Photovoltaic module accumulated snow load detection method, system and device and storage medium

Abstract

The invention discloses a photovoltaic module accumulated snow load detection method, system and device and a storage medium. Uniformly dividing the photovoltaic module into a plurality of test blocks; acquiring pressure data and accumulated snow depth data corresponding to each test block after accumulated snow appears by using a pressure sensor and a laser snow depth sensor; based on the pressure data and the accumulated snow depth data corresponding to each test block, respectively determining first and second load data corresponding to each test block; and determining target load data of each test block according to the first load data, the second load data and a preset weight coefficient. According to the invention, the photovoltaic module is divided into a plurality of test blocks, the pressure and the snow depth corresponding to each test block are respectively collected, the first load data and the second load data are respectively determined based on the two types of data, and the overall load data of the photovoltaic module are accurately determined according to the weight coefficient. The two testing methods are utilized, the situation that the accumulated snow loads at different positions of the assembly are different is considered, and the detection precision is greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of photovoltaic power generation, and in particular to a method, system, computing device and computer storage medium for detecting snow load on photovoltaic modules. Background Art

[0002] Because traditional fossil energy is a non-renewable energy source and can cause various types of environmental pollution during use, the use of various clean energy sources is increasing. Among them, solar energy is gaining increasing popularity due to its many advantages. Photovoltaic power generation projects are the most common application of solar energy.

[0003] However, photovoltaic panels need to be installed outdoors to receive sunlight, so they are exposed to different weather conditions throughout the year, which will have a certain impact on the photovoltaic panels. In particular, winter snowfall directly covers the photovoltaic panels, not only blocking them but also creating loads that put pressure on them.

[0004] The current patents in related fields all determine the load that the current photovoltaic modules can withstand by simulating the load state caused by rain and snow, but they do not accurately determine the load caused by actual snow accumulation. It is impossible to understand the actual snow load state of the photovoltaic modules and unable to respond to cleaning prompts. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention provides a photovoltaic module snow load detection method and a corresponding photovoltaic module snow load detection system, a computing device and a computer storage medium.

[0006] According to one aspect of the present invention, a method for detecting snow load on a photovoltaic module is provided, the method comprising:

[0007] Divide the photovoltaic panels evenly into multiple test blocks;

[0008] Using pressure sensors and laser snow depth sensors, the pressure data and snow depth data corresponding to each test block are obtained after snow accumulation occurs;

[0009] Determine first load data and second load data corresponding to each test block based on the obtained pressure data and snow depth data corresponding to each test block;

[0010] Target load data of each test block is determined according to the first load data, the second load data and a preset weight coefficient.

[0011] In the above solution, the step of evenly dividing the photovoltaic module into a plurality of test blocks further includes:

[0012] The number of test blocks is determined according to the number of cells contained in the photovoltaic module; wherein the number of cells contained in each test block and the number of test blocks are determined according to the number of cells contained in each row and the number of cells contained in each column of the photovoltaic module.

[0013] In the above solution, the use of a pressure sensor and a laser snow depth sensor to obtain pressure data and snow depth data corresponding to each test block after snow accumulation further includes:

[0014] A pressure sensor is set at each test block to obtain pressure data of each test block;

[0015] Laser snow depth sensors were installed on the photovoltaic modules to obtain snow depth data for each test block;

[0016] An additional pressure sensor is installed on the lower edge side of the photovoltaic module to obtain the lower edge pressure data; and a laser snow depth sensor is used to obtain the lower edge snow depth data.

[0017] In the above solution, the determining of the first load data and the second load data corresponding to each test block based on the obtained pressure data and snow depth data corresponding to each test block further includes:

[0018] Determine the first load data and the second load data corresponding to each test block according to the pressure data and the snow depth data respectively; wherein,

[0019] Calculating first load data of each test block based on the pressure data corresponding to the test block and the area of ​​the test block;

[0020] Based on the snow depth data and snow density corresponding to each test block, calculate the second load data of the test block.

[0021] P=ρ×h×k

[0022] Among them, P is the second load data; ρ is the snow density; h is the snow depth data; k is the non-uniformity coefficient.

[0023] In the above solution, determining the target load data of each test block according to the first load data, the second load data and the preset weight coefficient further includes:

[0024] Determining a first coefficient corresponding to the first load data and a second coefficient corresponding to the second load data from preset weight coefficients;

[0025] Calculating target load data based on the first load data and the corresponding first coefficient, and the second load data and the corresponding second coefficient;

[0026] According to the predetermined safe load threshold, an alarm is issued when the target load data exceeds the safe load threshold.

[0027] In the above solution, the calculation of the second load data of each test block based on the snow depth data and snow density corresponding to each test block further includes:

[0028] Build a snow type recognition model based on deep convolutional neural network technology;

[0029] An image training set is constructed based on historical image data acquired by the camera, and a snow type recognition model is trained to obtain a trained snow type recognition model;

[0030] The current snow image data is obtained based on the camera, and the trained snow type recognition model is input to obtain the snow type recognition result.

[0031] In the above solution, after obtaining the current snow image data based on the camera and inputting the trained snow type recognition model to obtain the snow type recognition result, the method further includes:

[0032] determining the current snow type according to the snow type identification result; wherein the snow types include new snow, compacted snow, and wet snow / ice mixture;

[0033] Determine the corresponding snow density according to the current snow type; the snow density corresponding to the new snow is ρ 新 ≈100kg / m 3 ; Snow density ρ corresponding to compacted snow 压实 ≈250kg / m 3 ; Snow density ρ corresponding to wet snow / ice mixture 混 ≈250kg / m 3 .

[0034] According to another aspect of the present invention, a photovoltaic module snow load detection system is provided, comprising: a division module, a data acquisition module, a calculation module, and a load determination module; wherein,

[0035] The division module is used to evenly divide the photovoltaic assembly into multiple test blocks;

[0036] The data acquisition module is used to obtain pressure data and snow depth data corresponding to each test block after snow accumulation occurs using a pressure sensor and a laser snow depth sensor;

[0037] The calculation module is configured to determine first load data and second load data corresponding to each test block based on the acquired pressure data and snow depth data corresponding to each test block;

[0038] The load determination module is used to determine target load data of each test block according to the first load data, the second load data and a preset weight coefficient.

[0039] According to another aspect of the present invention, there is provided a computing device, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus;

[0040] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the above-mentioned photovoltaic module snow load detection method.

[0041] According to another aspect of the present invention, a computer storage medium is provided, wherein the storage medium stores at least one executable instruction, and the executable instruction enables a processor to execute operations corresponding to the above-mentioned photovoltaic module snow load detection method.

[0042] According to the technical scheme provided in the application, the photovoltaic module is evenly divided into a plurality of test blocks; the pressure sensor and the laser snow depth sensor are used to obtain the pressure data and the snow depth data corresponding to each test block after snow appears; the first load data and the second load data corresponding to each test block are respectively determined based on the obtained pressure data and snow depth data corresponding to each test block; and the target load data of each test block is determined according to the first load data, the second load data and a preset weight coefficient. Based on the technical scheme provided in the application, the photovoltaic module is divided into a plurality of test blocks, the pressure data and the snow depth data of each test block are obtained by using the pressure sensor and the laser snow depth sensor, so that the data of each region of the entire photovoltaic module is more finely understood by obtaining the data of each test block, so as to solve the problem of uneven snow load of the photovoltaic module, help to obtain more accurate load data after integration, and the additional pressure sensor and the laser snow depth sensor are arranged at the lower edge side of the photovoltaic module, the related data of the lower edge where the load is increased due to snow sliding down and frame blocking accumulation are highlighted, the state of the entire photovoltaic module is better understood, and the accuracy of load data calculation is improved; the corresponding load data is calculated according to the collected pressure data and snow depth data, and the preset weight coefficient is combined, so as to further improve the accuracy of the target load data obtained finally, help the staff to understand the actual load data of the photovoltaic module under the snow condition, facilitate subsequent cleaning, maintenance and repair work, and understand the state and performance of the photovoltaic module. In addition, the snow type recognition model constructed by the deep convolutional neural network is used to analyze the snow image of the photovoltaic module obtained by the camera after training, and the snow type is scientifically and accurately recognized, so that the corresponding continuous density of different snow types is determined, the snow load data of the photovoltaic module is more accurate, the calculation error caused by the deviation of the snow type identification is avoided, and the damage and failure of the module caused by the load data error and the possible safety risks caused thereby are avoided, so that the overall safety cost is reduced.

[0043] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application can be achieved and obtained by the structure particularly pointed out in the written description and the accompanying drawings.

[0044] The technical scheme of the present application will be further described in detail below with the help of the accompanying drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0045] The accompanying drawings are included to provide a further understanding of the application, and are incorporated in and constitute a part of this specification, illustrate embodiments of the application, and together with the description serve to explain the application, and, do not limit the application. In the drawings:

[0046] Figure 1 A flowchart of a method for detecting snow load of a photovoltaic module according to an embodiment of the application is shown;

[0047] Figure 2 A flowchart of a data collection method for snow load of a photovoltaic module according to an embodiment of the application is shown;

[0048] Figure 3 A flowchart of a snow load calculation method for a photovoltaic module according to an embodiment of the application is shown;

[0049] Figure 4 A flowchart of a snow type identification and density determination method according to an embodiment of the application is shown;

[0050] Figure 5 A structural block diagram of a snow load detection system for a photovoltaic module according to an embodiment of the application is shown;

[0051] Figure 6 A structural diagram of a computing device according to an embodiment of the application is shown. DETAILED DESCRIPTION

[0052] The preferred embodiments of the application are described below in conjunction with the accompanying drawings, which should be understood as merely illustrative and explanatory, and are not intended to limit the application.

[0053] Figure 1 A flowchart of a method for detecting snow load of a photovoltaic module according to an embodiment of the application is shown, which includes the following steps:

[0054] Step S101, the photovoltaic module is evenly divided into a plurality of test blocks.

[0055] Specifically, the number of test blocks is determined according to the number of cell pieces contained in the photovoltaic module; wherein the number of cell pieces contained in each test block and the number of test blocks are determined according to the number of cell pieces contained in each row and the number of cell pieces contained in each column of the photovoltaic module.

[0056] Preferably, the test blocks are divided according to the actual distribution of the cell pieces, the number of cell pieces in each row and each column is divided in a relatively even manner to determine the number of cell pieces contained in each test block, and the number of test blocks is determined accordingly, and the specific allocation manner is divided according to actual needs, which is not limited here.

[0057] For example, if a PV module contains 72 cells, each row contains 9 cells, and each column contains 8 cells, each row is divided into 3 equal parts, and each column is divided into 4 equal parts. That is, each test block contains 3 cells per row and 2 cells per column, for a total of 6 cells. The entire PV module is divided into 12 test blocks.

[0058] Step S102: Using a pressure sensor and a laser snow depth sensor, obtain pressure data and snow depth data corresponding to each test block after snow accumulation occurs.

[0059] Step S103 : Based on the acquired pressure data and snow depth data corresponding to each test block, first load data and second load data corresponding to each test block are determined respectively.

[0060] Step S104 : determining target load data of each test block according to the first load data, the second load data and a preset weight coefficient.

[0061] According to a photovoltaic module snow load detection method provided by this embodiment, the photovoltaic module is evenly divided into multiple test blocks; using a pressure sensor and a laser snow depth sensor, the pressure data and snow depth data corresponding to each test block are obtained after snow accumulation occurs; based on the obtained pressure data and snow depth data corresponding to each test block, the first load data and the second load data corresponding to each test block are respectively determined; and the target load data of each test block is determined based on the first load data, the second load data and the preset weight coefficient. Through the photovoltaic module snow load detection method provided by this embodiment, the photovoltaic module is divided into multiple test blocks, and the pressure data and snow depth data of each test block are obtained by using a pressure sensor and a laser snow depth sensor. Therefore, by obtaining the data of each test block, the data of each area of ​​the entire photovoltaic module can be more precisely understood, so as to deal with the problem of uneven snow load on the photovoltaic module, which helps to obtain more accurate load data after integration and improve the accuracy of load data calculation; through the collected pressure data and snow depth data, the corresponding load data is calculated separately according to these two different data types, and combined with the preset weight coefficient, and then combined using the weight coefficient to further improve the accuracy of the target load data finally obtained, so as to help staff understand the actual load data of the photovoltaic module under snow conditions, facilitate subsequent cleaning, repair, and maintenance work, and understand the status and performance of the photovoltaic module.

[0062] Figure 2 A schematic flow chart of a method for collecting data on snow loads on photovoltaic modules according to an embodiment of the present invention is shown;

[0063] like Figure 2 As shown, the method includes the following steps:

[0064] Step S201 : Setting a pressure sensor at each test block to obtain pressure data of each test block.

[0065] Preferably, the pressure sensor is a strain gauge pressure sensor.

[0066] Step S202: A laser snow depth sensor is set on the photovoltaic module to obtain snow depth data of each test block.

[0067] Preferably, the laser snow depth sensor can be a pan-tilt laser rangefinder that horizontally scans the thickness of snow in different areas of the photovoltaic module.

[0068] In step S203 , a pressure sensor is additionally provided on the lower edge side of the photovoltaic module to obtain lower edge pressure data; and a laser snow depth sensor is used to obtain lower edge snow depth data.

[0069] Preferably, for snow accumulation at the lower edge of a photovoltaic module due to obstruction by the frame, a strain gauge pressure sensor is used to monitor the peak pressure area caused by snow sliding in real time, and a pan-tilt laser rangefinder is used to focus on measuring the height of the accumulation at the lower edge of the module due to obstruction by the frame. Furthermore, an early warning can be triggered when the snow thickness exceeds a preset warning thickness, which can be 15 cm.

[0070] According to the above method, by obtaining the data of each test block, we can understand the data of each area of ​​the entire photovoltaic module in more detail, so as to deal with the problem of uneven snow load on the photovoltaic module, which helps to obtain more accurate load data after integration. In addition, by setting additional pressure sensors and laser snow depth sensors on the lower edge of the photovoltaic module, we focus on obtaining relevant data at the lower edge where the load may increase due to the sliding of snow and the accumulation of frame obstruction, so as to better understand the status of the entire photovoltaic module and improve the accuracy of load data calculation.

[0071] Figure 3 A schematic flow chart of a method for calculating snow load on photovoltaic modules according to one embodiment of the present invention is shown;

[0072] like Figure 3 As shown, the method includes the following steps:

[0073] Step S301 : determining first load data and second load data corresponding to each test block according to pressure data and snow depth data respectively.

[0074] Specifically, based on the pressure data corresponding to each test block and the area of ​​the test block, first load data of the test block is calculated;

[0075] Based on the snow depth data and snow density corresponding to each test block, calculate the second load data of the test block.

[0076] P=ρ×h×k

[0077] Among them, P is the second load data; ρ is the snow density; h is the snow depth data; k is the non-uniformity coefficient.

[0078] Preferably, for the inclination angle of the photovoltaic module, if the inclination angle of the module is ≤30°, the stress at the bottom is concentrated, and the value of k ranges from 1.5 to 2; if the inclination angle is 30°<the inclination angle is ≤60°, the value of k ranges from 1 to 1.5; if the inclination angle is >60°, k=1.

[0079] Step S302 : determining target load data of each test block according to the first load data, the second load data and a preset weight coefficient.

[0080] Specifically, determining a first coefficient corresponding to the first load data and a second coefficient corresponding to the second load data from preset weight coefficients;

[0081] Target load data is calculated based on the first load data and the corresponding first coefficient, and the second load data and the corresponding second coefficient.

[0082] Preferably, first average load data and second average load data are calculated based on the first load data and the second load data corresponding to each test block;

[0083] Then, the product of the first average load data and the first coefficient is added to the product of the second average load data and the second coefficient to obtain the target load data, that is, target load data = first average load data × first coefficient + second average load data × second coefficient.

[0084] Preferably, the first coefficient and the second coefficient may be 0.6 and 0.4 respectively.

[0085] Furthermore, the method further comprises:

[0086] Step S303: According to a predetermined safe load threshold, an alarm is issued when the target load data exceeds the safe load threshold.

[0087] Preferably, after the photovoltaic modules leave the factory, a load test is conducted in advance on the same model to determine the safe load threshold.

[0088] According to the above method, the corresponding load data can be calculated from the collected pressure data and snow depth data according to the two different data types, the first average load data and the second average load data of the whole photovoltaic module are calculated according to the first and second load data of each test block, and the accuracy of the final target load data is further improved by combining the preset weight coefficient, which helps the staff to understand the actual load data of the photovoltaic module under the condition of snow accumulation, and facilitates the subsequent cleaning, maintenance and maintenance work, and the state and performance of the photovoltaic module. At the same time, based on the predetermined safety load threshold, the current load data is judged, and the alarm is completed when it is exceeded, effectively prompting the relevant staff to handle, further improving the safety and processing efficiency of the photovoltaic module under the condition of snow accumulation.

[0089] Figure 4 A flowchart of a snow type identification and density determination method according to an embodiment of the application is shown.

[0090] As Figure 4 shown, the method comprises the following steps:

[0091] Step S401, constructing a snow type identification model based on a deep convolutional neural network technology.

[0092] Preferably, the deep convolutional neural network can select a ResNet residual network to construct the snow type identification model.

[0093] Step S402, constructing an image training set based on historical image data obtained by the camera, training the snow type identification model, and obtaining a trained snow type identification model.

[0094] Step S403, obtaining current snow image data based on the camera and inputting the trained snow type identification model to obtain a snow type identification result.

[0095] Preferably, after the current snow image data is obtained based on the camera and input into the trained snow type identification model to obtain the snow type identification result, the method further comprises:

[0096] determining the current snow type according to the snow type identification result; wherein the snow type includes new snow, compacted snow and wet snow / ice mixture;

[0097] determining the corresponding snow density according to the current snow type; wherein the snow density of new snow is ρ 新 ≈100kg / m 3 ; the snow density of compacted snow is ρ 压实 ≈250kg / m 3; Snow density ρ corresponding to wet snow / ice mixture 混 ≈250kg / m 3 .

[0098] According to the above method, the snow type recognition model constructed by the deep convolutional neural network analyzes the snow images on the photovoltaic modules obtained by the camera after training, and scientifically and accurately identifies the snow type, thereby determining the corresponding density of different snow types, making the snow load data of the photovoltaic modules more accurate, avoiding calculation errors caused by deviations in snow type identification, and further avoiding the occurrence of module damage and failure due to load data errors, as well as various possible safety risks caused by this, thereby reducing the overall safety cost.

[0099] Figure 5 A structural block diagram of a photovoltaic module snow load detection system according to an embodiment of the present invention is shown;

[0100] like Figure 5 As shown, the system includes: a division module 501, a data acquisition module 502, a calculation module 503 and a load determination module 504; wherein,

[0101] The division module 501 is used to evenly divide the photovoltaic module into multiple test blocks.

[0102] Specifically, the division module 501 is further configured to:

[0103] The number of test blocks is determined according to the number of cells contained in the photovoltaic module; wherein the number of cells contained in each test block and the number of test blocks are determined according to the number of cells contained in each row and the number of cells contained in each column of the photovoltaic module.

[0104] The data acquisition module 502 is used to use a pressure sensor and a laser snow depth sensor to obtain pressure data and snow depth data corresponding to each test block after snow accumulation occurs.

[0105] Specifically, the data acquisition module 502 is further configured to:

[0106] A pressure sensor is set at each test block to obtain pressure data of each test block;

[0107] Laser snow depth sensors were installed on the photovoltaic modules to obtain snow depth data for each test block;

[0108] An additional pressure sensor is installed on the lower edge side of the photovoltaic module to obtain the lower edge pressure data; and a laser snow depth sensor is used to obtain the lower edge snow depth data.

[0109] The computing module 503 is configured to determine first load data and second load data corresponding to each test block based on the obtained pressure data and snow depth data of each test block.

[0110] Specifically, the computing module 503 is further configured to,

[0111] determine the first load data and the second load data corresponding to each test block according to the pressure data and the snow depth data, respectively; wherein,

[0112] calculate the first load data of each test block based on the pressure data corresponding to the test block and the area of the test block;

[0113] calculate the second load data of each test block based on the snow depth data corresponding to the test block and the snow density,

[0114] P = p x h x k

[0115] wherein P is the second load data; p is the snow density; h is the snow depth data; and k is a non-uniformity coefficient.

[0116] Preferably, the computing module 503 is further configured to,

[0117] construct a snow type recognition model based on a deep convolutional neural network technology;

[0118] construct an image training set based on historical image data obtained by the camera, train the snow type recognition model, and obtain a trained snow type recognition model;

[0119] obtain current snow image data based on the camera, input the trained snow type recognition model, and obtain a snow type recognition result.

[0120] Preferably, the computing module 503 is further configured to,

[0121] determine the current snow type according to the snow type recognition result; wherein the snow type includes new snow, compacted snow, and wet snow / ice mixture;

[0122] determine the corresponding snow density according to the current snow type; wherein the snow density p of new snow is 新 ≈ 100 kg / m 3 ; the snow density p of compacted snow is 压实 ≈ 250 kg / m 3 ; and the snow density p of wet snow / ice mixture is 混 ≈ 250 kg / m 3 .

[0123] The load determination module 504 is configured to determine target load data for each test block according to the first load data, the second load data, and a preset weight coefficient.

[0124] Specifically, the load determination module 504 is further configured to:

[0125] Determining a first coefficient corresponding to the first load data and a second coefficient corresponding to the second load data from preset weight coefficients;

[0126] Calculating target load data based on the first load data and its corresponding first coefficient, and the second load data and its corresponding second coefficient;

[0127] According to the predetermined safe load threshold, an alarm is issued when the target load data exceeds the safe load threshold.

[0128] The photovoltaic module snow load detection system provided according to the present embodiment includes: a division module, a data acquisition module, a calculation module and a load determination module; wherein the division module is used to evenly divide the photovoltaic module into multiple test blocks; the data acquisition module is used to use a pressure sensor and a laser snow depth sensor to obtain pressure data and snow depth data corresponding to each test block after snow accumulation occurs; the calculation module is used to determine the first load data and the second load data corresponding to each test block based on the obtained pressure data and snow depth data corresponding to each test block; the load determination module is used to determine the target load data of each test block based on the first load data, the second load data and a preset weight coefficient. The photovoltaic module snow load detection system provided by this embodiment divides the photovoltaic module into multiple test blocks, and uses pressure sensors and laser snow depth sensors to obtain pressure data and snow depth data for each test block. By obtaining data from each test block, a more detailed understanding of the data of each area of ​​the entire photovoltaic module can be obtained, so as to address the problem of uneven snow load on the photovoltaic module, which helps to obtain more accurate load data after integration. In addition, by setting additional pressure sensors and laser snow depth sensors on the lower edge of the photovoltaic module, the relevant data at the lower edge, where the load may increase due to sliding snow and frame obstruction accumulation, is focused on obtaining, thereby better understanding the status of the entire photovoltaic module and improving the accuracy of load data calculation. The corresponding load data is calculated based on the collected pressure data and snow depth data according to these two different data types, and combined with a preset weight coefficient, and then combined using the weight coefficient to further improve the accuracy of the final target load data, helping staff to understand the actual load data of the photovoltaic module under snow conditions, facilitating subsequent cleaning, repair, and maintenance work, as well as understanding the status and performance of the photovoltaic module. In addition, a snow type recognition model constructed through a deep convolutional neural network is used to analyze the snow images on photovoltaic modules obtained by the camera after training, and the snow type is scientifically and accurately identified, thereby determining the corresponding density of different snow types, making the snow load data of photovoltaic modules more accurate, avoiding calculation errors caused by deviations in snow type identification, and thus avoiding damage to components and failures caused by incorrect load data, as well as various possible safety risks caused by this, thereby reducing overall safety costs.

[0129] The present invention also provides a non-volatile computer storage medium, which stores at least one executable instruction. The executable instruction can execute a photovoltaic module snow load detection method in any of the above method embodiments.

[0130] Figure 6A schematic structural diagram of a computing device according to an embodiment of the present invention is shown. The specific embodiment of the present invention does not limit the specific implementation of the computing device.

[0131] like Figure 6 As shown, the computing device may include: a processor (processor) 602 , a communications interface (Communications Interface) 604 , a memory (memory) 606 , and a communication bus 608 .

[0132] in:

[0133] The processor 602 , the communication interface 604 , and the memory 606 communicate with each other via a communication bus 608 .

[0134] The communication interface 604 is used to communicate with other devices such as clients or other servers.

[0135] The processor 602 is configured to execute the program 610 , and specifically to execute the relevant steps in the above-mentioned embodiment of the method for detecting snow load on photovoltaic modules.

[0136] Specifically, the program 610 may include program codes, which include computer operating instructions.

[0137] Processor 602 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The one or more processors included in a computing device may be of the same type, such as one or more CPUs, or may be of different types, such as one or more CPUs and one or more ASICs.

[0138] The memory 606 is used to store the program 610. The memory 606 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0139] Program 610 can specifically be used to cause processor 602 to execute a photovoltaic module snow load detection method in any of the aforementioned method embodiments. The specific implementation of each step in program 610 can be found in the corresponding descriptions of the corresponding steps and units in the aforementioned photovoltaic module snow load detection method embodiment, and will not be repeated here. Those skilled in the art will clearly understand that for ease and brevity of description, the specific operating processes of the devices and modules described above can refer to the corresponding process descriptions in the aforementioned method embodiments, and will not be repeated here.

[0140] The algorithm and display provided herein are not inherently related to any particular computer, virtual system or other device. Various general-purpose systems can also be used together with the teachings based on this. According to the above description, it is obvious that the structure required for constructing this type of system. In addition, the present invention is not directed to any specific programming language. It should be understood that various programming languages ​​can be utilized to realize the content of the present invention described herein, and the above description of specific languages ​​is for the purpose of disclosing the best mode of the present invention.

[0141] In the description provided herein, numerous specific details are described. However, it is understood that embodiments of the present invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques are not shown in detail so as not to obscure the understanding of this description.

[0142] Similarly, it should be understood that in order to streamline the present disclosure and aid understanding of one or more of the various inventive aspects, in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the claims, inventive aspects lie in less than all the features of the individual embodiments disclosed above. Accordingly, the claims that follow the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of the invention.

[0143] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and in addition may be divided into multiple submodules or subunits or subcomponents. All features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed herein may be combined in any combination, except that at least some of such features and / or processes or units are mutually exclusive. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0144] Furthermore, those skilled in the art will appreciate that although some embodiments described herein include certain features included in other embodiments but not other features, combinations of features from different embodiments are intended to be within the scope of the present invention and to form different embodiments. For example, in the claims, any of the claimed embodiments may be used in any combination.

[0145] The various component embodiments of the present invention can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It will be appreciated by those skilled in the art that a microprocessor or digital signal processor (DSP) can be used in practice to implement some or all of the functions of some or all of the components in accordance with the embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and a computer program product) for executing a portion or all of the methods described herein. Such a program implementing the present invention can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0146] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for detecting snow load on photovoltaic modules, characterized in that: include: Divide the photovoltaic panels evenly into multiple test blocks; Using pressure sensors and laser snow depth sensors, the pressure data and snow depth data corresponding to each test block are obtained after snow accumulation occurs; Determine first load data and second load data corresponding to each test block based on the obtained pressure data and snow depth data corresponding to each test block; Target load data of each test block is determined according to the first load data, the second load data and a preset weight coefficient.

2. The method according to claim 1, characterized in that The step of evenly dividing the photovoltaic assembly into a plurality of test blocks further comprises: The number of test blocks is determined according to the number of cells contained in the photovoltaic module; wherein the number of cells contained in each test block and the number of test blocks are determined according to the number of cells contained in each row and the number of cells contained in each column of the photovoltaic module.

3. The method according to claim 1, characterized in that The method of using a pressure sensor and a laser snow depth sensor to obtain pressure data and snow depth data corresponding to each test block after snow accumulation occurs further includes: A pressure sensor is set at each test block to obtain pressure data of each test block; Laser snow depth sensors were installed on the photovoltaic modules to obtain snow depth data for each test block; An additional pressure sensor is installed on the lower edge side of the photovoltaic module to obtain the lower edge pressure data; and a laser snow depth sensor is used to obtain the lower edge snow depth data.

4. The method according to claim 1, wherein The method further includes determining first load data and second load data corresponding to each test block based on the obtained pressure data and snow depth data corresponding to each test block: Determine the first load data and the second load data corresponding to each test block according to the pressure data and the snow depth data respectively; wherein, Calculating first load data of each test block based on the pressure data corresponding to the test block and the area of ​​the test block; Based on the snow depth data and snow density corresponding to each test block, calculate the second load data of the test block. P=ρ×h×k Among them, P is the second load data; ρ is the snow density; h is the snow depth data; k is the non-uniformity coefficient.

5. The method according to claim 1, wherein The determining target load data of each test block according to the first load data, the second load data and the preset weight coefficient further includes: Determining a first coefficient corresponding to the first load data and a second coefficient corresponding to the second load data from preset weight coefficients; Calculating target load data based on the first load data and the corresponding first coefficient, and the second load data and the corresponding second coefficient; According to the predetermined safe load threshold, an alarm is issued when the target load data exceeds the safe load threshold.

6. The method according to claim 4, characterized in that The calculating the second load data of the test block based on the snow depth data and snow density corresponding to each test block further includes: Build a snow type recognition model based on deep convolutional neural network technology; An image training set is constructed based on historical image data acquired by the camera, and a snow type recognition model is trained to obtain a trained snow type recognition model; The current snow image data is obtained based on the camera, and the trained snow type recognition model is input to obtain the snow type recognition result.

7. The method according to claim 6, characterized in that After obtaining the current snow image data based on the camera and inputting the trained snow type recognition model to obtain the snow type recognition result, the method further includes: determining the current snow type according to the snow type identification result; wherein the snow types include new snow, compacted snow, and wet snow / ice mixture; Determine the corresponding snow density according to the current snow type; the snow density corresponding to the new snow is ρ 新 ≈100kg / m 3 ; Snow density ρ corresponding to compacted snow 压实 ≈250kg / m 3 ; Snow density ρ corresponding to wet snow / ice mixture 混 ≈250kg / m 3 .

8. A photovoltaic module snow load detection system, characterized in that: include: Division module, data acquisition module, calculation module and load determination module; wherein, The division module is used to evenly divide the photovoltaic assembly into multiple test blocks; The data acquisition module is used to obtain pressure data and snow depth data corresponding to each test block after snow accumulation occurs using a pressure sensor and a laser snow depth sensor; The calculation module is configured to determine first load data and second load data corresponding to each test block based on the acquired pressure data and snow depth data corresponding to each test block; The load determination module is used to determine target load data of each test block according to the first load data, the second load data and a preset weight coefficient.

9. A computing device, characterized in that include: A processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the photovoltaic module snow load detection method according to any one of claims 1 to 7.

10. A computer storage medium, characterized in that The storage medium stores at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the photovoltaic module snow load detection method according to any one of claims 1 to 7.

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