10 results about "Cloud droplet" patented technology

This invention discloses a small-sample Bayesian optimization sampling method and system based on cloud droplet data augmentation, belonging to the field of transportation engineering material design. Addressing the problems of limited initial samples and poor fit of the Bayesian optimization surrogate model in modified asphalt formulation design, this invention acquires initial small-sample data; constructs a clustering cloud model to obtain expectation, entropy estimates, and hyperentropy estimates; generates cloud droplet virtual samples using a forward cloud generator and merges them with the original samples to expand the dataset; trains a Gaussian process regression surrogate model based on the expanded data; constructs an expectation-improved acquisition function to optimize and solve candidate formulations; updates the data after physical testing verification; and iterates repeatedly until the termination condition is met to output the optimal formulation. This invention mines the distribution information of small-sample data through cloud droplet data augmentation, improves the accuracy and sampling efficiency of the surrogate model, significantly reduces the number of expensive physical tests, and lowers the cost of formulation design. It can be widely applied to the formulation optimization of modified asphalt and similar high-cost experimental materials.

The application discloses an atmospheric visibility prediction method considering cloud droplet concentration, wherein when a physical relation model in fog is constructed, the influence of cloud droplet concentration on visibility under different pollution conditions is additionally considered, and a diagnostic calculation formula of the visibility is derived; after reasonable cloud droplet concentration values are set, the formula is applied to main numerical prediction products such as GFS, ECMWF, CMA-MESO, etc., and is substituted into basic meteorological quantities, so that the diagnostic prediction result of the visibility can be obtained. The method is suitable for the visibility prediction under different pollution conditions, has very fast calculation speed, does not additionally occupy storage, does not need a large number of sample training, and has strong expansibility, thereby providing theoretical and technical support for the development of fine prediction and early warning technology of fog.

The application discloses a dam deformation anomaly identification method and device based on frost and ice optimization decomposition and probability sparse attention mechanism, and relates to the technical field of dam safety monitoring data processing. A frost and ice optimization algorithm is adopted, a global optimization is performed on a variational mode decomposition model by using a frost and ice search mechanism; a joint screening criterion of slope entropy and spectral energy is adopted to perform feature quantization and screening, and a pure residual component sequence is reconstructed; a reconstruction error sequence is determined based on an Informer deep time series prediction network; statistical analysis is performed by using a reverse cloud reasoning generator algorithm, a threshold interval is determined based on the statistical law of cloud droplet distribution; the reconstruction error sequence is mapped to the threshold interval point by point according to time, and if the amplitude of the reconstruction error jumps out of the interval boundary of the threshold interval at a time, it is determined that the dam deformation monitoring data corresponding to the time is abnormal, so as to complete the positioning of the abnormal value. The application aims to improve the intelligent and refined level of dam safety monitoring.

This invention discloses a scale-adaptive method for cloud droplet condensation processes in a microphysical parameterization scheme, relating to the field of meteorological technology. The method includes: S1, simulating the cloud droplet condensation conversion rate and the pre-condensation temperature and water vapor at each scale of 0.5 km, 1 km, and 3 km; S2, fitting a normal distribution of supersaturation at the 0.5 km scale at the 3 km scale; S3, dividing the normal distribution into M intervals; S4, analyzing the weighted cloud droplet condensation amount in each interval; S5, summing the weighted cloud droplet condensation amounts in each interval to obtain the total cloud droplet condensation amount corresponding to the supersaturation at the 3 km scale; S6, repeating S2-S5 to analyze the total cloud droplet condensation amount corresponding to the supersaturation at the 1 km scale; S7, using extrapolation to construct a scale-adaptive cloud droplet condensation parameterization scheme, achieving adaptation of the cloud droplet condensation process. This method effectively reduces the deviation of the cloud droplet condensation process at different spatial scales, meets the requirements of variable grid models, and improves the accuracy of simulations and short-term severe weather forecasts.

This invention discloses a fatigue state evaluation system and method for unmanned aerial vehicle (UAV) operators. It employs a strategy combining functional decomposition and task orientation to construct a hierarchical evaluation system encompassing four dimensions: eye-tracking perception, cognitive integration and attention allocation, task-related eye-tracking performance, and eye-tracking-fatigue iterative learning and adaptation. This system is quantitatively represented through 36 underlying indicators. A task-situation gating weighting model based on a normal cloud model and Shapley value game is constructed. Expert verbal ratings are converted into cloud drops to retain fuzziness and randomness. Shapley values ​​are used to eliminate redundant indicator coupling, and subjective and objective weights are dynamically integrated through task-situation gating factors. This invention can accurately quantify operator fatigue state in conjunction with task context, solving the problem that traditional static evaluation is difficult to adapt to dynamic environments and individual differences. It improves the robustness and accuracy of UAV inspection personnel status assessment and has strong applicability.

The application discloses a kind of based on multi-dimensional space-time graph convolution pipe risk dynamic perception and visualization method, comprising: constructing high-precision pipe internet of things perception matrix, collecting multi-physical field operation data, and constructing high-fidelity space-time tensor;From time-frequency domain and topological space domain dual dimension extraction data's deep feature;Construct double-flow space-time graph convolution attention network, output rich semantic high-dimensional risk characteristics;Introduce cloud model theory to construct adaptive risk entropy field, convert risk value into cloud drop distribution, realize the unified quantification of risk certainty and randomness;Based on force-oriented layout algorithm and particle system design dynamic three-dimensional visualization engine, risk data is mapped into holographic field effect in digital twin interface in real time;Finally, a hierarchical closed-loop response mechanism is established.The application not only significantly improves the identification rate of micro-leakage and early fault, but also greatly improves the operation and maintenance decision efficiency and situational awareness ability through immersive visualization means.

The application provides a laser radar-based aerosol-cloud droplet conversion stage identification method. The method provided by the application: obtaining echo signals of a main channel, a depolarization channel and a molecular channel; calculating particle scattering ratios and particle depolarization ratios according to the echo signals and an atmospheric molecule scattering model; counting samples of the particle scattering ratios and the particle depolarization ratios in an identification domain, determining a joint probability density distribution of the particle scattering ratios and the particle depolarization ratios based on a two-dimensional sample set, and generating a joint probability density scatter plot; setting a joint identification criterion according to microphysical process characteristics of each stage of aerosol-cloud droplet conversion, and identifying the aerosol-cloud droplet conversion stage according to the particle scattering ratios, the particle depolarization ratios and the joint identification criterion. The laser radar-based aerosol-cloud droplet conversion stage identification method provided by the application realizes dynamic and fine identification of a cloud formation stage.

The invention provides an aerosol-cloud droplet conversion stage identification method based on a laser radar. The method provided by the invention comprises the following steps: acquiring echo signals of a main channel, a depolarization channel and a molecular channel; calculating a particle scattering ratio and a particle depolarization ratio according to the echo signal and an atmospheric molecular scattering model; counting samples of the particle scattering ratio and the particle depolarization ratio in the identification domain, determining joint probability density distribution of the particle scattering ratio and the particle depolarization ratio based on the two-dimensional sample set, and generating a joint probability density scatter diagram; a combined identification criterion is set according to the microscopic physical process characteristics of each stage of aerosol-cloud droplet conversion, and the aerosol-cloud droplet conversion stage is identified according to the particle scattering ratio and the particle depolarization ratio in combination with the combined identification criterion. According to the aerosol-cloud droplet conversion stage identification method based on the laser radar, dynamic and fine identification of the cloud formation stage is realized.

This invention relates to the field of UAV flight path optimization technology, specifically to a method for optimizing rain enhancement environment detection paths based on UAVs. The method includes: acquiring a three-dimensional grid set of the target airspace and initial atmospheric background field data; collecting real-time data on supercooled water content, vertical airflow velocity, and cloud droplet number concentration using the UAV; configuring Gaussian process regression prior parameters based on the initial background field and updating the joint prediction distribution with real-time data; obtaining the predicted mean and information entropy variance of the rain enhancement catalytic potential by transforming the joint prediction distribution through a nonlinear activation function probability density transformation; constructing a collection function that integrates potential gains and path energy consumption costs, and iteratively detecting the grid corresponding to its maximum value as the next target waypoint until the optimal catalytic zone is locked. This invention achieves high-efficiency, low-energy dynamic path optimization under limited onboard fuel constraints, significantly improving the positioning accuracy and operational success rate of cloud seeding windows.