23 results about "Cloud height" patented technology

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.

This invention relates to a cloud height identification method integrating cloud radar and laser wind radar, belonging to the field of radio. The identification method includes: S1, obtaining the cloud base height and cloud top height by inverting the reflectivity product from the cloud radar in top-view mode; S2, obtaining the cloud base height and cloud top height by inverting the signal-to-noise ratio product from the laser wind radar in DBS mode; S3, performing time matching on the output products of the cloud radar and the laser wind radar, and then fusing the results obtained from the cloud radar and the laser wind radar to obtain the final cloud height identification result. This invention complements the characteristics of the two devices. The laser wind radar can compensate for the detection limitations of millimeter-wave radar in thin clouds and distinguish between cloud layers and aerosol layers, while the cloud radar can supplement the inability of laser wind radar to penetrate thick cloud layers, achieving accurate detection of cloud height across the entire cloud area.

To highly accurately measure cloud heights of clouds in the whole sky.SOLUTION: A cloud measurement system comprises: a stereo camera 30 that can change zoom magnification in an imaging direction and optically; a picture reception unit 204 for acquiring the imaged picture; a cloud edge detection unit 207 that recognizes a cloud displayed on the picture, detects a boundary of the cloud, and acquires an edge region including the boundary from a plurality of sub regions in the picture; a cloud imaging order setting unit 209 that on the basis of the edge region, sets target sub regions to be imaged while changing the zoom magnification of the camera and their imaging order; a camera control unit 210 that controls the stereo camera 30 so as to perform enlargement imaging on the target sub regions according to the set imaging order to acquire a stereo picture for each target sub region, a stereo vision distance measurement unit 202 for acquiring a distance to a cloud displayed on the stereo picture using a parallax, and a cloud bottom height estimation unit 203 for acquiring a cloud bottom height of the cloud on the basis of the acquired distance.SELECTED DRAWING: Figure 1

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.

To measure a cloud height with high accuracy for a long time.SOLUTION: A cloud measurement device includes: a stereo camera 30 for photographing a stereo image by a plurality of cameras that can change a pan angle and a tilt angle; a camera control unit 210 for controlling the pan angle and the tilt angle of each camera to control photographing; an image reception unit 204 for receiving the stereo image from the stereo camera; a matching coordinate acquisition unit for acquiring feature points of a subject captured in the stereo image, and acquiring a combination of reference coordinates at which the feature points should be located on the image and coordinates of the acquired feature points as matching coordinates; a direction difference detection unit 209 for acquiring a displacement amount of the feature points on the basis of the matching coordinates, and acquiring a difference amount of pan directions and tilt directions of the plurality of cameras to generate correction information; a correction processing unit 201 for correcting the stereo image on the basis of the correction information; and a cloud base height estimation unit 203 for estimating a cloud base height on the basis of the corrected stereo image.SELECTED DRAWING: Figure 1

The application relates to an artificial intelligence image recognition method based on deep learning, and belongs to the technical field of image processing. In the application, an optimization algorithm is used to optimize a Back Propagation neural network. Due to the strong global convergence ability and strong robustness of the swarm intelligence optimization algorithm, the slow convergence speed and the shortcoming of falling into a local optimum of the neural network can be well compensated, so that the whole system can accurately judge the shielding condition of a photovoltaic power station caused by cloud movement compared with a traditional artificial neural network prediction method and a cloud shielding prediction method considering the influence of cloud height. Meanwhile, the optimized long short-term memory network model can meet the application requirements of high precision, high efficiency and high stability of photovoltaic ultra-short-term power prediction under full climate conditions, so that efficient and economic operation of solar photovoltaics is possible, and the prediction precision of the ultra-short-term prediction can be effectively improved.

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 a boundary layer height inversion method based on a dynamic height threshold, and relates to the technical field of boundary layer height inversion, and the method comprises the steps: obtaining and preprocessing a laser radar signal; recognizing the cloud height according to the zero crossing point of the signal based on a slope method; determining a height range of searching when the cloud exists, adjusting an upper limit height according to the size of a signal in the range, and generating a limited range of searching when the cloud exists; and judging whether the cloud layer is in the boundary layer or not according to the slope of the signal in the corresponding limited range under the condition that the cloud exists, and recording the first target height or the second target height in the corresponding limited range as the boundary layer height according to the slope in the two conditions that the cloud layer is in the boundary layer and the cloud layer is outside the boundary layer. Through a method of dynamically adjusting the height threshold, the upper limit height threshold is adjusted in real time according to different conditions, and the influence of complex layer junctions such as a cloud layer, a suspended aerosol layer and a residual layer is effectively eliminated.

The invention discloses a cloud layer height measurement method and system based on a distributed camera, and the method comprises the steps: employing each image collection device in a distributed camera system to shoot a target cloud layer region, and obtaining each target cloud layer image; executing a two-stage screening process based on saturation features, neighborhood texture features and gray difference features of pixel points in each target cloud layer image to obtain a classification result of each pixel point in each target cloud layer image, and performing differentiation processing on all the pixel points to obtain each cloud layer optimization image; and performing feature point matching on the cloud layer optimization image to obtain first position data of each cloud layer target, and calculating height data of each cloud layer target according to difference features between each first position data and second position data of all the image acquisition devices. According to the cloud layer height measurement method and system based on the distributed camera provided by the embodiment of the invention, the accuracy of cloud layer height measurement is improved.

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 application provides a laser ceilometer system error compensation method based on regression analysis, and belongs to the technical field of laser ceilometers.The application divides a measurement range into four height intervals by constructing a layered height interval step regression equation set and establishing independent nonlinear regression equations, designs a double-layer game optimization framework to realize collaborative optimization of global error minimization and local fitting accuracy maximization, performs historical data preprocessing and data set division, implements a double-layer game model collaborative optimization algorithm to determine regression equation coefficients and neural network parameters, constructs an adaptive cloud height error correction model based on a Transformer architecture to realize real-time error compensation, and maintains optimal model performance through sliding time window monitoring and automatic retraining, thereby solving the technical problem that the measurement accuracy of a laser ceilometer is significantly affected by system errors caused by changes in atmospheric environmental parameters and device parameters.

The utility model provides a cloud layer height measuring system based on a laser radar. The cloud layer height measuring system comprises an XC7Z020 chip, a pulse laser, a laser receiving, converting and amplifying circuit, a secondary amplifying circuit, a comparator, a power supply and a high voltage generating circuit, the laser receiving, converting and amplifying circuit comprises an APD, a level conversion module and a TIA module. After the APD receives laser returned from a cloud layer, an optical signal is converted into a voltage signal through the level conversion module, and a generated current signal is converted into the voltage signal through the TIA module. The comparator converts an analog signal of the secondary amplification circuit into a digital signal and then sends the digital signal to the XC7Z020 chip; the power supply and high-voltage generation circuit converts alternating-current voltage into direct-current voltage to supply power to the laser receiving, converting and amplifying circuit and the pulse laser, long-distance detection can be achieved, the measurement precision is improved, and continuous and uninterrupted measurement can be achieved.

The invention belongs to the technical field of meteorological information processing and early warning in an air traffic control system, and particularly relates to a control tower local meteorological information early warning and runway light luminosity level auxiliary decision-making system, which comprises the following steps: acquiring and displaying the latest local meteorological forecast message and observation message, and giving an alarm when the local meteorological observation special message appears; by acquiring real-time data of an automatic meteorological observation system of the field, key meteorological parameters such as QNH (corrected sea level air pressure), VIS (runway visibility) and cloud height are dynamically monitored and displayed, and a corresponding alarm prompt is provided when the parameter change exceeds a preset threshold value; calculating a recommended runway light luminosity level in combination with the current meteorological condition and the sunrise and sunset time, comparing the recommended runway light luminosity level with an actual set value, and if the recommended runway light luminosity level is not consistent with the actual set value, providing early warning and runway light luminosity level mode adjustment suggestions to control personnel; a controller can conveniently carry out comprehensive judgment by combining data provided by the system with field conditions, so that the controller is assisted to timely make accurate runway light luminosity level setting and operation decisions, and the safety and efficiency of aviation operation are improved.

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 utility model provides an unmanned aerial vehicle cloud height measuring device, which relates to the technical field of unmanned aerial vehicle measurement and comprises an unmanned aerial vehicle main body and a measuring bin, clamping frames are welded on two sides of the bottom of the unmanned aerial vehicle main body, clamping blocks are welded on two sides of the top of the measuring bin, and the clamping blocks and the clamping frames are in sliding connection and are fixed through bolts. The front side of the measuring bin is provided with a barrier strip plate, the bottom of the measuring bin is provided with an auxiliary camera, the bottom of the front side of the measuring bin is provided with a partition plate, the device carries a millimeter wave radar rangefinder and a laser rangefinder, and cloud layer contour information can be rapidly obtained and preliminary detection is carried out by utilizing the penetrability of millimeter waves under severe weather such as cloud mist and light rain; the laser rangefinder can accurately calculate the height of the cloud base to centimeter-level precision by means of high-frequency laser pulses according to the light propagation speed and the round-trip time in sunny weather. The two distance measurement modes are rapidly switched according to needs, and an operator only needs to give an instruction through a ground control station.

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.

The invention relates to a lightning positioning method, device and system, and relates to the technical field of lightning positioning. The lightning positioning method comprises the steps of obtaining monitoring data of a target area in response to detection of lightning in the target area, the monitoring data comprising atmospheric electric field intensities corresponding to a plurality of monitoring points in the target area; obtaining thunderstorm cloud height information of the target area; determining latitude and longitude coordinates of lightning according to the monitoring data and the thunderstorm cloud height information through a pre-constructed lightning positioning model; and determining position information of the lightning in the target area according to the latitude and longitude coordinates of the lightning. According to the technical scheme, the lightning positioning precision can be improved on the basis of reducing the hardware cost.

The invention discloses a satellite cloud picture cloud classification method based on deep learning, and relates to the technical field of cloud picture detection, and the method specifically comprises the steps: S1, building a cloud layer coordinate system; s3, cloud picture type detection; s4, substituting the cloud picture information; s5, establishing a cloud picture information base; s6, constructing a learning network; s7, other climate conditions are substituted; the satellite cloud picture cloud classification method based on deep learning has the beneficial effects that future weather data is predicted by adopting a historical data deduction and multi-satellite data combination mode at the same time, so that weather information in a future period can be predicted more accurately; compared with a currently adopted cloud picture classification method based on a satellite, the method can focus on prediction of future weather information, and the technical purpose of satellite cloud picture monitoring is better achieved.