Method and device for determining numerical value of solar irradiance in preset time period

Abstract

The embodiment of the invention provides a method and device for determining the numerical value of solar irradiance in a preset time period, and relates to the technical field of solar energy. The method comprises the following steps: acquiring satellite cloud picture extrapolation data information and radar extrapolation data information, and performing data fusion processing on the satellite cloud picture extrapolation data information and the radar extrapolation data information to obtain fused feature data information; inputting the fused feature data information into a weather typing model, and processing the fused feature data information based on the weather typing model to obtain cloud picture feature data information; and inputting the cloud picture feature data information into a solar irradiance model, and performing prediction according to the cloud picture feature data information based on the solar irradiance model to obtain a numerical value of the solar irradiance in a preset time period. By adopting the technical scheme, the numerical value of the solar irradiance in the preset time period can be accurately predicted, so that the precision and timeliness of the numerical value of the solar irradiance can be met.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of solar energy technology, and more particularly to a method and device for determining a numerical value of solar irradiance in a preset time period. Background Art

[0002] With the widespread application of solar energy technology, accurate inversion of solar irradiance has become the key to improving the efficiency of solar energy utilization.

[0003] Existing solar irradiance prediction methods are often based on a single data source, such as relying solely on meteorological station data or satellite cloud image data. This method may not be able to fully capture the various factors that affect solar irradiance, resulting in limited accuracy and timeliness of solar irradiance prediction.

[0004] Therefore, there is an urgent need for a method for determining the value of solar irradiance in a preset time period, which can accurately predict the value of solar irradiance in the preset time period, and then meet the accuracy and timeliness of the value of solar irradiance, so as to achieve the purpose of real-time regulation of solar power generation system. Summary of the invention

[0005] In order to solve the above technical problem or at least partially solve the above technical problem, the embodiments of the present disclosure provide a method and device for determining the numerical value of solar irradiance in a preset time period.

[0006] A first aspect of an embodiment of the present disclosure provides a method for determining a numerical value of solar irradiance in a preset time period, the method comprising:

[0007] Acquire satellite cloud image extrapolation data information and radar extrapolation data information, and perform data fusion processing on the satellite cloud image extrapolation data information and the radar extrapolation data information to obtain fusion feature data information;

[0008] Inputting the fused feature data information into a weather classification model, and processing the fused feature data information based on the weather classification model to obtain cloud image feature data information;

[0009] The cloud image characteristic data information is input into a solar irradiance model, and prediction is performed based on the cloud image characteristic data information based on the solar irradiance model to obtain a value of solar irradiance in a preset time period.

[0010] In one example, the performing data fusion processing on the satellite cloud image extrapolated data information and the radar extrapolated data information to obtain fused feature data information includes:

[0011] Performing preliminary data processing on the satellite cloud image extrapolated data information and the radar extrapolated data information to obtain preliminary processing results;

[0012] Performing feature extraction on the preliminary processing result to obtain a feature processing result;

[0013] The feature processing result is subjected to data fusion processing to obtain the fused feature data information.

[0014] In one example, the performing preliminary data processing on the satellite cloud image extrapolated data information and the radar extrapolated data information to obtain preliminary processing results includes:

[0015] Performing temporal and / or spatial alignment processing on the satellite cloud image extrapolated data information and the radar extrapolated data information to obtain a first processing result;

[0016] Performing image enhancement processing on the first processing result to obtain a second processing result;

[0017] Perform image segmentation processing on the second processing result to obtain the preliminary processing result.

[0018] In one example, before obtaining satellite cloud image extrapolation data information and radar extrapolation data information, the method further includes:

[0019] Acquire meteorological satellite cloud image data information, analyze and process the meteorological satellite cloud image data information based on the SimVP model, and obtain satellite cloud image extrapolated data information;

[0020] Multi-level radar echo data information is obtained, and prediction processing is performed on the multi-level radar echo data based on a convolutional long short-term memory neural network model to obtain radar extrapolation data information.

[0021] In one example, the method further includes:

[0022] According to the value of the solar irradiance in the preset time period, the power value of the preset time period is calculated; wherein the power value of the preset time period is used to characterize the parameter value of the solar power generation system.

[0023] A second aspect of an embodiment of the present disclosure provides a device for determining a numerical value of solar irradiance in a preset time period, the device comprising:

[0024] An acquisition module is used to acquire satellite cloud image extrapolation data information and radar extrapolation data information, and perform data fusion processing on the satellite cloud image extrapolation data information and the radar extrapolation data information to obtain fusion feature data information;

[0025] A first processing module is used to input the fused feature data information into a weather classification model, and process the fused feature data information based on the weather classification model to obtain cloud image feature data information;

[0026] The prediction module is used to input the cloud image characteristic data information into a solar irradiance model, and make a prediction based on the cloud image characteristic data information based on the solar irradiance model to obtain the value of the solar irradiance in a preset time period.

[0027] A third aspect of an embodiment of the present disclosure provides an electronic device, comprising: a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the method of the first aspect above.

[0028] A fourth aspect of an embodiment of the present disclosure provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the method of the first aspect described above can be implemented.

[0029] A fifth aspect of an embodiment of the present disclosure provides a computer program product, including a computer program, which implements the method described in the first aspect when executed by a processor.

[0030] The disclosed embodiment provides a method and device for determining the value of solar irradiance in a preset time period, the method comprising: obtaining satellite cloud image extrapolation data information and radar extrapolation data information, performing data fusion processing on the satellite cloud image extrapolation data information and the radar extrapolation data information to obtain fused feature data information; inputting the fused feature data information into a weather classification model, processing the fused feature data information based on the weather classification model to obtain cloud image feature data information; inputting the cloud image feature data information into a solar irradiance model, predicting based on the cloud image feature data information based on the solar irradiance model to obtain the value of solar irradiance in a preset time period. By adopting this technical solution, the value of solar irradiance in a preset time period can be accurately predicted, thereby meeting the accuracy and timeliness of the value of solar irradiance, so as to achieve the purpose of real-time regulation of the solar power generation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0033] Figure 1It is a flow chart of a method for determining a numerical value of solar irradiance in a preset time period provided by an embodiment of the present disclosure;

[0034] Figure 2 It is a flow chart of a method for determining a numerical value of solar irradiance in a preset time period provided by an embodiment of the present disclosure;

[0035] Figure 3 It is a structural schematic diagram of a device for determining a numerical value of solar irradiance in a preset time period provided by an embodiment of the present disclosure;

[0036] Figure 4 It is a structural schematic diagram of an electronic device in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0037] In order to more clearly understand the above-mentioned objectives, features and advantages of the present disclosure, the scheme of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0038] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.

[0039] Figure 1 1 is a flow chart of a method for determining the value of solar irradiance in a preset time period provided by an embodiment of the present disclosure, and the method can be performed by an electronic device. The electronic device can be exemplarily understood as a device such as a mobile phone, a tablet computer, a laptop computer, a desktop computer, a smart TV, etc. Figure 1 As shown, the method provided in this embodiment includes the following steps:

[0040] S101, obtaining satellite cloud image extrapolation data information and radar extrapolation data information, performing data fusion processing on the satellite cloud image extrapolation data information and the radar extrapolation data information, and obtaining fusion feature data information.

[0041] In one example, the satellite cloud image extrapolation data information is used to characterize the satellite cloud image prediction results of a preset time period. The radar extrapolation data information is used to characterize the predicted radar echo data of a preset time period. After obtaining the satellite cloud image extrapolation data information and the radar extrapolation data information, the satellite cloud image extrapolation data information and the radar extrapolation data information are subjected to data fusion processing in time and / or space to obtain fused feature data information.

[0042] In one example, the preset time period may be 0 to 4 hours in the future.

[0043] S102: input the fused feature data information into a weather classification model, and process the fused feature data information based on the weather classification model to obtain cloud map feature data information.

[0044] In one example, the weather classification model is used to determine the weather type in a preset time period and extract irradiance characteristic information, wherein the weather type in the preset time period may be sunny, cloudy, rainy or foggy.

[0045] S103, inputting the cloud image characteristic data information into a solar irradiance model, and performing prediction based on the cloud image characteristic data information based on the solar irradiance model to obtain a value of solar irradiance in a preset time period.

[0046] In one example, the solar irradiance model is used to predict the solar irradiance value in a preset time period, wherein the solar irradiance model is obtained after training based on historical weather types, historical power station information, historical automatic station data, historical high-precision model forecasts, historical three-dimensional geographic information data, historical cloud map feature data information, historical solar altitude angles, and historical azimuth angle data.

[0047] The disclosed embodiment provides a method for determining the value of solar irradiance in a preset time period, the method comprising: obtaining satellite cloud image extrapolation data information and radar extrapolation data information, performing data fusion processing on the satellite cloud image extrapolation data information and the radar extrapolation data information to obtain fused feature data information; inputting the fused feature data information into a weather classification model, processing the fused feature data information based on the weather classification model to obtain cloud image feature data information; inputting the cloud image feature data information into a solar irradiance model, predicting based on the cloud image feature data information based on the solar irradiance model to obtain the value of solar irradiance in a preset time period. By adopting this technical solution, the value of solar irradiance in a preset time period can be accurately predicted, thereby meeting the accuracy and timeliness of the value of solar irradiance, so as to achieve the purpose of real-time regulation of the solar power generation system.

[0048] Figure 2 The flowchart of a method for determining the value of solar irradiance in a preset time period provided by an embodiment of the present disclosure is shown. The embodiment of the present disclosure is optimized on the basis of the above embodiment, and the embodiment of the present disclosure can be combined with various optional solutions in one or more of the above embodiments.

[0049] like Figure 2 As shown, the method for determining the numerical value of the solar irradiance in the preset time period may include the following steps:

[0050] S201, obtaining meteorological satellite cloud image data information, analyzing and processing the meteorological satellite cloud image data information based on the SimVP model, and obtaining satellite cloud image extrapolation data information.

[0051] In one example, the SimVP model is a model architecture that includes a convolutional attention module, a bidirectional autoencoder module, and a 3D self-attention spatiotemporal LSTM prediction module. This model architecture can fully extract features and use these features to predict satellite cloud image extrapolation data information. In this embodiment, the SimVP model is obtained after training based on historical meteorological satellite cloud image data information.

[0052] In one example, historical meteorological satellite cloud image data information of one image every 30 minutes is input into the SimVP model, and CBAM is used to extract features from the historical meteorological satellite cloud image data information based on the SimVP model. The CBAM module includes a channel attention mechanism and a spatial attention mechanism. The channel attention module focuses on the correlation between different channels, while the spatial attention module captures the spatial correlation between different pixel positions in the feature map. The features extracted by CBAM are input into the bidirectional autoencoder module, and the spatiotemporal features after learning and fusion are used to predict the satellite cloud image extrapolation data information of the preset time period. In the 3D self-attention spatiotemporal LSTM, 3D convolution and self-attention mechanism are integrated into LSTM to better extract the temporal and spatial information of meteorological satellite cloud image data information.

[0053] S202, obtaining multi-level radar echo data information, and performing prediction processing on the multi-level radar echo data based on a convolutional long short-term memory neural network model to obtain radar extrapolation data information.

[0054] In one example, based on the spatiotemporal correlation of multi-level radar echo data information, a convolutional long short-term memory neural network model is used to determine the radar extrapolated data information. Specifically, the amount of solar radiation is mainly affected by clouds. Low clouds and medium clouds (about 0 to 7 kilometers in height) directly block solar radiation, so radar echo data at an altitude of 0.5-7km is selected, generally 0.5km as a layer, with a total of 14 levels of radar echo data information.

[0055] In one example, a convolutional long short-term memory neural network model models the echo motion evolution process based on the spatiotemporal correlation of multi-level radar echo data information. An end-to-end encoder-predictor architecture is constructed as a generator for radar echo extrapolation. A convolutional neural network (CNN) structure is designed as a discriminator for adversarial training.

[0056] Specifically, the echoes of different height layers are converted into feature space and feature fusion is performed. The input 14-level echo is converted into feature space and feature mapping is performed using two convolutional layers. The first convolutional layer: the stride is 1×1, and the input echo is mapped from the image space to the feature space. The second convolutional layer: the stride is 2×2, and the features from different levels are integrated. The rectified linear unit (ReLU) activation function is used after the two convolutional layers to enhance the nonlinear feature extraction capability of the model. The evolution process of spatiotemporal echoes is modeled using ConvLSTM. The model contains three sigmoid gate functions (input gate, forget gate, and output gate). The input gate is used to control the addition of new information to the memory unit to model the process of echo growth and development; the forget gate is used to control the forgetting and discarding of past information to model the process of echo dissipation; the output gate is used to control the output update hidden state of the information in the memory unit. Echo feature separation and extrapolation: The echo features of the extrapolated height layer are separated from the ConvLSTM output and mapped back to the image space to obtain the final extrapolated echo. Specifically, two deconvolution layers are used. The stride of the first deconvolution layer is 2×2, which is used to separate the extrapolated height layer echo features, and the stride of the second convolution layer is 1×1, which is used to map features to images. The adversarial training strategy of the Generative Adversarial Network (GAN) is introduced, and a generator (encoder-predictor) and a CNN discriminator are used for adversarial training to obtain the final predicted echo.

[0057] Specifically, initialization: Initialize the parameters of the generator (encoder-predictor) and the discriminator. Randomly extract a batch of real radar echo data from the data set, and use the generator to generate a batch of fake radar echo data. Calculate the output of the discriminator for real data and the output for generated data, and calculate the loss function of the discriminator. Generally, binary cross entropy loss is used, and the formula is as follows:

[0058] loss D = -E(log(D real ))-E(log(1-D fake ));

[0059] Among them, Dreal is the output of real data, Dfake is the output of generated data, and lossD is the loss function of the discriminator.

[0060] Use the back-propagation algorithm to update the parameters of the discriminator. The optimizer can be SGD, Adam, etc. Train the generator: Use the generator to generate a batch of fake radar echo data, calculate the output of the discriminator for the generated data, and calculate the loss function of the generator, which is usually also a binary cross entropy loss. The formula is as follows:

[0061] loss G = -E(log(D fake ));

[0062] Among them, lossG is the loss function of the generator, Dfake is the output of the discriminator on the generated data, and fake_data is a batch of fake radar echo data generated by the generator.

[0063] Use the back propagation algorithm to update the parameters of the generator. The optimizer can be SGD, Adam, etc. Perform multiple rounds of iterative training until the generator and discriminator reach a relative equilibrium state. By monitoring the loss functions of the generator and discriminator, determine whether the model has converged. At the same time, use the test set to verify the echo extrapolation effect to ensure the quality of the generated radar extrapolation data information.

[0064] S203, obtaining satellite cloud image extrapolation data information and radar extrapolation data information.

[0065] In an example, this step can refer to the content of step S101, which will not be repeated here.

[0066] S204: Perform preliminary data processing on the satellite cloud image extrapolation data information and the radar extrapolation data information to obtain preliminary processing results.

[0067] In one example, preliminary data processing is performed on satellite cloud image extrapolation data information and radar extrapolation data information to obtain preliminary processing results, including:

[0068] Performing temporal and / or spatial alignment processing on the satellite cloud image extrapolation data information and the radar extrapolation data information to obtain a first processing result;

[0069] Performing image enhancement processing on the first processing result to obtain a second processing result;

[0070] Perform image segmentation processing on the second processing result to obtain a preliminary processing result.

[0071] In one example, the satellite cloud image in the satellite cloud image extrapolation data information and the radar image in the radar extrapolation data information are aligned in time and / or space, and the images are converted into a unified format to obtain a first processing result. The image contrast of the first processing result is adjusted, the cloud image features are enhanced, and a smoothing filter is used to remove noise to obtain a second processing result.

[0072] In one example, edge detection algorithms such as Canny edge detection or threshold segmentation algorithms such as Otsu threshold method are used to separate clouds from the background. Different cloud regions are marked using region growing algorithms or connected component analysis to obtain preliminary processing results.

[0073] S205: Perform feature extraction on the preliminary processing result to obtain a feature processing result.

[0074] In one example, corresponding cloud image features, such as the thickness, density, and moving speed of the cloud layer, are extracted from the segmented satellite cloud image and radar image in the preliminary processing results to obtain feature processing results.

[0075] S206: Perform data fusion processing on the feature processing results to obtain fused feature data information.

[0076] In one example, features extracted from satellite cloud images and radar images are fused to obtain fused feature data information, and the changes in cloud features in time and space are analyzed.

[0077] S207, inputting the fused feature data information into a weather classification model, processing the fused feature data information based on the weather classification model, and obtaining cloud map feature data information.

[0078] In an example, this step can refer to the content of step S102, which will not be repeated here.

[0079] S208, inputting the cloud image characteristic data information into a solar irradiance model, and performing prediction based on the cloud image characteristic data information based on the solar irradiance model to obtain a value of solar irradiance in a preset time period.

[0080] In one example, for different weather types, the system sets different adjacency matrices, and combines high-precision meteorological data, three-dimensional geographic information data, power station information, and solar altitude and azimuth data, etc., and uses the spatiotemporal graph convolutional network prediction model to construct solar irradiance models for sunny, cloudy, rainy, and foggy days, respectively, to obtain the solar irradiance values ​​for the preset time period. Specifically, according to the weather type classification, the collected data set is divided into training set, validation set, and test set, with data accounting for 70%, 20%, and 10%, respectively. Using the divided training set data, the spatiotemporal graph convolutional network models for sunny, cloudy, rainy, and foggy days are trained respectively. Using the validation set data, the hyperparameters of the solar irradiance model are adjusted. The hyperparameter adjustment includes learning rate, number of network layers, convolution kernel size, etc. By adjusting these parameters, the performance of the solar irradiance model can be optimized. Using the test set data, the forecast quality of the trained solar irradiance model is evaluated. Evaluation indicators can include root mean square error (RMSE), mean absolute error (MAE), TS indicators, etc. The lower the RMSE and MAE, the better, and the higher the TS, the better. According to the evaluation results of the test set, the solar irradiance model may need to be further optimized or adjusted, such as through data enhancement, model structure improvement or introduction of more features, to improve the prediction accuracy of the solar irradiance model.

[0081] S209, calculating a power value for a preset time period according to the value of the solar irradiance for the preset time period; wherein the power value for the preset time period is used to characterize a parameter value of a solar power generation system.

[0082] In one example, the preset time period can be the next 0 to 4 hours. After determining the value of the solar irradiance for the next 0 to 4 hours, the power value for the next 0 to 4 hours is calculated. Based on the relationship between the power value and the standard power value, the power value is adjusted to achieve the purpose of controlling the solar power generation system.

[0083] The disclosed embodiment provides a method for determining the value of solar irradiance in a preset time period, the method comprising: obtaining meteorological satellite cloud image data information, analyzing and processing the meteorological satellite cloud image data information based on the SimVP model to obtain satellite cloud image extrapolation data information, obtaining multi-level radar echo data information, predicting and processing the multi-level radar echo data based on the convolutional long short-term memory neural network model to obtain radar extrapolation data information, and obtaining satellite cloud image extrapolation data information and radar extrapolation data information. Performing preliminary data processing on the satellite cloud image extrapolation data information and the radar extrapolation data information to obtain preliminary processing results. Performing feature extraction on the preliminary processing results to obtain feature processing results. Performing data fusion processing on the feature processing results to obtain fused feature data information. Inputting the fused feature data information into the weather classification model, processing the fused feature data information based on the weather classification model to obtain cloud image feature data information. Inputting the cloud image feature data information into the solar irradiance model, predicting based on the cloud image feature data information based on the solar irradiance model, and obtaining the value of solar irradiance in the preset time period. According to the value of the solar irradiance in the preset time period, the power value of the preset time period is calculated; wherein the power value of the preset time period is used to characterize the parameter value of the solar power generation system. The technical solution can more accurately predict the value of the solar irradiance in the preset time period, thereby achieving the purpose of controlling the solar power generation system.

[0084] Figure 3 1 is a schematic diagram of a device for determining the value of solar irradiance in a preset time period provided by an embodiment of the present disclosure. The device for determining the value of solar irradiance in a preset time period can be understood as the above-mentioned electronic device or a part of the functional modules in the above-mentioned electronic device. Figure 3 As shown, the numerical determination device 30 of the solar irradiance in the preset time period includes:

[0085] The acquisition module 301 is used to acquire satellite cloud image extrapolation data information and radar extrapolation data information, and perform data fusion processing on the satellite cloud image extrapolation data information and the radar extrapolation data information to obtain fused feature data information.

[0086] The first processing module 302 is used to input the fused feature data information into the weather classification model, and process the fused feature data information based on the weather classification model to obtain cloud image feature data information.

[0087] The prediction module 303 is used to input the cloud image characteristic data information into the solar irradiance model, and make a prediction based on the cloud image characteristic data information based on the solar irradiance model to obtain the value of the solar irradiance in a preset time period.

[0088] In one example, the acquisition module 301 includes:

[0089] The first processing submodule is used to perform preliminary data processing on the satellite cloud image extrapolation data information and the radar extrapolation data information to obtain preliminary processing results.

[0090] The extraction submodule is used to extract features from the preliminary processing results to obtain feature processing results.

[0091] The second processing submodule is used to perform data fusion processing on the feature processing results to obtain fused feature data information.

[0092] In one example, the first processing submodule is specifically configured to:

[0093] The satellite cloud image extrapolation data information and the radar extrapolation data information are aligned in time and / or space to obtain a first processing result.

[0094] Perform image enhancement processing on the first processing result to obtain a second processing result.

[0095] Perform image segmentation processing on the second processing result to obtain a preliminary processing result.

[0096] In one example, the apparatus 30 further includes:

[0097] The second processing module 304 is used to obtain meteorological satellite cloud image data information, analyze and process the meteorological satellite cloud image data information based on the SimVP model, and obtain satellite cloud image extrapolation data information.

[0098] The third processing module 305 is used to obtain multi-level radar echo data information, and perform prediction processing on the multi-level radar echo data based on a convolutional long short-term memory neural network model to obtain radar extrapolation data information.

[0099] In one example, the apparatus 30 further includes:

[0100] The calculation module 306 is used to calculate the power value of the preset time period according to the value of the solar irradiance of the preset time period; wherein the power value of the preset time period is used to represent the parameter value of the solar power generation system.

[0101] The device provided in this embodiment can execute the method of any of the above embodiments, and its execution method and beneficial effects are similar, which will not be repeated here.

[0102] An embodiment of the present disclosure further provides an electronic device, which includes: a memory, in which a computer program is stored; and a processor, which is used to execute the computer program. When the computer program is executed by the processor, the method of any of the above embodiments can be implemented.

[0103] For example, Figure 4 Schematic diagram of the structure of an electronic device in the embodiment of the present disclosure. Figure 4 , which shows a schematic diagram of the structure of an electronic device 1000 suitable for implementing the embodiment of the present disclosure. The electronic device 1000 in the embodiment of the present disclosure may include but is not limited to mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 4 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0104] like Figure 4 As shown, the electronic device 1000 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 1001, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1008 into a random access memory (RAM) 1003. In the RAM 1003, various programs and data required for the operation of the electronic device 1000 are also stored. The processing device 1001, the ROM 1002, and the RAM 1003 are connected to each other via a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0105] Typically, the following devices may be connected to the I / O interface 1005: an input device 1006 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 1007 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 1008 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 may allow the electronic device 1000 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 4 The electronic device 1000 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0106] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device 1009, or installed from a storage device 1008, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0107] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0108] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0109] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0110] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains satellite cloud image extrapolation data information and radar extrapolation data information, performs data fusion processing on the satellite cloud image extrapolation data information and the radar extrapolation data information, and obtains fusion feature data information; inputs the fusion feature data information into the weather classification model, processes the fusion feature data information based on the weather classification model, and obtains cloud image feature data information; inputs the cloud image feature data information into the solar irradiance model, and predicts the cloud image feature data information based on the solar irradiance model to obtain the value of solar irradiance in a preset time period. The computer program code for performing the operation of the present disclosure can be written in one or more programming languages ​​or a combination thereof, and the programming languages ​​include but are not limited to object-oriented programming languages—such as Java, Smalltalk, C++, and also conventional procedural programming languages—such as "C" language or similar programming languages. The program code can be executed completely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0111] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0112] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit does not, in some cases, limit the unit itself.

[0113] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0114] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0115] The embodiments of the present disclosure also provide a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the method of any of the above embodiments can be implemented. The execution method and beneficial effects are similar and will not be repeated here.

[0116] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0117] The above are only specific embodiments of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to these embodiments herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for determining the value of solar irradiance in a preset time period, characterized in that: The method comprises: Acquire satellite cloud image extrapolation data information and radar extrapolation data information, and perform data fusion processing on the satellite cloud image extrapolation data information and the radar extrapolation data information to obtain fusion feature data information; Inputting the fused feature data information into a weather classification model, and processing the fused feature data information based on the weather classification model to obtain cloud image feature data information; The cloud image characteristic data information is input into a solar irradiance model, and a prediction is performed based on the cloud image characteristic data information based on the solar irradiance model to obtain a value of solar irradiance in a preset time period.

2. The method according to claim 1, characterized in that The step of fusing the satellite cloud image extrapolated data information and the radar extrapolated data information to obtain fused feature data information includes: Performing preliminary data processing on the satellite cloud image extrapolated data information and the radar extrapolated data information to obtain preliminary processing results; Performing feature extraction on the preliminary processing result to obtain a feature processing result; The feature processing result is subjected to data fusion processing to obtain the fused feature data information.

3. The method according to claim 2, characterized in that The preliminary processing of the satellite cloud image extrapolated data information and the radar extrapolated data information to obtain preliminary processing results includes: Performing temporal and / or spatial alignment processing on the satellite cloud image extrapolated data information and the radar extrapolated data information to obtain a first processing result; Performing image enhancement processing on the first processing result to obtain a second processing result; Perform image segmentation processing on the second processing result to obtain the preliminary processing result.

4. The method according to claim 1, characterized in that: Before obtaining the satellite cloud image extrapolation data information and the radar extrapolation data information, the method further includes: Acquire meteorological satellite cloud image data information, analyze and process the meteorological satellite cloud image data information based on the SimVP model, and obtain satellite cloud image extrapolated data information; Multi-level radar echo data information is obtained, and prediction processing is performed on the multi-level radar echo data based on a convolutional long short-term memory neural network model to obtain radar extrapolation data information.

5. The method according to claim 1, characterized in that: The method further comprises: According to the value of the solar irradiance in the preset time period, the power value of the preset time period is calculated; wherein the power value of the preset time period is used to characterize the parameter value of the solar power generation system.

6. A device for determining the value of solar irradiance in a preset time period, characterized in that: The device comprises: An acquisition module is used to acquire satellite cloud image extrapolation data information and radar extrapolation data information, and perform data fusion processing on the satellite cloud image extrapolation data information and the radar extrapolation data information to obtain fusion feature data information; A first processing module is used to input the fused feature data information into a weather classification model, and process the fused feature data information based on the weather classification model to obtain cloud image feature data information; The prediction module is used to input the cloud image characteristic data information into a solar irradiance model, and make a prediction based on the cloud image characteristic data information based on the solar irradiance model to obtain the value of the solar irradiance in a preset time period.

7. The device according to claim 6, characterized in that The acquisition module comprises: The first processing submodule is used to perform preliminary data processing on the satellite cloud image extrapolation data information and the radar extrapolation data information to obtain preliminary processing results; An extraction submodule, used for performing feature extraction on the preliminary processing result to obtain a feature processing result; The second processing submodule is used to perform data fusion processing on the feature processing results to obtain the fused feature data information.

8. An electronic device, characterized in that: include: A processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the method according to any one of claims 1 to 5.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

10. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 5 when being executed by a processor.

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