Wireless automatic charging method and device for photovoltaic cleaning robot

Abstract

The invention provides a wireless automatic charging method and device for a photovoltaic cleaning robot, and belongs to the technical field of photovoltaic cleaning robots. The method comprises the steps of determining a second cleaning pollution information set based on a first cleaning pollution information set, historical cleaning data and a preset pollution prediction model; and based on the second cleaning pollution information set, a preset pollution cleaning intensity curve and a preset pollution power generation influence curve, dynamic power consumption optimization is carried out, and after a cleaning strategy corresponding to the to-be-cleaned area is updated based on an optimization result, the photovoltaic cleaning robot is controlled to clean the photovoltaic panel until the cleaning terminal point of the corresponding photovoltaic panel is reached for the first time. And whether the corresponding photovoltaic panel is subjected to secondary cleaning or not is judged based on the residual electric quantity. If it is judged that secondary cleaning is conducted on the photovoltaic panel, a cleaning return path is generated based on the remaining pollution information set corresponding to the photovoltaic panel and the remaining electric quantity, so that a secondary cleaning task is executed along the cleaning return path, and whether automatic charging is conducted through the wireless charging device or not is determined based on the remaining electric quantity.

Description

Technical Field

[0001] The present application relates to the technical field of photovoltaic cleaning robots, and in particular to a wireless automatic charging method and device for photovoltaic cleaning robots. Background Art

[0002] In the context of the booming photovoltaic industry today, the cleaning and maintenance of photovoltaic panels plays a vital role in ensuring power generation efficiency. At present, the cleaning methods of photovoltaic panels mainly include manual cleaning and automatic cleaning. Manual cleaning methods rely on manual operation, which is inefficient and labor-intensive, and it is difficult to meet the cleaning needs of large-scale photovoltaic power stations.

[0003] In the field of automated cleaning, although the emergence of many photovoltaic cleaning robots has improved the cleaning rate to a certain extent, it has fallen into a series of technical difficulties that need to be overcome. On the one hand, most photovoltaic cleaning robots have serious shortcomings in power management, lacking accurate and effective power monitoring and scientific and reasonable management strategies, which directly leads to their inability to closely meet the actual needs of cleaning tasks, and dynamically and accurately plan the steps of cleaning tasks according to power consumption conditions, making the entire cleaning operation often fall into a state of chaos and disorder, and under the invasion of bad weather and the continued influence of the rainy season, this shortcoming has become more and more significant, and the negative impact has become more and more serious. On the other hand, in the face of the rich and diverse types of pollution on photovoltaic panels, such as bird droppings, dust, oil, algae, etc., the robot lacks a deep understanding of the relationship between different pollution characteristics and power consumption, and the cleaning mode is single and rigid. For example, the same power and duration are used to deal with sticky bird droppings and light dust, which not only wastes electricity but also cannot guarantee the cleaning effect, further aggravating the unreasonable consumption of electricity.

[0004] At the same time, focusing on the charging technology sector, most of the existing charging solutions are too simple and extensive. They only focus on the simple power replenishment behavior, but completely ignore the complex real-life situations during the execution of cleaning tasks and the potential and non-negligible impact of pollution factors on power. For example, because the relationship between the degree and type of pollution and power consumption is not taken into consideration, the cleaning robot often encounters the dilemma of sudden power loss without warning when executing the cleaning process, and even stops working midway due to rapid power depletion, which not only affects the continuity of the cleaning task, but also poses a potential threat to the normal operation of the photovoltaic panel. This series of problems are intertwined, setting up many obstacles for the photovoltaic cleaning robot to achieve flexible, intelligent and efficient wireless automatic charging and integrated cleaning operations, and has become a key bottleneck restricting the further development of the industry.

[0005] Based on this, there is an urgent need for a more flexible, intelligent and efficient technical solution for wireless automatic charging of photovoltaic cleaning robots. Summary of the invention

[0006] In order to solve the above problems, the embodiments of the present application provide a wireless automatic charging method and device for a photovoltaic cleaning robot.

[0007] On the one hand, an embodiment of the present application provides a wireless automatic charging method for a photovoltaic cleaning robot, the method comprising:

[0008] Determine a first cleaning pollution information set according to an image recognition result of an image of an area to be cleaned within a preset range; wherein the first cleaning pollution information set includes one or more pollution sequences; the pollution sequence includes at least pollution type, pollution occurrence frequency and pollution degree;

[0009] Determine a second cleaning contamination information set based on the first cleaning contamination information set, historical cleaning data and a preset contamination prediction model; the second cleaning contamination information set corresponds to the remaining to-be-cleaned area outside the preset range;

[0010] Based on the second cleaning pollution information set and the preset pollution cleaning intensity curve and the preset pollution power generation impact curve, a dynamic power consumption curve of the photovoltaic cleaning robot is generated;

[0011] The dynamic power consumption curve is input into the cleaning intensity optimization model, and after the cleaning strategy corresponding to the area to be cleaned is updated based on the optimization result, the photovoltaic cleaning robot is controlled to clean the photovoltaic panel until the cleaning end point of the corresponding photovoltaic panel is reached for the first time, so as to determine whether to perform a secondary cleaning on the corresponding photovoltaic panel based on the remaining power; wherein the same cleaning strategy corresponds to the same preset range;

[0012] If it is determined that the photovoltaic panel needs to be cleaned a second time, a cleaning return path is generated based on the residual contamination information set corresponding to the photovoltaic panel and the remaining power, so that the photovoltaic cleaning robot returns to the initial position after performing the secondary cleaning task along the cleaning return path, and determines whether to automatically charge through the wireless charging device based on the remaining power.

[0013] In one implementation of the present application, the method is applied to a rail-mounted photovoltaic cleaning robot hung on the edge of a photovoltaic panel; before determining the first cleaning pollution information set based on an image recognition result of an image of an area to be cleaned within a preset range, the method further includes:

[0014] Determining whether the photovoltaic cleaning robot is at the initial position or the end edge of the previous preset range;

[0015] If yes, obtain the surface image of the photovoltaic panel in the driving direction captured by the image acquisition device; wherein the image acquisition device is arranged on the body of the photovoltaic cleaning robot;

[0016] According to the pre-stored size information corresponding to the preset range, the current position of the photovoltaic cleaning robot is used as the starting edge reference point to crop the surface image of the photovoltaic panel to obtain the image of the area to be cleaned corresponding to the preset range; wherein the area shape and size of the image of the area to be cleaned are consistent with the preset range; the area of ​​the photovoltaic panel is composed of an integer multiple of the area corresponding to the preset range;

[0017] Input the image of the area to be cleaned into a pre-trained image recognition model to determine each contaminated grid area within the preset range according to the model output result; wherein the contaminated grid area is obtained based on clustering contaminated grids of each contamination type; and one contaminated grid area includes a plurality of contaminated grids corresponding to the same contamination type;

[0018] The first cleaning contamination information set is determined based on each of the contaminated grid areas and the contamination feature extraction model.

[0019] In one implementation of the present application, based on each of the contaminated grid areas and the contamination feature extraction model, determining the first cleaning contamination information set specifically includes:

[0020] Determine the pollution feature information corresponding to each of the pollution grids through the pollution feature extraction model; wherein the pollution feature information at least includes a color feature value, a texture feature value, a shape feature value, an area feature value and a reflectivity feature value associated with the pollution degree;

[0021] Calculating the product and value of the pollution feature information of the pollution grid and the preset pollution feature weight to determine a corresponding pollution degree coefficient; the pollution degree coefficient is used to quantify the pollution degree;

[0022] Calculate the average value of each pollution degree coefficient corresponding to the same pollution grid area, and calculate the first product value of the average value and the pollution degree weight of the corresponding pollution type as the pollution degree assessment value;

[0023] Matching the pollution level assessment value with a plurality of gradient threshold intervals to determine that the pollution level corresponding to the corresponding matching gradient threshold interval is the pollution level of the pollution type, and adding it to the corresponding pollution sequence;

[0024] The number of occurrences of the polluted grids of each pollution type is counted respectively, so as to determine the pollution occurrence frequency according to the ratio of the number of occurrences to the total number of polluted grids, and add it to the corresponding pollution sequence to obtain the first cleaning pollution information set.

[0025] In one implementation of the present application, determining a second cleaning pollution information set based on the first cleaning pollution information set, historical cleaning data, and a preset pollution prediction model specifically includes:

[0026] Determine, according to the current position, whether the first cleaning pollution information set has a preset range that has been cleaned;

[0027] If yes, the corresponding cleaned photovoltaic panel surface image and the historical cleaning pollution information set and historical cleaning strategy are used as the historical cleaning data and sent to the cloud server to output the corresponding pollution degree identification deviation value based on the comparison result of the cleaned photovoltaic panel surface image and a number of corresponding preset cleaning residual pollution sample images; wherein the pollution degree identification deviation value is used to correct one or more preset parameters in the preset pollution prediction model;

[0028] After receiving the pollution degree identification deviation value from the cloud server, the preset pollution prediction model is adjusted, and the first cleaning pollution information set is input into the adjusted preset pollution prediction model to determine the second cleaning pollution information set according to the output result.

[0029] In one implementation of the present application, the method further includes:

[0030] When it is determined that the first cleaning pollution information set does not contain the cleaned preset range, sending the image of the area to be cleaned to the cloud server so that the cloud server matches it in a historical case database;

[0031] If the match is successful, the matched historical case is used as the historical cleansing data;

[0032] Otherwise, the preset default cleaning data is used as the historical cleaning data.

[0033] In one implementation of the present application, based on the second cleaning pollution information set and the preset pollution cleaning intensity curve and the preset pollution power generation impact curve, a dynamic power consumption curve of the photovoltaic cleaning robot is generated, specifically including:

[0034] Determine, in chronological order, a pollution degree sequence corresponding to the second cleaning pollution information set within a preset cleaning cycle;

[0035] Based on the contamination degree sequence and the preset contamination cleaning intensity curve, a second cleaning intensity curve corresponding to the second cleaning contamination information set is generated; wherein the abscissa of the preset contamination cleaning intensity curve is the contamination degree, and the ordinate is the cleaning intensity value; the cleaning intensity value at least corresponds to the cleaning power;

[0036] Based on the second cleaning intensity curve and the preset pollution power generation influence curve, a second power generation efficiency curve corresponding to the second cleaning pollution information set is generated; wherein the abscissa of the preset pollution power generation influence curve is cleanliness, and the ordinate is power generation efficiency value; the cleanliness is obtained based on the cleaning intensity value, the pollution type, the pollution degree and the preset cleaning cleanliness comparison table; the cleanliness is positively correlated with the power generation efficiency value;

[0037] The second cleaning intensity curve and the second power generation efficiency curve are input into the following formula to generate the dynamic power consumption curve:

[0038]

[0039] Among them, E represents the power consumption of the dynamic power consumption curve within the cleaning cycle t; I(t) represents the cleaning intensity value at time t; C1 is the power consumption per unit time of cleaning; G(t) represents the power generation efficiency value at time t; C2 is the power consumption per unit time in the standby state.

[0040] In one implementation of the present application, after the dynamic power consumption curve is input into the cleaning intensity optimization model, the method further includes:

[0041] A multi-objective optimization algorithm is established with the objective function being the minimization of energy consumption and the maximization of cleaning efficiency corresponding to the dynamic power consumption curve and the cleaning intensity being greater than a cleaning intensity threshold being a constraint condition;

[0042] The formula for maximizing the cleaning efficiency is:

[0043]

[0044] Where η represents the cleaning efficiency; P(t) represents the actual cleaning power at time t; P max represents the maximum cleaning power of the photovoltaic cleaning robot;

[0045] Based on a preset genetic algorithm, the multi-objective optimization algorithm is solved to obtain an optimized cleaning intensity curve, and the optimized cleaning intensity curve is used as the optimization result to update the cleaning strategy; the cleaning strategy at least includes the cleaning intensity for photovoltaic pollution of different pollution types and different pollution degrees.

[0046] In one implementation of the present application, based on the residual pollution information set corresponding to the photovoltaic panel and the remaining power, a cleaning return path is generated, specifically including:

[0047] Determining the remaining pollution information set according to the cleaned area image corresponding to the cleaned photovoltaic panel;

[0048] According to the pollution degree corresponding to each residual pollution in the residual pollution information set, in descending order of pollution degree, a secondary cleaning priority sequence is generated;

[0049] Determine the corresponding surplus power based on the remaining power and the planned power corresponding to the remaining cleaning task amount;

[0050] Determine corresponding secondary cleaning residual pollution and corresponding residual pollution position according to the surplus power, the secondary cleaning priority sequence and the thorough cleaning intensity corresponding to each residual pollution;

[0051] According to the distance between the remaining contamination position and the cleaning end point, the remaining contamination position is added to the return cleaning node of the cleaning return path in order from near to far.

[0052] In one implementation of the present application, judging whether to perform secondary cleaning on the corresponding photovoltaic panel based on the remaining power specifically includes:

[0053] Calculate the difference between the remaining power and the planned power corresponding to the remaining cleaning task amount as the surplus power; wherein the planned power at least includes the power required for the photovoltaic cleaning robot to return to the initial position;

[0054] Determine whether the residual contamination information set contains residual contamination with a contamination degree greater than a preset secondary cleaning threshold;

[0055] If yes, and the surplus power meets the thorough cleaning intensity of the corresponding residual pollution, it is determined to perform secondary cleaning on the corresponding photovoltaic panel; wherein the thorough cleaning intensity at least includes the cleaning power required to completely clean the residual pollution;

[0056] Otherwise, the photovoltaic cleaning robot is controlled to return to the initial position to clean the next photovoltaic panel with the remaining cleaning task amount or perform wireless automatic charging.

[0057] On the other hand, an embodiment of the present application further provides a wireless automatic charging device for a photovoltaic cleaning robot, the device comprising:

[0058] A first determination module is used to determine a first cleaning pollution information set according to an image recognition result of an image of an area to be cleaned within a preset range; wherein the first cleaning pollution information set includes one or more pollution sequences; the pollution sequence at least includes pollution type, pollution occurrence frequency and pollution degree;

[0059] A second determination module is used to determine a second cleaning contamination information set based on the first cleaning contamination information set, historical cleaning data and a preset contamination prediction model; the second cleaning contamination information set corresponds to the remaining to-be-cleaned area outside the preset range;

[0060] A first generating module, configured to generate a dynamic power consumption curve of the photovoltaic cleaning robot based on the second cleaning pollution information set, a preset pollution cleaning intensity curve, and a preset pollution power generation impact curve;

[0061] An input module, used for inputting the dynamic power consumption curve into the cleaning intensity optimization model, so as to update the cleaning strategy corresponding to the area to be cleaned based on the optimization result, and then control the photovoltaic cleaning robot to clean the photovoltaic panel until the cleaning end point of the corresponding photovoltaic panel is reached for the first time, so as to determine whether to perform a secondary cleaning on the corresponding photovoltaic panel based on the remaining power; wherein the same cleaning strategy corresponds to the same preset range;

[0062] The second generation module is used to generate a cleaning return path based on the residual pollution information set corresponding to the photovoltaic panel and the remaining power if it is determined that the photovoltaic panel needs to be cleaned for the second time, so as to return the photovoltaic cleaning robot to its initial position after performing the second cleaning task along the cleaning return path, and determine whether to automatically charge through a wireless charging device based on the remaining power.

[0063] Compared with the prior art, the present invention has the following significant effects:

[0064] Through the above technical solution, this application uses image recognition to determine the first set of cleaning pollution information, combines historical data with prediction models, and clarifies the pollution of the remaining area to be cleaned, providing a basis for accurate cleaning. At the same time, a dynamic power consumption curve is generated based on pollution information and related curves, and the optimization model is input to achieve dynamic matching of cleaning intensity and power consumption, thereby improving power utilization efficiency. After the first cleaning, it is also possible to determine whether to perform a second cleaning based on the remaining power and remaining pollution information, and reasonably plan the return route to avoid ineffective work. After completing the task, the photovoltaic cleaning robot automatically decides whether to charge wirelessly based on the remaining power, thereby realizing an intelligent closed loop of cleaning and charging.

[0065] The above solution significantly improves the intelligence and automation level of cleaning and charging of photovoltaic cleaning robots. Through intelligent, automated and refined management methods, it effectively solves the current technical problems of insufficient power management level of photovoltaic panel cleaning robots, simple and extensive charging solutions, and difficulty in achieving flexible, intelligent and efficient wireless automatic charging. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0067] Figure 1 A schematic diagram of a flow chart of a wireless automatic charging method for a photovoltaic cleaning robot in an embodiment of the present application;

[0068] Figure 2 This is a structural schematic diagram of a photovoltaic cleaning system corresponding to a wireless automatic charging method for a photovoltaic cleaning robot in an embodiment of the present application;

[0069] Figure 3 This is a schematic diagram of the structure of a wireless automatic charging device for a photovoltaic cleaning robot in an embodiment of the present application. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0071] The embodiments of the present application provide a wireless automatic charging method and device for a photovoltaic cleaning robot, which are used to solve the current technical problems of insufficient photovoltaic panel cleaning power management level of photovoltaic cleaning robots, simple and extensive charging solutions, and difficulty in achieving flexible, intelligent and efficient wireless automatic charging.

[0072] The wireless automatic charging technology solution for photovoltaic cleaning robots provided in this application allows the robot to clean photovoltaic panels without being affected by weather. For example, in the rainy season, the photovoltaic cleaning robot can always maintain a full charge by relying on wireless automatic charging. It can perform cleaning tasks multiple times a day, increase the frequency of cleaning photovoltaic panels, and allow the photovoltaic panels to have the shortest contamination period, thereby improving the efficiency of photovoltaic power generation.

[0073] The following describes in detail various embodiments of the present application in conjunction with the accompanying drawings.

[0074] The present application embodiment provides a wireless automatic charging method for a photovoltaic cleaning robot, such as Figure 1 As shown, the method may include steps S101-S105:

[0075] S101, determining a first cleaning contamination information set according to an image recognition result of an image of an area to be cleaned within a preset range.

[0076] The first cleaning contamination information set includes one or more contamination sequences, which at least include contamination type, contamination occurrence frequency and contamination degree.

[0077] The execution subject of the wireless automatic charging method for the photovoltaic cleaning robot can be a microcontroller unit (MCU) inside the photovoltaic cleaning robot, or an electronic device connected to the single-chip microcomputer of the photovoltaic cleaning robot, and the connection includes a wired connection and a wireless connection. Electronic devices include but are not limited to handheld devices, computing devices, and servers with wireless communication functions. This application describes an embodiment with the execution subject being the MCU, which is only for illustrative purposes and the execution subject is not limited to the MCU.

[0078] In an embodiment of the present application, the above method is applied to a rail-mounted photovoltaic cleaning robot hung on the edge of a photovoltaic panel. The above method is applied to photovoltaic power generation scenarios, especially large-area photovoltaic power stations.

[0079] Before determining the first cleaning pollution information set according to the image recognition result of the image of the area to be cleaned within the preset range, the method further includes:

[0080] Determine whether the photovoltaic cleaning robot is at the initial position or the end edge of the last preset range. When it is determined that the photovoltaic cleaning robot is at the initial position or the end edge of the last preset range, obtain the surface image of the photovoltaic panel in the driving direction collected by the image acquisition device. Wherein, the image acquisition device is set on the body of the photovoltaic cleaning robot. According to the size information corresponding to the preset range stored in advance, the current position of the photovoltaic cleaning robot is used as the starting edge reference point, and the surface image of the photovoltaic panel is cropped to obtain the image of the area to be cleaned corresponding to the preset range. Wherein, the area shape and size of the image of the area to be cleaned are consistent with the preset range. The area of ​​the photovoltaic panel is composed of an integer multiple of the area corresponding to the preset range. The image of the area to be cleaned is input into a pre-trained image recognition model to determine each contaminated grid area within the preset range according to the output result of the model. Wherein, the contaminated grid area is obtained based on clustering the contaminated grids of each pollution type. A contaminated grid area includes a number of contaminated grids corresponding to the same pollution type. Based on each contaminated grid area and the pollution feature extraction model, a first cleaning pollution information set is determined.

[0081] In other words, the photovoltaic cleaning robot is equipped with a position sensing module, which can determine the current driving position in real time. The initial position can be understood as the robot is still at the edge of the photovoltaic panel and has not entered the track that contacts the photovoltaic panel. The previous preset range can be understood as the preset range where the photovoltaic cleaning robot has cleaned the same photovoltaic panel. The end edge of the previous preset range is the boundary line between the cleaned preset range and another uncleaned preset range. Figure 2A photovoltaic cleaning system is shown, the photovoltaic cleaning robot is 201, the first preset range is 202, its end edge is 203, the second preset range is 204, the end edge of the second preset range is 205, the initial position is 206, the driving track of the photovoltaic cleaning robot is 207, and the cleaning end point is 208.

[0082] If it is determined that the photovoltaic cleaning robot is at the initial position or the end edge of the previous preset range at this time, and is not at the end point of the photovoltaic panel cleaning, and there is surplus power to perform subsequent cleaning tasks, then the image acquisition device is controlled to capture the surface image of the photovoltaic panel in the direction of travel. The image acquisition device is arranged on the body of the photovoltaic cleaning robot. The image acquisition device can be provided with multiple cameras, such as two, which are respectively arranged in front and behind the body, and can capture the surface image of the photovoltaic panel in the direction of travel and away from the direction of travel. The MCU of the present application also pre-stores the size information of the preset range, and the preset range can evenly divide the area of ​​the photovoltaic panel. Among them, the photovoltaic panel surface image contains at least one area corresponding to the preset range, that is, the actual photovoltaic panel surface size corresponding to the photovoltaic panel surface image captured by the image acquisition device is greater than or equal to the size corresponding to the preset range. The MCU will use the current position as the starting edge reference point, such as Figure 2 The terminating edge in the image is the starting edge reference point of the cropping operation, and the image of the area to be cleaned corresponding to the size information of the preset range is cropped. Subsequently, the image of the area to be cleaned is divided into an image composed of polluted grids through an image recognition model trained by a number of polluted grid area sample images and pollution type labels, and each polluted grid carries a label of the pollution type; the image recognition model can be a convolutional neural network model. The present application can further divide the image of the area to be cleaned into images containing one or more polluted grid areas, and the polluted grid areas can be obtained by clustering the polluted grids. Clustering can use a density-based spatial clustering algorithm (Density-Based Spatial Clustering of Applications with Noise, DBSCAN) to cluster the polluted grids of each pollution type to obtain the polluted grid area.

[0083] For example, when clustering, each polluted grid is regarded as a data point, and the polluted grids corresponding to the same pollution type and adjacent in space are clustered together. A polluted grid area contains several polluted grids corresponding to the same pollution type.

[0084] Through the above processing, the present application can obtain accurate and finely divided pollution grid areas, which can be used to accurately plan the cleaning strategy for pollution on the one hand, and on the other hand, cluster the refined pollution grids into pollution grid areas, and plan cleaning according to the area to improve the cleaning efficiency.

[0085] Furthermore, the MCU will further generate a first cleaning pollution information set through each pollution grid area and pollution feature extraction model.

[0086] Specifically, based on each contaminated grid area and the contamination feature extraction model, a first cleaning contamination information set is determined, specifically including:

[0087] The pollution feature information corresponding to each polluted grid is determined by the pollution feature extraction model. Among them, the pollution feature information at least includes color feature values, texture feature values, shape feature values, area feature values ​​and reflectivity feature values ​​associated with the pollution degree. The product and value of the pollution feature information of the polluted grid and the preset pollution feature weight are calculated to determine the corresponding pollution degree coefficient. The pollution degree coefficient is used to quantify the pollution degree. The average value of each pollution degree coefficient corresponding to the same polluted grid area is calculated, and the first product value of the average value and the pollution degree weight of the corresponding pollution type is calculated as the pollution degree assessment value. The pollution degree assessment value is matched with multiple gradient threshold intervals to determine that the pollution degree corresponding to the corresponding matching gradient threshold interval is the pollution degree of the pollution type, and is added to the corresponding pollution sequence. The number of occurrences of polluted grids of each pollution type is counted respectively, so as to determine the pollution occurrence frequency according to the ratio of the number of occurrences to the total number of polluted grids, and add it to the corresponding pollution sequence to obtain the first cleaning pollution information set.

[0088] Among them, the pollution feature extraction model can be a pre-trained neural network model, which is used to extract the following feature dimensions corresponding to each pollution grid: color features, texture features, shape features, area features and reflectivity features. Specifically, color features such as bird droppings and dust have obvious color differences, which can be used to intuitively reflect pollution characteristics; the texture of pollution can reflect its physical properties. The texture of oil pollution may be smooth and flowing, while the texture of algae pollution is rough and granular. The more complex and special the texture, the more likely it is to imply serious pollution; the shape of pollution can also reflect its formation mechanism and degree of harm. Pollution with regular shapes and clear edges may be a special pollutant in a small local area. Compared with irregular shapes and large-scale diffusion pollution, the severity is different; the size of the pollution area is directly related to the scope of pollution. Large-area pollution covers photovoltaic panels, reducing the area receiving light and having a great impact on power generation efficiency; the normal reflectivity of photovoltaic panels is relatively stable, pollution will change its reflection characteristics, and pollutant coverage will reduce the reflectivity, and the more serious the pollution, the greater the change in reflectivity.

[0089] The above-mentioned characteristic values ​​corresponding to different characteristic dimensions can be pre-scored by experts, and different characteristic values ​​can correspond to different levels of correlation with pollution levels. The pollution degree coefficient formula is as follows:

[0090]

[0091] Where Pll(i,j) represents the pollution degree coefficient of the polluted grid corresponding to the coordinate (i,j) in the preset range, and w k is the contamination feature weight of the kth feature dimension, N is the number of feature dimensions, for example, N is equal to 5, f k (i,j) represents the eigenvalue of the kth feature dimension of the contaminated grid.

[0092] For example, the pollution feature information includes the following feature values ​​[a1, a2, a3, a4, a5], where a1 is the color feature value, a2 is the texture feature value, a3 is the shape feature value, a4 is the area feature value, and a5 is the reflectivity feature value. Taking the pollution type of bird droppings as an example, the preset pollution feature weights corresponding to the bird droppings pollution type are [w1, w2, w3, w4, w5], and the pollution degree coefficient is w 1* a1+w 2* a2+w 3* a3+w 4* a4+w 5* a5. This application integrates multiple parameter features to quantify the degree of pollution in order to regulate the output power of the robot and effectively plan the power of the robot.

[0093] Subsequently, the present application also calculates the average value of each pollution degree coefficient in the same pollution grid area, and at the same time calculates the first product value of the calculated average value and the pollution degree weight of the corresponding pollution type to obtain the pollution degree assessment value of the pollution grid area. The pollution degree weight is used to unify the dimensions of the pollution degrees of various pollution types. Different pollution types can correspond to different pollution degree weights, which can be set by the user. The present application does not make specific restrictions on this. Furthermore, the MCU can match the pollution degree assessment value with multiple gradient threshold intervals corresponding to different pollution degrees. The pollution degree classification standards such as mild, moderate, and severe are only exemplary and are not specifically limited here. According to the gradient threshold intervals in which each pollution degree assessment value is located, that is, matching the gradient threshold interval, the pollution degree corresponding to each pollution grid area can be obtained, and then added to the pollution sequence. The specific value of the gradient threshold interval can be set by the user during actual use, and the present application does not make specific restrictions on this.

[0094] Subsequently, the MCU can also count the number of contaminated grids of each contamination type within the preset range, and calculate the ratio of the number of contaminated grids to the total number of contaminated grids contained in the preset range according to the contamination type, thereby obtaining the contamination frequency of different contamination types within the preset range, which together with the above-mentioned contamination degree constitutes the first cleaning contamination information set.

[0095] In one embodiment of the present application, if the preset range is always a fixed size, it may also cause unreasonable cleaning. For example, one side of the preset range is seriously polluted, while the other side is less polluted. If a unified cleaning strategy is provided according to the preset range, it is obviously not reasonable. Therefore, the present application also provides the following embodiments, including:

[0096] Calculate the second product value of each pollution degree coefficient corresponding to the same pollution type and the pollution degree weight of the corresponding pollution type respectively. Compare each second product value with the preset range division threshold to determine whether the pollution grids corresponding to the second product value greater than the preset range division threshold are concentrated on one side of the half of the preset range. The concentration is on one side of the half of the preset range, and the number of pollution grids corresponding to the second product value greater than the preset range division threshold is greater than the predetermined value. One side of the half of the preset range is the side perpendicular to the driving direction of the photovoltaic cleaning robot, after the preset range is divided in half. If so, divide the original preset range in half.

[0097] That is to say, according to the pollution type, each pollution degree coefficient is multiplied by the pollution degree weight of the corresponding pollution type to obtain the second product values ​​corresponding to each pollution grid. Subsequently, the second product value is compared with the preset range division threshold. The preset range division threshold can be set by the user and is not specifically limited here. Next, the distribution of polluted grids with second product values ​​greater than the preset range division threshold is counted to determine whether this part of the polluted grids is concentrated on one side of the preset range. If so, the preset range is further divided according to the one-half division principle. Otherwise, the preset range is not adjusted. Figure 2 In the middle of a preset range, a straight line parallel to the end edge is used to divide the preset range equally, that is, the preset range is divided into two halves. Thus, the image of the area to be cleaned is obtained more accurately, and the subsequent steps are further performed.

[0098] After obtaining the first cleaning pollution information set, in order to predict the pollution conditions of the remaining areas to be cleaned outside the preset range, the present application will then combine historical cleaning data and a preset pollution prediction model to determine a second cleaning pollution information set.

[0099] S102, determining a second cleaning pollution information set based on the first cleaning pollution information set, historical cleaning data and a preset pollution prediction model.

[0100] The second cleaning contamination information set corresponds to the remaining area to be cleaned outside the preset range.

[0101] In the embodiment of the present application, based on the first cleaning pollution information set, historical cleaning data and a preset pollution prediction model, the second cleaning pollution information set is determined, specifically including:

[0102] Determine whether the first cleaning pollution information set has a preset cleaned range according to the current position. When it is determined that the first cleaning pollution information set has a preset cleaned range, the corresponding cleaned photovoltaic panel surface image and the historical cleaning pollution information set and the historical cleaning strategy are used as historical cleaning data and sent to the cloud server to output the corresponding pollution degree identification deviation value based on the comparison result of the cleaned photovoltaic panel surface image and several corresponding preset cleaning residual pollution sample images. Among them, the pollution degree identification deviation value is used to correct one or more preset parameters in the preset pollution prediction model. After receiving the pollution degree identification deviation value from the cloud server, adjust the parameters of the preset pollution prediction model, and input the first cleaning pollution information set into the preset pollution prediction model after parameter adjustment to determine the second cleaning pollution information set according to the output result.

[0103] Specifically, the preset cleaning range can be understood as for the same row of photovoltaic panels, such as the photovoltaic cleaning robot is in Figure 2 In the middle position, the first preset range is the cleaned preset range, and the MCU can make a judgment based on the current position of the photovoltaic cleaning robot. At this time, the MCU will control the image acquisition device to acquire the surface image of the first preset range again, obtain the surface image of the cleaned photovoltaic panel, and use the first cleaning pollution information set corresponding to the first preset range as the historical cleaning pollution information set, and use the cleaning strategy of the first preset range as the historical cleaning strategy to obtain historical cleaning data. Subsequently, the historical cleaning data is sent to the cloud server, and the cloud server performs high-computation operations.

[0104] The cloud server can pre-store a number of preset cleaning legacy pollution sample images, and the preset cleaning legacy pollution sample images have a corresponding relationship with the corresponding cleaning pollution information sample set and cleaning strategy sample. First, the cloud server determines the similar sample image that is closest to the surface image of the cleaned photovoltaic panel (such as the largest cosine similarity); then, the historical cleaning pollution information set, the historical cleaning strategy and the cleaning pollution information sample set and the cleaning strategy sample corresponding to the similar sample image are compared respectively, which can be obtained by calculating the Euclidean distance or the cosine distance. For example, if the Euclidean distance is greater than the preset deviation threshold, it means that there is a pollution degree identification deviation. The pollution degree identification deviation value is obtained by matching the calculated Euclidean distance with the preset deviation value comparison table.

[0105] The preset deviation threshold and the preset deviation comparison table can be set by the user according to actual use, and this application does not make specific restrictions on this. The MCU can correct the preset parameters in the preset pollution prediction model by presetting the pollution degree identification deviation value, wherein the correspondence between the preset pollution degree identification deviation value and the preset parameter can be provided by an expert. The preset pollution degree identification deviation value can increase or decrease the preset parameters. Then, the second cleaning pollution information set is further generated using the preset pollution prediction model after parameter adjustment and the first cleaning pollution information set.

[0106] The preset pollution prediction model may be a pre-trained machine learning model, which can generate a second cleaning pollution information set of the photovoltaic panel that needs to be cleaned next based on the first cleaning pollution information set.

[0107] In another embodiment of the present application, the above-mentioned step S102 of adjusting parameters can be performed once after a preset time period, instead of once after a preset range cleaning, thereby reducing the frequency of interaction between the MCU and the cloud server. The preset time period can be set by the user, such as one day, one week, etc., and the present application does not make specific limitations on this.

[0108] Through the above scheme, the preset pollution prediction model can be updated in time according to the actual cleaning situation, thereby improving the accuracy of generating the cleaning pollution information set.

[0109] In addition, when it is determined that the first cleaning pollution information set does not have a preset cleaned range, the image of the area to be cleaned is sent to the cloud server so that the cloud server can match it in the historical case database. If the match is successful, the matched historical case is used as the historical cleaning data. Otherwise, the preset default cleaning data is used as the historical cleaning data.

[0110] That is to say, the MCU determines that the photovoltaic cleaning robot does not have a preset cleaned range at this time. Generally, the photovoltaic cleaning robot is in the initial position at this time, and the MCU sends the image of the area to be cleaned to the cloud server. The cloud server can match whether there is a historical case for the image of the area to be cleaned from the historical case database. The historical case contains a historical cleaning data sample corresponding to the image of the area to be cleaned. Then the cloud server performs the above-mentioned second cleaning pollution information set determination step through the matched historical case. In the event of a match failure, the cloud server can use the default cleaning data pre-set by the user as the historical cleaning data.

[0111] Through the above scheme, combined with historical cleaning data, the first cleaning pollution information set that has been collected can be accurately predicted for the second cleaning pollution information set of the subsequent photovoltaic panels, so as to reasonably plan the robot power and perform charging management.

[0112] S103, generating a dynamic power consumption curve of the photovoltaic cleaning robot based on the second cleaning pollution information set, the preset pollution cleaning intensity curve, and the preset pollution power generation impact curve.

[0113] In the embodiment of the present application, based on the second cleaning pollution information set and the preset pollution cleaning intensity curve and the preset pollution power generation impact curve, a dynamic power consumption curve of the photovoltaic cleaning robot is generated, specifically including:

[0114] Determine the pollution degree sequence corresponding to the second cleaning pollution information set within the preset cleaning cycle in chronological order. Generate a second cleaning intensity curve corresponding to the second cleaning pollution information set based on the pollution degree sequence and the preset pollution cleaning intensity curve. The horizontal coordinate of the preset pollution cleaning intensity curve is the pollution degree, and the vertical coordinate is the cleaning intensity value. The cleaning intensity value corresponds to at least the cleaning power. Generate a second power generation efficiency curve corresponding to the second cleaning pollution information set based on the second cleaning intensity curve and the preset pollution power generation impact curve. The horizontal coordinate of the preset pollution power generation impact curve is the cleanliness, and the vertical coordinate is the power generation efficiency value. The cleanliness is obtained based on the cleaning intensity value, pollution type, pollution degree and the preset cleaning cleanliness comparison table. The cleanliness is positively correlated with the power generation efficiency value.

[0115] It should be noted that the above-mentioned cleaning pollution information set corresponds to a preset range, and a cleaning pollution information set can contain multiple pollution sequences, so there are also multiple pollution degree values. The present application can sum up and calculate the average value of multiple pollution degrees for the same cleaning pollution information set to obtain the pollution degree corresponding to the preset range. At the same time, a preset range corresponds to the position of a photovoltaic panel, and the preset range has a time sequence before and after cleaning. The present application can generate the pollution degree sequence of all preset ranges contained in the second cleaning pollution information set in time order.

[0116] Subsequently, the cleaning intensity value sequence corresponding to the pollution degree sequence is matched by the preset pollution cleaning intensity curve, and then the second cleaning intensity curve is generated in chronological order. Next, the cleaning intensity value in the second cleaning intensity curve, each pollution type and its pollution intensity within the preset range corresponding to the cleaning intensity value, are matched with the preset cleaning cleanliness comparison table, which contains the mapping relationship between different pollution types, preset ranges of different pollution intensities and cleaning intensity values ​​during cleaning, and the cleanliness after cleaning. The power generation efficiency value corresponding to the cleanliness of each preset range is searched from the preset pollution power generation impact curve, and the second power generation efficiency curve is generated in the above chronological order.

[0117] Furthermore, the second cleaning intensity curve and the second power generation efficiency curve are input into the following formula to generate a dynamic power consumption curve:

[0118]

[0119] Wherein, E represents the power consumption of the dynamic power consumption curve within the cleaning cycle T. I(t) represents the cleaning intensity value at time t. C1 represents the power consumption per unit time of cleaning. G(t) represents the power generation efficiency value at time t. C2 represents the power consumption per unit time of standby state.

[0120] Through the above solution, the two influence curves of the second cleaning intensity curve and the second power generation efficiency curve can be combined to more reasonably generate a dynamic power consumption curve.

[0121] S104, inputting the dynamic power consumption curve into the cleaning intensity optimization model, and updating the cleaning strategy corresponding to the area to be cleaned based on the optimization result, and then controlling the photovoltaic cleaning robot to clean the photovoltaic panel until it reaches the cleaning end point of the corresponding photovoltaic panel for the first time, so as to determine whether to perform a second cleaning on the corresponding photovoltaic panel based on the remaining power.

[0122] The same cleaning strategy corresponds to the same preset range.

[0123] In the embodiment of the present application, after the dynamic power consumption curve is input into the cleaning intensity optimization model, the method further includes:

[0124] The dynamic power consumption curve corresponds to minimizing energy consumption and maximizing cleaning efficiency as the objective function, and the cleaning intensity is greater than the cleaning intensity threshold as the constraint condition, and a multi-objective optimization algorithm is established. Among them, the formula corresponding to maximizing cleaning efficiency is:

[0125]

[0126] Where η represents the cleaning efficiency. P(t) represents the actual cleaning power at time t. max Indicates the maximum cleaning power of the photovoltaic cleaning robot.

[0127] The multi-objective optimization algorithm is as follows:

[0128]

[0129] Based on the preset genetic algorithm, the multi-objective optimization algorithm is solved to obtain an optimized cleaning intensity curve, and the optimized cleaning intensity curve is used as the optimization result to update the cleaning strategy. The cleaning strategy at least includes the cleaning intensity for photovoltaic pollution of different pollution types and different pollution degrees.

[0130] In other words, the present application can achieve the solution of the above multi-objective optimization algorithm through iterative operations of a preset genetic algorithm, or can be solved through other algorithms, which are not specifically limited here. Through the preset genetic algorithm of the above example, an optimized cleaning intensity curve can be solved, and the optimized cleaning intensity curve corresponds to the cleaning power used in different preset ranges, where a uniform cleaning power is used in the same preset range.

[0131] For example, for lightly polluted photovoltaic panels, a lower cleaning intensity can be used to save energy. For heavily polluted photovoltaic panels, the cleaning intensity needs to be increased to ensure cleaning efficiency. For different types of pollutants (such as dust, bird droppings, etc.), the cleaning intensity can be further adjusted according to their characteristics and impact on the performance of the photovoltaic panel.

[0132] This application can be implemented. For example, if a piece of bird droppings requires 20% power to clean, but after cleaning, it may not be able to reach the subsequent task normally, this application can pre-judge the power consumption required for cleaning in the subsequent task, so as to flexibly adjust the intensity of cleaning the bird droppings. If the required intensity makes it impossible to complete the subsequent task, the bird droppings cleaning power here is adjusted according to the subsequent task. In addition, after the subsequent task is completed, it is determined whether to carry out the bird droppings exclusive treatment based on the remaining power and the power generation.

[0133] The pollution targeted by this application includes but is not limited to bird droppings, algae, leaves and debris, metal oxides, as well as pollutants containing chemical substances such as acid rain, industrial waste gas, and bacterial plaques formed by various microorganisms on the surface of photovoltaic panels.

[0134] After obtaining the cleaning strategy as described above, the photovoltaic cleaning robot can be controlled to clean the area to be cleaned in the first cleaning pollution information set. At the same time, the above steps S101-S104 will be executed in real time to update the cleaning strategy in time until the photovoltaic panels in the same row are cleaned and the return conditions are met.

[0135] In one embodiment of the present application, the above-mentioned determination of whether to perform secondary cleaning on the corresponding photovoltaic panel based on the remaining power specifically includes:

[0136] Calculate the difference between the remaining power and the planned power corresponding to the remaining amount of cleaning tasks as the surplus power. The planned power at least includes the power required for the photovoltaic cleaning robot to return to the initial position. Determine whether the residual pollution information set has residual pollution with a degree of pollution greater than the preset secondary cleaning threshold. In the case where it is determined that the residual pollution information set has residual pollution with a degree of pollution greater than the preset secondary cleaning threshold, and the surplus power meets the thorough cleaning intensity of the corresponding residual pollution, it is determined that the corresponding photovoltaic panel is to be cleaned twice. The thorough cleaning intensity at least includes the cleaning power required to completely clean the residual pollution. Otherwise, control the photovoltaic cleaning robot to return to the initial position to clean the next photovoltaic panel with the remaining amount of cleaning tasks or perform wireless automatic charging.

[0137] In other words, before the photovoltaic cleaning robot returns to its initial position, it will further determine whether to perform a second cleaning of the photovoltaic panels on the return journey based on the power level. It should be noted that when the rail-mounted photovoltaic cleaning robot performs a cleaning task, it will clean the same row of photovoltaic panels and then return to its initial position, where the walking robot will replace the photovoltaic cleaning robot and clean the next row of photovoltaic panels.

[0138] The MCU of this application can obtain the remaining power of the battery management module set inside the photovoltaic cleaning robot in real time, and can also predict the planned power corresponding to the subsequent remaining cleaning tasks based on the power consumption of the cleaned photovoltaic panels. For example, if the power consumption of the cleaned photovoltaic panels is 10% for a row of photovoltaic panels, and the remaining cleaning tasks are 7 rows of photovoltaic panels, then the planned power is 70%+M, where M is the preset power consumption of the robot returning to the initial position. If the remaining power is 90%, then the surplus power is 20%-MR. R is the charging warning power. When the remaining power of the photovoltaic cleaning robot is less than the charging warning power, it is determined that wireless charging is required. The charging warning power can be set by the user according to the actual usage scenario, and this application does not make specific restrictions on this.

[0139] At the same time, the present application also determines the residual pollution information set, which is obtained by collecting the images of the cleaned areas of each preset range of the cleaned photovoltaic panel through the image acquisition device, and processing each cleaned area image respectively through the above-mentioned image recognition model and pollution feature extraction model. The MCU will also determine whether there is a pollution degree value greater than the preset secondary cleaning threshold in the residual pollution information set corresponding to the same row of photovoltaic panels. The preset secondary cleaning threshold is a threshold value pre-set by the user, which is not specifically limited here. The residual pollution with a pollution degree value greater than the preset secondary cleaning threshold is regarded as pollution that needs to be thoroughly cleaned. At the same time, the MCU also determines whether the surplus power meets the thorough cleaning intensity (i.e., the cleaning power for thorough cleaning) for thorough cleaning. If it is satisfied, the photovoltaic panel will be cleaned twice, otherwise it will return to the initial position directly. Among them, in addition to the cleaning power, the above-mentioned cleaning intensity can also include the cleaning time, such as the duration of continuous flushing or scrubbing of a certain position.

[0140] In addition, the present application can also wirelessly charge the robot immediately once the photovoltaic cleaning robot returns to its initial position, thereby keeping the robot fully charged at all times. For example, after performing a row of cleaning and returning to the initial position, it will be charged until it reaches the next row of photovoltaic panels. The robot is also wirelessly charged during the movement, which increases the charging frequency and enables the robot's power to meet the needs of performing multiple cleaning tasks a day, thereby allowing the robot to clean the photovoltaic panels more flexibly.

[0141] S105, if it is determined that the photovoltaic panel needs to be cleaned a second time, a cleaning return path is generated based on the residual pollution information set corresponding to the photovoltaic panel and the remaining power, so that the photovoltaic cleaning robot returns to the initial position after performing the secondary cleaning task along the cleaning return path, and determines whether to automatically charge through the wireless charging device based on the remaining power.

[0142] In the embodiment of the present application, based on the residual pollution information set and the remaining power corresponding to the photovoltaic panel, a cleaning return path is generated, which specifically includes:

[0143] According to the image of the cleaned area corresponding to the cleaned photovoltaic panel, the residual pollution information set is determined. According to the pollution degree corresponding to each residual pollution in the residual pollution information set, a secondary cleaning priority sequence is generated in the order of pollution degree from large to small. Based on the remaining power and the planned power corresponding to the remaining task volume to be cleaned, the corresponding surplus power is determined. According to the surplus power, the secondary cleaning priority sequence and the thorough cleaning intensity corresponding to each residual pollution, the corresponding secondary cleaning residual pollution and the corresponding residual pollution location are determined. According to the distance between the residual pollution location and the cleaning end point, the residual pollution location is added to the return cleaning node of the cleaning return path in order from near to far.

[0144] That is to say, during the secondary cleaning, the present application will further divide the secondary cleaning priority according to the degree of pollution of the residual pollution corresponding to each preset range. At the same time, the secondary cleaning plan is carried out according to the surplus power, the secondary cleaning priority sequence and the thorough cleaning intensity. The present application calculates the power consumption corresponding to each residual pollution in turn according to the preset thorough cleaning time, the cleaning power and cleaning time corresponding to the thorough cleaning intensity, and generates the power consumption sequence in the order of the secondary cleaning priority sequence. Then, the present application accumulates and calculates the power consumption and value of the power consumption sequence in turn, until the power consumption and value are greater than the surplus power value for the first time, the last power consumption during the cumulative calculation is eliminated, and it is judged whether the power consumption and value after elimination are less than the surplus power value. If so, the residual pollution corresponding to the power consumption corresponding to the cumulative calculation is used as the secondary cleaning residual pollution, and each secondary cleaning residual pollution is cleaned in order from near to far. If it is judged that the power consumption and value after elimination are still greater than the surplus power value, the elimination operation is continued until the power consumption and value after elimination are less than the surplus power value.

[0145] For example, the secondary cleaning priority sequence is [x2, x1, x3, x4, x5], x2 is the pollution degree corresponding to the second preset range, if the surplus power is 10%, the power consumption sequence corresponding to the secondary cleaning priority sequence is [x2·t, x1·t, x3·t, x4·t, x5·t], x2·t corresponds to 5% power, x1·t corresponds to 3% power of the robot battery, and x3·t corresponds to 3% power of the battery. 5%+3%=8%, at this time the sum of power consumption is less than the surplus power value, and 5%+3%+3%=11%, at this time the sum of power consumption is greater than the surplus power value, then at this time the residual pollution corresponding to x2 and x1 is regarded as the secondary cleaning residual pollution, and the positions L2 and L1 corresponding to the second preset range and the first preset range respectively are the return cleaning nodes. At this time, the second preset range corresponding to x2 is closer to the cleaning end point than the first preset range corresponding to x1, so the return cleaning path is: L2→L1, that is, the photovoltaic cleaning robot moves to the second preset range first, cleans the secondary cleaning residual pollution in the second preset range, and then moves to the first preset range for secondary cleaning.

[0146] Through the above technical solution, this application uses image recognition to determine the first set of cleaning pollution information, combines historical data with prediction models, and clarifies the pollution of the remaining area to be cleaned, providing a basis for accurate cleaning. At the same time, a dynamic power consumption curve is generated based on pollution information and related curves, and the optimization model is input to achieve dynamic matching of cleaning intensity and power consumption, thereby improving power utilization efficiency. After the first cleaning, it is also possible to determine whether to perform a second cleaning based on the remaining power and remaining pollution information, and reasonably plan the return route to avoid ineffective work. After completing the task, the photovoltaic cleaning robot automatically decides whether to charge wirelessly based on the remaining power, thereby realizing an intelligent closed loop of cleaning and charging.

[0147] The above solution significantly improves the intelligence and automation level of cleaning and charging of photovoltaic cleaning robots. Through intelligent, automated and refined management methods, it effectively solves the current technical problems of insufficient power management level of photovoltaic panel cleaning robots, simple and extensive charging solutions, and difficulty in achieving flexible, intelligent and efficient wireless automatic charging.

[0148] Figure 3 A schematic diagram of the structure of a wireless automatic charging device for a photovoltaic cleaning robot provided in an embodiment of the present application is shown in FIG. Figure 3 As shown, the wireless automatic charging device 300 for the photovoltaic cleaning robot includes:

[0149] The first determination module 301 is used to determine the first cleaning pollution information set based on the image recognition result of the image of the area to be cleaned within the preset range. The first cleaning pollution information set includes one or more pollution sequences. The pollution sequence at least includes the pollution type, the frequency of pollution occurrence and the pollution degree. The second determination module 302 is used to determine the second cleaning pollution information set based on the first cleaning pollution information set, the historical cleaning data and the preset pollution prediction model. The second cleaning pollution information set corresponds to the remaining area to be cleaned outside the preset range. The first generation module 303 is used to generate a dynamic power consumption curve of the photovoltaic cleaning robot based on the second cleaning pollution information set and the preset pollution cleaning intensity curve and the preset pollution power generation impact curve. The input module 304 is used to input the dynamic power consumption curve into the cleaning intensity optimization model, so as to update the cleaning strategy corresponding to the area to be cleaned based on the optimization result, and then control the photovoltaic cleaning robot to clean the photovoltaic panel until the cleaning end point of the corresponding photovoltaic panel is reached for the first time, so as to determine whether to perform a second cleaning on the corresponding photovoltaic panel based on the remaining power. The same cleaning strategy corresponds to the same preset range. The second generation module 305 is used to generate a cleaning return path based on the residual pollution information set and the remaining power corresponding to the photovoltaic panel if it is determined that the photovoltaic panel needs to be cleaned for the second time, so as to return the photovoltaic cleaning robot to the initial position after performing the second cleaning task along the cleaning return path, and determine whether to automatically charge through the wireless charging device based on the remaining power.

[0150] In the embodiment of the present application, the above device is applied to a rail-mounted photovoltaic cleaning robot hung on the edge of a photovoltaic panel. Based on the image recognition result of the image of the area to be cleaned within a preset range, before determining the first cleaning pollution information set, the device can also:

[0151] Determine whether the photovoltaic cleaning robot is at the initial position or the end edge of the previous preset range. If so, obtain the surface image of the photovoltaic panel in the driving direction collected by the image acquisition device. Wherein, the image acquisition device is set on the body of the photovoltaic cleaning robot. According to the size information corresponding to the preset range stored in advance, the current position of the photovoltaic cleaning robot is used as the starting edge reference point, and the surface image of the photovoltaic panel is cropped to obtain the image of the area to be cleaned corresponding to the preset range. Wherein, the area shape and size of the image of the area to be cleaned are consistent with the preset range. The area of ​​the photovoltaic panel is composed of an integer multiple of the area corresponding to the preset range. The image of the area to be cleaned is input into a pre-trained image recognition model to determine each contaminated grid area within the preset range according to the output result of the model. Wherein, the contaminated grid area is obtained based on clustering the contaminated grids of each pollution type. A contaminated grid area includes a number of contaminated grids corresponding to the same pollution type. Based on each contaminated grid area and the pollution feature extraction model, a first cleaning pollution information set is determined.

[0152] The second determining module 302 is specifically configured to:

[0153] The pollution feature information corresponding to each polluted grid is determined by the pollution feature extraction model. Among them, the pollution feature information at least includes color feature values, texture feature values, shape feature values, area feature values ​​and reflectivity feature values ​​associated with the pollution degree. The product and value of the pollution feature information of the polluted grid and the preset pollution feature weight are calculated to determine the corresponding pollution degree coefficient. The pollution degree coefficient is used to quantify the pollution degree. The average value of each pollution degree coefficient corresponding to the same polluted grid area is calculated, and the first product value of the average value and the pollution degree weight of the corresponding pollution type is calculated as the pollution degree assessment value. The pollution degree assessment value is matched with multiple gradient threshold intervals to determine that the pollution degree corresponding to the corresponding matching gradient threshold interval is the pollution degree of the pollution type, and is added to the corresponding pollution sequence. The number of occurrences of polluted grids of each pollution type is counted respectively, so as to determine the pollution occurrence frequency according to the ratio of the number of occurrences to the total number of polluted grids, and add it to the corresponding pollution sequence to obtain the first cleaning pollution information set.

[0154] The second determining module 302 is further configured to:

[0155] According to the current position, determine whether the first cleaning pollution information set has a preset range that has been cleaned. If so, the corresponding cleaned photovoltaic panel surface image and historical cleaning pollution information set and historical cleaning strategy are used as historical cleaning data and sent to the cloud server to output the corresponding pollution degree identification deviation value based on the comparison result of the cleaned photovoltaic panel surface image and several corresponding preset cleaning residual pollution sample images. Among them, the pollution degree identification deviation value is used to correct one or more preset parameters in the preset pollution prediction model. After receiving the pollution degree identification deviation value from the cloud server, adjust the parameters of the preset pollution prediction model, and input the first cleaning pollution information set into the preset pollution prediction model after parameter adjustment to determine the second cleaning pollution information set according to the output result.

[0156] The device can also:

[0157] When it is determined that the first cleaning pollution information set does not have a preset cleaned range, the image of the area to be cleaned is sent to the cloud server so that the cloud server can match it in the historical case database. If the match is successful, the matched historical case is used as the historical cleaning data. Otherwise, the preset default cleaning data is used as the historical cleaning data.

[0158] The first generating module 303 is specifically used for:

[0159] Determine the pollution degree sequence corresponding to the second cleaning pollution information set within the preset cleaning cycle in chronological order. Generate a second cleaning intensity curve corresponding to the second cleaning pollution information set based on the pollution degree sequence and the preset pollution cleaning intensity curve. The horizontal coordinate of the preset pollution cleaning intensity curve is the pollution degree, and the vertical coordinate is the cleaning intensity value. The cleaning intensity value corresponds to at least the cleaning power. Generate a second power generation efficiency curve corresponding to the second cleaning pollution information set based on the second cleaning intensity curve and the preset pollution power generation impact curve. The horizontal coordinate of the preset pollution power generation impact curve is the cleanliness, and the vertical coordinate is the power generation efficiency value. The cleanliness is obtained based on the cleaning intensity value, pollution type, pollution degree and the preset cleaning cleanliness comparison table. The cleanliness is positively correlated with the power generation efficiency value.

[0160] The second cleaning intensity curve and the second power generation efficiency curve are input into the following formula to generate a dynamic power consumption curve:

[0161]

[0162] Wherein, E represents the power consumption of the dynamic power consumption curve within the cleaning cycle T. I(t) represents the cleaning intensity value at time t. C1 represents the power consumption per unit time of cleaning. G(t) represents the power generation efficiency value at time t. C2 represents the power consumption per unit time of standby state.

[0163] The device can also:

[0164] A multi-objective optimization algorithm is established with the dynamic power consumption curve corresponding to minimizing energy consumption and maximizing cleaning efficiency as the objective function and the cleaning intensity being greater than the cleaning intensity threshold as the constraint condition.

[0165] Among them, the formula for maximizing cleaning efficiency is:

[0166]

[0167] Where η represents the cleaning efficiency. P(t) represents the actual cleaning power at time t. max Represents the maximum cleaning power of the photovoltaic cleaning robot. Based on the preset genetic algorithm, the multi-objective optimization algorithm is solved to obtain the optimized cleaning intensity curve, and the optimized cleaning intensity curve is used as the optimization result to update the cleaning strategy. The cleaning strategy at least includes the cleaning intensity for photovoltaic pollution of different pollution types and different pollution degrees.

[0168] The second generating module 305 is specifically used for:

[0169] According to the image of the cleaned area corresponding to the cleaned photovoltaic panel, the residual pollution information set is determined. According to the pollution degree corresponding to each residual pollution in the residual pollution information set, a secondary cleaning priority sequence is generated in the order of pollution degree from large to small. Based on the remaining power and the planned power corresponding to the remaining task volume to be cleaned, the corresponding surplus power is determined. According to the surplus power, the secondary cleaning priority sequence and the thorough cleaning intensity corresponding to each residual pollution, the corresponding secondary cleaning residual pollution and the corresponding residual pollution location are determined. According to the distance between the residual pollution location and the cleaning end point, the residual pollution location is added to the return cleaning node of the cleaning return path in order from near to far.

[0170] The input module 304 is also specifically used for:

[0171] Calculate the difference between the remaining power and the planned power corresponding to the remaining amount of cleaning tasks as the surplus power. The planned power at least includes the power required for the photovoltaic cleaning robot to return to the initial position. Determine whether there is any residual pollution in the residual pollution information set with a pollution degree greater than the preset secondary cleaning threshold. If so, and the surplus power meets the thorough cleaning intensity of the corresponding residual pollution, determine to perform a secondary cleaning on the corresponding photovoltaic panel. The thorough cleaning intensity at least includes the cleaning power required to completely clean the residual pollution. Otherwise, control the photovoltaic cleaning robot to return to the initial position to clean the next photovoltaic panel with the remaining amount of cleaning tasks or perform wireless automatic charging.

[0172] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0173] The device and method provided in the embodiments of the present application correspond one to one, and therefore, the device also has similar beneficial technical effects as the corresponding method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the device will not be repeated here.

[0174] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0175] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A wireless automatic charging method for a photovoltaic cleaning robot, characterized in that: The method comprises: Determine a first cleaning pollution information set according to an image recognition result of an image of an area to be cleaned within a preset range; wherein the first cleaning pollution information set includes one or more pollution sequences; the pollution sequence includes at least pollution type, pollution occurrence frequency and pollution degree; Determine a second cleaning contamination information set based on the first cleaning contamination information set, historical cleaning data and a preset contamination prediction model; the second cleaning contamination information set corresponds to the remaining to-be-cleaned area outside the preset range; Based on the second cleaning pollution information set and the preset pollution cleaning intensity curve and the preset pollution power generation impact curve, a dynamic power consumption curve of the photovoltaic cleaning robot is generated; The dynamic power consumption curve is input into the cleaning intensity optimization model, and after the cleaning strategy corresponding to the area to be cleaned is updated based on the optimization result, the photovoltaic cleaning robot is controlled to clean the photovoltaic panel until the cleaning end point of the corresponding photovoltaic panel is reached for the first time, so as to determine whether to perform a secondary cleaning on the corresponding photovoltaic panel based on the remaining power; wherein the same cleaning strategy corresponds to the same preset range; If it is determined that the photovoltaic panel needs to be cleaned a second time, a cleaning return path is generated based on the residual contamination information set corresponding to the photovoltaic panel and the remaining power, so that the photovoltaic cleaning robot returns to the initial position after performing the secondary cleaning task along the cleaning return path, and determines whether to automatically charge through the wireless charging device based on the remaining power.

2. A wireless automatic charging method for a photovoltaic cleaning robot according to claim 1, characterized in that: The method is applied to a rail-mounted photovoltaic cleaning robot hung on the edge of a photovoltaic panel; before determining the first cleaning pollution information set based on the image recognition result of the image of the area to be cleaned within a preset range, the method also includes: Determining whether the photovoltaic cleaning robot is at the initial position or the end edge of the previous preset range; If yes, obtain the surface image of the photovoltaic panel in the driving direction captured by the image acquisition device; wherein the image acquisition device is arranged on the body of the photovoltaic cleaning robot; According to the pre-stored size information corresponding to the preset range, the current position of the photovoltaic cleaning robot is used as the starting edge reference point to crop the surface image of the photovoltaic panel to obtain the image of the area to be cleaned corresponding to the preset range; wherein the area shape and size of the image of the area to be cleaned are consistent with the preset range; the area of ​​the photovoltaic panel is composed of an integer multiple of the area corresponding to the preset range; Input the image of the area to be cleaned into a pre-trained image recognition model to determine each contaminated grid area within the preset range according to the model output result; wherein the contaminated grid area is obtained based on clustering contaminated grids of each contamination type; and one contaminated grid area includes a plurality of contaminated grids corresponding to the same contamination type; The first cleaning contamination information set is determined based on each of the contaminated grid areas and the contamination feature extraction model.

3. A wireless automatic charging method for a photovoltaic cleaning robot according to claim 2, characterized in that: Determining the first cleaning pollution information set based on each of the pollution grid areas and the pollution feature extraction model specifically includes: Determine the pollution feature information corresponding to each of the pollution grids through the pollution feature extraction model; wherein the pollution feature information at least includes a color feature value, a texture feature value, a shape feature value, an area feature value and a reflectivity feature value associated with the pollution degree; Calculating the product and value of the pollution feature information of the pollution grid and the preset pollution feature weight to determine the corresponding pollution degree coefficient; the pollution degree coefficient is used to quantify the pollution degree; Calculate the average value of each pollution degree coefficient corresponding to the same pollution grid area, and calculate the first product value of the average value and the pollution degree weight of the corresponding pollution type as the pollution degree assessment value; Matching the pollution level assessment value with a plurality of gradient threshold intervals to determine that the pollution level corresponding to the corresponding matching gradient threshold interval is the pollution level of the pollution type, and adding it to the corresponding pollution sequence; The number of occurrences of the polluted grids of each pollution type is counted respectively, so as to determine the pollution occurrence frequency according to the ratio of the number of occurrences to the total number of polluted grids, and add it to the corresponding pollution sequence to obtain the first cleaning pollution information set.

4. A wireless automatic charging method for a photovoltaic cleaning robot according to claim 2, characterized in that: Determining a second cleaning pollution information set based on the first cleaning pollution information set, historical cleaning data, and a preset pollution prediction model specifically includes: Determine, according to the current position, whether the first cleaning pollution information set has a preset range that has been cleaned; If yes, the corresponding cleaned photovoltaic panel surface image and the historical cleaning pollution information set and historical cleaning strategy are used as the historical cleaning data and sent to the cloud server to output the corresponding pollution degree identification deviation value based on the comparison result of the cleaned photovoltaic panel surface image and a number of corresponding preset cleaning residual pollution sample images; wherein the pollution degree identification deviation value is used to correct one or more preset parameters in the preset pollution prediction model; After receiving the pollution degree identification deviation value from the cloud server, the preset pollution prediction model is adjusted, and the first cleaning pollution information set is input into the adjusted preset pollution prediction model to determine the second cleaning pollution information set according to the output result.

5. A wireless automatic charging method for a photovoltaic cleaning robot according to claim 4, characterized in that: The method further comprises: When it is determined that the first cleaning pollution information set does not contain the cleaned preset range, sending the image of the area to be cleaned to the cloud server so that the cloud server matches it in a historical case database; If the match is successful, the matched historical case is used as the historical cleansing data; Otherwise, the preset default cleaning data is used as the historical cleaning data.

6. A wireless automatic charging method for a photovoltaic cleaning robot according to claim 1, characterized in that: Based on the second cleaning pollution information set and the preset pollution cleaning intensity curve and the preset pollution power generation impact curve, a dynamic power consumption curve of the photovoltaic cleaning robot is generated, specifically including: Determine, in chronological order, a pollution degree sequence corresponding to the second cleaning pollution information set within a preset cleaning cycle; Based on the contamination degree sequence and the preset contamination cleaning intensity curve, a second cleaning intensity curve corresponding to the second cleaning contamination information set is generated; wherein the abscissa of the preset contamination cleaning intensity curve is the contamination degree, and the ordinate is the cleaning intensity value; the cleaning intensity value at least corresponds to the cleaning power; Based on the second cleaning intensity curve and the preset pollution power generation influence curve, a second power generation efficiency curve corresponding to the second cleaning pollution information set is generated; wherein the abscissa of the preset pollution power generation influence curve is cleanliness, and the ordinate is power generation efficiency value; the cleanliness is obtained based on the cleaning intensity value, the pollution type, the pollution degree and the preset cleaning cleanliness comparison table; the cleanliness is positively correlated with the power generation efficiency value; The second cleaning intensity curve and the second power generation efficiency curve are input into the following formula to generate the dynamic power consumption curve: E=∫0 T [I(t)·C1+(1-G(t))·C2]dt Among them, E represents the power consumption of the dynamic power consumption curve within the cleaning cycle T; I(t) represents the cleaning intensity value at time t; C1 is the power consumption per unit time of cleaning; G(t) represents the power generation efficiency value at time t; C2 is the power consumption per unit time in the standby state.

7. A wireless automatic charging method for a photovoltaic cleaning robot according to claim 6, characterized in that: After inputting the dynamic power consumption curve into the cleaning intensity optimization model, the method further includes: A multi-objective optimization algorithm is established with the objective function of minimizing energy consumption and maximizing cleaning efficiency corresponding to the dynamic power consumption curve and the constraint condition that the cleaning intensity is greater than the cleaning intensity threshold; The formula for maximizing the cleaning efficiency is: Where η represents the cleaning efficiency; P(t) represents the actual cleaning power at time t; P max represents the maximum cleaning power of the photovoltaic cleaning robot; Based on a preset genetic algorithm, the multi-objective optimization algorithm is solved to obtain an optimized cleaning intensity curve, and the optimized cleaning intensity curve is used as the optimization result to update the cleaning strategy; the cleaning strategy at least includes the cleaning intensity for photovoltaic pollution of different pollution types and different pollution degrees.

8. The wireless automatic charging method for a photovoltaic cleaning robot according to claim 1, characterized in that: Based on the residual pollution information set corresponding to the photovoltaic panel and the remaining power, a cleaning return path is generated, specifically including: Determining the remaining pollution information set according to the cleaned area image corresponding to the cleaned photovoltaic panel; According to the pollution degree corresponding to each residual pollution in the residual pollution information set, in descending order of pollution degree, a secondary cleaning priority sequence is generated; Determine the corresponding surplus power based on the remaining power and the planned power corresponding to the remaining cleaning task amount; Determine corresponding secondary cleaning residual pollution and corresponding residual pollution position according to the surplus power, the secondary cleaning priority sequence and the thorough cleaning intensity corresponding to each residual pollution; According to the distance between the remaining contamination position and the cleaning end point, the remaining contamination position is added to the return cleaning node of the cleaning return path in order from near to far.

9. The wireless automatic charging method for a photovoltaic cleaning robot according to claim 1, characterized in that: Determining whether to perform secondary cleaning on the corresponding photovoltaic panel based on the remaining power specifically includes: Calculate the difference between the remaining power and the planned power corresponding to the remaining cleaning task amount as the surplus power; wherein the planned power at least includes the power required for the photovoltaic cleaning robot to return to the initial position; Determine whether the residual contamination information set contains residual contamination with a contamination degree greater than a preset secondary cleaning threshold; If yes, and the surplus power meets the thorough cleaning intensity of the corresponding residual pollution, it is determined to perform secondary cleaning on the corresponding photovoltaic panel; wherein the thorough cleaning intensity at least includes the cleaning power required to completely clean the residual pollution; Otherwise, the photovoltaic cleaning robot is controlled to return to the initial position to clean the next photovoltaic panel with the remaining cleaning task amount or perform wireless automatic charging.

10. A wireless automatic charging device for a photovoltaic cleaning robot, characterized in that: The device comprises: A first determination module is used to determine a first cleaning pollution information set according to an image recognition result of an image of an area to be cleaned within a preset range; wherein the first cleaning pollution information set includes one or more pollution sequences; the pollution sequence at least includes pollution type, pollution occurrence frequency and pollution degree; A second determination module is used to determine a second cleaning contamination information set based on the first cleaning contamination information set, historical cleaning data and a preset contamination prediction model; the second cleaning contamination information set corresponds to the remaining to-be-cleaned area outside the preset range; A first generating module, configured to generate a dynamic power consumption curve of the photovoltaic cleaning robot based on the second cleaning pollution information set, a preset pollution cleaning intensity curve, and a preset pollution power generation impact curve; An input module, used for inputting the dynamic power consumption curve into the cleaning intensity optimization model, so as to update the cleaning strategy corresponding to the area to be cleaned based on the optimization result, and then control the photovoltaic cleaning robot to clean the photovoltaic panel until the cleaning end point of the corresponding photovoltaic panel is reached for the first time, so as to determine whether to perform a secondary cleaning on the corresponding photovoltaic panel based on the remaining power; wherein the same cleaning strategy corresponds to the same preset range; The second generation module is used to generate a cleaning return path based on the residual pollution information set corresponding to the photovoltaic panel and the remaining power if it is determined that the photovoltaic panel needs to be cleaned for the second time, so as to return the photovoltaic cleaning robot to its initial position after performing the second cleaning task along the cleaning return path, and determine whether to automatically charge through a wireless charging device based on the remaining power.

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