Photovoltaic panel anomaly analysis method and device, electronic equipment and storage medium

Abstract

The embodiment of the application provides a photovoltaic panel abnormality analysis method and device, electronic equipment and storage medium, and belongs to the technical field of Internet of Things. The method comprises the following steps: acquiring image sensor parameters of a solar camera, real-time output power of a photovoltaic panel, a current timestamp and device latitude and longitude information; performing illumination intensity analysis based on the image sensor parameters to obtain equivalent illumination intensity of the current environment; performing solar azimuth analysis based on the current timestamp and the device latitude and longitude information to obtain solar spatial position parameters; performing output power analysis based on the equivalent illumination intensity, the solar spatial position parameters and preset photovoltaic panel installation posture parameters to obtain theoretical output power of the photovoltaic panel at the current moment; and performing power abnormality analysis based on the theoretical output power of the photovoltaic panel and the real-time output power of the photovoltaic panel to obtain target power abnormality information. The embodiment of the application can automatically and accurately analyze the power abnormality reason of the photovoltaic panel.

Description

Technical Field

[0001] This application relates to the field of Internet of Things (IoT) technology, and in particular to a method, apparatus, electronic device, and storage medium for analyzing anomalies in photovoltaic panels. Background Technology

[0002] Solar-powered cameras, as crucial outdoor surveillance equipment, rely on photovoltaic panels to convert solar energy into electricity to maintain operation. However, in practical applications, the power output of photovoltaic panels is easily affected by various factors. One is installation-related; if the orientation and tilt angle are not precisely adjusted according to local latitude and longitude, it will lead to a permanent loss of power generation efficiency. Another factor is environmental and shading, such as tree growth, building obstruction, dust accumulation, and bird droppings, all of which can cause abnormally low output power. Furthermore, aging of the equipment itself or wiring faults can also lead to performance degradation.

[0003] Current technologies for diagnosing the operating status of photovoltaic panels rely on manual inspections or simple power threshold alarms. Manual inspections are costly and inefficient. Simple power threshold alarms cannot distinguish between power drops caused by normal factors such as weather changes and abnormal factors such as equipment malfunctions or shading, resulting in a high false alarm rate and failing to achieve accurate and automated diagnosis. Therefore, there is an urgent need in this field for an automated and highly accurate method for analyzing photovoltaic panel power anomalies. Summary of the Invention

[0004] The main objective of this application is to provide a photovoltaic panel anomaly analysis method, device, electronic device, and storage medium, which aims to automatically and accurately analyze the causes of photovoltaic panel power anomalies.

[0005] To achieve the above objectives, a first aspect of this application proposes a method for analyzing anomalies in photovoltaic panels, the method comprising: Acquire image sensor parameters of the solar camera, real-time output power of the photovoltaic panel, current timestamp, and device latitude and longitude information; Based on the image sensor parameters, an illumination intensity analysis is performed to obtain the equivalent illumination intensity of the current environment; Based on the current timestamp and the device's latitude and longitude information, solar orientation analysis is performed to obtain solar spatial position parameters; Based on the equivalent light intensity, the solar spatial position parameters, and the preset photovoltaic panel installation attitude parameters, the output power is analyzed to obtain the theoretical output power of the photovoltaic panel at the current moment. Power anomaly analysis is performed based on the theoretical output power and real-time output power of the photovoltaic panel to obtain target power anomaly information.

[0006] In some embodiments, the image sensor parameters include exposure time, gain value, and gamma value; The step of performing illumination intensity analysis based on the image sensor parameters to obtain the equivalent illumination intensity of the current environment includes: The equivalent illumination intensity is obtained by performing reverse deduction based on the exposure time, the gain value, and the gamma value.

[0007] In some embodiments, the process of inversely extrapolating from the exposure time, the gain value, and the gamma value to obtain the equivalent illumination intensity includes: The current exposure level is obtained by multiplying the exposure time and the gain value. The reference sensitivity of the solar camera is corrected based on the gamma value to obtain the corrected sensitivity coefficient in the current state. Obtain the correlation mapping relationship between the equivalent light intensity and the exposure index; Based on the corrected sensitivity coefficient and the relevant mapping relationship, the exposure index is reverse-engineered to obtain the equivalent light intensity.

[0008] In some embodiments, the process of inversely extrapolating from the exposure time, the gain value, and the gamma value to obtain the equivalent illumination intensity includes: Based on the gamma value, the preset image grayscale reference value is subjected to gamma correction processing to obtain a linearized brightness reference. Obtain the physical reference sensitivity of the solar-powered camera at unity gain and unit exposure time; Based on the linearized brightness reference, the exposure time, and the gain value, the exposure ratio of the current scene relative to the physical reference sensitivity is obtained; The equivalent light intensity of the current environment is obtained by reverse calculation based on the exposure ratio.

[0009] In some embodiments, the step of performing solar azimuth analysis based on the current timestamp and the device's latitude and longitude information to obtain solar spatial position parameters includes: The current timestamp is parsed to obtain date and time information; Based on the date information, time information, and device latitude and longitude information, solar azimuth analysis is performed to obtain the solar altitude angle and solar azimuth angle, which characterize the direction of solar incidence, and serve as the solar spatial position parameters.

[0010] In some embodiments, the step of performing power anomaly analysis based on the theoretical output power and real-time output power of the photovoltaic panel to obtain target power anomaly information includes: A power deviation index is obtained by comparing the theoretical output power of the photovoltaic panel with the real-time output power of the photovoltaic panel. Obtain multiple power deviation indicators within a historical preset time period and construct deviation temporal distribution characteristics; Based on the aforementioned deviation time-series distribution characteristics, the abnormal power output state of the photovoltaic panel is classified and processed to obtain the abnormal attribution result, which is then identified as the target power abnormality information.

[0011] In some embodiments, the classification of abnormal power output states of the photovoltaic panel based on the deviation time-series distribution characteristics to obtain anomaly attribution results includes: In response to the deviation time-series distribution characteristics showing that the power deviation index appears regularly within a specific time window each day, the abnormal attribution result is determined to be local occlusion; In response to the deviation time-series distribution characteristics showing that the power deviation index persists throughout the entire time period without obvious fluctuation patterns, the abnormal attribution result is determined to be dust accumulation or general shading. In response to the deviation time-series distribution characteristics showing that the power deviation index has been present continuously and has a constant value since the initial installation time of the equipment, the abnormal attribution result is determined to be an abnormal installation angle. In response to the deviation time-series distribution characteristics showing that the real-time output power of the photovoltaic panel suddenly drops to zero, the abnormal attribution result is determined to be a device circuit failure.

[0012] To achieve the above objectives, a second aspect of this application provides a photovoltaic panel anomaly analysis device, the device comprising: The information acquisition module is used to acquire the image sensor parameters of the solar camera, the real-time output power of the photovoltaic panel, the current timestamp, and the latitude and longitude information of the device. The illumination intensity analysis module is used to perform illumination intensity analysis based on the image sensor parameters to obtain the equivalent illumination intensity of the current environment; The solar azimuth analysis module is used to perform solar azimuth analysis based on the current timestamp and the device's latitude and longitude information to obtain solar spatial position parameters. The theoretical power analysis module is used to perform output power analysis based on the equivalent irradiance, the solar spatial position parameters, and the preset photovoltaic panel installation attitude parameters to obtain the theoretical output power of the photovoltaic panel at the current moment. A power anomaly analysis model is used to perform power anomaly analysis based on the theoretical output power and real-time output power of the photovoltaic panel to obtain target power anomaly information.

[0013] To achieve the above objectives, a third aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the photovoltaic panel anomaly analysis method described in the first aspect.

[0014] To achieve the above objectives, a fourth aspect of the present application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the photovoltaic panel anomaly analysis method described in the first aspect.

[0015] The photovoltaic panel anomaly analysis method, device, electronic equipment, and storage medium proposed in this application acquire image sensor parameters of a solar camera, real-time output power of the photovoltaic panel, current timestamp, and device latitude and longitude information. Based on the image sensor parameters, it performs light intensity analysis to obtain the equivalent light intensity of the current environment. Based on the current timestamp and device latitude and longitude information, it performs solar azimuth analysis to obtain solar spatial position parameters. Based on the equivalent light intensity, solar spatial position parameters, and preset photovoltaic panel installation attitude parameters, it performs output power analysis to obtain the theoretical output power of the photovoltaic panel at the current moment. Based on the theoretical output power and real-time output power of the photovoltaic panel, it performs power anomaly analysis to obtain target power anomaly information. Therefore, this application quantifies the current actual ambient light conditions by utilizing the physical correlation between the image sensor parameters of a solar camera and the ambient brightness, obtaining the equivalent light intensity of the current environment. This allows for the identification of weather conditions and avoids the influence of weather conditions. Consequently, in the final power anomaly analysis, it can automatically eliminate power reduction components caused by environmental factors such as cloudy days, thus accurately identifying the real anomalies caused by equipment failure or shading, effectively reducing the false alarm rate. Furthermore, by comparing the calculated theoretical output power of the photovoltaic panel with the real-time output power of the photovoltaic panel, it can automatically monitor the working status of the photovoltaic panel around the clock. It can identify continuous power loss caused by installation angle deviation or long-term shading without manual on-site inspection, achieving low-cost and automated photovoltaic panel status diagnosis. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the photovoltaic panel anomaly analysis method provided in the embodiments of this application; Figure 2 yes Figure 1 A flowchart illustrating step S102 in the process; Figure 3 yes Figure 1 Another flowchart of step S102 in the process; Figure 4 yes Figure 1 A flowchart illustrating step S103 in the process; Figure 5 yes Figure 1 A flowchart illustrating step S105 in the process; Figure 6 yes Figure 5 A flowchart illustrating step S503 in the process; Figure 7 This is a schematic diagram of the structure of the photovoltaic panel anomaly analysis device provided in the embodiments of this application; Figure 8 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0018] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0020] This application provides a photovoltaic panel anomaly analysis method, apparatus, electronic device, and storage medium, which aims to automatically and accurately analyze the causes of photovoltaic panel power anomalies.

[0021] The photovoltaic panel anomaly analysis method, apparatus, electronic device, and storage medium provided in this application are specifically described through the following embodiments. First, the photovoltaic panel anomaly analysis method in this application embodiment is described.

[0022] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0023] Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0024] The photovoltaic panel anomaly analysis method provided in this application relates to the field of Internet of Things (IoT) technology. This method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application implementing the photovoltaic panel anomaly analysis method, but is not limited to the above forms.

[0025] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0026] Figure 1 This is an optional flowchart of the photovoltaic panel anomaly analysis method provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S101 to S105.

[0027] Step S101: Obtain the image sensor parameters of the solar camera, the real-time output power of the photovoltaic panel, the current timestamp, and the latitude and longitude information of the device; Step S102: Analyze the illumination intensity based on the image sensor parameters to obtain the equivalent illumination intensity of the current environment; Step S103: Perform solar orientation analysis based on the current timestamp and device latitude and longitude information to obtain solar spatial position parameters; Step S104: Based on the equivalent light intensity, solar spatial position parameters and preset photovoltaic panel installation attitude parameters, perform output power analysis to obtain the theoretical output power of the photovoltaic panel at the current moment; Step S105: Perform power anomaly analysis based on the theoretical output power and real-time output power of the photovoltaic panel to obtain target power anomaly information.

[0028] Steps S101 to S105 as shown in the embodiments of this application involve acquiring the image sensor parameters of the solar camera, the real-time output power of the photovoltaic panel, the current timestamp, and the latitude and longitude information of the device; performing light intensity analysis based on the image sensor parameters to obtain the equivalent light intensity of the current environment; performing solar azimuth analysis based on the current timestamp and the latitude and longitude information of the device to obtain the solar spatial position parameters; performing output power analysis based on the equivalent light intensity, the solar spatial position parameters, and the preset photovoltaic panel installation attitude parameters to obtain the theoretical output power of the photovoltaic panel at the current moment; and performing power anomaly analysis based on the theoretical output power and the real-time output power of the photovoltaic panel to obtain the target power anomaly information. Therefore, this application quantifies the current actual ambient light conditions by utilizing the physical correlation between the image sensor parameters of a solar camera and the ambient brightness, obtaining the equivalent light intensity of the current environment. This allows for the identification of weather conditions and avoids the influence of weather conditions. Consequently, in the final power anomaly analysis, it can automatically eliminate power reduction components caused by environmental factors such as cloudy days, thus accurately identifying the real anomalies caused by equipment failure or shading, effectively reducing the false alarm rate. Furthermore, by comparing the calculated theoretical output power of the photovoltaic panel with the real-time output power of the photovoltaic panel, it can automatically monitor the working status of the photovoltaic panel around the clock. It can identify continuous power loss caused by installation angle deviation or long-term shading without manual on-site inspection, achieving low-cost and automated photovoltaic panel status diagnosis.

[0029] In step S101 of some embodiments, the main task is to synchronously acquire multi-dimensional data. Specifically, the solar-powered camera, as a terminal device, periodically collects its own status data, i.e., image sensor parameters, during operation or under specific triggering conditions. Image sensor parameters include, for example, exposure time, gain, and gamma, which directly reflect the optical settings information of the camera during imaging. Simultaneously, the current electrical performance indicators of the photovoltaic panel, i.e., real-time output power, can be read, which typically involves real-time monitoring of output voltage and output current. Furthermore, to determine the astronomical background of the external environment, the current timestamp and the latitude and longitude information pre-configured or real-time located by the device can also be obtained.

[0030] In step S102 of some embodiments, since the camera's automatic exposure algorithm automatically adjusts imaging parameters according to ambient brightness—for example, reducing gain and shortening exposure time in strong light and vice versa in low light—there is a significant mapping relationship between these image sensor parameters and ambient brightness. By calling a pre-calibrated image sensor photosensitivity model or looking up the corresponding mapping table, the obtained exposure time, gain value, and other parameters can be reverse-engineered and calculated to quantify the current equivalent light intensity of the shooting scene. This achieves accurate perception of ambient lighting conditions without increasing the hardware cost of an independent light sensor.

[0031] Please see Figure 2 In some embodiments, the image sensor parameters include exposure time, gain value, and gamma value. Step S102 becomes a reverse deduction process based on the exposure time, gain value, and gamma value to obtain the equivalent illumination intensity. Step S102 may include, but is not limited to, steps S201 to S204. Step S201: Perform a product operation based on the exposure time and the gain value to obtain the current exposure index; Step S202: Correct the reference sensitivity of the solar camera based on the gamma value to obtain the corrected sensitivity coefficient under the current state. Step S203: Obtain the correlation mapping relationship between equivalent light intensity and exposure index; Step S204: Based on the corrected sensitivity coefficient and related mapping relationship, the exposure index is reverse-engineered to obtain the equivalent light intensity.

[0032] In step S201 of some embodiments, it is first necessary to quantify the total amount of light-sensitive resources invested by the camera to acquire the current image. Exposure time and gain value are the two most critical factors determining image brightness. Exposure time determines the duration of light entering the sensor, while gain value determines the amplification factor of the electrical signal. The acquired exposure time and gain value can be multiplied to obtain the exposure index. The exposure index numerically quantifies the total exposure level required by the image sensor to output a standard grayscale image under the current lighting conditions. Under the control of the automatic exposure algorithm, the exposure index is negatively correlated with the ambient brightness; that is, a larger value means weaker ambient light, and a smaller value means stronger ambient light.

[0033] In step S202 of some embodiments, the reference sensitivity of the solar camera is numerically corrected using the current gamma value. Since the gamma value setting directly affects the nonlinear characteristics of the image sensor's photoelectric response curve, different gamma settings will result in different signal outputs from the same light input. Using a preset correction algorithm or coefficient table, the device's preset static reference sensitivity is dynamically adjusted based on the current gamma value to generate a corrected sensitivity coefficient. This corrected sensitivity coefficient reflects the actual photoelectric conversion reference capability of the image sensor under the current specific gamma configuration.

[0034] In step S203 of some embodiments, a pre-built data model can be retrieved from the storage unit. This data model defines the numerical correspondence between equivalent light intensity and exposure index, or a pre-built mathematical formula that reflects the relevant mapping relationship between equivalent light intensity and exposure index.

[0035] This correlation is typically obtained through experimental calibration, recording the exposure index values ​​generated by the camera's automatic exposure for different physical light intensity values ​​under standard test conditions. This relationship is represented as a deterministic mathematical function curve or a discrete lookup table, used to describe the monotonic mapping between exposure parameter combinations and ambient light levels.

[0036] In step S204 of some embodiments, a reverse inference operation is performed based on the above calculation results. The calculated exposure index is used as an input variable and substituted into the relevant mapping relationship to initially match the corresponding illuminance value. The obtained corrected sensitivity coefficient is then used to perform weighted compensation or linear correction on the initial value, and finally the equivalent illuminance of the current environment is calculated. This completes the conversion from the camera's internal control parameters to the physical quantity of the external environment.

[0037] Through steps S201 to S204, high-precision illumination inversion was achieved via a layered processing approach. By integrating exposure time and gain values ​​into a unified exposure index, linear characterization of imaging parameters was achieved. Furthermore, by introducing gamma values ​​to correct for photosensitivity, calculation errors caused by nonlinear configurations were eliminated. Finally, by combining pre-calibrated mapping relationships for back-calculation, it was ensured that ambient light intensity could be accurately quantified using existing imaging hardware without the need for external photosensors, providing accurate data input for subsequent photovoltaic power theoretical calculations.

[0038] Please see Figure 3 In some embodiments, step S102 may also include, but is not limited to, steps S301 to S304: Step S301: Perform gamma correction on the preset image grayscale reference value according to the gamma value to obtain a linearized brightness reference. Step S302: Obtain the physical reference sensitivity of the solar camera at unity gain and unit exposure time; Step S303: Based on the linearized brightness reference, exposure time, and gain value, obtain the exposure ratio of the current scene relative to the physical reference sensitivity; Step S304: Based on the exposure ratio, the equivalent light intensity of the current environment is derived by reverse calculation.

[0039] In step S301 of some embodiments, the nonlinear characteristics of the image signal are first addressed. Because cameras typically perform gamma encoding on image signals to adapt to human visual characteristics or display device standards, the preset image grayscale reference value used for automatic exposure control is in a nonlinear domain. By reading the currently configured gamma value, a degamma correction process is performed on the preset image grayscale reference value using an inverse gamma function or a reverse lookup table. This eliminates the nonlinear compression effect of the gamma curve on brightness, restoring the nonlinear grayscale value to a linearized brightness reference that is linearly related to the number of physical photons.

[0040] In step S302 of some embodiments, the inherent photoelectric conversion characteristic parameters of the image sensor are obtained. This parameter is defined as the physical reference sensitivity, which numerically characterizes the response amplitude of the image sensor of a specific model to a standard light source under reference conditions of no signal electronic amplification (i.e., unity gain) and an exposure time of unit time (e.g., 1 second or 1 millisecond). This physical reference sensitivity serves as the absolute reference anchor point for system calculations, used to eliminate the influence of individual differences in sensor hardware or different reference operating points in subsequent calculations.

[0041] In step S303 of some embodiments, the difference factor between the current imaging state and the reference state is calculated. This can be achieved by using a linearized brightness reference as the numerator and the product of the current exposure time and the gain value as the denominator, or by employing appropriate logarithmic subtraction and combining it with the acquired physical reference sensitivity to obtain the exposure ratio. The numerical value of the exposure ratio reflects the sum of the signal integral and amplification applied by the solar camera relative to the reference state in order to enhance the weak light signal in the current environment to the target brightness level.

[0042] In step S304 of some embodiments, since there is a definite physical inverse or direct proportional relationship between the exposure ratio and the luminous flux density incident on the sensor surface in the imaging system, the exposure ratio can be used in conjunction with a preset photometric conversion coefficient to perform reverse deduction, so as to map the dimensionless proportional value into a value with physical units, thereby obtaining an equivalent illuminance that accurately characterizes the current ambient brightness.

[0043] Through steps S301 to S304, a linear reference is obtained by first performing gamma correction, and then the exposure ratio is calculated by combining the physical reference sensitivity. This scheme strictly follows the physical laws of photoelectric conversion and avoids the calculation deviation caused by directly using nonlinear gamma data. This processing method makes the calculation of light intensity independent of specific empirical fitting curves, but based on the underlying physical response characteristics of the sensor, significantly improving the linearity and accuracy of light inversion under different gamma settings and different light dynamic ranges.

[0044] In step S103 of some embodiments, the accurate date and time are determined by parsing the obtained current timestamp, and geometric calculations are performed using a solar position algorithm in combination with the latitude and longitude coordinates of the device.

[0045] Please see Figure 4 In some embodiments, step S103 may include, but is not limited to, steps S401 to S402: Step S401: Parse the current timestamp to obtain date and time information; Step S402: Based on the date information, time information, and equipment latitude and longitude information, perform solar azimuth analysis to obtain the solar altitude angle and solar azimuth angle, which characterize the direction of solar incidence, as solar spatial position parameters.

[0046] In step S401 of some embodiments, the received current timestamp undergoes time-domain data parsing and structured conversion processing. The current timestamp is typically recorded as a long integer or standard UTC string format, which is mapped back to the time components under a specific calendar. Date information, including year, month, and day, is extracted to determine the Earth's position in its orbit and the corresponding seasonal characteristics. Simultaneously, time information, including hour, minute, and second, is extracted to determine the phase state of the Earth's rotation. This two-dimensional information provides the necessary time reference for determining the Sun's relative position in the celestial coordinate system.

[0047] In step S402 of some embodiments, a geometric calculation program based on the principles of spherical astronomy is executed. The solar declination angle is calculated based on the date information; the solar declination angle reflects the latitude of the subsolar point on the Earth's surface. Combining the time information with the longitude value in the device's latitude and longitude information, the solar hour angle is calculated; the solar hour angle reflects the longitude difference between the observation point and the subsolar meridian. Subsequently, the latitude value, solar declination angle, and solar hour angle from the device's latitude and longitude information are substituted into the coordinate transformation formula to calculate the solar altitude angle and solar azimuth angle. The solar altitude angle numerically represents the vertical angle between the sun's rays and the ground plane, and the solar azimuth angle numerically represents the deflection angle of the sun's projection on the horizontal plane relative to true north. These two angular parameters together constitute the solar spatial position parameters that can accurately pinpoint the solar spatial vector.

[0048] Through steps S401 and S402, real-time mathematical reconstruction of the sun's trajectory is achieved through precise time analysis and astronomical coordinate transformation. Without relying on expensive external solar tracking sensors or illumination direction sensors, the algorithm can accurately calculate the sun's incident direction at any given moment using only time and geographical coordinates. This provides precise geometric input for subsequent calculations of theoretical radiation reception based on the photovoltaic panel's installation attitude, ensuring the spatial accuracy of the theoretical power calculation model.

[0049] In step S104 of some embodiments, a theoretical benchmark is constructed by integrating ambient light intensity, celestial position, and device attitude. The calculated solar spatial position parameters are spatially geometrically matched with preset photovoltaic panel installation attitude parameters, i.e., the actual installation orientation and tilt angle of the photovoltaic panel, to calculate the angle of incidence of sunlight relative to the photovoltaic panel surface. Then, combined with the equivalent irradiance, the radiant energy actually projected onto the effective area of ​​the photovoltaic panel is evaluated. Subsequently, using a photovoltaic power generation conversion model, this radiant energy is mapped into electrical energy, and the theoretical output power of the photovoltaic panel under the current irradiance conditions and solar position is calculated; that is, the theoretical output power that the photovoltaic panel should produce if the equipment is functioning normally. This theoretical value is a reference standard that dynamically changes with the environment.

[0050] In step S105 of some embodiments, the health status of the solar camera is diagnosed by comparing theoretical values ​​with actual values. The calculated theoretical output power of the photovoltaic panel can be compared with the actual real-time output power of the photovoltaic panel. If the real-time output power is consistently and significantly lower than the theoretical output power, for example, lower than 70% of the theoretical value, then an anomaly is determined to exist.

[0051] Please see Figure 5 In some embodiments, step S105 may include, but is not limited to, steps S501 to S503: Step S501: Based on the theoretical output power of the photovoltaic panel and the real-time output power of the photovoltaic panel, a power deviation index is obtained by comparing and analyzing the power output power. Step S502: Obtain multiple power deviation indicators within a historical preset time period and construct deviation time-series distribution characteristics; Step S503: Based on the deviation time series distribution characteristics, classify the abnormal power output state of the photovoltaic panel, obtain the abnormal attribution result, and determine it as the target power abnormal information.

[0052] In step S501 of some embodiments, the theoretical output power of the photovoltaic panel is used as a reference benchmark under ideal operating conditions, and the real-time output power of the photovoltaic panel is used as an observed value under actual operating conditions. Both are used as input variables in the comparison logic. Through difference calculation or ratio calculation, a value representing the degree of deviation between the two is obtained, namely the power deviation index. The power deviation index quantifies the loss of photovoltaic panel power generation efficiency at the current moment relative to the theoretical expectation, providing direct data support for determining whether the solar camera is in an abnormal operating state.

[0053] In step S502 of some embodiments, multiple power deviation indicators generated within a preset historical time period are retrieved from the data storage unit, and these discrete indicator data are arranged and fitted according to the time sequence of their generation. Through this process, a deviation time-series distribution feature is constructed. This feature objectively records the law of power loss evolution over time in the form of a curve or statistical distribution, revealing whether the power anomaly is sudden, periodic, or continuous and constant.

[0054] In step S503 of some embodiments, the waveform shape or statistical law of the deviation time sequence distribution characteristics is analyzed and matched with a preset fault model or fault cause.

[0055] Through steps S501 to S503, the introduction of a time-series analysis mechanism significantly improves the depth and accuracy of fault diagnosis. Instead of relying solely on low power at a single moment to trigger an alarm, it analyzes the evolution of power deviation over time to accurately identify the cause of the anomaly. This approach can precisely distinguish between regular fluctuations caused by building obstruction, continuous attenuation caused by dust accumulation, and sudden interruptions caused by equipment damage. This provides maintenance personnel with clearly identifiable root cause information, greatly reducing the difficulty and cost of on-site troubleshooting.

[0056] Furthermore, the temporal characteristics of this power deviation can be analyzed. If the deviation occurs regularly only during specific time periods each day, anomaly information pointing to localized shading is generated; if the deviation is irregular but persistent, anomaly information pointing to dust accumulation or widespread shading is generated; if the deviation has existed since the initial installation and is extremely large, anomaly information pointing to an incorrect installation angle is generated; if the power suddenly drops to zero, anomaly information indicating equipment failure is generated. Finally, target power anomaly information containing specific causes can be output.

[0057] Please see Figure 6 In some embodiments, step S503 may include, but is not limited to, steps S601 to S604: Step S601: In response to the deviation time series distribution characteristics showing that the power deviation index appears regularly within a specific time window each day, the abnormal attribution result is determined to be local occlusion. Step S602: In response to the deviation time-series distribution characteristics showing that the power deviation index continues to exist throughout the entire time period and has no obvious fluctuation pattern, the abnormal attribution result is determined to be dust accumulation or general shading. Step S603: In response to the deviation time sequence distribution characteristics showing that the power deviation index has been present continuously since the initial installation time of the equipment and the value is constant, the abnormal attribution result is determined to be an abnormal installation angle. Step S604: In response to the deviation time sequence distribution characteristics showing that the real-time output power of the photovoltaic panel suddenly drops to zero, the abnormal attribution result is determined to be a device circuit failure.

[0058] In step S601 of some embodiments, the focus is on identifying the periodicity of the power deviation over time. As the sun moves across the sky, its position relative to a fixed obstacle, such as a nearby building, tree, or utility pole, changes continuously. If the power deviation is detected to consistently occur within a relatively fixed time period each day, such as a specific number of hours in the afternoon, while the power output is normal at other times, this highly time-reproducible deviation pattern is determined to conform to the projection pattern of a fixed obstruction, thereby identifying the abnormal attribution as local shading.

[0059] In step S602 of some embodiments, the persistence and randomness characteristics of the power deviation are analyzed. When dust, dirt, or snow accumulates on the surface of the photovoltaic panel, its light transmittance decreases overall, causing the photovoltaic panel's power generation capacity to be lower than theoretically expected throughout the day, unlike shading which disappears with changes in the sun's angle. If the power deviation index is detected to persist throughout the entire time period, and the fluctuation of the deviation amplitude does not exhibit obvious time correlation or a specific waveform, it is determined that this all-weather efficiency degradation is consistent with the characteristics of the surface covering, thereby identifying the abnormal attribution result as dust accumulation or general shading.

[0060] In step S603 of some embodiments, a historical data backtracking mechanism covering the entire lifecycle is introduced. Historical records from the initial power-on installation of the equipment are retrieved, and the difference between theoretical power and real-time power is compared and analyzed. If the data indicates that from the first day of equipment operation, there is always a constant and significant difference between the real-time output power and the theoretical output power, and this difference ratio does not fundamentally change with seasonal or weather variations, it can be determined that this inherent and persistent deviation stems from a mismatch between the physical installation geometric parameters and the theoretically calculated parameters, thus identifying the abnormal attribution result as an abnormal installation angle.

[0061] In step S604 of some embodiments, the abrupt change characteristics of the power value are monitored. The output status of the photovoltaic panel is scanned in real time. When it is detected that the real-time output power drops sharply from the normal value to zero or close to zero in a short period of time, and this state continues to be maintained while the light conditions still meet the power generation requirements, it can be determined that the phenomenon is not a gradual decrease caused by environmental factors, but a hard fault caused by electrical connection interruption or component damage, thereby determining the abnormal attribution result as a device circuit failure.

[0062] Through steps S601 to S604, by performing multi-dimensional logical analysis on the time distribution characteristics and numerical evolution characteristics of power deviation data, accurate classification of the causes of anomalies is achieved. It can automatically distinguish different types of problems caused by external environment (shading, dust accumulation), human construction (installation angle), and internal hardware (circuit fault), thereby providing maintenance personnel with clear and targeted maintenance suggestions, avoiding indiscriminate on-site investigation of all anomalies, and significantly improving the pertinence and efficiency of maintenance decisions.

[0063] This application embodiment acquires the image sensor parameters of a solar camera, the real-time output power of a photovoltaic panel, the current timestamp, and the device's latitude and longitude information. Based on the image sensor parameters, it performs light intensity analysis to obtain the equivalent light intensity of the current environment. Based on the current timestamp and the device's latitude and longitude information, it performs solar azimuth analysis to obtain the solar spatial position parameters. Based on the equivalent light intensity, the solar spatial position parameters, and preset photovoltaic panel installation attitude parameters, it performs output power analysis to obtain the theoretical output power of the photovoltaic panel at the current moment. Based on the theoretical output power and the real-time output power of the photovoltaic panel, it performs power anomaly analysis to obtain target power anomaly information. Therefore, this application quantifies the current actual ambient light conditions by utilizing the physical correlation between the image sensor parameters of a solar camera and the ambient brightness, obtaining the equivalent light intensity of the current environment. This allows for the identification of weather conditions and avoids the influence of weather conditions. Consequently, in the final power anomaly analysis, it can automatically eliminate power reduction components caused by environmental factors such as cloudy days, thus accurately identifying the real anomalies caused by equipment failure or shading, effectively reducing the false alarm rate. Furthermore, by comparing the calculated theoretical output power of the photovoltaic panel with the real-time output power of the photovoltaic panel, it can automatically monitor the working status of the photovoltaic panel around the clock. It can identify continuous power loss caused by installation angle deviation or long-term shading without manual on-site inspection, achieving low-cost and automated photovoltaic panel status diagnosis.

[0064] Please see Figure 7 This application also provides a photovoltaic panel anomaly analysis device, which can implement the above-mentioned photovoltaic panel anomaly analysis method. The device includes: The information acquisition module is used to acquire the image sensor parameters of the solar camera, the real-time output power of the photovoltaic panel, the current timestamp, and the latitude and longitude information of the device. The illumination intensity analysis module is used to perform illumination intensity analysis based on image sensor parameters to obtain the equivalent illumination intensity of the current environment; The solar orientation analysis module is used to perform solar orientation analysis based on the current timestamp and device latitude and longitude information to obtain solar spatial position parameters. The theoretical power analysis module is used to analyze the output power based on the equivalent irradiance, solar spatial position parameters, and preset photovoltaic panel installation attitude parameters to obtain the theoretical output power of the photovoltaic panel at the current moment. The power anomaly analysis model is used to perform power anomaly analysis based on the theoretical output power and real-time output power of the photovoltaic panel, and to obtain target power anomaly information.

[0065] The specific implementation method of the photovoltaic panel anomaly analysis device is basically the same as the specific implementation method of the photovoltaic panel anomaly analysis method described above, and will not be repeated here.

[0066] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described photovoltaic panel anomaly analysis method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0067] Please see Figure 8 , Figure 8 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 801 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 802 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 802 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 802 and called and executed by the processor 801 using the photovoltaic panel anomaly analysis method of the embodiments of this application. The 803 input / output interface is used to implement information input and output. The communication interface 804 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 805 transmits information between various components of the device (e.g., processor 801, memory 802, input / output interface 803, and communication interface 804); The processor 801, memory 802, input / output interface 803, and communication interface 804 are connected to each other within the device via bus 805.

[0068] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described photovoltaic panel anomaly analysis method.

[0069] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0070] The photovoltaic panel anomaly analysis method, device, electronic device, and storage medium provided in this application embodiment acquire image sensor parameters of a solar camera, real-time output power of the photovoltaic panel, current timestamp, and device latitude and longitude information. Based on the image sensor parameters, it performs light intensity analysis to obtain the equivalent light intensity of the current environment. Based on the current timestamp and device latitude and longitude information, it performs solar azimuth analysis to obtain solar spatial position parameters. Based on the equivalent light intensity, solar spatial position parameters, and preset photovoltaic panel installation attitude parameters, it performs output power analysis to obtain the theoretical output power of the photovoltaic panel at the current moment. Based on the theoretical output power and real-time output power of the photovoltaic panel, it performs power anomaly analysis to obtain target power anomaly information. Therefore, this application quantifies the current actual ambient light conditions by utilizing the physical correlation between the image sensor parameters of a solar camera and the ambient brightness, obtaining the equivalent light intensity of the current environment. This allows for the identification of weather conditions and avoids the influence of weather conditions. Consequently, in the final power anomaly analysis, it can automatically eliminate power reduction components caused by environmental factors such as cloudy days, thus accurately identifying the real anomalies caused by equipment failure or shading, effectively reducing the false alarm rate. Furthermore, by comparing the calculated theoretical output power of the photovoltaic panel with the real-time output power of the photovoltaic panel, it can automatically monitor the working status of the photovoltaic panel around the clock. It can identify continuous power loss caused by installation angle deviation or long-term shading without manual on-site inspection, achieving low-cost and automated photovoltaic panel status diagnosis.

[0071] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0072] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0073] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0074] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0075] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0076] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0077] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. The coupling or direct coupling or communication connection between the shown or discussed units may be through some interfaces, or indirect coupling or communication connection between the apparatus or units, and may be electrical, mechanical, or other forms.

[0078] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0079] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0080] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0081] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for analyzing anomalies in photovoltaic panels, characterized in that, The method includes: Acquire image sensor parameters of the solar camera, real-time output power of the photovoltaic panel, current timestamp, and device latitude and longitude information; Based on the image sensor parameters, an illumination intensity analysis is performed to obtain the equivalent illumination intensity of the current environment; Based on the current timestamp and the device's latitude and longitude information, solar orientation analysis is performed to obtain solar spatial position parameters; Based on the equivalent light intensity, the solar spatial position parameters, and the preset photovoltaic panel installation attitude parameters, the output power is analyzed to obtain the theoretical output power of the photovoltaic panel at the current moment. Power anomaly analysis is performed based on the theoretical output power and real-time output power of the photovoltaic panel to obtain target power anomaly information.

2. The method according to claim 1, characterized in that, The image sensor parameters include exposure time, gain value, and gamma value; The step of performing illumination intensity analysis based on the image sensor parameters to obtain the equivalent illumination intensity of the current environment includes: The equivalent illumination intensity is obtained by performing reverse deduction based on the exposure time, the gain value, and the gamma value.

3. The method according to claim 2, characterized in that, The process of inversely extrapolating from the exposure time, the gain value, and the gamma value to obtain the equivalent illumination intensity includes: The current exposure level is obtained by multiplying the exposure time and the gain value. The reference sensitivity of the solar camera is corrected based on the gamma value to obtain the corrected sensitivity coefficient in the current state. Obtain the correlation mapping relationship between the equivalent light intensity and the exposure index; Based on the corrected sensitivity coefficient and the relevant mapping relationship, the exposure index is reverse-engineered to obtain the equivalent light intensity.

4. The method according to claim 2, characterized in that, The process of inversely extrapolating from the exposure time, the gain value, and the gamma value to obtain the equivalent illumination intensity includes: Based on the gamma value, the preset image grayscale reference value is subjected to gamma correction processing to obtain a linearized brightness reference. Obtain the physical reference sensitivity of the solar-powered camera at unity gain and unit exposure time; Based on the linearized brightness reference, the exposure time, and the gain value, the exposure ratio of the current scene relative to the physical reference sensitivity is obtained; The equivalent light intensity of the current environment is obtained by reverse calculation based on the exposure ratio.

5. The method according to claim 1, characterized in that, The solar azimuth analysis based on the current timestamp and the device's latitude and longitude information yields solar spatial position parameters, including: The current timestamp is parsed to obtain date and time information; Based on the date information, time information, and device latitude and longitude information, solar azimuth analysis is performed to obtain the solar altitude angle and solar azimuth angle, which characterize the direction of solar incidence, and serve as the solar spatial position parameters.

6. The method according to claim 1, characterized in that, The power anomaly analysis based on the theoretical output power and real-time output power of the photovoltaic panel is used to obtain target power anomaly information, including: A power deviation index is obtained by comparing the theoretical output power of the photovoltaic panel with the real-time output power of the photovoltaic panel. Obtain multiple power deviation indicators within a historical preset time period and construct deviation temporal distribution characteristics; Based on the aforementioned deviation time-series distribution characteristics, the abnormal power output state of the photovoltaic panel is classified and processed to obtain the abnormal attribution result, which is then identified as the target power abnormality information.

7. The method according to claim 6, characterized in that, The process of classifying the abnormal power output states of the photovoltaic panel based on the deviation time-series distribution characteristics to obtain anomaly attribution results includes: In response to the deviation time-series distribution characteristics showing that the power deviation index appears regularly within a specific time window each day, the abnormal attribution result is determined to be local occlusion; In response to the deviation time-series distribution characteristics showing that the power deviation index persists throughout the entire time period without obvious fluctuation patterns, the abnormal attribution result is determined to be dust accumulation or general shading. In response to the deviation time-series distribution characteristics showing that the power deviation index has been present continuously and has a constant value since the initial installation time of the equipment, the abnormal attribution result is determined to be an abnormal installation angle. In response to the deviation time-series distribution characteristics showing that the real-time output power of the photovoltaic panel suddenly drops to zero, the abnormal attribution result is determined to be a device circuit failure.

8. A photovoltaic panel anomaly analysis device, characterized in that, The device includes: The information acquisition module is used to acquire the image sensor parameters of the solar camera, the real-time output power of the photovoltaic panel, the current timestamp, and the latitude and longitude information of the device. The illumination intensity analysis module is used to perform illumination intensity analysis based on the image sensor parameters to obtain the equivalent illumination intensity of the current environment; The solar azimuth analysis module is used to perform solar azimuth analysis based on the current timestamp and the device's latitude and longitude information to obtain solar spatial position parameters. The theoretical power analysis module is used to perform output power analysis based on the equivalent irradiance, the solar spatial position parameters, and the preset photovoltaic panel installation attitude parameters to obtain the theoretical output power of the photovoltaic panel at the current moment. A power anomaly analysis model is used to perform power anomaly analysis based on the theoretical output power and real-time output power of the photovoltaic panel to obtain target power anomaly information.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the photovoltaic panel anomaly analysis method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the photovoltaic panel anomaly analysis method according to any one of claims 1 to 7.

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