133 results about "Convection" patented technology

The invention relates to the technical field of data processing, and discloses a hazardous chemical substance leakage and diffusion prediction method and system based on an unmanned ship, and a storage medium. The method comprises the following steps: acquiring water quality, weather and geographic data through an unmanned ship sensor, and processing to obtain an initial data set; calculating a diffusion range according to the data, optimizing a monitoring area, and generating a cooperative monitoring track; performing exception elimination and smoothing processing on the data, and constructing spatial distribution; solving a convection diffusion equation, and establishing a three-dimensional flow field model; analyzing a hazardous chemical substance diffusion path, and predicting pollutant distribution; and establishing a risk assessment system, and generating emergency disposal suggestions and decision-making schemes. According to the application, rapid and accurate monitoring of the polluted area is realized through the multi-parameter sensor array carried by the unmanned ship, and accurate prediction of the hazardous chemical substance pollutant diffusion process is realized in combination with multi-source data fusion processing and a three-dimensional flow field simulation technology.

The invention relates to the technical field of weather forecast, discloses a method for classifying severe convection weather forecast, and aims to solve the problem that complexity of severe convection weather requires multi-dimensional data support, so that a three-dimensional data acquisition network covering'ground-air-sky 'needs to be constructed. Ground observation data need to include minute-level rainfall, hourly air temperature and humidity (emphatically paying attention to humidity difference between 850hPa and 500hPa and reflecting unstable stratification) and 10-minute average wind speed of a meteorological station, and high-altitude detection data need to extract temperature vertical profiles (calculating convective condensation height LCL) and wind speed vertical shear (shear values of 0-3km and 0-6km) at 08 o'clock and 20 o'clock every day. According to the method for classifying severe convection weather forecast, new signals (such as sudden cloud top brightness temperature drop) observed in real time are rapidly absorbed, and meanwhile, the method is adaptive to severe convection characteristic differences of different areas (such as mountainous areas and plains) and different seasons, so that the forecast precision is improved, and the requirements of refined disaster prevention for high-accuracy and high-timeliness forecast are met.

The present invention relates to the field of meteorological technology, and discloses a method for identifying and forecasting severe convection based on deep learning. The method first constructs a severe convection identification model and a forecasting model, which are used for feature classification and prediction of weather occurrence time, location and intensity, respectively. The two models are trained by a deep learning algorithm, and their residuals are used to construct a residual fitting model to correct the forecast results. Combined with real-time and historical meteorological data, the method can generate accurate severe convective weather identification and forecasting, effectively make up for the limitations of traditional methods, improve forecast accuracy and timeliness, and is of great value to disaster prevention and mitigation and ensuring stable social and economic development.

The invention provides a mountain wind power plant fan icing intelligent forecasting method and system, and relates to the field of fan icing forecasting. The method comprises the following steps: determining a meteorological element threshold value of a fan in an icing scene and a meteorological element threshold value of the fan in an ice melting scene; establishing a temperature-wind speed-power dynamic response model; constructing a three-layer linkage model for fan icing prediction; the first-layer model judges an icing condition in a preset range around the station based on a meteorological element threshold value in an icing scene; the second-layer model is started before the first-layer model judges the icing condition, and a convection radiation ice melting scheme is coupled when the meteorological condition reaches a meteorological element threshold value in an ice melting scene; and on the premise of the second-layer model, the third-layer model timely triggers graded early warning based on a temperature-wind speed-power dynamic response model, and outputs a forecasting and early warning result. According to the method, the regional adaptability and precision of fan icing forecasting are improved, risk level early warning is optimized, and about 10% of power generation loss of a wind power plant is reduced.

The invention is suitable for the technical field of rainfall forecast correction, and provides a regional short-term rainfall quantitative forecast machine learning correction method integrated with weather system information, and the method comprises the steps: determining an initial forecast factor based on meteorological elements in an ECMWF mode, the meteorological elements comprise a precipitation field, ground transformation, convection effective potential energy, UV wind fields of 500hPa, 850hPa and 925hPa, relative humidity, temperature and vertical speed; taking the dichotomy labels corresponding to the 500hPa slot line and the tangent line between 850hPa and 925hPa as newly added forecast factors, and constructing a four-dimensional training set according to the initial forecast factors and the newly added forecast factors; converting the four-dimensional training set into a three-dimensional training set; training a random forest regression model through the three-dimensional training set, and outputting a rainfall forecast field within a preset time; according to the CMASH3 mode rainfall forecast data and a preset judgment logic, dynamically correcting the rainfall forecast field within a preset time, and generating a target rainfall forecast product; and the accuracy of heavy rainfall forecasting is effectively improved.

The invention provides a ground wind speed measurement and calculation correction method and device considering convective complex environment characteristics. The method comprises the following steps: acquiring original historical data of a target station; preprocessing the original historical data to generate standardized historical data; according to the threshold values of the wind speed, the duration time and the convection parameters, the type of the gale process is judged on the basis of standardized historical data such as meteorological observation and forecast, early warning information and disaster-causing events, and a common case, a cold air systematic gale case and a convective gale case are screened out; the method comprises the following steps: training and debugging LSTM, XGboost and Stacking models under each category based on standardized historical data such as weather forecast and weather observation according to different gale process categories, and generating a wind speed measurement and calculation classification correction model; and for real-time wind speed measurement and calculation, judging the type of a gale process, and automatically matching a proper wind speed measurement and calculation correction model in the wind speed measurement and calculation classification correction model to correct wind speed measurement and calculation.

The invention relates to the technical field of artificial intelligence and aviation technology crossing, and discloses a multi-environment aircraft trajectory prediction method based on a space-time self-attention mechanism, and the method comprises the steps: obtaining historical trajectory data of a target aircraft and synchronous meteorological data, namely wind speed, wind direction and air pressure; s2, extracting time sequence features of the historical trajectory through a time-space self-attention network (TSAN), wherein the time sequence features specifically comprise local time sequence feature extraction by adopting a time convolution block; according to the multi-environment aircraft trajectory prediction method based on the space-time self-attention mechanism, the prediction precision is remarkably improved, on a TrajAir high-dynamic data set, the average displacement error (ADE) is reduced by 27.7% to 0.68 m, and the final displacement error (FDE) is reduced by 26.7% to 1.37 m. The FDE is reduced to 1.29 m (39.7%) from 2.14 m in severe convection weather, and the problem of track abrupt change caused by wind direction jump of a traditional model is effectively solved.

The invention discloses a decision control system and method based on temperature field and atmosphere analysis, and relates to the technical field of intelligent control. A monitoring module in the system obtains a temperature set and a gas concentration set; the trend analysis module constructs a three-dimensional model and establishes a coordinate system, a variation function is combined with a Kriging equation to supplement a temperature set to generate an expanded temperature set, the three-dimensional model is discretized into a plurality of nodes and is matched with points in the expanded temperature set through nearest neighbor search, a heat conduction equation and a convection boundary are set, and a finite element matrix equation set is constructed; solving to obtain a temperature field model and carrying out statistics to generate a temperature trend curve; a decision module compares the temperature trend curve, the gas concentration set and a process standard to obtain an abnormal section number, and analyzes a section temperature difference and a concentration error vector of the abnormal section number by adopting a fuzzy PID control algorithm to generate an adjustment vector; and the execution module performs automatic adjustment based on the adjustment vector, so that intelligent decision adjustment based on temperature field and atmosphere conjoint analysis is realized.

The invention provides an oil shale formation temperature field simulation method and system for fluid injection heating mining, a storage area is divided into a plurality of unit mining spaces according to geologic feature data of the storage area to be mined, a mass conservation operation model corresponding to fluid injection in-situ mining is constructed for each unit mining space based on the principle of mass conservation, and the temperature field of the oil shale formation is simulated. According to the principle that high-temperature fluid carries heat to conduct heat transfer and exchange with a rock mass system in a conduction and convection mode and the influence on a temperature field in a rock mass, an energy operation model corresponding to fluid injection in-situ mining is built; preset initial parameters are introduced, the mass conservation operation model and the energy operation model are solved based on the discrete process, initial mass field data and energy field data are calculated, and then temperature field data of subsequent time are calculated in combination with a Newton iteration method. According to the scheme, the limitation of low precision of existing temperature field simulation is overcome, and rigorous and accurate temperature field space-time distribution of the oil shale under fluid injection heating is obtained.

The invention discloses a tornado identification method and device under a power failure, equipment and a medium, and belongs to the field of power weather forecast. According to the method, meteorological data of a three-dimensional space of a power grid fault area are obtained, high-altitude and low-altitude circulation situation analysis is carried out respectively to obtain a weather background trend and a small and medium-scale weather process, and a vertical structure model under an air temperature-air pressure logarithmic coordinate system is constructed to analyze thermal instability, dynamic conditions and convection intensity characteristics. And performing comprehensive judgment by combining a high-altitude feature judgment condition and a ground feature judgment condition. According to the method, the limitation of traditional radar monitoring in a complex terrain area is broken through, precise identification of the tornado is achieved through multi-dimensional meteorological element coupling analysis, an objective basis is provided for power grid fault attribution, and the disaster prevention emergency capacity of electric power facilities in extreme weather is remarkably improved.

The invention relates to the technical field of meteorological monitoring, in particular to a satellite fusion internet-of-things meteorological early warning issuing method, which comprises the following steps of: firstly, acquiring data of a stationary satellite, a low-orbit satellite, millimeter wave communication, ground optics and an electric field, and calibrating and registering to form a unified observation set; a multi-modal diagram is constructed, the optimal transmission mapping meeting mass conservation is obtained through quantum annealing, and three-dimensional wind speed, specific humidity and temperature fields are reconstructed to generate potential energy tensors; establishing a potential energy function based on the potential energy tensor, and judging convection nucleation through Hamiltonian Monte Carlo sampling minimum action path; a coring event is packaged into an early warning message, a signature and a delay function timestamp are added, then the early warning message is written into a right and interest proof block chain, a named data network controlled by reinforcement learning is used for self-adaptive copying and distribution, and meanwhile, an action quantity threshold value and a regularization coefficient are dynamically adjusted. According to the invention, minute-level advance, second-level issuing and whole-process traceable meteorological early warning are realized.

The invention belongs to the technical field of meteorology, and discloses a typhoon intensity prediction and residual error correction method and system based on a double-branch convolutional neural network, and the method comprises the steps: obtaining satellite-typhoon data; data preprocessing and space-time matching: constructing and cleaning a sample set; building a double-branch CNN (Content Node Network)-DenseNet network; model training; and carrying out residual analysis and system error correction. According to the method, key physical information such as severe convection and rapid enhancement of a typhoon core area is fully mined by combining a traditional multi-channel satellite cloud picture and an infrared-water vapor channel difference physical indication factor, and the perception ability of the model to a complex typhoon structure and a development process is effectively improved.

The embodiment of the invention discloses a photovoltaic power characteristic difference prediction method and system under different weather conditions and a medium. The method comprises the following steps: determining thermodynamic diagram data carrying dust type distribution information according to particle size analysis data; determining airflow turbulence characteristic parameters based on the three-dimensional velocity field data and the pressure distribution data; constructing a dust removal effect model according to the thermodynamic diagram data and the airflow turbulence characteristic parameters, and establishing a mapping relation between turbulence intensity and a dust removal effect; a power recovery speed prediction model is constructed according to the power output data of the photovoltaic panel under different dust removal effects, and a mapping relation between the dust removal effects and the power recovery speed is established; constructing a quantitative relation model under the severe convection weather condition based on the mapping relation; and according to the quantitative relation model and a preset photovoltaic power prediction model under a non-severe convection weather condition, constructing a photovoltaic power characteristic difference prediction model to perform photovoltaic power prediction under different weather conditions, thereby realizing accurate prediction of power recovery of the photovoltaic power station under severe convection weather.

The invention discloses a buried pipe temperature response calculation method considering underground water seepage and ground temperature gradient, and belongs to the field of building environment and energy application engineering.The buried pipe temperature response calculation method comprises the steps that a shallow buried pipe heat exchanger is regarded as a finite long-line heat source model, and when underground water seepage passes, the shallow buried pipe heat exchanger is regarded as a finite long-line heat source seepage model; an energy control equation containing a soil heat conduction item, an underground water seepage convection item and a ground temperature gradient correlation item at the same time is established, and initial temperature distribution based on ground temperature gradient, ground surface fixed wall temperature and boundary conditions of an underground limited boundary are set; and finally, obtaining an analytical solution expression of the change of the temperature at any position of the underground soil along with the time through analytical solution. According to the method, the defect that a traditional model ignores key environmental factors is overcome, the heat transfer process of heat conduction and convection coupling can be accurately simulated, the accuracy of temperature field prediction and the reliability of long-term performance evaluation are remarkably improved, meanwhile, the complexity of numerical calculation is avoided, and the calculation efficiency is improved.

The invention discloses a thunderstorm gale regression analysis method and system based on multi-stream parameters, and relates to the technical field of data processing. The method comprises the following steps: collecting multi-stream parameters in real time, and constructing a real-time multi-stream parameter sequence; mapping the real-time multi-flow-parameter sequence to a pre-constructed multi-flow-parameter coupling sensitivity field model, and calculating to generate a real-time convection sensitivity field; inputting the real-time convection sensitivity field into the physical consistency constraint model to generate real-time structured convection characteristics; main regression analysis under multi-flow parameter regression is executed, and a main regression result is established; and after the real-time local disturbance index is calculated, disturbance residual compensation of the main regression result is triggered, dynamic weight adjustment is carried out on the main regression result after residual compensation by utilizing seasonal phase characteristics, and a thunderstorm gale regression analysis result is output. The technical problem that thunderstorm gale prediction accuracy is insufficient in the prior art is solved, and the technical effect of improving thunderstorm gale regression analysis result accuracy is achieved.

The invention discloses a plateau rainfall fusion method and a plateau rainfall fusion system based on multi-source meteorological collaboration, and relates to the technical field of plateau rainfall fusion methods, and the method comprises the steps: obtaining a multi-source rainfall product, a meteorological covariable and high-resolution digital elevation model data, carrying out the time-space alignment and normalization processing of the multi-source rainfall product and the meteorological covariable, and obtaining the high-resolution digital elevation model data; forming a multi-source meteorological data set; based on physical variables in the multi-source meteorological data set, constructing four physical process links of water vapor transportation, terrain uplift, convection triggering and cloud microphysics, generating attention weight vectors through sub attention branches corresponding to the links, obtaining a causal attention weight matrix after weighted fusion, and performing weighted fusion on multi-source rainfall products to obtain a multi-source rainfall data set; generating a causal fusion feature map; wavelet decomposition is carried out on the causal fusion feature map and meteorological variables, large-scale components and small-scale components are separated out, and meanwhile the terrain uplift rate and the included angle between the wind direction and the slope direction are calculated according to the digital elevation model data and the wind field components.

The invention belongs to the technical field of oil and gas field development, and discloses a temperature distribution calculation method, system, equipment and medium for reservoir in-situ electric heating, and the method comprises the following steps: constructing a physical stratum model of a near wellbore zone stratum; based on the physical stratum model, constructing a mathematical physical equation of heat conduction and heat convection phenomena in the stratum by combining the law of conservation of energy; calculating oil phase flow velocity data of an oil phase in the physical stratum model; calculating internal heat source intensity distribution data generated by in-situ electric heating in the physical stratum model; and substituting the oil phase flow velocity data and the internal heat source intensity distribution data into the mathematical physical equation, and obtaining the temperature distribution data of the stratum through integral solution. According to the technical scheme, the precision and reliability of temperature distribution calculation can be improved.

The invention discloses a convective gale recognition and analysis method based on machine learning, and belongs to the technical field of weather forecast and artificial intelligence technology crossing, and the method comprises the following steps: multi-source data acquisition and feature fusion: obtaining multi-source meteorological data of a target region in a preset time window, the multi-source meteorological data at least comprises numerical mode forecast data, satellite remote sensing data and ground observation data. According to the invention, by introducing the space-time deep learning model and combining the three-dimensional convolution and recurrent neural network, the evolution rule of the weather system in the space-time dimension is effectively captured, and the perspectiveness and continuity of convective gale recognition are improved. According to the invention, by integrating an interpretability technology and a credibility evaluation mechanism, the transparency and reliability of the model in business application are improved. According to the method, through a self-supervised pre-training and lightweight fusion strategy, the adaptability and the calculation efficiency of the model are improved while the performance is ensured.

The invention belongs to the technical field of power transmission channel meteorological monitoring, and particularly relates to a power transmission channel strong convection strong wind observation method and system. Determining an observation mode adopted for strong convection gale observation according to the environmental condition parameters; determining a strong convection gale observation result of the target power transmission channel based on the selected observation mode; the observation mode comprises laser or microwave. According to the technical scheme provided by the invention, the advantages of high sensitivity of the laser wind measurement technology and strong penetration, weak attenuation and the like of the microwave wind measurement technology are fused, the laser radar and the microwave radar are systematically integrated, and the current external environment is automatically judged, so that laser / microwave is adaptively switched to serve as a main channel for strong convection strong wind observation; the observation efficiency and the observation accuracy are improved, and then accurate observation of strong convection strong wind of the power transmission channel is achieved.

The invention discloses a ResNet-based lightning-three-dimensional proxy reflectivity inversion method, which belongs to the technical field of application meteorology, and comprises the following steps: obtaining lightning positioning observation data, screening, quality control and classification of lightning positioning observation records, determining grids, counting data at grid points of the grids, and processing the counting data. Calculating a space coefficient, a time coefficient and a space-time coefficient, sorting the lightning positioning observation records of each grid point, processing the sorted lightning positioning observation records and other data to obtain the maximum proxy reflectivity, obtaining the three-dimensional proxy reflectivity through the maximum proxy reflectivity and the characteristics of the atmospheric environment where the three-dimensional proxy reflectivity is located, and calculating the spatial coefficient, the time coefficient and the space-time coefficient of the three-dimensional proxy reflectivity. Therefore, three-dimensional proxy reflectivity inversion is realized. According to the method, inversion of the three-dimensional proxy reflectivity is more accurate, and the problem that the severe convection weather monitoring and early warning capacity is insufficient in a radar detection blind area is effectively solved.

The invention discloses a Doppler weather radar-based static meteorological satellite convection primary product evaluation method, relates to the technical field of meteorological satellite convection primary product evaluation, and solves the problems that the existing method does not consider the splitting and merging conditions of cloud clusters, cannot invert and calculate the satellite cloud wind guide when there is less cloud or no cloud, and is inconvenient to use. And the convection primary product cannot be evaluated. According to the method, a convection primary event is extracted from three-dimensional weather radar data to serve as a real condition, a static meteorological satellite convection primary product is evaluated, and the method comprises the following steps of: identifying the convection primary event in the excessive Puler weather radar by taking a reflectivity value greater than or equal to 35dBz as a standard; radar convection inception is taken as a real condition, a neighborhood method oriented to a convection inception area is adopted, the hit rate, the empty report rate, the TS test index and the time advance of satellite convection inception are calculated, and a geostationary satellite convection inception product is objectively evaluated from the time-space perspective, so that the method can be better applied to early warning of severe convection weather.

The invention discloses an inflow temperature calculation method and device for a coupled oil reservoir-wellbore model.The inflow temperature calculation method comprises the steps that firstly, for an oil reservoir grid corresponding to a wellbore section, a radial flow area from the equivalent radius of the oil reservoir grid to the well wall is defined; secondly, a radial steady-state temperature second-order ordinary differential equation describing temperature distribution in the area is established, and the equation comprehensively takes heat convection, viscous dissipation caused by pressure gradient, Joule-Thomson effect, heat conduction and other micro heat effects into account; thirdly, setting boundary conditions for coupling the oil reservoir and the wellbore state for the equation; and finally, solving the equation with complete configuration to obtain radial temperature distribution, and determining the temperature at the well wall as the accurate inflow temperature. Compared with the prior art, the method has the advantages that the inflow temperature model with more complete mechanism and more accurate solution is established, so that the accuracy and reliability of output profile inversion interpretation are remarkably improved.

The application discloses a non-orthogonal transducer array three-dimensional ultrasonic wind speed and direction instrument which can be carried on a unmanned aerial vehicle and applied to the field of wind speed and direction instruments, and the non-orthogonal transducer array is adopted to reduce the influence of turbulence on wind measurement accuracy; when the turbulence degree generated by one group of transducers is relatively large, the other two groups of transducers can compensate the data, so that high-precision wind speed and direction measurement is realized; meanwhile, when the unmanned aerial vehicle encounters strong wind, the probe fixing rod will be broken, so that the unmanned aerial vehicle is prevented from losing control due to the large wind area of the orthogonal annular support, and the fixed base plate and the elastic thin rod are arranged to recycle the broken and fallen orthogonal annular support and ultrasonic probe, so that the high-altitude falling object is not easy to form, and the orthogonal annular support and the ultrasonic probe are effectively protected, the subsequent recycling and reuse of the orthogonal annular support and the ultrasonic probe are facilitated, and the wind direction and speed detection in strong convection weather such as strong wind is applicable.

This invention relates to the field of AI weather forecasting technology and discloses a weather forecasting method based on an AI weather forecasting model and a WRF model. The method includes: inputting ERA5 global atmospheric reanalysis gridded data into the AI ​​weather forecasting model to obtain an assimilated background field; constructing an effective multi-source observation vector based on observation data from ground automatic weather stations, radiosonde observation data, geostationary meteorological satellite radiance data, and Doppler radar VAD wind profile data; solving for the optimal atmospheric analysis field; extracting atmospheric state variables from the optimal atmospheric analysis field and writing them into the WRF model's side boundary driving file, driving the WRF model to complete the integration operation, and obtaining a gridded forecast field of meteorological elements. This method enables the WRF boundary condition update frequency to be higher than the traditional 6-hour static scheme, effectively characterizing the rapid evolution of the atmospheric state in the boundary region at the sub-hourly scale, thereby improving the WRF model's forecasting capability for high-impact weather events such as severe convection.

The application discloses a LES super-high-resolution TC atmospheric flow field numerical simulation method and system, and relates to the fields of atmospheric observation, numerical assimilation and high-resolution numerical simulation. The method is characterized in that: three-dimensional dynamic-thermal information is obtained by implementing airship platform radar observation and downward sounding observation in the core area of a tropical cyclone; the radar radial wind and profile observation operators are preprocessed and constructed; a data assimilation strategy is adopted to integrate multi-source observation in a multi-nested grid numerical model to generate a three-dimensional initial / reanalysis field suitable for large eddy simulation; then, a large eddy simulation solver is configured on the innermost grid covering the boundary layer of the tropical cyclone; fine horizontal grids and boundary layer encryption vertical stratification are adopted to explicitly analyze the boundary layer vortex, small-scale convection and turbulent structure; and a super-high-resolution three-dimensional flow field product for fine evaluation of strong wind and disaster risk is output.

The present invention discloses a heat exchange sleeve device and method for efficiently exploiting geothermal resources, and relates to the technical field of energy recovery equipment. The present invention includes a geothermal layer, wherein a geothermal pipe is installed inside the geothermal layer, and a heat-absorbing metal sheet is installed on the bottom surface of the geothermal pipe; a circulation component is provided on the top of the geothermal layer, and the circulation component includes a main water tank. The present invention can circulate and recover the heat energy in the geothermal layer through the setting of the circulation component. When the working medium circulating fluid enters the geothermal pipe, it forms convection around the geothermal fluid, realizes heat transfer, and effectively improves the utilization efficiency of thermal energy. Through the setting of the diversion component, the opening and closing of the first circulation pipe and the second circulation pipe can be controlled. By changing the circulation direction of the second circulation pipe, the working medium circulating fluid can be concentratedly introduced into the auxiliary water tank, which is convenient for use in an external low-temperature environment, further improving the utilization efficiency of thermal energy.

The invention discloses a power transmission line fault prediction and strategy generation method and system in severe convective weather, and the method comprises the steps: obtaining the grid weather forecast data of a target region, extracting severe convective weather parameters, calculating the meteorological threat index of a power transmission line based on the data, dividing the power transmission line into a plurality of line units, and carrying out the fault prediction of the power transmission line. Extracting anti-disaster toughness attributes of the line units, fusing meteorological threat indexes of meteorological grid points of the positions where the line units are located, predicting failure rates of the line units through a pre-trained machine learning model, determining risk levels of the line units according to the failure rates, and combining a preset risk level-disposal rule mapping library to determine the risk levels of the line units. And generating and pushing a corresponding coping strategy for each line unit. According to the method, the fault rate of the individual line unit is accurately and quantitatively predicted by using the pre-trained machine learning model, the early warning accuracy, the strategy performability and the operation and maintenance resource use efficiency are greatly improved, and an effective technical support is provided for ensuring safe and stable operation of a power grid in extreme weather.

The present invention discloses a near-term extrapolation forecasting method for fast-moving quasi-linear convection. The method comprises the following steps: within half an hour of the onset of the forecast, the echo is extrapolated using the semi-Lagrangian extrapolation method based on the mixed extrapolation vector field of the convective monomer; and half an hour after the onset of the forecast, the echo is extrapolated using the semi-Lagrangian extrapolation method based on the extrapolation vector field of the convective organization propagation characteristics. This method can improve the short-term extrapolation forecast accuracy of fast-moving quasi-linear convection and significantly enhance the early warning capability of fast-moving quasi-linear convection, representing a technological breakthrough for meteorological disaster prevention and mitigation of severe convection.

The invention discloses a polar region temperature difference phase change power generation device and method, and belongs to the technical field of renewable energy sources. The device comprises a vertical shaft wind generating set, an energy storage tank, a heat convection circulation pipeline system and a floating body device, the energy storage tank is filled with a low-boiling-point refrigerant medium, and the surface of the heat convection circulation pipeline system is covered with a thermoelectric power generation piece. When temperature difference exists between the environment temperature and the energy storage tank, the high-temperature end medium is vaporized to form pressure difference to drive steam to push the turbine to generate electricity; after acting, the steam is condensed and releases heat at the low-temperature end, and waste heat secondarily generates power through the thermoelectric power generation sheet; by adapting to media with different boiling points, the device can adapt to polar region seasons and day and night temperature difference changes, and continuous and stable power generation is achieved; the device is simple in structure, free of pollution and suitable for polar regions and other large-temperature-difference environments.

The invention discloses a mountain area power transmission line rime icing prediction method and system, and relates to the technical field of power transmission line icing prediction.The mountain area power transmission line rime icing prediction method comprises the following steps that the vorticity gradient of terrain disturbance is calculated based on terrain features and wind speed vectors in meteorological data; a momentum relaxation equation of the supercooled water drops is established according to the vorticity gradient, and the deviation degree between the water drop movement track and the airflow streamline is solved; according to the deviation degree, carrying out space redistribution on the content of supercooled liquid water in the meteorological data to obtain the content of effective liquid water; based on the effective liquid water content, iteratively solving the real-time ice density by using an energy balance physical model and calculating the initial value of the icing thickness; and performing feature coupling on the initial value of the icing thickness and the vorticity gradient, inputting a machine learning model to predict a systematic deviation value, and correcting the initial value of the icing thickness to obtain the final icing thickness. According to the method, the influence of terrain disturbance on the flow field is quantified through the vorticity gradient, and the prediction precision of rime icing of the power transmission line in the mountainous area is remarkably improved.