80 results about "Convection" patented 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 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 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 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 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 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 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 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 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.

The invention relates to a method and a system for simulating dynamic evolution of a nitrogen element in a melting process of an anti-hydrogen steel laser powder bed. The method comprises the following steps: constructing a three-dimensional model of a powder bed through a discrete element method, and simulating different energy input characteristics by adopting Gaussian, flat-topped, annular and Bessel laser heat sources; establishing a heat transfer model following mass, momentum and energy conservation by using a volumetric method, and setting natural convection, a thermal radiation boundary, recoil pressure and gas-liquid interface changes; according to a macrosegregation and solute migration mechanism, a motion model of a nitrogen element between a liquid phase and a solid phase is constructed, a critical nitrogen content threshold value is introduced, and the gasification escape and bubble forming process of the nitrogen element is simulated. According to the method, on the basis of the molten pool temperature field and flow field detailed nitrogen element distribution / escape simulation result, the molten pool dynamic mode and melt convective heat transfer and mass transfer evolution law under the laser time-domain airspace shaping condition can be clarified, the nitrogen element distribution / escape characteristics are clarified, and the influence mechanism of time-domain airspace laser shaping regulation on pore defect formation is revealed.

This invention discloses a method for designing a thermostat in a porous medium thermal convection environment based on phase change materials. The thermostat employs a double-layer structure. Under the background of uniform flow and linear temperature change, three concentric circles are set in the middle of the background region, with the small circle forming the central region. The inner annular region, outer annular region, and background region are all covered with isotropic porous media. The inner annular region is the functional region, which uses a phase change material with asymmetric thermal conductivity and permeability. The changes in thermal conductivity and permeability with temperature satisfy the temperature trapping condition to maintain a constant temperature in the central region. The ratio of thermal conductivity and permeability of the media between the outer annular region and the background region is governed by the same geometric relationship, satisfying the dual stealth conditions of thermal field and flow field. This invention provides a new thermal control scheme for porous medium systems, microfluidic systems, and other fields.

The application discloses a kind of submarine plateau subduction three-dimensional spherical dynamics model construction methods, steps are as follows: solving mass, momentum, energy and composition conservation equation under the influence of neglecting compressible fluid dynamics;Apply mantle convection activity;Apply viscosity jump;Shape independent plate tectonics behavior;Embedding with multi-parameter attribute submarine plateau;After model test, finally complete the construction of three-dimensional spherical earth dynamics model.The three-dimensional numerical model constructed adopts three-dimensional spherical tectonic method with independent plate tectonics behavior, by introducing different sizes and different rheological state parameters, fine description is carried out to submarine plateau.The model can not only accurately reproduce the bending and fracture of plate and other geodynamic phenomena in the process of submarine plateau subduction, but also can reveal the difference of local trench tectonic response of different size and rheological property submarine plateau, and realize the cross-scale simulation from local structure to global structure influence.

The invention relates to the technical field of incompressible thermal fluid simulation, and discloses an incompressible thermal fluid simulation method and device and a storage medium, and the method comprises the steps: calculating the convection flux of a particle distribution function and a temperature distribution function of a grid unit according to the particle distribution function and the temperature distribution function on a ghost unit and the information of the grid unit; executing a collision term of the particle distribution function and the temperature distribution function of the grid unit according to the particle distribution function and the temperature distribution function on the ghost unit; according to the convection flux, the collision term and the particle distribution function and the temperature distribution function of the updated grid unit, any large-scale complex flow field can be processed, and the grid precision at the boundary can be improved at the complex boundary, so that the information at the boundary layer of the incompressible heat flow field can be simulated more accurately; the method can be used for simulating the practical application problem of large-scale incompressible heat flow and simulating a large-scale complex heat flow field.

The application provides a weather radar network wind field self-adaptive correction method and system, the method comprises the following steps: according to the radar coverage condition of the multi-radar network in the target area, adopting the local least square method or the three-dimensional variation method based on multi-radar observation to carry out wind field inversion, and obtaining a first wind field; according to the second wind field obtained by the observation equipment in the target area, the first wind field is fused and corrected; based on the digital elevation model data, the terrain forced correction is carried out on the fused and corrected wind field, and the final wind field is obtained. The application can obtain more fine and more physically consistent real-time wind field under the condition of complex terrain and strong convection, and provide more reliable data support for the monitoring, prediction and mechanism research of disastrous weather.

The invention discloses a method for preventing a power transmission line in advance in severe convection weather, and belongs to the technical field of power transmission line maintenance. The method comprises the steps of collecting meteorological data of a meteorological station and historical and actual measurement data of a weather radar, constructing and training an improved weather prediction model, and outputting a weather data prediction result; the unmanned aerial vehicle laser radar scans to obtain a three-dimensional point cloud model of the power transmission line, extracts tower and topographic features, analyzes lightning stroke risks and outputs lightning danger levels; acquiring real-time meteorological data of the tower, correcting the weather prediction model, and outputting a danger level of wind; prevention measures are provided in advance according to danger levels of thunder and wind. Through multi-source data fusion and dynamic model correction, the problems of insufficient refinement, poor real-time performance and lack of effective prevention measures in the prior art are solved, the safe operation guarantee capability of the power transmission line is improved, and the operation and maintenance cost is optimized.

The application discloses a kind of salt cavern helium storage wellbore-cavity-surrounding rock temperature field prediction method.Real-time monitoring salt cavern helium storage operation dynamics, including but not limited to wellhead pressure and injection-production rate and other data, based on the collected helium injection-production operation data, combined with the basic parameters of wellbore composite structure, the numerical calculation of helium flow and heat transfer equation is carried out;Again based on the prediction result of helium temperature field in wellbore, the convection law of helium in cavity is solved, and then the coupling heat transfer between cavity and surrounding rock is calculated, to realize the accurate prediction of wellbore-cavity-surrounding rock temperature field finally.

The invention discloses a Leidenfrost effect three-dimensional numerical simulation method based on a double-distribution function lattice Boltzmann method coupled with a finite difference method. The method comprises the following steps: 1, constructing a three-dimensional computational domain and defining lattice point types; 2, initializing a lattice point type, a fluid domain density field, a velocity field, a temperature field and a distribution function; 3, calculating fluid domain density, pressure and thermal diffusion coefficient according to the distribution function; 4, calculating resultant force of molecular interaction force and volume force among different phase states based on a pseudo-potential model; 5, calculating a fluid domain collision process; 6, calculating a migration process; 7, treating wall surface boundary conditions; 8, updating the fluid domain temperature field by using the temperature distribution function, and updating the solid domain temperature field by using a finite difference method; and 9, carrying out loop iteration on the steps 3-8 until the preset simulation time is reached. According to the invention, the whole process of the Leidenfrost phenomenon generated when the liquid drops impact the high-temperature wall surface can be stably and accurately simulated, and an accurate and reliable numerical simulation tool is provided for the design of efficient heat dissipation schemes such as spray cooling and the like.

The invention provides a severe convection identification method and device based on GNSS occultation observation data, and the method comprises the steps: collecting original observation data, carrying out the induction and sorting of the original observation data according to an established real-time data pool, and carrying out the quality control of the sorted original observation data, so as to obtain quality control observation data; performing pattern gridding on the quality control observation data, and interpolating the quality control observation data to numerical pattern grid points through a multi-source constraint adaptive interpolation algorithm to form severe convection single puzzle data; performing data assimilation on the strong convection monomer puzzle data based on a Bayesian-Gaussian mixture assimilation model constrained by observation data to generate an analysis field; and driving a global weather forecasting mode to carry out numerical forecasting hour by hour according to the analysis field so as to identify the severe convection monomers. According to the invention, the precision and efficiency of meteorological data processing, fusion, forecasting and severe convective weather monitoring and early warning are significantly improved, and the precise forecasting capability of severe convective weather is improved.

The invention discloses a four-dimensional data assimilation method and system fused with space-time observation, and relates to the technical field of numerical weather forecast, and the method comprises the steps: generating a small set containing a plurality of set members based on a current analysis field, and enabling the set members to be generated through applying initial disturbance; performing short-time integration on the small set, covering and assimilating a time window, and outputting state variable sequences of set members at different time layers; and calculating a background error covariance evolved along with time by using the state variable sequence. According to the four-dimensional data assimilation method provided by the invention, the background error covariance evolved along with time and the static background error covariance are dynamically fused, the change characteristics of the atmospheric system on different time and spatial scales are effectively captured, especially in weather violent change areas such as strong oblique pressure and active convection, the error description precision is remarkably improved, and the method has the advantages of being high in robustness and high in robustness. The fusion mode of multiple types of observation data is optimized, and the propagation efficiency of observation information in a time window is enhanced.

The invention discloses a buried pipe group heat transfer simulation method considering hydraulic-geological boundary geometric mismatch, and belongs to the technical field of geotechnical engineering and underground energy storage. The method comprises the following steps: identifying a spatial relationship between an underground water level interface and a geological layering interface based on a geometric algorithm, executing in-layer sub-layer discretization processing, and generating a calculation sub-layer sequence with a hydraulic attribute mark; whether geometric mismatch exists or not is judged by comparing the depth of the top and bottom plates of each geological layer with the buried depth of the underground water level; constructing a composite analytic calculation model, and performing full space-time coupling solution on the transient thermal network in the borehole and the external heterogeneous moving finite line source field; and outputting a long-term thermal response prediction result, and performing optimization evaluation on the buried pipe system layout based on prediction data. According to the method, the complex boundary condition that the underground water level line does not coincide with the geological stratified interface is accurately described, and full coupling calculation of transient heat transfer in the pipe and stratified non-uniform convection-diffusion heat transfer outside the pipe is achieved.

The invention relates to the technical field of weather classification forecasting, and discloses a severe convection weather classification forecasting method, system and device and a medium, and the method comprises the steps: obtaining historical data of a target meteorological station, carrying out the first division operation of the historical data, and providing a basis for the subsequent data processing. The second division operation further distinguishes the training set and the test set, and provides a reliable data source for the training of the quantum machine learning classifier. Through establishment of the quantum machine learning classifier, especially a plurality of different classifiers included in the quantum machine learning classifier, the diversity and accuracy of classification are improved. A training subset is randomly formed as input, so that the generalization ability of the model is enhanced. The application of the preset ensemble learning strategy not only optimizes the weight of the quantum machine learning classifier, but also improves the stability and reliability of the final prediction result through a voting mechanism. Compared with the prior art, the method has the advantage that the forecasting efficiency and accuracy are remarkably improved.