19results about How to "Increase computational cost" patented technology

A CFD (computational fluid dynamics)-based wind turbine generator load simulation method under a high slope comprises the following steps: step 1, acquiring topographic data and position information of the high slope: acquiring the topographic data and position information of the high slope through a multi-source data acquisition means; step 2, constructing a high slope simplified physical model according to the high slope topographic data and the machine position information; 3, a CFD tool is used for flow field numerical simulation calculation, and according to the constructed high slope simplified physical model, the CFD tool is used for obtaining the wind speed distribution and turbulence intensity distribution conditions of the whole wind wheel face of the wind turbine generator; step 4, generating a target wind seed file by using a TurbSim tool; 5, establishing a high slope wind file library; step 6, calculating the dynamic response of the whole machine; and obtaining an accurate load result of the wind turbine generator under the high slope terrain. The accuracy of the simulation result is improved, and the safety, the power generation efficiency and the service life of the wind turbine generator are improved.

The invention discloses a diffusion model self-guiding-based image or video generation method and system. The method comprises the steps that the input content is a noise potential variable and a future offset step length of a current time step; the diffusion model predicts the score network twice based on the input content to obtain a conditional score network of the current time step and the future offset time step; carrying out linear combination on the two conditional score networks to generate a combined conditional score network; denoising the noise potential variable of the current time step by using a combined condition score network; calculating and updating a potential variable of the previous time step according to a denoising result; in each time step, the self-guiding strategy is adopted to carry out denoising processing on the noise, and noise prediction is optimized; and after de-noising processing of all time steps, a de-noising potential variable is finally output as a final model generation result, and an image or a video is obtained. According to the invention, the quality of the generated image or video can be obviously improved.

The invention provides an MRI (Magnetic Resonance Imaging) reconstruction method of a subspace diffusion model based on orthogonal decomposition. The MRI reconstruction method comprises the following steps: forming an open source brain data set; a subspace diffusion model based on orthogonal decomposition is constructed, and k-space data is decomposed into subspaces for diffusion, so that data distribution is better captured, and robustness is kept in a noise environment; designing an iterative solution algorithm for extracting prior information in the model; and applying an iterative solution algorithm to the model to test the test data set, and outputting a final restored image. According to the method, the learning of effective information in complex distribution is promoted, the inherent high-dimensional extrapolation challenge is solved, and the calculation cost related to a diffusion model is reduced. Comprehensive experiments on different data sets clearly show that the method is superior to other methods in the aspects of reconstruction speed and quality.

The invention discloses a Beidou navigation unmanned aerial vehicle inspection path planning method based on a simulated annealing algorithm. The method comprises the following steps: S1, reading latitude and longitude data: reading latitude and longitude data of an inspection target point and a base from a file or a database; s2, calculating a distance matrix: calculating an actual distance between any two points according to the longitude and latitude data, and constructing the distance matrix; s3, constructing a solution space: constructing all path sets; s4, selecting an initial solution: selecting an initial path from the path set as a current solution; s5, setting an initial temperature: setting the initial temperature of a simulated annealing algorithm; s6, loop iteration: performing an iteration process of a simulated annealing algorithm; and S7, generating a new solution: generating a new path as the new solution by using a transformation method 2 or a transformation method 3 according to the current solution. The method is used for solving the problem that a genetic algorithm and a particle swarm optimization algorithm are relatively weak in theoretical convergence guarantee, and particularly are possibly difficult to meet theoretical conditions in practical application.

The invention provides a student classroom behavior detection method based on Transform and task dynamic alignment. On one hand, the GLTB is added into the backbone network to enhance the feature extraction capability. Wherein the CGLU is used for enhancing the non-linear feature expression capability, and the global feature extraction capability of the MHSA is combined, so that the method has higher feature extraction capability. And on the other hand, a neck structure network improved by GLSA is adopted, global features can be effectively extracted, fine-grained local features can be captured, and interaction between global information and local information can be promoted. According to the method, when a complex task is processed, key information in the image can be identified and understood more accurately. In addition, a novel detection head ETADH is adopted, interactive features of classification and positioning tasks are learned through a feature extractor, and task decomposition is performed through a task decomposition module. In this way, the interactivity between the two tasks is enhanced, and the detection precision is improved by effectively utilizing the associated information between the two tasks.

The invention relates to the technical field of remote sensing, and discloses an unfavorable geology detection method, system and device based on multi-source aerial remote sensing data fusion, and the method comprises the steps: obtaining laser radar data, hyperspectral image data, CCD image data, infrared image data and integrated navigation data of a target area, and carrying out the preprocessing; carrying out space matching processing on the preprocessed laser radar data, the preprocessed hyperspectral image data, the preprocessed CCD image data and the preprocessed infrared image data; fusing the laser radar data, the hyperspectral image data, the CCD image data and the infrared image data after application space matching processing for geological detection to obtain different geological detection data; and obtaining an unfavorable geological detection result of the target area in combination with different geological detection data. According to the method, the multi-source data fusion processing precision can be improved, multi-source heterogeneous information is effectively integrated, comprehensive and efficient detection of various unfavorable geologies is realized, the misjudgment and missed judgment risks are reduced, and the credibility of unfavorable geology detection results is improved.

The embodiment of the invention provides a self-planning method for a mathematical geometry problem of an intelligent agent, which can be applied to the technical field of artificial intelligence. The self-planning method for the mathematical geometry problem of the intelligent agent comprises the following steps: generating a seed image according to scene demand information of the mathematical geometry problem; performing complex feature planning on the seed image based on a preset guide search rule until a target mathematical problem set is output; and performing problem solving path matching on at least one target mathematical problem in the target mathematical problem set through a preset language model to generate a problem solving planning path set. The embodiment of the invention further provides a self-planning method and device for the mathematical geometry problem of the intelligent agent, equipment, a storage medium and a program product.

The invention discloses a multi-channel sampling circuit hybrid error modeling and cyclic calibration method, and belongs to the technical field of signal sampling. The method comprises the following steps: constructing a system error model, and introducing polynomial series to realize model simplification; generating a reference signal participating in error cycle calibration according to the simplified hybrid error model; initializing a frequency response function of each sampling channel, and calculating an initial tap coefficient of an error correction module; a cascade multi-order error correction structure is used for completing reference signal error iteration correction, so that the reference signal error continuously approaches to an input signal; correcting the signal to calculate a loss function, and completing estimation and updating of a tap coefficient; and judging whether the loss function reaches a convergence condition or not until hybrid error cyclic calibration is realized. On the basis of the reference signal constructed by the hybrid error model, low-complexity online blind cycle calibration is realized, the precision and completeness of the system error model are improved, the quantity of parameters to be estimated and the number of sample points for calibration are remarkably reduced, and the calibration efficiency is improved.

The present invention discloses a method for data transmission between a fluid domain and a solid domain during a fluid-solid coupling simulation process, and belongs to the technical field of fluid-solid coupling simulation of impeller machinery. The method comprises: obtaining data information on the known data side of the fluid-solid coupling interface to form a known data set; obtaining data information on the unknown data side of the fluid-solid coupling interface to form an unknown data set; selecting a target node in the unknown data set; finding the three nodes 1, 2, and 3 closest to the target node in the known data set; determining the distance d from the target node to the plane where nodes 1, 2, and 3 are located, and if d is less than or equal to a preset threshold, calculating the target node data; selecting the next target node in the unknown data set, and repeating the above steps until all nodes in the unknown data set obtain corresponding data information. The present invention can realize accurate data transmission at any grid scale, and does not significantly increase the time of data mapping.

The invention provides a time-varying network-considered large-scale travel order connection plan automatic generation method and device and a readable storage medium, and the method comprises the steps: carrying out the parameter configuration through reading order and vehicle information; carrying out space-time clustering and local path planning on the orders, and merging on-the-way orders; and assigning the orders by using a genetic algorithm, and generating a connection plan of each vehicle / driver. According to the method, the calculation amount is effectively reduced, the solving efficiency is improved, the workload of workers is reduced, and the utilization rate of the connection vehicles is improved.

The invention discloses a method for calculating the edge drop control efficiency coefficient of a 20-roller cold rolling mill by using the opening degree, and relates to the technical field of cold rolling. A more applicable edge drop control efficiency coefficient calculation model is proposed, and the edge drop control efficiency coefficient is obtained by the opening degree of the first intermediate roll and the strip. The method does not depend on a specific production line, and the edge drop control efficiency coefficient curve obtained has stronger applicability, which can improve the adjustment accuracy of the first intermediate roll of the 20-roller cold rolling mill, thereby quickly and effectively controlling the edge drop of silicon steel and improving the silicon steel finished product rate.

The invention discloses a transient interaction characteristic analysis method for an optical storage direct-flexible system in a high-temperature and high-humidity environment, and the method comprises the steps: firstly building a differential algebraic model of a dynamic behavior, and initializing a state and parameters; then, adopting an SAS method to construct an approximate solution, and dynamically adjusting model parameters according to real-time temperature and humidity; establishing a linear equation of a coupling relationship, recursively solving a high-order coefficient, and calculating residual error evaluation precision; solving the high-order nonlinear model by utilizing a parallelization calculation technology, constructing a local RLC topology to carry out transient simulation, recording a time-varying result of a system state, and analyzing a coupling effect of each component; and finally, adjusting the key variable by adopting a self-adaptive control algorithm. According to the invention, based on the SAS calculation method, for the dynamic response of the optical storage direct-flexible system in the high-temperature and high-humidity environment, efficient transient analysis is realized, and the operation reliability and the regulation and control capability of the system are remarkably improved. The method is applied to an on-line monitoring and control platform of a power system, and real-time optimization is realized.

This invention discloses a method and system for binocular image super-resolution reconstruction based on cross-scale disparity prior, comprising: S1: acquiring the original left feature map and the original right feature map corresponding to the low-resolution left and right binocular images; S2: using a binocular attention module to perform cross-view interaction on the original left feature map and the original right feature map respectively, to obtain the interacted left feature map and the interacted right feature map; S3: inputting the interacted left feature map and the interacted right feature map into the cross-scale disparity attention module, and fusing them to obtain an aggregated feature map; S4: inputting the aggregated feature map into a cascaded dynamic upsampling reconstruction network to obtain the reconstructed super-resolution binocular image. This invention can fully utilize disparity information and obtain more realistic and higher-quality super-resolution images in a shorter running time.

The application discloses a small target detection feature enhancement method and system based on cross-scale local contrast modulation and a medium, and aims to solve the problems of blindness and easy amplification of noise in small target feature enhancement. The method comprises the following steps: acquiring shallow high-resolution features and deep low-resolution features; generating a small target priori graph by using the deep features, and constructing a local contrast feature on the shallow features; fusing the priori graph and the contrast feature to generate a modulation coefficient graph; and modulating and enhancing the shallow features by using the modulation coefficient graph and the contrast feature, and sending the enhanced shallow features into a detection head for target detection. The application selectively enhances the small target features by fusing the position priori of deep semantic and the local contrast of shallow physical, improves the detection precision, and suppresses the noise.

The invention discloses a method and a system for accelerating a train-ballast track-roadbed system MBD-DEM-FDM coupling simulation model based on a self-adaptive PID (Proportion Integration Differentiation) algorithm. The method comprises the following steps: firstly, establishing a data interaction interface of MBD and DEM-FDM modules, and calculating and transmitting an acting force and a counter-acting force; a self-adaptive PID control algorithm is introduced, loading force is dynamically corrected according to interface errors, and fast approximation balance in a single time step is achieved; the convergence state of the system is monitored through a sliding window error judgment mechanism, a local fuzzy self-learning or global beetle antenna search optimization algorithm is intelligently triggered according to the convergence state, and PID control parameters are dynamically adjusted in real time and globally optimized. According to the method, a traditional time-consuming space iteration balance process is converted into dynamic feedback adjustment of a time domain, the problems that an existing coupling method is low in calculation efficiency, poor in numerical stability and difficult to apply to a large-scale model are effectively solved, and an efficient and reliable simulation tool is provided for design, operation and maintenance of a railway system.

The application provides a dynamic gesture recognition method based on light-weight multivariate space-time convolution, which comprises the following steps: constructing a space feature extraction module based on a pseudo 3D gated attention fusion network; extracting multi-scale space features by using the space feature extraction module; injecting a guided heat map by using a gated attention fusion module to enhance key region features and suppress background interference, so as to obtain a space refinement feature sequence; decomposing the space refinement feature sequence into multiple sub-variables; capturing long-range and local time dependence in parallel by using a modern convolution module; and modeling the intra-variable and inter-variable relationships respectively by using a decoupling feature interaction network, so as to obtain a dynamic gesture recognition result. By combining the multivariate feature decomposition strategy with the modern convolution, the double-branch design advantage of the modern convolution is to balance long-range dependence and local details, solve the limitations of traditional convolution in capturing long-range time dependence, and model complex dynamics.

The invention provides a data feature extraction method and system based on artificial intelligence. The method comprises the steps of obtaining heterogeneous data of a plurality of clients; performing feature extraction according to the heterogeneous data to generate a standardized feature set; performing model aggregation processing according to the standardized feature set to determine important features; determining target data in the heterogeneous data according to the important features; performing data preprocessing according to the target data to generate standardized data; determining sensitive information in the standardized data according to the standardized data and a preset deep learning model; and determining target features according to the sensitive information and the standardized data. Reasonable data feature extraction can be performed according to the importance degree of the data while privacy data is protected, so that the processing time of data feature extraction is greatly reduced, and the complexity of data processing and the calculation cost are reduced.

The invention provides a multi-scale Patch mixed drug demand prediction system and method, and relates to the technical field of data processing, and the system comprises a data obtaining module which obtains sequence data reflecting drug demands; the data block division module is used for converting the data block into a multi-scale data block matrix; the dimension mapping module is used for dimensionally mapping the data block matrix; the mixing module is used for mixing characteristics among different data blocks in a mapping result and characteristics in refined data blocks; the mixing module is used for mixing and refining the mixed and refined characteristic matrix; the residual error connection module is used for fusing the two characteristic matrixes of the two mixing modules; the channel mixing module is used for carrying out feature weighted recombination on the fusion feature matrix among channels through a mixer; the prediction module is used for predicting a channel mixed feature matrix; and the multi-scale fusion module is used for fusing the multi-scale prediction results to obtain demand prediction results, wherein the demand prediction results comprise prediction results of Xipai gingival fixing liquid, Zukamu particles, cold asthma Zupao particles and the like. The invention aims to improve the drug demand prediction accuracy.

The application relates to a lightweight cell positioning method and system, which comprises the following steps: inputting an image to be processed into a cell positioning model; wherein the cell positioning model is a model obtained after lightweight processing, and a differential convolution and an attention module are introduced into the cell positioning model; in the front end of an initial stage, gradient information of an image is enhanced through differential convolution to obtain a feature map containing the gradient information; in each subsequent stage, multi-channel convolution is performed on the feature map, and the feature map is adaptively optimized through the attention module to obtain an optimized feature map; and cell positioning information is obtained based on the optimized feature map. According to the scheme, the attention module is introduced into the cell positioning model, the cell positioning model can pay attention to dense cell parts in a scene, lightweight processing is performed, the calculation cost of the cell positioning model is reduced, and the cell positioning model can be applied to more low-computing-power scenes.