CN115512290A - Photovoltaic panel efficiency monitoring method and system based on image recognition technology - Google Patents
Photovoltaic panel efficiency monitoring method and system based on image recognition technology Download PDFInfo
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Abstract
The invention discloses a photovoltaic panel efficiency monitoring method and system based on an image recognition technology, wherein the method comprises the following steps of 1: obtaining historical output power data and non-shielding photo data of the photovoltaic panel, calculating according to the historical output power data to obtain a historical efficiency output power interval and a maximum change slope, and extracting edge features of the photo data to obtain reference edge feature data; and 2, step: acquiring output power data and video stream data of a photovoltaic panel in a target detection area in real time; and step 3: judging whether the output power data exceeds a historical efficiency output power interval or the maximum change slope; and 4, step 4: processing the video stream data according to an edge identification algorithm to obtain edge characteristic data of the photovoltaic panel; and 5: comparing the edge feature data with the reference edge feature data to obtain an image feature difference; step 6: and judging whether the image characteristic difference exceeds a preset similarity value.
Description
Technical Field
The invention relates to the field of photovoltaic panel monitoring, in particular to a photovoltaic panel efficiency monitoring method and system based on an image recognition technology.
Background
The photovoltaic power generation mainly comprises a photovoltaic panel inclination angle, photovoltaic panel battery component conversion efficiency (the photovoltaic power station has the problem that the component efficiency and the electrical element performance are gradually reduced in the service life cycle, the generated energy is gradually reduced year by year, the quality problems of components and inverters, various factors such as circuit layout, series-parallel connection loss and cable loss, dust shielding, shadow and object shielding are eliminated besides the natural aging factors), and the factors directly influence the photovoltaic conversion efficiency;
at present, in the influence factors aiming at the photoelectric conversion efficiency of the photovoltaic panel, the inclination angle of the photovoltaic panel is dynamically adjusted by a partial solution, but aiming at the factors of the conversion efficiency of a battery assembly of the photovoltaic panel, dust shielding and shadow object shielding, the efficiency of the photovoltaic panel is mainly monitored by a monitoring disc and daily manual inspection, particularly the manual inspection is relied, and the labor cost and the working efficiency are lower.
Disclosure of Invention
The invention aims to solve the technical problems that at present, aiming at the factors of conversion efficiency, dust shielding and shadow object shielding of a photovoltaic panel battery assembly, the efficiency of a photovoltaic panel is mainly monitored by means of a supervisory panel and daily manual inspection, particularly, the efficiency of the photovoltaic panel is monitored by means of manual inspection, the labor cost and the working efficiency are low, the invention provides a photovoltaic panel efficiency monitoring method based on an image recognition technology, the invention also provides a photovoltaic panel efficiency monitoring system based on the image recognition technology, the photovoltaic panel dust shielding and the shadow object shielding are automatically monitored by means of image recognition assistance, data real-time monitoring is performed on the output power of the photovoltaic panel, and the photovoltaic panel efficiency is monitored by means of combination of the image recognition assistance, so that supervisory panel personnel can refer to the photovoltaic panel and arrange daily maintenance work, and the defects caused by the prior art are overcome.
In order to solve the technical problems, the invention provides the following technical scheme:
in a first aspect, a photovoltaic panel efficiency monitoring method based on an image recognition technology includes the following steps:
step 1: obtaining historical output power data and non-shielding photo data of a photovoltaic panel, calculating according to the historical output power data to obtain a historical efficiency output power interval [ min, max ] and a maximum change slope of the historical output power within a certain time period as s, and extracting edge features according to the photo data to obtain reference edge feature data;
step 2: acquiring output power data and video stream data of a photovoltaic panel in a target detection area in real time;
and step 3: judging whether the output power data exceeds the historical efficiency output power interval or the maximum change slope;
if not, executing the step 1;
if yes, executing step 4;
and 4, step 4: processing the video stream data according to an edge recognition algorithm to obtain edge feature data of the photovoltaic panel;
and 5: comparing the edge feature data with the reference edge feature data to obtain image feature difference;
and 6: judging whether the image characteristic difference exceeds a preset similarity value, wherein the preset similarity value is set and adjusted according to the actual operation condition;
if the number exceeds the preset value, outputting the photovoltaic panel to accumulate dust or shadow objects for shielding;
if the conversion efficiency of the photovoltaic panel battery pack is not exceeded, outputting abnormal conversion efficiency of the photovoltaic panel battery pack;
according to the corresponding output evaluation, the operation personnel can reasonably arrange the maintenance task, specifically arrange the cleaning of the photovoltaic panel or the working of the assembly maintenance task;
when the photovoltaic panel is monitored, an element fault diagnosis technology of a photovoltaic panel battery pack can be introduced, a battery pack element fault diagnosis model is established according to a mechanism model of an element or a certain expert rule, and is combined into a monitoring method, when the efficiency of the photovoltaic panel is abnormal, the cleanliness of the photovoltaic panel on site is high, and no influence factor is shielded by other objects, fault diagnosis is carried out on the photovoltaic panel battery pack element, and a more refined guidance suggestion can be brought to the monitoring method.
The photovoltaic panel efficiency monitoring method based on the image recognition technology comprises the following steps of calculating according to the historical output power data to obtain a historical efficiency output power interval [ min, max ] and a maximum change slope in a certain period of time:
after the historical output power data are cleaned, screening is carried out to obtain power data under normal working of the photovoltaic panel and recording the power data as p;
substituting p into the formula 1 to calculate to obtain a minimum value and a maximum value, wherein the interval [ min, max ] of the minimum value and the maximum value is the historical efficiency output power;
equation 1:
wherein: the expected value of X is equal to the linear combination of one or several post-falls, plus a constant term, plus a random error, c is a constant term,
is the autocorrelation coefficient, ε t Is a random error value assumed to have a mean equal to 0 and a standard deviation equal to σ; σ is assumed to be invariant for any t;
and deriving the maximum change slope of the historical output power by using a least square method.
The photovoltaic panel efficiency monitoring method based on the image recognition technology comprises the following specific steps of deriving the maximum change slope of the historical output power by adopting a least square method:
the derivation calculation is carried out according to the truncated form y = kx + b, and the coordinate value (x) of n points in the plane is calculated 1 ,y 1 )、(x 2 ,y 2 )、(x 3 ,y 3 )、……(x n ,y n ) Substituting any two points into a formula to obtain a slope value k and a constant b which change between every two points;
k = (y) is obtained by derivation according to a slope formula 2 -y 1 )/(x 2 -x 1 ) And taking the maximum value of the obtained slopes as the maximum change slope. In the above method for monitoring efficiency of a photovoltaic panel based on an image recognition technology, a specific method for extracting edge features according to the photo data to obtain reference edge feature data in step 1 is as follows:
and carrying out median filtering denoising processing on the photo data, then carrying out feature extraction by adopting CNN (convolutional neural network), and carrying out feature fusion by using BP neural network to obtain the reference edge feature data.
The photovoltaic panel efficiency monitoring method based on the image recognition technology, wherein the specific method for processing the video stream data according to the edge recognition algorithm to obtain the edge feature data of the photovoltaic panel in the step 4 is as follows:
carrying out imaging processing on the video stream data to obtain a plurality of pieces of image data;
carrying out pixel point detection on the image data and connecting the pixel points to form contour pixel points;
detecting boundary points of the image data with the contour pixel points, integrating the boundary points with the contour pixel points, and removing false pixel points to obtain the edge feature data;
in the photovoltaic panel efficiency monitoring method based on the image recognition technology, after the video stream data is subjected to the imaging processing to obtain a plurality of pieces of image data, the image data is subjected to the noise reduction processing.
The above photovoltaic panel efficiency monitoring method based on the image recognition technology, wherein the edge recognition algorithm is Roberts operator (Roberts operator) or Prewitt operator (first order differential operator) or Sobel operator (Sobel operator) or Canny operator (multi-stage edge detection algorithm) or Laplacian operator, the edge detection of the image is realized based on the gradient of the image, and the obtained gradient of the image is converted into the method of performing convolution operation on the image by using various operators;
in order to achieve the purpose of evaluating the efficiency of the photovoltaic panel, preferably, an edge recognition second-order Laplacian operator is adopted to extract the edge characteristics of the photovoltaic panel, the Laplacian operator is an isotropic second-order differential operator with rotation invariance, and the response obtained by processing the individual pixel points by the operator is stronger than the response obtained by processing the edge points, so that the Laplacian operator is not suitable for processing images with higher noise intensity, and if the detected images have higher noise, low-pass filtering is required to remove the noise;
laplacian edge extraction process: 1) Gaussian blur-noise cancellation; 2) Turning gray scale; 3) Laplacian-second derivative calculation Laplacian; 4) Taking an absolute value, wherein an edge image can be obtained; 5) Carrying out threshold binarization processing, enhancing edge characteristics and obtaining a more obvious edge image;
the Laplacian operator of an image function is a second derivative (f is a second differentiable real function) defined as:
the expression of the discrete form suitable for digital image processing is as follows:
the Laplacian operator generated by the above formula 3 is divided into a four-neighborhood operator and an eight-neighborhood operator, wherein the four-neighborhood operator calculates gradient in four directions of a central pixel neighborhood, and the eight-neighborhood operator calculates gradient in eight directions, and the operators are defined as follows:
the Laplacian operator can find that when the gray values of the pixel per se and the pixel in the neighborhood are the same, the operation result after the Laplacian operator is processed is zero;
when the gray value of the pixel point is higher than the average gray value of the pixel points in the neighborhood, the operation result is positive; otherwise, the number is negative;
the edge points of the image can thus be determined by the zero crossings between the positive and negative peaks.
The Laplacian operator is particularly sensitive to noise, and therefore in order to obtain a better edge detection effect, the image needs to be subjected to blurring and smoothing to remove high-frequency noise in the image.
In a second aspect, a photovoltaic panel efficiency monitoring system based on an image recognition technology comprises a data processing module, a data acquisition module, a judgment module, a feature extraction module and a comparison module;
the data processing module is used for acquiring historical output power data and non-shielded photo data of the photovoltaic panel, calculating according to the historical output power data to obtain a historical efficiency output power interval [ min, max ] and a maximum change slope in a certain period of historical output power as s, and extracting edge features according to the photo data to obtain reference edge feature data;
the data acquisition module is used for acquiring output power data and video stream data of a photovoltaic panel in a target detection area in real time;
the judging module is used for acquiring the output power data, the historical efficiency output power interval and the maximum change slope, judging whether the output power data exceeds the historical efficiency output power interval or the maximum change slope, and generating feedback data transmitted to the data processing module and the feature extraction module;
the characteristic extraction module is used for processing the video stream data according to an edge recognition algorithm to obtain edge characteristic data of the photovoltaic panel;
the comparison module is used for comparing the edge feature data with the reference edge feature data to obtain image feature difference;
the judging module is also used for judging whether the image characteristic difference exceeds a preset similarity value, if so, outputting the shielding of dust or shadow objects accumulated on the photovoltaic panel, and if not, outputting the abnormal conversion efficiency of the photovoltaic panel battery pack.
In a third aspect, a computer-readable storage medium has stored thereon a computer program which, when executed by a processor, performs the steps of the method of any one of the first aspect.
According to the technical scheme provided by the photovoltaic panel efficiency monitoring method and system based on the image recognition technology, the invention has the following technical effects:
the invention adopts the image recognition to assist in automatically monitoring the dust shielding and shadow object shielding of the photovoltaic panel, assists in carrying out data real-time monitoring on the output power of the photovoltaic panel, and monitors the efficiency of the photovoltaic panel by combining the two, so that a supervisory personnel can refer to the photovoltaic panel and arrange daily maintenance work.
Drawings
FIG. 1 is a flow chart of a photovoltaic panel efficiency monitoring method based on image recognition technology;
fig. 2 is a schematic structural diagram of a photovoltaic panel efficiency monitoring system based on an image recognition technology.
Wherein the reference numbers are as follows:
the system comprises a data processing module 100, a data acquisition module 200, a judgment module 300, a feature extraction module 400 and a comparison module 500.
Detailed Description
In order to make the technical means, the inventive features, the objectives and the effects of the invention easily understood and appreciated, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the specific drawings, and it is obvious that the described embodiments are a part of the embodiments of the present invention, but not all of the embodiments.
All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
It should be understood that the structures, ratios, sizes, etc. shown in the drawings and attached to the description are only for understanding and reading the disclosure of the present invention, and are not intended to limit the practical conditions of the present invention, so that the present invention has no technical significance, and any modifications of the structures, changes of the ratio relationships, or adjustments of the sizes, should still fall within the scope of the technical contents of the present invention without affecting the efficacy and the achievable purpose of the present invention.
In addition, the terms "upper", "lower", "left", "right", "middle" and "one" used in the present specification are for clarity of description, and are not intended to limit the scope of the present invention, and the relative relationship between the terms and the terms is not to be construed as a scope of the present invention.
The invention provides a photovoltaic panel efficiency monitoring method and system based on an image recognition technology, aiming at monitoring the photovoltaic panel efficiency by combining an image recognition auxiliary method for automatically monitoring the dust shielding and shadow object shielding of a photovoltaic panel and an auxiliary method for monitoring the output power of the photovoltaic panel in real time, so that supervision personnel can refer and arrange daily maintenance work.
As shown in fig. 1, a method for monitoring efficiency of a photovoltaic panel based on an image recognition technology includes the following steps:
step 1: obtaining historical output power data and non-shielding photo data of the photovoltaic panel, calculating according to the historical output power data to obtain a historical efficiency output power interval [ min, max ] and a maximum change slope in a certain period of historical output power as s, and extracting edge features according to the photo data to obtain reference edge feature data;
step 2: acquiring output power data and video stream data of a photovoltaic panel in a target detection area in real time;
and step 3: judging whether the output power data exceeds a historical efficiency output power interval or the maximum change slope;
if not, executing the step 1;
if yes, executing step 4;
and 4, step 4: processing the video stream data according to an edge identification algorithm to obtain edge characteristic data of the photovoltaic panel;
and 5: comparing the edge feature data with the reference edge feature data to obtain an image feature difference;
and 6: judging whether the image characteristic difference exceeds a preset similarity value, and setting and adjusting the preset similarity value according to the actual running condition;
if the quantity exceeds the preset value, outputting the blocking of the photovoltaic panel by accumulating dust or shadow objects;
if not, the conversion efficiency of the photovoltaic panel battery pack is abnormal;
according to the corresponding output evaluation, the operation personnel can reasonably arrange the maintenance task, specifically arrange the cleaning of the photovoltaic panel or the working of the assembly maintenance task;
when the photovoltaic panel is monitored, an element fault diagnosis technology of a photovoltaic panel battery pack can be introduced, a battery pack element fault diagnosis model is established according to a mechanism model of an element or a certain expert rule, and is combined into a monitoring method, when the efficiency of the photovoltaic panel is abnormal, the cleanliness of the photovoltaic panel on site is high, and no influence factor is shielded by other objects, fault diagnosis is carried out on the photovoltaic panel battery pack element, and more detailed guidance suggestions can be brought to the monitoring method.
The photovoltaic panel efficiency monitoring method based on the image recognition technology comprises the following steps of calculating according to historical output power data to obtain a historical efficiency output power interval [ min, max ] and a maximum change slope in a certain period of time:
after the historical output power data are cleaned, screening to obtain power data of the photovoltaic panel under normal work and recording the power data as p;
substituting p into the formula 1 to calculate to obtain a minimum value and a maximum value, wherein the interval [ min, max ] of the minimum value and the maximum value is historical efficiency output power;
equation 1:
wherein: the expected value of X is equal to the linear combination of one or several post-falls, plus a constant term, plus a random error, c is a constant term,
is the autocorrelation coefficient, epsilon t Is a random error value assumed to have a mean equal to 0 and a standard deviation equal to σ; σ is assumed to be invariant for any t;
and deriving the maximum change slope of the historical output power by using a least square method.
The photovoltaic panel efficiency monitoring method based on the image recognition technology comprises the following specific steps of adopting a least square method to deduce the maximum change slope of historical output power:
the derivation calculation is performed according to the truncated form y = kx + b, and the coordinate value (x) of n points in the plane is calculated 1 ,y 1 )、(x 2 ,y 2 )、(x 3 ,y 3 )、……(x n ,y n ) Substituting any two points into a formula to obtain a slope value k and a constant b which change between every two points;
k = (y) is obtained by derivation according to a slope formula 2 -y 1 )/(x 2 -x 1 ) And taking the maximum value of the obtained slopes as the maximum change slope. The photovoltaic panel efficiency monitoring method based on the image recognition technology is characterized in that the specific method for extracting the edge features according to the photo data to obtain the reference edge feature data in the step 1 is as follows:
and carrying out median filtering denoising processing on the photo data, then adopting CNN to carry out feature extraction, and carrying out feature fusion through a BP neural network to obtain reference edge feature data.
The photovoltaic panel efficiency monitoring method based on the image recognition technology is characterized in that the specific method for processing the video stream data according to the edge recognition algorithm to obtain the edge characteristic data of the photovoltaic panel in the step 4 is as follows:
performing imaging processing on video stream data to obtain a plurality of pieces of image data;
carrying out pixel point detection on the image data and connecting the pixel points to form contour pixel points;
carrying out boundary point detection on the image data with the contour pixel points, integrating the boundary points with the contour pixel points, and removing false pixel points to obtain edge feature data;
according to the photovoltaic panel efficiency monitoring method based on the image recognition technology, after the video stream data is subjected to imaging processing to obtain a plurality of pieces of image data, the image data is subjected to noise reduction processing.
The above photovoltaic panel efficiency monitoring method based on the image recognition technology, wherein the edge recognition algorithm is Roberts operator (Roberts operator) or Prewitt operator (first order differential operator) or Sobel operator (Sobel operator) or Canny operator (multi-stage edge detection algorithm) or Laplacian operator, the edge detection of the image is realized based on the gradient of the image, and the obtained gradient of the image is obtained by performing convolution operation on the image by using various operators;
in order to achieve the purpose of evaluating the efficiency of the photovoltaic panel, preferably, an edge recognition second-order Laplacian operator is adopted to extract the edge characteristics of the photovoltaic panel, the Laplacian operator is an isotropic second-order differential operator with rotation invariance, and the response obtained by processing the individual pixel points by the operator is stronger than the response obtained by processing the edge points, so that the Laplacian operator is not suitable for processing images with higher noise intensity, and if the detected images have higher noise, low-pass filtering is required to remove the noise;
laplacian edge extraction flow: 1) Gaussian blur-noise cancellation; 2) Turning gray scale; 3) Laplacian-second derivative calculation Laplacian; 4) Taking an absolute value-here to obtain an edge image; 5) Carrying out threshold binarization processing, enhancing edge characteristics and obtaining a more obvious edge image;
the Laplacian operator of an image function is a second derivative (f is a second differentiable real function) defined as:
the expression mode of the discrete form suitable for digital image processing is as follows:
the Laplacian operator generated by the above formula 3 is divided into a four-neighborhood operator and an eight-neighborhood operator, the four-neighborhood operator calculates gradients in four directions of the central pixel neighborhood, and the eight-neighborhood operator calculates gradients in eight directions, and the operators are defined as follows:
the Laplacian operator can find that when the gray values of the pixel per se and the pixel in the neighborhood are the same, the operation result after the Laplacian operator is processed is zero;
when the gray value of the pixel point is higher than the average gray value of the pixel points in the neighborhood, the operation result is positive; otherwise, the number is negative;
the edge points of the image can thus be determined by the zero crossings between the positive and negative peaks.
The Laplacian operator is particularly sensitive to noise, so in order to obtain a better edge detection effect, the image needs to be subjected to fuzzy smoothing processing to remove high-frequency noise in the image.
As shown in fig. 2, in a second aspect, a photovoltaic panel efficiency monitoring system based on an image recognition technology includes a data processing module 100, a data collecting module 200, a determining module 300, a feature extracting module 400, and a comparing module 500;
the data processing module 100 is configured to obtain historical output power data and non-occlusion photo data of the photovoltaic panel, calculate according to the historical output power data to obtain a historical efficiency output power interval [ min, max ] and a maximum change slope of the historical output power within a certain time period as s, and extract edge features according to the photo data to obtain reference edge feature data;
the data acquisition module 200 is used for acquiring output power data and video stream data of a photovoltaic panel in a target detection area in real time;
the judging module 300 is configured to obtain the output power data, the historical efficiency output power interval, and the maximum change slope, judge whether the output power data exceeds the historical efficiency output power interval or the maximum change slope, and generate feedback data transmitted to the data processing module 100 and the feature extraction module 400;
the feature extraction module 400 is configured to process video stream data according to an edge recognition algorithm to obtain edge feature data of the photovoltaic panel;
the comparison module 500 is configured to compare the edge feature data with the reference edge feature data to obtain an image feature difference;
the judging module 300 is further configured to judge whether the image feature difference exceeds a preset similarity value, if so, output that the photovoltaic panel accumulates dust or is shielded by a shadow object, and if not, output that the conversion efficiency of the photovoltaic panel battery assembly is abnormal.
In a third aspect, a computer-readable storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps of the method of any one of the first aspects.
In the embodiments of the present application, the disclosed system, apparatus and method may be implemented in other ways;
for example, the division of a unit or a module is only one logic function division, and there may be another division manner in actual implementation;
for example, a plurality of units or modules or components may be combined or may be integrated into another system;
in addition, functional units or modules in the embodiments of the present application may be integrated into one processing unit or module, or may exist separately and physically.
It should be understood that, in the various embodiments of the present application, the sequence numbers of the processes do not mean the execution sequence, and the execution sequence of the processes should be determined by the functions and the inherent logic thereof, and should not constitute any limitation to the implementation process of the embodiments of the present application.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a machine-readable storage medium;
therefore, the technical solution of the present application may be embodied in the form of a software product, which may be stored in a machine-readable storage medium and may include several instructions to cause an electronic device to execute all or part of the processes of the technical solution described in the embodiments of the present application;
the storage medium may include various media that can store program codes, such as ROM, RAM, removable disk, hard disk, magnetic disk, or optical disk.
In summary, the photovoltaic panel efficiency monitoring method and system based on the image recognition technology can automatically monitor the shielding of dust and shadow objects of the photovoltaic panel by the aid of image recognition, simultaneously monitor the output power of the photovoltaic panel in real time in an auxiliary manner, and monitor the efficiency of the photovoltaic panel by the aid of the combination of the image recognition and the data, so that supervision personnel can refer to the efficiency monitoring method and system and arrange daily maintenance work.
Specific embodiments of the invention have been described above. It is to be understood that the invention is not limited to the particular embodiments described above, in that devices and structures not described in detail are understood to be implemented in a manner common in the art; various changes or modifications may be made by one skilled in the art within the scope of the claims without departing from the spirit of the invention, and without affecting the spirit of the invention.
Claims (9)
1. A photovoltaic panel efficiency monitoring method based on an image recognition technology is characterized by comprising the following steps:
step 1: obtaining historical output power data and non-shielded photo data of a photovoltaic panel, calculating according to the historical output power data to obtain a historical efficiency output power interval [ min, max ] and a maximum change slope of the historical output power within a certain period of time as s, and extracting edge features according to the photo data to obtain reference edge feature data;
step 2: acquiring output power data and video stream data of a photovoltaic panel in a target detection area in real time;
and 3, step 3: judging whether the output power data exceeds the historical efficiency output power interval or the maximum change slope;
if not, executing the step 1;
if yes, executing step 4;
and 4, step 4: processing the video stream data according to an edge recognition algorithm to obtain edge feature data of the photovoltaic panel;
and 5: comparing the edge feature data with the reference edge feature data to obtain image feature difference;
step 6: judging whether the image characteristic difference exceeds a preset similarity value or not;
if the quantity exceeds the preset value, outputting the photovoltaic panel to accumulate dust or shadow object shielding;
and if not, outputting that the conversion efficiency of the photovoltaic panel battery pack is abnormal.
2. The method for monitoring the efficiency of the photovoltaic panel based on the image recognition technology as claimed in claim 1, wherein the method for obtaining the maximum change slope between the historical efficiency output power interval [ min, max ] and a certain period of time by calculation according to the historical output power data is as follows:
cleaning the historical output power data, and screening to obtain power data of the photovoltaic panel under normal working as p;
substituting p into the formula 1 to calculate to obtain a minimum value and a maximum value, wherein the interval [ min, max ] of the minimum value and the maximum value is the historical efficiency output power;
equation 1:
wherein: the expected value of X is equal to the linear combination of one or several post-falls, plus a constant term, plus a random error, c is a constant term,
for the autocorrelation coefficients,. Epsilon.t is a random error value assumed to be a mean equal to 0 and a standard deviation equal to σ; σ is assumed to be invariant for any t;
and deriving the maximum change slope of the historical output power by using a least square method.
3. The photovoltaic panel efficiency monitoring method based on the image recognition technology as claimed in claim 2, wherein the specific method for deriving the maximum change slope of the historical output power by using the least square method is as follows:
the derivation calculation is performed according to the truncated form y = kx + b, and the coordinate value (x) of n points in the plane is calculated 1 ,y 1 )、(x 2 ,y 2 )、(x 3 ,y 3 )、……(x n ,y n ) Substituting any two points into a formula to obtain a slope value k and a constant b which change between every two points;
deriving k = (y) according to a slope formula 2 -y 1 )/(x 2 -x 1 ) And taking the maximum value of the obtained slopes as the maximum change slope.
4. The method for monitoring the efficiency of the photovoltaic panel based on the image recognition technology as claimed in claim 3, wherein the specific method for extracting the edge feature according to the photo data to obtain the reference edge feature data in the step 1 is as follows:
and carrying out median filtering denoising processing on the photo data, then carrying out feature extraction by adopting CNN (convolutional neural network), and carrying out feature fusion by a BP neural network to obtain the reference edge feature data.
5. The method according to claim 4, wherein the specific method for processing the video stream data according to the edge recognition algorithm to obtain the edge feature data of the photovoltaic panel in step 4 is as follows:
carrying out imaging processing on the video stream data to obtain a plurality of pieces of image data;
carrying out pixel point detection on the image data and connecting the pixel points to form contour pixel points;
and detecting boundary points of the image data with the contour pixel points, integrating the boundary points with the contour pixel points, and removing false pixel points to obtain the edge feature data.
6. The method as claimed in claim 5, wherein a noise reduction process is further performed on the image data after the video stream data is imaged to obtain a plurality of pieces of image data.
7. The photovoltaic panel efficiency monitoring method based on the image recognition technology as claimed in any one of claims 1 to 6, wherein the edge recognition algorithm is a Roberts operator or a Prewitt operator or a Sobel operator or a Canny operator or a Laplacian operator.
8. A photovoltaic panel efficiency monitoring system based on an image recognition technology is characterized by comprising a data processing module, a data acquisition module, a judgment module, a feature extraction module and a comparison module;
the data processing module is used for acquiring historical output power data and non-shielded photo data of the photovoltaic panel, calculating according to the historical output power data to obtain a historical efficiency output power interval [ min, max ] and a maximum change slope in a certain period of historical output power as s, and extracting edge features according to the photo data to obtain reference edge feature data;
the data acquisition module is used for acquiring output power data and video stream data of a photovoltaic panel in a target detection area in real time;
the judging module is used for acquiring the output power data, the historical efficiency output power interval and the maximum change slope, judging whether the output power data exceeds the historical efficiency output power interval or the maximum change slope, and generating feedback data transmitted to the data processing module and the feature extraction module;
the characteristic extraction module is used for processing the video stream data according to an edge recognition algorithm to obtain edge characteristic data of the photovoltaic panel;
the comparison module is used for comparing the edge characteristic data with the reference edge characteristic data to obtain an image characteristic difference;
the judging module is also used for judging whether the image characteristic difference exceeds a preset similarity value, if so, outputting the shielding of dust or shadow objects accumulated on the photovoltaic panel, and if not, outputting the abnormal conversion efficiency of the photovoltaic panel battery pack.
9. A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, which computer program, when being executed by a processor, carries out the steps of the method according to any one of claims 1-7.
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