52 results about "Sky imaging" patented technology

The invention relates to the field of solar photovoltaic power generation, in particular to a multi-mode ultra-short-term solar irradiation prediction method and system based on a sky image, and the method comprises the steps: collecting a sky image and meteorological data shot by an all-sky imager; preprocessing the collected image, extracting cloud layer information through graying and a normalized red-blue ratio, and extracting dynamic characteristics by using a dense optical flow method; a 3D-ResNet model is adopted to extract spatial features of the image, and an Informer model is utilized to perform time sequence coding on historical irradiation data; and through a multi-modal interaction fusion matrix based on an attention mechanism, carrying out alignment fusion on the spatial features of the sky image and the time sequence features of the irradiation, and generating a predicted value of the solar irradiation. According to the invention, the precision of ultra-short-term solar irradiation prediction can be improved, and especially in a time range of 5-10 minutes, the method has relatively strong real-time performance and accuracy.

The invention discloses an all-sky cloud imaging real-time simulation method of a three-dimensional structure, which is used for realizing real-time rapid simulation of a cloud image with set spatial resolution per hour, and comprises the following steps: obtaining basic geographic information data of a research area; carrying out background field data fusion; interpolating the ground meteorological data collected in real time to the grid points of the research area by adopting an interpolation method; obtaining surface meteorological analysis and background field data, and realizing three-dimensional temperature analysis according to a static relation; obtaining an initial guessing field of three-dimensional cloud analysis, and obtaining a three-dimensional cloud analysis field in combination with three-dimensional temperature analysis; an initial three-dimensional water vapor analysis field is obtained according to the background field data, and three-dimensional water vapor analysis is realized by performing optimization iteration solution on a target function; obtaining soil humidity analysis according to the surface meteorological analysis and the three-dimensional cloud analysis field; according to the surface meteorological analysis, the three-dimensional temperature analysis, the three-dimensional cloud analysis field and the three-dimensional water vapor analysis, derivative product analysis is achieved; and simulating to obtain all-sky imaging data.

The invention discloses a solar irradiance prediction system and method based on dual attention multi-modal fusion. The method comprises the following steps: performing spatial feature extraction on a sky imaging observation map after preprocessing and time synchronization by using a MobileNetV2 network to obtain a sky imaging time sequence feature sequence; inputting the sky imaging time sequence feature sequence into a DAT model to obtain a global sky imaging time sequence feature sequence, a local sky imaging time sequence feature sequence and a local convolution position coding residual term; obtaining an aggregated sky imaging time sequence feature sequence according to the global sky imaging time sequence feature sequence, the local sky imaging time sequence feature sequence and the local convolution position coding residual item; performing time-dependent modeling on the meteorological observation data by adopting a long short-term memory network to obtain a meteorological observation data feature sequence; and performing feature fusion on the aggregated sky imaging time sequence feature sequence and the meteorological observation data feature sequence through a trans-attention mechanism to obtain a fused joint feature sequence for solar irradiance prediction.

The invention provides a photovoltaic ultra-short-term power prediction method based on quantum. The method comprises the steps of performing quantization processing on historical actual power, historical weather information, historical sky imaging information and weather forecast information of a photovoltaic power station to be predicted to generate a photovoltaic power station power wave function, a historical weather information wave function, a historical sky imaging wave function and a weather forecast wave function; constructing a time correlation function according to the historical weather information wave function, the historical sky imaging wave function and the weather forecast wave function; constructing an interaction function between information at different moments; and constructing a power prediction model based on the weather forecast wave function, the time correlation function, the interaction function and the photovoltaic power station power wave function so as to carry out photovoltaic ultra-short-term power prediction. Therefore, the influence of various factors on the photovoltaic power can be accurately captured based on the quantum information processing technology, so that the accuracy and reliability of photovoltaic power prediction are effectively improved.

The invention relates to the technical field of photovoltaic power prediction, in particular to an ultra-short-term prediction system and method based on image analysis, and the system comprises an all-sky imaging data processing module, a cloud layer dynamic feature calculation module, a solar irradiance change prediction module, a photovoltaic power generation power calculation module and a prediction model optimization training module. According to the method, the cloud layer thickness, transparency, movement direction and coverage rate change are calculated, the cloud layer dynamic characteristic analysis precision is enhanced, and the radiation scattering coefficient is calculated in combination with the direct solar light shielding ratio, the cloud layer characteristics and the solar angle, so that the irradiance calculation is more accurate, the illumination change rate is analyzed, and the short-term irradiance trend is dynamically adjusted; the total power is calculated and the shielding influence is adjusted in combination with component arrangement, so that the power prediction is more practical, the input data is normalized, the time sequence sample is enhanced, the dynamic adjustment of the model parameters is optimized, and the adaptive capacity and the prediction stability are improved.

The invention discloses a multi-mode fusion photovoltaic power prediction method for all-sky imager data. The method comprises the following steps: obtaining time sequence sky image data and photovoltaic array historical photovoltaic power data in an all-sky imager; extracting features of the time sequence sky image data and the historical photovoltaic power data to obtain modal feature information of the time sequence sky image data and modal feature information of the historical photovoltaic power data; fusing the modal feature information of the time sequence sky image data and the modal feature information of the historical photovoltaic power data to obtain a feature matrix; obtaining a final potential feature according to the feature matrix; training by utilizing the final potential features to obtain a photovoltaic power prediction model; and predicting the photovoltaic power by using the photovoltaic power prediction model. According to the method, feature extraction processing is carried out on the time sequence sky image data and the historical photovoltaic power data, and prediction errors caused by intermittency and uncertainty of solar power generation are effectively reduced.

The present invention discloses a full-field refined DNI prediction method. At least two total-sky imagers are used to determine the actual position of a cloud, and then a shadow position is determined on the basis of a solar angle; and the thickness of the cloud is determined by means of the imaging brightness of the cloud, and then a DNI value is predicted. The method specifically comprises the following steps: performing cloud identification, cloud image speed calculation, cloud actual-position calculation, cloud / shadow actual-speed calculation, shadow position prediction, cloud thickness extraction, DNI mapping, and DNI prediction. In the method, at least two total-sky imagers or pinhole cameras are used to perform a DNI prediction operation, and a DNI change at each specific position in a heliostat field can be accurately predicted, such that the power generation efficiency is improved.

The invention provides a multi-climate zone-oriented photovoltaic car shed assembly inclination angle adjusting method and system, and the method comprises the steps: obtaining a low-altitude topographic cumulus real-time distribution image and sky brightness data through an all-sky imager, and combining the speed and direction of valley wind collected by an anemorumbometer, generating a meteorological feature data set including a cloud body edge, a shadow boundary and a moving path; extracting a shadow coverage state of the high-altitude shed from the shadow evolution time sequence, if the high-altitude shed is not in shadow coverage, determining that a solar panel of the high-altitude shed maintains a target direct light receiving inclination angle, and generating an inclination angle maintaining instruction; the inclination angle adjusting instruction and the inclination angle maintaining instruction are distributed to solar panel control units of low-altitude and high-altitude carports, servo motors are driven to adjust panel angles, and inclination angle optimization is completed; and evaluating the data set according to the optimization effect, calculating the deviation between the photoelectric conversion efficiency and the preset expected scattered light capture efficiency, and updating the terrain constraint parameters according to the deviation.

The invention relates to the technical field of new energy photovoltaic power stations, and provides a numerical weather forecast data updating method and system, and the method comprises the steps: obtaining multi-source weather data and power data; carrying out downscaling processing on the meteorological satellite cloud picture data, and carrying out initial weather prediction to obtain initial weather forecast data; carrying out secondary fusion processing on the initialized meteorological forecast data, the meteorological station data and the sky imager monitoring data, and then carrying out secondary meteorological prediction to obtain multi-source meteorological fusion data; performing three-time fusion processing on the multi-source meteorological fusion data and the power data, and then performing three-time meteorological prediction to obtain historical available meteorological forecast data; sky imager monitoring data and meteorological station data in the next four hours are obtained in real time, historical available weather forecast data are corrected through the obtained data, and real-time available weather forecast data are obtained. According to the invention, high-precision numerical weather forecast data can be obtained, so that the power prediction accuracy of the photovoltaic power station is improved.

The invention relates to the field of weather forecast, and discloses a DNI prediction method based on all-sky imaging and clear sky DNI fitting, which comprises the following steps: screening clear sky DNI data from DNI historical data, fitting a clear sky DNI model, and then carrying out clear sky DNI calculation by using the clear sky DNI model obtained by fitting to obtain clear sky DNI at each time point; inputting an all-sky image obtained in real time and an all-sky image obtained in the previous one minute into the trained double-picture convolutional neural network model, and predicting to obtain a clear sky index after # imgabs0 # minute; and multiplying the predicted clear sky index of the # imgabs 1 # after the minute by the corresponding clear sky DNI to obtain a predicted DNI value of the # imgabs 2 # after the minute. The method disclosed by the invention can reflect the change of actual DNI more accurately, has better prediction accuracy and strong adaptability, can be used for tower type solar thermal power generation systems in different regions, and is beneficial to reducing the operation cost of a photo-thermal power station.

The invention provides a temperature and humidity sensor external device of an all-sky imager, and is applied to the technical field of temperature and humidity sensors. The device comprises an imager body, a mounting ring is mounted on the edge of the top of the imager body, a gear ring is connected to the outer wall of the mounting ring, a mounting seat is slidably mounted on the mounting ring, a mounting cover is mounted on the mounting seat, and a sun shade is connected to the outer wall of the mounting cover. And the revolution assembly is used for driving the sensor probe to revolve by taking the imager main body as the center. Through the arrangement of the sun shade, the sun shade can prevent sunlight from directly irradiating the sensor probe to influence the temperature measurement accuracy; through the arrangement of the revolution assembly, the mounting seat can rotate on the mounting ring by utilizing wind power, so that the sensor probe on the mounting seat continuously moves, the sensor probe is uniformly contacted with air around the imager main body, and the accuracy of temperature and humidity measurement is improved.

The invention relates to the technical field of solar radiation prediction, in particular to a satellite-ground combined ultra-short-term solar radiation prediction method and system. Comprising the following steps: S1, collecting current state data and historical big data based on solar radiation; s2, preprocessing the ground-based cloud picture and the air-based cloud picture; and S3, identifying cloud layer state data based on the constructed cloud layer identification model. And S4, the solar radiation attenuation prediction model is adopted to identify the total solar radiation attenuation rate, and a radiation attenuation table is constructed. And S5, predicting cloud layer state change data and satellite observation total radiation change data by adopting an extrapolation method, and further calculating to obtain future short-term ground observation total radiation prediction data. According to the method, by combining observation of a stationary satellite and an all-sky imager, satellite-ground combined observation of cloud conditions of different height layers is realized, the influence of cloud in a whole atmospheric column on solar short-wave radiation is fully considered, the prediction precision is maintained, the operation process is optimized, and the precision and efficiency of solar radiation attenuation prediction are improved.

The invention provides a photoelectric solar irradiance meter self-calibration device for outdoor monitoring and a calibration method, and belongs to the technical field of photovoltaic monitoring. The device comprises a sky imager, a solar irradiance meter for monitoring, a standard solar irradiance meter, a sealed darkroom, a three-dimensional adjusting bracket and a waterproof sealed box, wherein a dual-channel temperature collector, a dual-channel voltage collector and a control, data processing and storage module are integrated in the waterproof sealed box. The sealed darkroom normally protects the standard irradiance meter to prevent the standard irradiance meter from aging and dust deposition; the dual-channel collector synchronously collects temperature and voltage of a standard solar irradiance meter for monitoring, and supports a calibration algorithm to realize dynamic updating of sensitivity; the three-dimensional adjusting bracket ensures that the device is parallel to the mounting plane of the photovoltaic module, and the angle response matching degree is improved; the sky imager judges the calibration weather condition to guarantee the effectiveness of the calibration environment; the control module realizes automation of a self-calibration process, adapts to an outdoor long-term monitoring scene, and realizes continuous and accurate measurement of photovoltaic solar irradiance.

1. Name of the product in this design: Intelligent All-Sky Imager. 2. Purpose of this design: This design is used for all-weather intelligent observation of cloud cover and cloud base, and directly outputs cloud cover information and cloud height information. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

The present application relates to the technical field of photovoltaic power generation, and particularly relates to an ultra-short-term photovoltaic power prediction method, system, device and medium. The present application fuses two types of heterogeneous observation data of all-sky imagers and geostationary satellites, respectively calculates cloud transmittance time series of different historical windows, utilizes the real-time and non-lagged characteristics of all-sky imagers to capture minute-level cloud changes, and utilizes the wide-range observation capability of geostationary satellites to obtain the macro trend of cloud movement, thereby effectively making up for the deficiencies of a single data source in timeliness and spatial coverage. On this basis, a pre-trained time series prediction model is used to estimate the cloud transmittance of a future target period, and is further converted into ground surface irradiance and photovoltaic power, which can significantly improve the accuracy and stability of ultra-short-term power prediction, and provide more reliable technical support for power grid dispatching and photovoltaic power station operation.

This invention relates to the field of cloud tracking technology, specifically to a cloud tracking visual recognition photovoltaic power monitoring device, comprising an all-sky imager, a protective component, a blocking component, and a cooling component on the outer side of the all-sky imager; the protective component includes a protective shell, and a fixing ring is provided inside the protective shell, with a gear ring rotatably embedded inside the fixing ring, and a protective lens fixedly connected to the outer surface of the gear ring, the protective lens being a spherical fisheye composite beam splitter; the purpose of this invention is to solve the problem that existing all-sky imagers, during long-term outdoor operation, have their core imaging lenses directly exposed to complex external environments, lacking a stable and long-lasting physical isolation protection system, and the lens surface is easily contaminated with various pollutants such as sand, salt spray, bird droppings, and insect carcasses.

The present invention relates to the field of weather forecasting and discloses a DNI prediction method based on full-sky imaging and clear-sky DNI fitting. The method comprises the following steps: filtering clear-sky DNI data from historical DNI data, fitting a clear-sky DNI model, and then using the fitted clear-sky DNI model to calculate the clear-sky DNI to obtain the clear-sky DNI at each time point; inputting the real-time full-sky imaging and the full-sky imaging of the preceding minute into a trained dual-image convolutional neural network model to predict the clear-sky index for the next minute; and multiplying the predicted clear-sky index for the next minute by the corresponding clear-sky DNI to obtain the predicted DNI value for the next minute. The method disclosed in the present invention can more accurately reflect actual DNI changes, has better prediction accuracy, and is highly adaptable. It can be used in tower-type solar thermal power generation systems in different regions, helping to reduce the operating costs of concentrated solar power plants.

The utility model discloses an integrated all-sky meteorological monitoring device, which comprises a box body, an ultrasonic meteorological instrument, an all-sky imager, a total solar radiation sensor, a power supply module and a communication module, and is characterized in that the ultrasonic meteorological instrument is arranged on one side of the top of the box body, and the all-sky imager is arranged on the other side of the top of the box body; a total solar radiation sensor is arranged along the horizontal direction of the top of the box body and the inclination direction of the front side of the box body respectively, the power supply module and the communication module are arranged in the box body, and the ultrasonic meteorological instrument, the all-sky imager and the total solar radiation sensors are electrically connected with the power supply module and the communication module respectively. The ultrasonic meteorological instrument, the all-sky imager, the total solar radiation sensor, the power supply module, the communication module and other components are integrated on the box body, the portability is high, the use is flexible, the field installation and debugging are convenient, and the installation and use efficiency is further improved.

The invention relates to the technical field of cloud cover recognition, and discloses a double-camera all-sky imager cloud cover recognition system, which comprises a visible light camera, an infrared camera, a graphics processor connected with the visible light camera and the infrared camera, and a deep learning framework connected with the graphics processor, according to the system, an SSTD-Unet cloud cover segmentation network is adopted to train independent models for visible light and infrared images, and the SSTD-Unet cloud cover segmentation network is of a symmetrical structure composed of a down-sampling coding part, a bottleneck part and an up-sampling decoding part. The cloud cover identification system can realize stable and high-precision cloud cover identification under various weather and illumination conditions, and compared with a traditional threshold segmentation method, the cloud cover identification system has the advantages that the performance is obviously improved, and a reliable solution is provided for realizing high-precision, full-automatic and all-weather cloud cover identification.

The invention relates to the technical field of intelligent traffic, in particular to a highway intelligent sensing and real-time monitoring system, which comprises an intelligent sensing end, a monitoring end and a monitoring end, and is characterized in that the intelligent sensing end comprises a PTZ (Pan / Tilt / Zoom) camera and an all-sky imager; the monitoring cloud platform is in real-time communication with the intelligent sensing end, and comprises a vehicle detection model carrying a neural network algorithm, a road detection model carrying the neural network algorithm, an imaging adjustment module, a sample set updating module, a plan updating module and an alarm management module; the method is used for judging whether vehicle stop, vehicle congestion and slow driving, vehicle traffic accidents, vehicle retrograde driving, road flaws, road fire disasters and road spilled objects occur in the road images or not, and then alarming and plan display are carried out. According to the invention, high-accuracy detection of various road events is realized, and effective support is provided for road traffic safety management and operation.

The invention provides a carbon flux metering inversion method and system for a Saggohm new energy base, and the method comprises the steps: obtaining the physical parameters, topographic data and image data of the Saggohm new energy base, and building a three-dimensional geometric model through a three-dimensional reconstruction technology; performing space grid processing on the model area to generate an LBM computational grid; inputting the image data into the trained ground parameter model, and outputting a ground boundary parameter and a ground source item parameter of each grid unit; cloud layer information is monitored through an all-sky imager, and ground irradiation intensity distribution is calculated by adopting a Monte Carlo method in combination with a sun trajectory tracking model; acquiring an atmosphere input boundary parameter and a source item parameter; solving the LBM model to obtain carbon dioxide concentration, flow velocity, temperature and pollutant concentration distribution; and the carbon flux of the base is calculated based on the simulation result, so that high-precision, low-cost and spatially continuous inversion calculation of the large-range carbon flux of the Sagomean region is realized.

The invention belongs to the technical field of cloud measurement, and particularly relates to a cloud region identification and cloud amount calculation method and system based on an all-sky imager, a normalized difference processing mode is selected to identify a cloud region, and a brightness threshold is introduced for a sky image which is strong in sun illumination and is not completely covered by a cloud layer; selecting an initial threshold value based on a maximum between-class variance method, and selecting a brightness threshold value for the sky image into which the brightness threshold value is introduced by using the maximum between-class variance method; performing cloud region preliminary identification on the whole-sky image through the initial threshold value, and adjusting the threshold value according to image features to realize cloud region identification; performing morphological smoothing processing on the sky image after the cloud region identification; geometric correction is carried out on the image after smoothing processing, and the cloud cover is calculated; the method has the advantages that the sky image is processed through differentiation, the brightness threshold value is introduced into the strong light interference cloud region, the noise influence is reduced by combining morphological smoothing processing, the cloud region recognition precision is effectively improved, and the cloud amount calculation error is finally reduced through geometric correction.

The application relates to the field of DNI prediction, and discloses a DNI prediction method based on a dense optical flow method and a deep learning network, which comprises the following steps: performing distortion correction on images collected by a full-sky imager; according to images at two time points before and after distortion correction, the speed of each pixel point of a current image is calculated by using a dense optical flow method; when the maximum pixel speed is less than a specified speed threshold, a static model is used to predict the DNI at a future time point, otherwise, a motion model is used to predict the DNI at the future time point; when the motion model is used, a trained deep learning network model is used to predict the clear sky index at the future time point, and then the clear sky index at the future time point is multiplied by the clear sky DNI at the future time point, so that the predicted DNI at the future time point can be obtained. The method disclosed by the application can more accurately reflect the change of the actual DNI, has better prediction accuracy, and is highly adaptable.

The present invention discloses a multi-modal fusion photovoltaic power prediction method for all-sky imager data, comprising the following steps: obtaining the time-series sky image data in the all-sky imager and the historical photovoltaic power data of the photovoltaic array; respectively extracting features from the time-series sky image data and the historical photovoltaic power data to obtain the modal feature information of the time-series sky image data and the modal feature information of the historical photovoltaic power data; fusing the modal feature information of the time-series sky image data and the modal feature information of the historical photovoltaic power data to obtain a feature matrix; obtaining the final latent features according to the feature matrix; training a photovoltaic power prediction model using the final latent features; and predicting the photovoltaic power using the photovoltaic power prediction model. The present invention performs feature extraction processing on the time-series sky image data and the historical photovoltaic power data, effectively reducing the prediction error caused by the intermittency and uncertainty of solar power generation.

The invention discloses a transformer area output prediction device suitable for transformer area micro-grid autonomy, which comprises a master device and a plurality of slave devices, and is characterized in that the master device is connected in parallel with a sky acquisition imager and a meteorological element sensor to acquire weather data; and the master device is in data parallel connection with the plurality of slave devices so as to record and manage the high-time-resolution data from the plurality of slave devices. Aiming at higher requirements of a novel digital intelligent power distribution network on distributed photovoltaic output prediction, the output prediction device which is low in cost, higher in prediction time resolution and lower in energy consumption is provided. The problem that at present, distributed photovoltaic connected to a distribution network side lacks ultra-short-term high-time-resolution power generation power prediction is solved.

The present invention discloses a system and method for atmospheric observations. The system (100) comprises a sky observer (102) comprising a sky imager (104) to capture and communicate a sky image for analyzing cloud characteristics, a robotic sun photometer (106) to track and communicate the sun and measure aerosol properties, a pyranometer (108) with a shadowband (110) and a sensor unit (120) to measure environmental conditions and collect and communicate sensor data surrounding the sky observer (102). The system (100) comprises a communication network (114) communicatively connected to the sky observer (102) to collect aggregated atmospheric data comprising the sky image, aerosol properties, different components of solar irradiance and the sensor data and transmit the aggregated atmospheric data to a server (114). The server (114) receives and analyzes the aggregated atmospheric data to estimate impact of atmospheric conditions on solar energy production in a solar plant.

This invention relates to the field of solar energy control and discloses a solar energy intelligent control system based on brightness gradation extraction, comprising: a panoramic perception module for acquiring first image data of the entire sky field of view; a near-sun tracking sampling module including a dual-axis servo frame and a narrow-angle camera mounted thereon, wherein the narrow-angle camera uses a physical light-reducing component to suppress the pixel brightness of the central region of the sun to the unsaturated range, for acquiring second image data of the near-sun region; and an edge processing unit electrically connected to the panoramic perception module and the near-sun tracking sampling module, for executing brightness gradation logic based on the first and second image data and generating converter control commands, thus solving the overexposure problem of traditional all-sky imagers near the direct sunlight point.

The application discloses a kind of measurement method and measurement system of the spectral distribution of PAR, including by the geometric calibration and radiation calibration of all-sky imager, the observation zenith angle and observation azimuth of each pixel of all-sky image and RGB waveband radiance value are acquired;Through cloud detection algorithm, all-sky image is divided into clear sky and cloudy condition;For clear sky all-sky image, combined with radiation transfer model, PAR spectral radiation distribution algorithm reconstruction is carried out to obtain the spectral distribution of PAR;For the image with cloud, a deep learning model is used to obtain the spectral distribution of PAR, and finally the spectral distribution of the whole PAR is obtained.The application has the advantages that: the spectral distribution of PAR is reconstructed using all-sky image, and by combining physical radiation model and deep learning model, the spectral irradiance of visible light band reaching the ground, i.e.PAR spectral distribution information, is obtained from the image captured by all-sky imager.

The present invention discloses a full-sky cloud imaging system based on a neuromorphic visual sensor, comprising a full-sky imager, a sunshade belt, a data acquisition module, and a data calculation module; the data acquisition module comprises a hemispherical mirror and an event-driven sensor for acquiring sky-related data; the full-sky imager uses an event-driven sensor for shooting, the event-driven sensor being arranged on a support arm of the full-sky imager and located above the center of the hemispherical mirror; the sunshade belt covers the surface of the hemispherical mirror; and the data acquisition module is connected to the data calculation module via a transmission module. The present invention has higher temporal resolution and dynamic range, and can more accurately identify and track targets by monitoring the movement of objects in the image. It can also measure cloud cover and cloud speed, ultimately achieving full-sky cloud cover and cloud movement speed measurement and forecasting of the probability of clouds blocking the sun.

The invention discloses a regional cloud field collaborative prediction and correction method based on a multi-site sky imaging network, and the method comprises the following steps: constructing and deploying the multi-site sky imaging network, and selecting and deploying sparsely distributed network sites; collecting and preprocessing multi-source observation data; carrying out graph structure modeling and node feature extraction; space-time diagram neural network prediction: constructing a model integrating space-time convolution and a diagram neural network for prediction; performing multi-source observation data fusion and dynamic correction, fusing the multi-source observation data, and performing dynamic correction and virtual completion when the data is abnormal or missing; intelligently generating a missing cloud picture by adopting an adversarial network; and outputting a regional cloud field prediction result and early warning information, outputting a future short-time scale regional cloud field prediction result, and automatically generating the early warning information. According to the method, the authenticity and accuracy of regional cloud field collaborative prediction can be improved, so that support is provided for scheduling decision-making of wind and light power generation, and the problem that the scheduling capability of a wind and light power generation station cannot be well improved by using a cloud picture prediction technology at present is solved.