10 results about "Cloud height" patented technology

The invention relates to the technical field of photovoltaic power generation, in particular to a photovoltaic power generation power prediction method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining multi-source heterogeneous data, such as historical power generation power, NWP, satellite remote sensing and cloud height, corresponding to a prediction time point, carrying out the preprocessing, and inputting the preprocessed data into four deep learning sub-models, the method comprises the following steps: respectively outputting corresponding future power prediction sequences according to a historical power analysis model, a numerical prediction analysis model, a cloud region analysis model and a cloud height analysis model, and fusing the sequences to obtain a photovoltaic power generation power prediction result. By integrating multi-source data and multi-model analysis fusion, local cloud cluster changes can be more accurately captured, deep features of multi-modal data are mined, physical process influences are considered, and the precision and reliability of photovoltaic power generation power prediction are effectively improved.

1. The name of the design product: handheld laser ceilometer. 2. The use of the design product: intelligent handheld sensor for measuring cloud height. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view.

A cloud measuring system includes: a stereo camera capturing stereo images by cameras each changing pan and tilt angles; a camera control section controlling the pan and tilt angles to control imaging; a reception section receiving the stereo images; a matching coordinate acquisition section acquiring a feature point of an object in the stereo images, and acquiring, as matching coordinates, a combination of reference coordinates where the feature point is to be located in the images, and coordinates of the acquired feature point; a difference detection section acquiring a displacement amount of the feature point from the matching coordinates, acquiring direction difference amounts in pan and tilt directions from the displacement amount, and generating correction information; a correction processing section correcting the stereo images on the basis of the correction information; and a height estimation section estimating a cloud height on the basis of the corrected stereo images.

1. Name of the product in this design: Cloud Gauge. 2. Purpose of this design: This design is used to measure data such as cloud base height, cloud thickness, and number of cloud layers. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: 3D view 1.

The present application relates to a kind of solar field cloud shelter monitoring and control method, system based on multisensor data fusion.The method includes: the low-density scattered solar radiation sensor network and high-density overall solar radiation sensor network are cooperatively collected basic data in deployment, combined with adaptive filtering technique to filter noise interference, then based on the inverse deduction direct solar radiation intensity of radiation balance equation;While aiming at shelter area, non-shelter area uses differentiated interpolation and extrapolation method;Through convolutional neural network, cloud layer features are extracted, cloud movement trajectory and shelter range are predicted;Introduce geographic information system data, according to the projection length and shape of topographic relief correction cloud shadow;While fusing meteorological satellite data and cloud height data to construct cloud layer three-dimensional model;Based on direct solar radiation intensity distribution map and cloud shelter trajectory prediction result, the working state of heliostat in shelter area is dynamically adjusted, to avoid invalid operation and energy waste.

The invention provides an accurate ceilometer comparison and measurement method, and belongs to the technical field of ceilometers, the method comprises the steps: building an aerosol scattering feature discrimination matrix, removing an aerosol layer, reserving a real cloud layer signal, employing a cloud layer boundary Bayesian inference algorithm based on maximum likelihood estimation to obtain the posterior probability distribution of cloud base height, and carrying out the comparison and measurement of the cloud base height. Constructing a double-layer game optimization model to improve the cloud layer recognition precision and minimize the data registration error, calculating the cloud layer movement speed by adopting a cloud layer motion vector estimation algorithm, carrying out space-time interpolation correction, and calculating a correlation coefficient and a linear regression slope to judge the comparison qualification. And increment deviation analysis is carried out on all maximum cloud height values to judge full-scale performance, so that the technical problem that the comparison accuracy is reduced due to space-time mismatching of observation data of a reference instrument and a tested instrument caused by dynamic movement of the cloud layer is solved.

The invention discloses an infrared cloud height inversion method based on multiple optical thickness sets and residual weighting, and the method comprises the steps: generating a simulation curve set changing with zenith angles in advance through a radiation transmission mode under a preset atmosphere profile condition, and carrying out the interpolation of a curve to a zenith angle matrix corresponding to an instrument view field, so as to construct a multi-dimensional radiation lookup table; interpolating the multi-dimensional radiation lookup table according to the total content of the atmospheric water vapor at the current time to generate a cloud height-radiation relation sub-table; for each cloud pixel, a cloud height candidate value of the cloud pixel exists under each optical thickness grade, a residual error between actually measured radiation corresponding to the cloud height candidate value and the cloud height-radiation simulation curve is calculated, an optical thickness residual error of each grade is obtained, an optical thickness weight is constructed, the multiple cloud height candidate values are subjected to weighted fusion, and a cloud height-radiation simulation curve is obtained; and obtaining a cloud height inversion result with cloud pixels. The influence of optical thickness uncertainty on cloud height inversion can be weakened through a multi-light-thickness set and a residual weight mechanism, and the cloud height inversion precision and stability are improved.

This invention provides an accurate cloud height comparison method, belonging to the field of cloud height technology. The invention establishes an aerosol scattering feature discrimination matrix to eliminate the aerosol layer and retain the true cloud signal. It employs a maximum likelihood estimation-based Bayesian inference algorithm for cloud boundaries to obtain the posterior probability distribution of cloud base height. A two-layer game optimization model is constructed to improve cloud identification accuracy and minimize data registration errors. A cloud motion vector estimation algorithm is used to calculate cloud movement speed and perform spatiotemporal interpolation correction. Correlation coefficients and linear regression slopes are calculated to determine the pass / failability of the comparison. Incremental deviation analysis is performed on all maximum cloud height values ​​to determine full-range performance. This invention solves the technical problem of decreased comparison accuracy caused by spatiotemporal mismatch between the observation data of the reference instrument and the tested instrument due to dynamic cloud movement.

The application provides a FY-4 sea cloud base height correction method considering a buoy cloud height instrument, and belongs to the technical field of sea cloud base height correction. The application collects the buoy cloud height instrument and FY-4 satellite cloud base height data, performs sea surface reflection pollution correction on the satellite data, identifies the cloud layer structure by using K-means clustering, extracts the multi-scale fractal dimension features of the buoy and satellite data and calculates the difference parameters, organizes the correction data into a four-dimensional correction tensor, extracts the low-rank structure by using CANDECOMP decomposition, inputs the decomposition result and the fractal dimension difference parameters into a cloud base height bidirectional optimization model, and realizes the dynamic balance between the accuracy improvement and the spatiotemporal stability maintenance in the model by the game mechanism of the accuracy optimization sub-model and the stability optimization sub-model, so that the technical problem that the sea cloud base height precision and the spatiotemporal stability of the satellite inversion are difficult to be considered is solved.

The application relates to a lightning positioning method, device and system, and relates to the technical field of lightning positioning. The lightning positioning method comprises the following steps: in response to detecting that lightning occurs in a target area, obtaining monitoring data of the target area, wherein the monitoring data comprises atmospheric electric field intensity corresponding to a plurality of monitoring points in the target area; obtaining thunderstorm cloud height information of the target area; determining longitude and latitude coordinates of lightning through a pre-constructed lightning positioning model according to the monitoring data and the thunderstorm cloud height information; and determining position information of the lightning in the target area according to the longitude and latitude coordinates of the lightning. The technical scheme can improve lightning positioning precision on the basis of reducing hardware cost.