12 results about "Random mixing" patented technology

The invention discloses an offshore wind power operation and maintenance method considering uncertainty and opportunity collaborative maintenance. The method is used for solving the technical problems that an offshore wind plant is high in operation and maintenance cost and large in uncertainty. The method comprises the following steps: firstly, acquiring wind speed, wave height, electricity price, wind speed and point prediction data thereof, and a prediction value of residual life of a wind turbine generator component; generating a group uncertain scene as the input of a subsequent optimization model through a space-time probability model; constructing a two-stage random mixed integer programming model as an optimization model, and setting decision variables and constraint conditions of the model; and finally, solving the two-stage random mixed integer programming model every day by adopting a rolling time domain optimization algorithm, and outputting a maintenance scheduling plan. The embodiment proves that compared with an existing advanced scheduling strategy, the offshore wind power operation and maintenance economy can be further improved, and the availability and robustness of the scheduling strategy are enhanced.

The application discloses an automatic homogenizing storage system for solid materials with poor fluidity, which comprises a feeding system, a stock bin system, a stock bin bottom material taking system and the like, and a plurality of screw material pushers for pushing the materials into a mixing pipe are arranged at different horizontal positions of the mixing pipe. Since the application adopts a mechanical mode to simultaneously take materials at multiple points in space, the random mixing of the materials is strengthened, and therefore the homogenizing efficiency of the system is high, the homogenizing effect is good, the degree of automation is high, and the system is compact in structure and good in environmental protection effect.

This invention relates to the fields of computer vision and machine learning, specifically to a lightweight classification and segmentation method and system for LiDAR point clouds based on GRSNet. The method includes: acquiring LiDAR point cloud data; generating a representative point set using a sampling method based on the golden ratio to reduce preprocessing complexity; inputting the point set into a GRSNet network for feature extraction, which consists of multiple processing stages. Each stage constructs a local region through a Sample_and_Group module, uses two SA_Ghost Block modules to learn shared weights and extract deep aggregation features, and performs feature aggregation through a Mixed Pooling module with randomly selected pooling methods; finally, classification or segmentation is completed based on the extracted features. This invention, through innovative sampling methods, a lightweight dual-path feature extraction module, and a randomized mixed pooling mechanism, significantly reduces the number of model parameters and computational complexity while maintaining high-precision classification and segmentation performance.

The present disclosure relates to the technical field of communication, in particular to a centerless distributed network optimization training method, device and equipment and medium. The method comprises: based on the connection relationship between N distributed nodes in a centerless distributed network, a random mixing matrix is obtained, a preset initialization parameter is obtained, and an initial model of an i-th node in the N nodes is obtained; a target compression algorithm is determined from at least one compression algorithm that satisfies a preset communication compression condition; based on the row random mixing matrix, the initialization parameter and the target compression algorithm, the initial model of the i-th node is iteratively trained, and when a training stop condition is satisfied, a trained model of the i-th node is obtained. The scheme is suitable for generalized communication compression assumptions containing relative and absolute compression errors, so that a more suitable compression method can be selected to improve communication efficiency while reducing communication cost.

This invention discloses a robust relative navigation method for aircraft based on a hybrid distribution under non-Gaussian noise, belonging to the technical field of computation, estimation, or counting. To address the problem of filter divergence caused by non-stationary heavy-tail noise in time-varying environments during relative navigation, this invention introduces a Dirichlet random mixture vector fusion of Gaussian, Student's t, and multivariate K-distributions, proposing a Gaussian-Student's t-multivariate K-distribution modeling of measurement likelihood. Then, by minimizing the KLD of the true posterior probability density function and the approximate posterior probability density function using variational Bayesian techniques, the approximate posterior estimates of the aircraft's relative motion state and filter parameters are obtained, yielding the target's state information relative to the aircraft and solving for relative position and velocity. Finally, a nonlinear filter based on the Gaussian-Student's t-multivariate K-distribution is derived to improve relative navigation accuracy for angle-only relative navigation of aircraft in time-varying environments.

The application provides a method and system for removing random mixed noise in dynamic electrocardiogram, and relates to the field of digital signal processing of bioelectric signals. A noise signal is used to perform noise adding processing on an original noise-free electrocardiogram signal to obtain a noise-added dynamic electrocardiogram signal, and a training data set is formed. A denoising model is trained until the distortion degree of the noise-free electrocardiogram signal output by the denoising model and the original noise-free electrocardiogram signal is minimum in amplitude and angle. The dynamic electrocardiogram signal to be denoised is input into the trained denoising model to generate and output a noise-free dynamic electrocardiogram signal. The application uses a two-step method of frequency domain filtering and time domain filtering to remove random noise or random mixed noise in the electrocardiogram signal. The frequency domain filtering effectively removes noise outside a specific frequency range, and the effective frequency band range of the dynamic electrocardiogram signal is retained. The time domain filtering uses a deep learning algorithm for denoising, learns the characteristic matrix of the useful signal and the noise, and thus obtains a reconstructed signal with less distortion.

The application belongs to the field of power system automation, and discloses a power dispatching model solving optimization system and method, which comprises a bottleneck identification module, a mixed integer programming acceleration module, a random mixed integer programming acceleration module and an optimization engine integration module. The bottleneck identification module is used for identifying the solving bottleneck of the power dispatching model and generating a solving optimization strategy according to the solving bottleneck. The mixed integer programming acceleration module is used for processing the mixed integer programming model in the power dispatching model, ensuring fast convergence and high-precision solving. The random mixed integer programming acceleration module is used for processing the random mixed integer programming model in the power dispatching model, ensuring the efficiency and reliability of the dispatching scheme of the solving result in a complex uncertain environment. The optimization engine integration module is used for modularly packaging the power dispatching model solving algorithm and establishing a unified calling interface for calling and dynamically calling the calculation resources of the power dispatching model solving process. The application can meet the efficiency, real-time performance and reliability requirements of the power dispatching model solving of the new power system and ensure the stable operation of the power system.

The application discloses a sine sweep plus random mixed signal separation method, and belongs to the field of digital signal processing.The method is as follows: a sine sweep plus random mixed time domain signal is provided, the phase of the sine sweep signal is taken as a reference, the mixed signal is sampled once every interval of the same phase, the obtained sampling signal is subjected to time normalization processing, that is, is reconstructed into a sine fixed frequency plus random mixed signal, then the correlation integral method is used to obtain the instantaneous amplitude and instantaneous phase of the signal, the original sine sweep signal is recovered, the obtained sine sweep signal is subtracted from the original sine sweep plus random mixed signal, and the random signal is obtained, and thus the signal separation is completed.The method can quickly and accurately identify the amplitude and phase of the sine sweep signal in the mixed signal, then subtracts the sine sweep signal from the mixed signal, leaves the random signal, and realizes the separation of the sine sweep signal and the random signal.

The invention belongs to the technical field of multimodal transport logistics service network design, and particularly relates to an optimization method and system for multimodal transport service network design based on uncertain requirements, and the system comprises a two-stage random mixed integer programming model; wherein the first-stage decision variable is a 0-1 discrete variable and is used for representing whether a node is selected as a freight hub position, and the second-stage decision variable comprises a network flow continuous variable of a freight demand, representing the distribution number of goods in a logistics network, and further comprises distribution decision variables of different types of goods vehicles in a freight hub; belongs to discrete integer variables; according to the method, a two-stage random mixed integer programming model is constructed for the problems of random hub site selection and vehicle distribution in multimodal transport service network design under uncertain requirements, and an accelerated Benders decomposition algorithm is adopted to solve the model to obtain an accurate solution.

The invention relates to the technical field of fishing gears, in particular to a fishing device capable of mixing materials freely, which comprises a base, a fishhook, a hook line, a sub line, a space bean, a connector, a counterweight, a beam splitting hook device, a plurality of blades and a water soluble line for binding. According to the automatic mixing and diffusing fishing device, a user puts bait into the cavity, then the bait is wrapped through the blades, the user does not need to specially bond the bait with water or other adhesives in the whole process, and the bait easy to loosen can be used. The bait does not need to be specially kneaded into a ball and is relatively loose, the bait can be always kept upward and the base is kept downward in the water entering process due to the balance weight, the water-soluble line is melted several minutes after the bait enters the water, and the blades are automatically opened under the action of expansion of the bait, so that the atomized bait is formed. And in the process of pulling up the fishhook towards the water surface, the fishline is shrunk through the hook binding device, and meanwhile, the fishhook is wrapped through the blades, so that the phenomenon of bottom hanging can be avoided when the fishhook is pulled up.

The application discloses a kind of generalized theory numerical calculation method of the thermal conductivity of multiphase random mixing porous medium, the method includes the following steps: step 1, obtain the porosity of soil sample;Step 2, obtain the thermal conductivity of soil particles;Step 3, obtain the effective thermal conductivity of soil sample and the full state thermal conductivity surface under the porosity of soil sample;Step 4, obtain the volumetric water content and ice content of soil sample.The application innovatively proposes the strategy of using extreme state to calculate the thermal conductivity of soil particles, realizes the double check of parameters, eliminates the calculation error caused by the difference of soil mineral composition, and can obtain the effective thermal conductivity of soil body by calculation using conventional soil parameters.Using the method of the application, the thermal conductivity of soil body can be predicted with high precision only by using easily obtained physical parameters, and online monitoring and inversion analysis can be carried out.This is especially suitable for large-scale engineering projects and soil thermal response prediction, which greatly saves time and economic cost.

The invention relates to the field of storage, in particular to a method and device for verifying robustness and data consistency under FW hot upgrade and a medium. The method comprises the following steps: firstly, ensuring that the state of an SSD to be tested is normal, recording SMART information before testing, carrying out full-disk low-lattice operation on the SSD to be tested, creating two physical partitions on the SSD to be tested, writing a test file into a first partition, calculating and storing a Hash check code of the test file, and issuing high-pressure random hybrid read-write to a created second partition by using an FIO tool. And performing K times of FW hot upgrade and hot degradation operation, and performing power-down operation on the test machine in the upgrade and degradation process. And after all upgrading and downgrading tests are completed, calculating the hash check code of the test file again. And comparing the SMART Log information and the Hash check code before and after the test to obtain a test result. According to the method, the firmware upgrading pressure and abnormal interference in a real complex scene are simulated, and the robustness and data consistency of the SSD to be tested during firmware upgrading are comprehensively evaluated.