43 results about "Scatterometer" patented technology

The invention relates to the technical field of new energy power generation prediction, in particular to a new energy power prediction method fusing typhoon meteorological information and a micrometeorological prediction result, which comprises the following steps: collecting multi-source observations such as a satellite scatterometer, a radar wind profile and laser wind measurement, and unifying coordinates; constructing a mesoscale wind field by adopting four-dimensional variational assimilation, inferring a micrometeorological field by utilizing a spectrum embedding diffusion network, and generating a multi-scale meteorological field by frequency domain phase consistency fusion; probabilistic wind speed is sampled in the countercurrent model meeting the condition of mass and momentum conservation, an energy conservation graph neural network is input, the wake effect is coupled, and a unit power quantile value is obtained; the power probability is sent to a risk weighting model, the weight is adjusted in real time according to the peak load, the reserve capacity and the climbing rate risk, a dispatching power curve and uncertainty are output, and grid-connected power errors are used for periodically updating the weight and the parameters of the last layer of the reversible model. According to the invention, the safety acceptance margin of the power grid to new energy is obviously improved.

The invention belongs to the technical field of ocean data processing, and discloses a GNSS-R and scatterometer combined same-orbit time sequence sea surface wind speed inversion method and system, and the method comprises the following steps: obtaining historical satellite data of ocean surface wind, carrying out the preprocessing of the historical satellite data, and obtaining the preprocessed data, constructing a training sample set in combination with the reference wind speed and the surge auxiliary data; constructing an initial wind speed inversion model by using a plurality of single-moment multi-frequency information fusion modules and multi-moment interactive attention enhancement modules, and training the initial wind speed inversion model by using the training sample set to obtain a same-orbit time sequence sea surface wind speed inversion model; and obtaining a time delay-Doppler correlation power diagram, GNSS-R observation parameters, scatterometer parameters and effective wave height of storm and surge, and completing sea surface wind speed inversion by using the same-orbit time sequence sea surface wind speed inversion model to obtain sea surface wind speed prediction data.

The invention provides a satellite scatterometer Ku wave band observation value rainfall influence correction method, and relates to the field of sea surface wind inversion methods, and the method comprises the steps: obtaining Ku wave band observation data, C wave band data and rainfall rate data of a satellite scatterometer, carrying out wind field matching and wind speed correction under a rain-free condition, so as to obtain an NRCS reference value corresponding to a sea surface real wind speed, and carrying out the calculation of the NRCS reference value; constructing to obtain a scatterometer rainfall matching data set; according to the scatterometer rainfall matching data set, the influence of rainfall on the scatterometer observation value is analyzed, and a Ku waveband rainfall influence conceptualization model is constructed; and calculating rainfall influence conceptualization model parameters and generating a deviation lookup table. According to the invention, on the basis of a classic scatterometer wind field inversion process, the problem of large inversion wind field deviation caused by the influence of rainfall on the observation value of the Ku wave band scatterometer in a medium-low rainfall rate range is solved.

The invention belongs to the field of optics, and particularly discloses a light spot shrinking device for an angle resolution scatterometer. Through the first lens, the beam shaping element and the second lens which are sequentially arranged along the light path, beam focusing, beam shaping and collimation are carried out in sequence, the collimated beam with the diameter smaller than that of the beam of the light source is formed, the collimated beam is combined with the subsequent light path, and the light spot shrinking function can be achieved at the detection end of the objective lens.

The invention discloses a scatterometer in-orbit satellite-ground integrated calibration method, system and device, a medium and a product, and relates to the field of radar calibration, and the method comprises the steps: screening scatterometer observation signals, carrying out the quality inspection of the downloading data of a scatterometer, extracting the quality inspection data in an external calibration working time range, and carrying out the detection of the quality inspection data in the external calibration working time range; determining a pitch angle and an azimuth angle of the satellite-to-earth connection line according to the quality inspection data, performing calibration by combining with a scatterometer observation signal received by a ground calibration station to obtain an antenna gain of the scatterometer, and performing interpolation on the antenna gain of the scatterometer to obtain a satellite-to-earth connection line; determining an actual antenna pattern of the scatterometer and determining a comparison result of the actual antenna pattern and an antenna pattern measured in a laboratory before emission; and calculating a theoretical backscattering coefficient according to the antenna gain and the amplification gain of the active calibrator, determining an actual backscattering coefficient according to the quality inspection data and the antenna gain of the scatterometer in combination with a measured antenna pattern, further determining an absolute calibration coefficient, and completing a calibration task. According to the invention, the calibration precision and the inspection capability of the scatterometer are improved.

The invention relates to a test method for evaluating the anti-shearing capability of cosmetics by using a multiple light scatterometer and application of the test method. The test method comprises the following steps: initially scanning a sample by using the multiple light scatterometer; the method comprises the following steps: fixing a sample in a simulated transportation vibration test bed, and after simulated transportation vibration, carrying out node scanning on the sample by using a multiple light scattering instrument; and calculating the light intensity change rate of the sample after the simulated transportation vibration, and evaluating the anti-shearing capability of the cosmetics. According to the invention, the multiple light scatterometer is creatively combined with the simulation transportation vibration test bench, and the sample microstructure measurement capability of the multiple light scatterometer is utilized to characterize the uniform state and the stability condition of the sample after simulation transportation vibration, so that the anti-shearing capability of the cosmetics is tested, and the detection accuracy is improved. And whether the sample changes after being sheared can be evaluated more accurately.

The invention discloses a method and a device for correcting nonlinear deviation of a backscattering coefficient, relates to the technical field of active microwave remote sensing, and aims to solve the problem of poor measurement precision of the backscattering coefficient of a microwave scatterometer in the prior art. The method comprises the following steps: acquiring measurement data and truth value data of the satellite microwave scatterometer; performing data preprocessing on the measurement data and the truth value data to obtain an initial sample data set; constructing an azimuth angle uniform distribution calibration model data set by using a dynamic uniform distribution construction method and a random error elimination method based on the initial sample data set; correcting the back scattering coefficient nonlinear deviation of the satellite microwave scatterometer based on the azimuth angle uniform distribution calibration model data set; based on this, accurate correction of the non-linear deviation of the backscattering coefficient of the satellite microwave scatterometer is realized, and the measurement accuracy of the backscattering coefficient of the satellite microwave scatterometer is improved.

The invention discloses an NRCS determination method for a Ku wave band scatterometer under a rainfall condition, and relates to the field of satellite ocean remote sensing and meteorological detection.The method comprises the steps that the action mechanism of rainfall on radar signals in the atmosphere is considered, and a sea surface radar back scattering theoretical model is constructed based on a wind-generated sea surface wave spectrum and a dual-scale Bragg scattering theory; obtaining a rainfall attenuation coefficient, a normalized radar backscattering cross section contributed by raindrop scattering and dual-polarization normalized radar backscattering cross sections of a C wave band and a Ku wave band based on a sea surface radar backscattering theoretical model; according to the rainfall attenuation coefficient, the normalized radar backscattering cross section contributed by raindrop scattering and the dual-polarization normalized radar backscattering cross sections of the C wave band and the Ku wave band, the normalized radar backscattering cross section of the Ku wave band scatterometer under the rainfall condition is obtained; according to the method, the accurate normalized radar back scattering cross section of the Ku wave band scatterometer is obtained under the rainfall condition.

The invention discloses a refined ammonia injection adjusting system for a coal-fired power plant, and relates to the technical field of environmental protection of coal-fired power plants. Comprising a data acquisition module which is composed of a plurality of sensors and is used for monitoring parameters in real time; the central processing module receives the data acquired by the data acquisition module and then generates a control instruction; the ammonia spraying execution module is used for receiving the control instruction of the central processing module and performing ammonia spraying treatment; the data management module is internally provided with a data storage unit, and the data storage unit is used for storing data in a topological node storage form; the data retrieval unit is used for performing data retrieval according to user input; the data analysis unit is used for analyzing the data and then providing a visual chart for a user to observe, and the sensor module comprises an infrared spectrum analyzer and a laser scattering instrument. The nitrogen oxide emission concentration is stably controlled to be 50 mg / Nm < 3 > or below, and the national environmental protection standard is met.

This invention discloses a joint inversion method for sea surface wind speed, comprising: jointly inverting observation data from a microwave scatterometer and a spaceborne GNSS-R over an overlapping observation area to obtain a first wind speed inversion result for that area; independently inverting the observation data from the microwave scatterometer to obtain a second wind speed inversion result; expanding the first wind speed inversion result to the second wind speed inversion result to obtain a third wind speed inversion result for the complete observation area of ​​the microwave scatterometer; independently inverting the observation data from a synthetic aperture radar to obtain a fourth wind speed inversion result; and expanding the fourth wind speed inversion result to the third wind speed inversion result to obtain a final joint inversion result. The technical solution provided by this invention performs joint inversion based on observation data from a microwave scatterometer, a spaceborne GNSS-R, and a synthetic aperture radar, thereby obtaining a final joint inversion result with a large observation area, high accuracy, and high resolution.

The present invention belongs to the technical fields of active microwave remote sensing, extraction of linear frequency modulation transmitted signals of scatterometers, and ocean data processing. Specifically, it relates to a method and device for extracting positive and negative slope linear frequency modulation transmitted signals of a spaceborne scatterometer. The method includes: using a spaceborne microwave scatterometer to perform conical scanning on an area to be observed within a swath through a fan beam, and obtaining in real time the scanning azimuth angle for this observed area; based on the scanning azimuth angle for this observed area obtained in real time, determining whether the antenna transmits a linear frequency modulation pulse signal with a negative slope or a linear frequency modulation pulse signal with a positive slope.

The present invention relates to the field of renewable energy power generation prediction technology, and in particular to a renewable energy power prediction method that integrates typhoon meteorological information and micrometeorological forecast results. The method comprises the following steps: collecting multi-source observations such as satellite scatterometers, radar wind profiles, and laser wind measurement and unifying their coordinates; constructing a mesoscale typhoon field using four-dimensional variational assimilation, inferring the micrometeorological field using a spectral embedding diffusion network, and generating a multiscale meteorological field using frequency domain phase consistency fusion; sampling probabilistic wind speeds in a conditional reversible flow model that satisfies mass and momentum conservation, inputting an energy conservation graph neural network and coupling the wake effect to obtain unit power quantiles; feeding the power probabilities into a risk-weighted model, adjusting the weights in real time according to peak load, spare capacity, and ramp rate risks, outputting a dispatching power curve and uncertainty, and periodically updating the weights and the last layer parameters of the reversible model using the grid-connected power error. The present invention significantly improves the safe acceptance margin of the power grid for renewable energy.

The present application belongs to the technical field of marine remote sensing, and particularly relates to a kind of full wind speed inversion methods based on spaceborne microwave active and passive combination physical statistics network, comprising the following steps: obtaining the brightness temperature data and backscattering coefficient of microwave radiometer and scatterometer, and performing data quality control;Construct sample data set;Load physical statistics constraint deep learning inversion algorithm model for training;Physical statistics constraint deep learning inversion algorithm model includes: input layer, network layer, physical layer, output layer;Network layer is composed of residual neural network, and physical layer is composed of Log formula of microwave radiometer and Log formula of microwave scatterometer to form microwave statistical matrix P matrix, which is a nonlinear matrix, and provides physical statistics constraint for the whole model;Output the wind speed of current full sea area.The present application effectively solves the problem of lack of physical support in the process of deep learning in marine parameter inversion, and provides high-quality and reliable wind speed products suitable for full wind speed range.

This invention discloses a space-ground coordinated radiometric characteristic calibration method for spaceborne active microwave sensors in the field of radar Earth observation technology. It constructs a space-ground coordinated calibration system consisting of a radiation reference source satellite and a ground calibration processing station. The method includes: determining the orbital parameters of the radiation reference source satellite; the satellite transmitting signals to the ground station in Earth observation mode, using a known RCS target on the ground to calibrate its own antenna gain and link gain; receiving signals from the satellite to be calibrated in Earth observation mode, accurately calculating and transmitting echo signals based on the calibrated parameters; and the satellite to be calibrated calibrating its own transmit / receive link gain based on the received echo signals. This invention can be widely applied to the on-orbit radiometric calibration of active microwave sensors such as synthetic aperture radar, altimeters, and microwave scatterometers, significantly improving calibration frequency and accuracy, supporting multi-band, high-stability quantitative remote sensing tasks, and providing reliable technical support for precise observation in fields such as ocean monitoring, weather forecasting, and topographic mapping.

The invention discloses a high-resolution and high-flux small-angle scatterometer which comprises a focus lens which is an ellipsoid multilayer film focus lens, the optical surface shape of the focus lens is determined by an ellipsoid, and a light source and a detector are arranged at the positions of a first focus and a second focus of the ellipsoid respectively. The focusing lens focuses the X-ray from the light source to the light sensing plane of the detector, and the focus of the straight-through light falls on the plane of the detector. According to the invention, the ellipsoid multilayer film focus lens is introduced to form a confocal light path, and the light source and the detector are respectively located at two focuses of the ellipsoid. Photons cut off by a traditional slit are actively collected, focused and utilized, and the luminous flux is greatly improved while the high resolution is kept; the light spot size d is matched with the pixel size of the detector, so that the light spot size d is controlled, and the light spot size d cooperates with the distance L to determine the angular resolution. In this way, the resolution performance of the device can be precisely customized, and the angular resolution and the incident luminous flux can be improved at the same time.

The present invention discloses an on-orbit directional pattern calibration method for a satellite-borne microwave scatterometer based on an active calibrator. The method comprises: obtaining the elevation angle and azimuth angle of the beam at different times within the scatterometer antenna system based on the position information of the active calibrator; obtaining the elevation angle and azimuth angle of the beam at different times within the active calibrator antenna system based on the pointing information and position information of the active calibrator; obtaining the data power received by the active calibrator at different times based on the power signal received by the active calibrator and the elevation angle and azimuth angle of the beam at different times within the active calibrator antenna system; and obtaining the antenna pattern of the scatterometer based on the data power received by the active calibrator at different times. The present invention solves the problem of directional pattern calibration under rapidly changing scatterometer antenna coordinate systems and compensates for the modulation effect of the active calibrator on the satellite-borne directional pattern, thereby improving the on-orbit test accuracy of the antenna pattern.

The invention relates to the technical field of ocean engineering, in particular to a high-resolution sea ice detection method based on a rotating fan-shaped beam scatterometer, the sea ice detection method can realize ice and water distinguishing in 10km grids in south and north polar regions, and the sea ice can be detected by utilizing first-level data of the scatterometer and a polar stereographic projection conversion method. The spatial resolution of an existing scatterometer sea ice monitoring method is improved, a sliding time window is adopted for collecting training data, dynamic training and updating are conducted on the model, and the problem that radar backscattering coefficient ice-water characteristics change greatly along with seasons is solved to a certain extent.

The invention provides a foaming agent performance evaluation method. The method comprises the following steps: preparing a foaming agent solution; starting the multiple light scatterometer, setting operation parameters of the multiple light scatterometer, and stabilizing the multiple light scatterometer with the set operation parameters for a period of time; stirring the foaming agent solution to completely foam the foaming agent to obtain a to-be-detected sample, placing the to-be-detected sample in a sample cell of a multiple light scatterometer which has set operating parameters and is stabilized, detecting the to-be-detected sample according to the set operating parameters, and collecting spectral signals; and converting the spectral signal into image information, and evaluating the performance of the foaming agent based on the spectral signal and the image information. According to the method, based on multiple light scattering, the comprehensive performance of the foaming power and the stability of the foaming agent can be evaluated, and the evaluation accuracy is improved.

The invention discloses an overlay error measurement system and method based on an angle resolution scatterometer, and the system comprises a computer control and processing system, a light source and a to-be-measured sample, and the axis of the light source is perpendicular to the axis of the to-be-measured sample. A collimating lens, a polarizing film, a first diaphragm, a first relay lens group and a first beam splitter prism are sequentially arranged along the irradiation direction of the light source, and the first diaphragm is placed at the conjugate plane position of the rear focal plane of the microscope objective; the alignment error measuring system based on the angle resolution scatterometer has the beneficial effects that the alignment error measuring system based on the angle resolution scatterometer is designed by utilizing the advantages of low cost, no damage, high efficiency and the like of an angle resolution scatterometer measuring technology. And on the basis of the basic principle that the positive and negative primary light intensity difference and the overlay error are approximately in a linear relationship within a certain range, a neural network model is trained for data fitting. And obtaining a predicted overlay error through the measured scattered light intensity information and the trained neural network.

The invention belongs to the technical field of ocean remote sensing, and particularly relates to a satellite-borne microwave active and passive combination-based physical statistical network full wind speed inversion method, which comprises the following steps of: acquiring brightness temperature data and backscattering coefficients of a microwave radiometer and a scatterometer, and performing data quality control; constructing a sample data set; loading a physical statistical constrained deep learning inversion algorithm model for training; the physical statistical constraint deep learning inversion algorithm model comprises an input layer, a network layer, a physical layer and an output layer; the network layer is composed of a residual neural network, and the physical layer is composed of a microwave statistical matrix P matrix which is composed of a Log formula of a microwave radiometer and a Log formula of a microwave scatterometer, is a nonlinear matrix and provides physical statistical constraints for the whole model; outputting the current wind speed of the whole sea area. The problem that deep learning lacks physical support in the ocean parameter inversion process is effectively solved, and a high-quality reliable wind speed product suitable for the whole wind speed range is provided.

The present invention relates to the fields of satellite remote sensing, ocean remote sensing, and meteorological technology, and particularly relates to a method for simultaneously retrieving wind speed and precipitation using a Ku-band microwave scatterometer. The method includes: selecting relevant parameters of the wind observation unit of the Ku-band scatterometer that meet the quality control requirements, and inputting them into a specific model established and trained in advance for simultaneously retrieving wind speed and precipitation using the Ku-band scatterometer, so as to achieve the simultaneous estimation of wind speed and precipitation under the set conditions, where the set conditions are that the wind speed is less than 14 m / s and the precipitation is less than 10 mm / h. Based on a general method of support vector machines, the present invention establishes a specific model for identifying the precipitation influence area of the Ku-band scatterometer, removing the precipitation influence on wind speed, and quantitatively estimating precipitation information, so as to achieve the simultaneous estimation of wind speed and precipitation below 14 m / s (meters per second) and 10 mm / h (millimeters per hour) of precipitation.

The application discloses a kind of satellite-borne scatterometer comprehensive calibration and verification method based on multi-objective.The method is after satellite-borne scatterometer on-orbit operation, high-precision calibration is realized using on-board inner calibration and star-ground integrated outer calibration means, supplemented with ocean calibration, rain forest calibration, cross calibration and other calibration verification means, to ensure on-orbit observation accuracy.In addition, the method connects on-board real-time link inner calibration, star-ground integrated outer calibration, ocean calibration, rain forest calibration, cross calibration and other multi-target calibration and verification technologies from the angle of business use and information feedback, forms a set of practical complete satellite-borne scatterometer calibration and verification method, greatly improves the completeness of whole process of business system.

The present invention relates to the field of marine engineering technology, and in particular to a high-resolution sea ice detection method based on a rotating fan-beam scatterometer. The sea ice monitoring method can distinguish between ice and water within a 10km grid in the Arctic and Antarctic regions. By utilizing scatterometer level 1 data and a polar stereographic projection conversion method, the spatial resolution of existing scatterometer sea ice monitoring methods is improved. In addition, a sliding time window is used to collect training data and dynamically train and update the model, which to a certain extent solves the problem that the ice-water characteristics of the radar backscatter coefficient vary greatly with the seasons.

The invention discloses a double-frequency scatterometer wind field joint inversion method based on deep learning, solves the problems of insufficient precision and poor adaptability of an existing inversion technology, and belongs to the technical field of space remote sensing and marine environment monitoring. The inversion method comprises the following steps: simulating satellite-borne multi-angle measurement data of the dual-band scatterometer to obtain simulation data; constructing physical enhancement features based on a scatterometer sea surface scattering mechanism; establishing a double-branch deep learning wind field inversion model which comprises a full wind speed model and a branch wind speed model, sharing a feature extraction backbone network, and respectively realizing wind speed and wind direction inversion by two output branches; training the full wind speed model and the sub-wind speed model based on the simulation data; and performing wind field inversion based on the trained full wind speed model and sub-wind speed model.

The invention relates to the technical field of ocean exploration, in particular to an active and passive microwave combined sea surface multi-element inversion method and system without a background field. According to the method, based on multi-band and multi-polarization combined observation data of a satellite-borne active and passive microwave imager and a microwave scatterometer, a linear regression model is constructed by using a microwave radiation transmission model and historical atmospheric ocean data, and initial inversion results of sea surface salinity, sea surface temperature, sea surface wind speed, total water vapor amount and total liquid water amount are obtained. On the basis, a least square method is adopted to carry out staged optimization inversion on observation data of different frequency bands, inversion results of all sea surface elements are updated step by step, and fine inversion of sea surface salinity and sea surface wind speed is realized through active and passive joint inversion. According to the method, collaborative inversion of multiple elements of the sea surface is realized under the condition of not depending on external background field data, and the inversion precision and stability are improved.

The application provides a method and device for in-orbit calibration of scatterometer backscattering coefficients based on optical remote sensing features, and relates to the technical field of ocean remote sensing calibration. The method obtains ocean optical remote sensing images and scatterometer backscattering coefficient observation data that satisfy preset matching conditions, and extracts optical features representing sea surface roughness changes from the ocean optical remote sensing images, so that the optical remote sensing information can be used as an external constraint source independent of the scatterometer microwave observation system. Further, by performing geometric normalization processing on the optical features and aggregating the geometric normalization processed optical features to a wind vector element scale, a statistical corresponding relationship between the element scale optical features and the scatterometer backscattering coefficient observation data can be established at a unified scale, and the availability and reference stability of the paired samples are improved.

A scatterometer for measuring a property of a target on a substrate includes a radiation source, a detector, and a processor. The radiation source produces a radiated spot on the target. The scatterometer adjusts a position of the radiated spot along a first direction across the target and along a second direction that is at an angle with respect to the first direction. The detector receives radiation scattered by the target. The received radiation is associated with positions of the radiated spot on the target along at least the first direction. The detector generates measurement signals based on the positions of the radiated spot on the target. The processor outputs, based on the measurement signals, a single value that is representative of the property of the target. The processor also combines the measurement signals to output a combined signal and derives, based on the combined signal, the single value.

The invention discloses a scatterometer calibration task planning method and device, equipment, a medium and a product, and relates to the field of micro-nano satellite networking calibration task planning, and the method comprises the steps: constructing a target characteristic model of a backscattering coefficient; independent variables of the target characteristic model of the backscattering coefficient are a preset sampling moment and an incident angle and an azimuth angle measured by the satellite scatterometer at an observation point at the preset sampling moment, and dependent variables are the backscattering coefficient measured by the satellite scatterometer at the observation point at the preset sampling moment; obtaining an initial state of reinforcement learning according to the target characteristic model of the backscattering coefficient; the state comprises a backscattering coefficient; based on the initial state of reinforcement learning, adopting a reinforcement learning method to obtain actions under each iteration number; the action is a scatterometer calibration task planning scheme; and evaluating the action under each iteration number to obtain an optimal scatterometer calibration task planning scheme. According to the invention, the calibration efficiency can be improved, and the adaptability to extreme or unseen observation scenes can be enhanced.

The invention provides an offshore wind field secondary fusion method and device based on satellite-borne active and passive microwave remote sensing, and the method comprises the steps: obtaining multi-source satellite-borne remote sensing sea surface wind data, and taking numerical prediction data or reanalysis data as first background field data; performing gridding processing on the scatterometer sea surface wind field data and the radiometer sea surface wind speed data, performing east-west direction and south-north direction wind speed component calculation on the scatterometer sea surface wind field data, and performing 10m height wind field extraction and equivalent neutral wind conversion on background field data; performing space coverage range matching processing and time window matching synthesis processing on the scatterometer sea surface wind field data, the radiometer sea surface wind speed data and the first background field data; performing first fusion on the scatterometer sea surface wind field data to obtain a first sea wind field with seamless grid points; and taking the first offshore wind field as second background field data, carrying out second fusion on the radiometer sea surface wind speed data, and generating a second offshore wind field with seamless grid points.

The invention discloses a cloud layer directional scheduling method based on a low earth orbit satellite and a 5G network, and belongs to the technical field of artificial influence weather. According to the method, a cloud electromagnetic fingerprint spectrum is generated through cross-domain fusion data of a low-orbit satellite constellation and a ground scatterometer array; deploying a federated learning framework based on 5G MEC edge nodes, and constructing a cloud layer behavior prediction model with a transfer learning capability; a low earth orbit satellite and 5G base station cooperative beam forming technology is adopted, and quantum encryption is combined to generate a scheduling instruction; constructing a multi-modal intervention effect evaluation matrix by using 5G inductance integration characteristics; constructing a cross-regional computing power pool through an inter-satellite laser link, and dynamically allocating resources based on a particle swarm optimization algorithm; a block chain technology is introduced to form a self-evolution scheduling closed loop. According to the method, accurate perception, prediction, intervention and evaluation of the cloud layer are realized, the precision, efficiency and safety of directional regulation and control of the cloud layer are improved, and technical support is provided for coping with extreme weather.