest to the boundary line of the path avoidance area; Based on the set avoidance distance, the critical point is translated along the normal direction of the boundary line of the path avoidance area at the critical point to generate an adjustment point; The intermediate straight line segment is divided 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 ending point of the intermediate straight line segment. Following the order of the priority traversal sequence, 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 of the detour path avoidance area boundary line.

5. A path planning system for a cleaning robot used for photovoltaic panels, characterized in that, include: The acquisition module is used to acquire three-dimensional elevation data and temperature distribution data of the photovoltaic array, as well as current and voltage data of the photovoltaic strings; The identification module is used to identify regions of abrupt height changes and shadow boundary regions on the surface of the photovoltaic panel based on the three-dimensional elevation data and temperature distribution data. The positioning module is used to generate a current-voltage characteristic curve based on the current-voltage data, and to locate the hot spot region associated with the shadow boundary region based on the abnormal peak points in the current-voltage characteristic curve. The generation module is used to generate a cleaning path plan based on the spatial coordinate information of the height change region, the shadow boundary region and the hot spot region. The cleaning path plan is used to define the cleaning priority of the path avoidance region and the hot spot region. Based on the aforementioned three-dimensional elevation data and temperature distribution data, regions of abrupt height changes and shadow boundary regions on the surface of the photovoltaic panel are identified, including: Compare 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, mark the point with the larger height value as an elevation change point. Connect all adjacent elevation change points to form a change boundary line. Define the area enclosed by the change boundary line as the height change area on the surface of the photovoltaic panel. The temperature value of each measurement point in the temperature distribution data is correlated with the corresponding position on the surface of the photovoltaic panel, and the temperature change rate of each measurement point and its adjacent measurement points within a preset range is calculated. The measurement points where the temperature change rate exceeds the set temperature threshold are marked as shadow boundary points. All adjacent shadow boundary points are connected to form a shadow boundary line. The area enclosed by the shadow boundary line is defined as the preliminary shadow boundary region. Remove the shadow boundary points within the height abrupt change region from the initial shadow boundary region to obtain the shadow boundary region; 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 region associated with the shadow boundary region is located, including: Based on the measurement time sequence, the current measurement values ​​and voltage measurement values ​​of the photovoltaic string in the current and voltage data are arranged to generate a data sequence; The voltage measurement values ​​in the data sequence are sorted in ascending order to generate a sorted data sequence. The voltage measurement values ​​and corresponding current measurement values ​​in the sorted data sequence are connected to generate a current-voltage characteristic curve. Identify the target segment in the current-voltage characteristic curve where the current measurement value increases monotonically with the voltage measurement value and the current value drops sharply; select the data point with the largest current measurement value from the target segment as the abnormal peak point. Based on the voltage and current measurements corresponding to the abnormal peak points, and in conjunction with the preset position mapping relationship of the photovoltaic strings, the physical location of the photovoltaic string to which the abnormal peak points belong is determined. Calculate the shortest spatial distance between the physical location and the boundary line of the shadow boundary region. 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 region. Based on the spatial coordinate information of the height abrupt change region, the shadow boundary region, and the hot spot region, a cleaning path plan is generated, including: Mark the mutation boundary line of the highly abrupt region as the path avoidance region boundary line; Calculate the minimum distance between the center points of each hot spot region, use the reciprocal of the minimum distance as the density reference value, and sort the hot spot regions according to the density reference value to generate a priority traversal sequence; Calculate the directional distribution of the shadow boundary points in the shadow boundary region to determine the main direction of travel based on the directional distribution; Based on the priority traversal sequence, a segmented straight path connecting the center points of all hot spot regions is generated. The segmented straight path is combined with the priority traversal sequence to form a cleaning path plan. The segmented straight path bypasses the boundary line of the path avoidance area.

6. 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 invoked 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 4.

7. A computer storage medium, characterized in that, The system contains a computer program that, when executed by a computer, implements a path planning method for a cleaning robot for photovoltaic panels as described in any one of claims 1 to 4.

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