143results about How to "Reduce computing time" patented technology

The invention provides a method for quickly establishing a three-dimensional near-seafloor speed model in a shallow sea area; the method comprises the following steps of extracting initial informationand extracting a common-center-point gather; calculating the slope of the initial travel time in the common-center-point gather; judging whether a shot point is located in the seawater layer or not,and if yes, correcting the shot point to the seafloor; calculating the speed and the depth of inflection points corresponding to respective offset distances; performing linear interpolation on the speed at each depth to obtain a one-dimensional speed model of the common center point; after all the common-center point domain gathers are processed, adopting the linear interpolation method, calculating the speed values of the rest grid points by utilizing the one-dimensional speed model of all common center points, and a speed model is output. By adoption of the method for quickly establishing the three-dimensional near-seafloor speed model in the shallow sea area, the influence of the seawater layer on ray propagation is overcome, and the influence of multiple excitation receiving modes on an inversion result is eliminated, and the problem that the conventional method needs to carry out multi-ray tracing forward modeling is avoided.

The invention discloses a free-form surface three-axis ball-end cutter equal approximation error finish machining tool path generation method which comprises the following steps: firstly, importing a free-form surface model to be machined, and setting data such as the radius, the line spacing, the approximation error maximum allowable value and the precision of a ball-end cutter; secondly, a group of section planes are planned according to the row spacing to intersect with the curved surface, the intersecting line serves as a cutter contact curve, and an equal-bow-height-error cutter contact iterative search method for driving cutter contact adjustment through the geometric distance is provided for the cutter contact curve on the section planes; the maximum distance between a cutter cutting envelope surface and a cutter contact track line is used as an approximation error, and an adaptive discrete method is adopted to carry out approximation error calculation; and finally, the cutter location points of the equal-bow-height-error cutter contact points serve as initial values, an equal-error cutter location point calculation method of step length self-adaptive adjustment iteration is provided, approximation errors between the cutter location points are all within an allowable range, and therefore the free-form surface three-axis ball-end cutter equal approximation error finish machining cutter path is obtained.

The invention discloses an automatic extraction method for semiconductor device model parameters, and the method comprises the steps: obtaining a multi-target response through the key parameters of a semiconductor device model, and constructing and training an agent model to predict a target response mean value and uncertainty parameters; constructing a multi-objective optimization collaborative circulation framework assisted by an agent model; generating a candidate parameter population in key parameters according to a position updating mechanism of a multi-objective optimization algorithm, predicting candidate parameters by using a trained proxy model, selecting the candidate parameters according to a prediction result to carry out real simulation evaluation, adding an obtained real sample into a non-dominated file, and updating the file based on a non-dominated relationship. Adding a training set dynamic updating agent model into the real sample, generating a potential search trajectory, selecting a representative representative Pareto approximate solution from the potential search trajectory, and updating the position of the population and the direction of the next-generation search trajectory according to the representative solution selected from the file; and terminating the circulation, outputting the solution set in the file, and checking.

The invention relates to the technical field of underground water simulation, in particular to an underground water numerical simulation method coupled with vegetation ecology. The method realizes bidirectional coupling between groundwater and vegetation ecology through iteration, and specifically comprises the following steps: inverting vegetation coverage based on groundwater burial depth; inversion of actual evapotranspiration is carried out based on underground water burial depth and vegetation coverage; and finally, updating the net supply amount of the groundwater model based on evapotranspiration until convergence. According to the method, a statistical function relation is established to replace a complex physical model, and drainage and overflow cell water levels are corrected in iteration to ensure calculation stability.

The invention provides a reference path generation method and device. The method comprises the following steps: acquiring environment sensing data and vehicle real-time state data, wherein the environment sensing data comprises road information, obstacle information and a road adhesion coefficient; generating an initial path set according to the road information and the obstacle information; vehicle state adaptive dynamic constraints are constructed, the vehicle state adaptive dynamic constraints comprise vehicle dynamic parameters, and the vehicle state adaptive dynamic constraints associate values of the vehicle dynamic parameters with vehicle real-time states and road adhesion coefficients; the value of the vehicle dynamics constraint index is adaptively adjusted according to the change of the vehicle real-time state or the road adhesion coefficient; and according to the vehicle real-time state data and the environmental perception data, optimizing each initial path in the initial path set under the vehicle state adaptive dynamics constraint to generate a reference path which simultaneously meets the geometric feasibility and the physical performability.

The application discloses a kind of high-pressure fuel filter prediction model construction method and flow prediction method, including two prediction models, method specifically includes: using computational fluid dynamics method carries out fluid dynamics simulation calculation to high-pressure fuel filter, constructs working condition sample data set;The size of filter hole, quantity, filter working pressure, fuel viscosity and filter pressure drop are used as input variable, and filter flow is used as output variable, two new high-pressure filter general nonlinear flow prediction models are established using artificial neural network and regression prediction method respectively, the flow characteristics of different models of filter in working interval can be accurately predicted using the neural network prediction method proposed in the application at any working condition point, and the error is low;The flow of different models of filter under different conditions can be quickly and accurately obtained, the accuracy meets the engineering needs, while greatly shortening the experimental time and calculation time, saving design cost.

The application relates to a real-valued super-resolution direction estimation method of a high-order acoustic field sensor array, which uses a received signal covariance matrix to approximately calculate an estimated value of noise power, avoids the iterative operation process of noise power in the prior art, improves the calculation efficiency, and reduces the floating-point operation amount. By constructing an augmented matrix, the array receiving data matrix and the array manifold matrix with a multi-dimensional structure of an element are made into Hermitian matrices, so that the unitary transformation processing of the related parameters of the high-order acoustic field sensor array is realized. On the basis of obtaining the array receiving data matrix and the array manifold matrix in the real number domain, the array receiving data matrix and the array manifold matrix are applied to a sparse approximate minimum variance method with a variable exponential factor, and the completely real-valued sparse approximate minimum variance direction estimation with a variable exponential factor is realized.

The present disclosure provides a capital construction graphic calculation method, system, electronic device and storage medium, which belongs to the technical field of capital construction engineering and engineering cost. The method determines the overlapping area between the steel bars and the concrete by obtaining the point cloud data of the steel bars and the concrete and the steel bar spacing parameter, extracts the boundary point set of the overlapping area to generate a first grid unit, generates a second grid unit according to the steel bar spacing parameter, corrects the first grid unit through the node coordinate offset between the first grid unit and the second grid unit to generate a third grid unit, and determines the gap filling ratio of the concrete according to the third grid unit to form a continuous boundary model. In this way, the discontinuous boundary error caused by the curved surface overlapping area between the components in the traditional graphic calculation method is effectively eliminated, so that the real geometric shape of the components can be accurately reflected, the precision of the component volume and material consumption calculation is improved, the component volume calculation precision can be improved to ±0.3%, and the material consumption budget accuracy rate reaches 98%.

This invention discloses a method, apparatus, device, and medium for malware detection based on subspace ensemble learning. The method includes: acquiring the installation package file of the software to be detected; parsing the installation package file to obtain the application programming interface (API) call relationships of the software to be detected; determining the API call relationship graph based on the API call relationships; filtering each API in the call relationship graph to obtain a target API set; and determining the software detection result of the software to be detected based on the target API set and a feature subspace ensemble learning model. This invention obtains the target API set by filtering the API call relationships and employs a feature subspace ensemble learning model composed of multiple machine learning algorithms, which can capture malicious behavior in software from different perspectives, thereby improving the accuracy of malware detection.

The application discloses a space-time convolution short-time traffic flow prediction method based on complementary ensemble empirical mode decomposition, and comprises the following steps: acquiring original traffic flow data and performing data preprocessing; and inputting the traffic flow data into a pre-constructed CEEMD-GAT-LSTM model to perform short-time traffic flow prediction. The prediction model comprises the following steps: performing signal decomposition on the traffic flow data by using CEEMD, fully mining the spatial features of the data by using GAT, and fully mining the time features of the data by using LSTM, so that the learning efficiency of the neural network is improved. The application can effectively improve the prediction accuracy of short-time traffic flow prediction and provide favorable reference value for the traffic department.

The invention discloses a turbine blade flutter evaluation method adopting a single-channel harmonic balance model. The method comprises the following steps: 1, carrying out structural grid division on a turbine blade to obtain a vibration mode, a vibration frequency and a vibration pitch diameter of a vibration blade; 2, performing fluid grid division on the turbine blade to obtain grid coordinates under a cylindrical coordinate system; 3, aligning the coordinates of the structural grid and the fluid grid to obtain a vibration mode of the fluid grid; 4, adopting a Doppler frequency shift effect to obtain blade vibration frequencies, vibration pitch diameters and inter-blade phase angles of other blade rows except the blade row where the vibration blades are located; 5, realizing mutual transmission of upstream information and downstream information of the rotor-stator interface; 6, single-channel unsteady harmonic balance analysis is achieved; and step 7, evaluating the stability of the turbine blade flutter system. According to the method, the flutter of the multiple rows of turbine blades is evaluated, and the method has the characteristics of low calculation cost and high evaluation accuracy.

This invention discloses a method, device, and storage medium for bird species identification based on bird calls. The method includes: (1) acquiring several segments of bird calls and preprocessing them; (2) filtering the power spectrum of the bird calls using two different filter banks, extracting the coefficients of the filtered signals, and then combining the two sets of coefficients, the short-time energy of the bird calls, and the short-time zero-crossing rate of the bird calls to form the feature vector of the current bird calls; (3) constructing a nonlinear classification model and using a prey optimization method to find the optimal kernel function in the nonlinear classification model; (4) inputting the extracted bird call feature vector into the nonlinear classification model for learning; and (5) extracting the feature vector of the bird call to be identified and inputting the feature vector into the learned nonlinear classification model to identify the bird species. This invention has low complexity and high accuracy.

This application provides a data processing method and a readable storage medium based on a matrix processor. The method includes: reading W first elements from a first matrix and sending the W first elements to a computing unit of the matrix processor for calculation; wherein W is greater than the width N of the first matrix and less than or equal to the number K of the computing units of the matrix processor; repeating the above steps until the number of remaining elements in the first matrix is ​​less than W; and in response to the number of remaining elements in the first matrix being non-zero, sending the remaining elements to the computing unit for calculation. This method can improve the utilization rate of the matrix processor's computing units, reduce the number of calculation cycles, shorten the calculation time, and fully utilize the computing units of the matrix processor.

The application discloses a cotton bale detection method based on multi-modal data timestamp synchronization and GPU / CPU collaborative acceleration, collects multi-modal data, marks timestamps, and performs GPU accelerated distortion correction on cotton bale images; the multi-modal data is synchronized and integrated according to timestamps; a main program adopts Nodelet combined with C++ multi-threading technology to process the reception of multi-modal data and the publication of multi-modal sensor coordinate systems in parallel, the main program running optimizes the transmission efficiency of multi-modal data; a cotton bale detection model is constructed, the optimized multi-modal data are input for training, the cotton bale detection model after training is optimized using TensorRT, and cotton bale images are input for inference, cotton bale target recognition data are obtained and transmitted to a CPU for post-processing operation, and OpenMP is used to accelerate point cloud traversal in the main program to match the cotton bale target recognition data with laser radar point cloud data; the matched multi-modal data are subjected to point cloud feature and plane fitting calculation, and the rectangular frame, pose information and score of the cotton bale target are obtained.

This invention provides a method for simulating buckling of subsea pipelines based on the pipeline element method, addressing the problems of low computational efficiency, complex modeling, and low accuracy in existing methods. Based on Euler-Bernoulli beam theory, this invention models the pipeline as a series of efficient pipeline elements. Internal pressure is converted into equivalent temperature rise, and the effective thermal expansion force generated by the effective temperature rise is applied to both ends of the pipeline element to construct the total potential energy equation. The second derivative of the total potential energy equation is performed to obtain the tangent stiffness matrix of the pipeline element. An updated Lagrange method is used for coordinate transformation. The secant relationship is used to calculate the pipeline element and soil resistance. A Newton-Raphson incremental-iterative numerical program is established to perform nonlinear analysis of the pipeline, progressively solving for the deformation and stress of the pipeline under complex loads. This invention significantly reduces the number of elements, improves computational efficiency, and accurately simulates nonlinear pipe-soil interactions. It is applicable to various pipelines, including short pipes, long pipes, and corroded pipes, as well as complex working conditions such as high temperature and high pressure.

The application discloses a parallel polynomial gradient calculation system and method based on a CMOS nerve synapse transistor, and relates to the technical field of calculation systems.The parallel polynomial gradient calculation system based on the CMOS nerve synapse transistor adopts a basic calculation unit containing at least one CMOS transistor, and configures the CMOS transistor to exhibit the dual-mode characteristics of neuron behavior and synapse behavior in a floating substrate operation mode, and meanwhile, combines a parallel array architecture, so that the application realizes a neuromorphic calculation hardware based on a standard CMOS process, utilizes mature CMOS manufacturing technology, avoids special materials and post-integration processes required by emerging devices such as memristors, thereby improving the manufacturability and yield of the system, and reducing production costs.Meanwhile, the inherent device consistency and long-term stability of the CMOS transistor effectively overcome the problems of large differences between devices and state drift in the memristor scheme, and realize high-precision polynomial gradient calculation.

The application discloses a kind of based on clustering partition large-scale indoor navigation path planning method, mainly for large-scale indoor space based on navigation network model path calculation time-consuming long, can cause the problem of slow response speed of navigation system, proposed offline clustering partition, offline area path pre-computation, online fast global path calculation method, belong to indoor path planning technical field.The application is automatically clustered by using clustering method according to the attribute of indoor space, and indoor path planning is carried out by the method of offline calculation and online loading, so as to solve the problem of time-consuming in large-scale indoor space path calculation, improve the response speed of navigation system in path planning stage, and has certain research significance and practical value for the research and development of indoor route navigation system.

The invention discloses an energy storage power station collaborative planning method and system based on uncertainty propagation analysis. In order to solve the problems that an existing energy storage planning method is indefinite in physical mechanism and insufficient in interpretability, the planning method comprises the steps that operation data of a power system are obtained, and an analysis alternating current probabilistic power flow model used for establishing an analysis relation between system voltage statistical characteristics and line power statistical characteristics is constructed based on the operation data; constructing a propagation matrix representing the uncertainty propagation characteristics of the system based on the analysis of the AC probabilistic power flow model; performing spectral analysis on the propagation matrix to identify one or more dominant propagation modes which play a dominant role in the uncertainty of the system; and based on the one or more dominant propagation modes, determining a planning scheme of at least one type of energy storage power station, the category of the planning scheme including the access position capacity of the energy storage power station. According to the method, energy storage configuration is guided by using an uncertainty physical propagation mechanism, and the risk control effect, the calculation efficiency and the engineering interpretability of a planning scheme are remarkably improved.

The invention aims to provide a gene sequence information comparison and correction method and device. The method comprises the following steps: inputting data to be subjected to third-generation sequencing; performing rough comparison on the data by using a halving comparison strategy to obtain data after rough comparison; the data obtained after rough comparison are analyzed, and analyzed data are obtained; a comparison database is constructed; performing BLASN fine comparison on the analyzed data and a comparison database to obtain data after fine comparison; setting a sequence query to be compared, and aligning any data after fine comparison with the sequence query to be compared; in the process of performing query alignment on the finely-compared data and the compared sequence, performing voting on the basic group at any position to generate a new basic group, and judging the problem of the current sequence through the new basic group; the method and the device have good correction capability and time efficiency.

The application discloses an interferometric constellation orbit optimization method based on a second-order state transition tensor and belongs to the field of space technology. The method of the application is as follows: an orbit dynamics model and a configuration stability index model of an interferometric constellation spacecraft are established, an orbit deviation propagation expression is given based on a second-order state transition tensor, and a second-order analytical mapping expression of configuration stability of the interferometric constellation and initial orbit deviation is derived; an iterative deviation of the initial state of the spacecraft is given; an initial value of the constellation configuration with strong stability is obtained through iteration, the constellation configuration satisfying long-term stability is obtained through recursive initial value, the interferometric constellation is realized to be long-term stable according to the constellation configuration, and the detection precision of the interferometric constellation is improved. The application has the advantages of high-precision space measurement constellation configuration stability, high efficiency, is favorable for fast iterative optimization of the high-precision space measurement constellation configuration, and is favorable for improving the stability and detection performance of the high-precision space measurement constellation configuration.

The present application relates to the technical field of geological engineering, and especially provides a slope dangerous rock identification and monitoring method based on unmanned aerial vehicle inspection, comprising the following steps: step 1: collecting slope image information by adopting unmanned aerial vehicle inspection to obtain a first point cloud model, and performing vegetation removal pretreatment on the first point cloud model; step 2: extracting a main joint normal vector direction of the slope based on the first point cloud model; step 3: determining a dangerous rock position of the slope based on the main joint normal vector direction of the slope and by using an equatorial projection analysis method and a normal vector matching method; and step 4: repeatedly performing steps 1 to 3 on the same slope according to a preset time interval to obtain a second point cloud model, and obtaining a slope change result after point cloud model registration and relative distance calculation are performed on the second point cloud model and the first point cloud model. The purpose is to solve the problem of insufficient non-contact rapid identification of slope dangerous rocks and achieve the technical effect of subsequent slope change monitoring.

The application discloses a bearing fault diagnosis method and system based on a CS-SHAP model, bearing operation signals are collected through acceleration, temperature and acoustic emission multi-sensor, multi-dimensional fault features are extracted from time domain, frequency domain and time-frequency domain after wavelet transform or EMD algorithm denoising to complete preprocessing, feature data is divided into similar clusters by K-means clustering, SHAP values are calculated cluster by cluster and weighted according to cluster data density or diagnosis influence degree to obtain feature importance score, a SVM classifier based on RBF kernel function is trained with screened key features, model parameters are optimized through 5-fold cross validation, new collected data is preprocessed and input into the trained model to obtain diagnosis results, feature SHAP values are calculated and visualized by using the CS-SHAP model, and key factors of faults are analyzed. The application realizes accurate diagnosis of bearing faults and interpretability of diagnosis results, and provides technical support for intelligent operation and maintenance of industrial equipment.

This invention discloses a general fast modulation method for a VIENNA rectifier, comprising: 1) establishing a mathematical model of nine switches based on the mathematical model of the VIENNA rectifier, obtaining the equality constraints between the duty cycle of the nine switches and the modulation wave; 2) establishing two equations satisfying the control voltage constraint through newly defined variables, and providing a general solution satisfying the equality constraint conditions; 3) obtaining six states based on the different directions of the three-phase current; obtaining inequality constraints under different states based on these six states; and obtaining the upper and lower limits of the free variables based on the inequality constraints under the six states; 4) solving for the compensation amount for neutral point potential balance control starting from the control equation for the capacitor neutral point potential control of the VIENNA rectifier. The modulation model and solution process of the VIENNA rectifier disclosed in this invention have a small computational workload, significantly reducing the computation time compared to the SVPWM modulation technology of three-level converters.

The application discloses a speech separation method and device based on a multi-channel full convolution time domain network, comprising the following steps: (1) obtaining a plurality of noisy mixed multi-channel speech signals containing different sound sources, noise and reverberation, and taking corresponding pure single-channel speech signals as labels to form a training data set; (2) establishing a multi-channel full convolution time domain network, wherein the multi-channel full convolution time domain network comprises an encoder, a separator, a point multiplication module and a decoder, and the parameters of the encoder are fixed Gammatone filter coefficients; (3) inputting the training data set into the multi-channel full convolution time domain network for training; and (4) inputting a noisy mixed multi-channel speech signal to be separated into the multi-channel full convolution time domain network to obtain a pure single-channel speech signal after sound source separation. The application has better separation effect.

This invention discloses an efficient simulation method for non-Gaussian processes based on a hybrid HPM and JTM model, belonging to the field of structural dynamics technology. This invention achieves rapid simulation of non-Gaussian processes through parameter estimation using Hermitian polynomial and Johnson transformation models based on machine learning, explicit transformation from non-Gaussian correlation functions to Gaussian correlation functions, and fast simulation of non-Gaussian processes based on linear filtering. While ensuring accuracy, this method effectively improves the efficiency of non-Gaussian process simulation and is applicable to non-Gaussian process simulation in various engineering fields.

This invention relates to a method and system for calculating the thin film thickness distribution on the surface of an ellipsoidal reflector. The method includes the following steps: First, calculating the radial beam incident angle distribution curve on the surface of the ellipsoidal optical element; then, calculating the optimized film thickness corresponding to the minimum and maximum beam incident angles, respectively, and using these as the initial range for optimizing the film thickness distribution; then, starting from the minimum beam incident angle, using the optimized film thickness at the current incident angle as the lower limit of the film thickness optimization range for the next incident angle, gradually increasing the incident angle, and finally obtaining the optimized film thickness at all angles through three iterations; finally, visualizing the film thickness distribution and the reflectivity at the working wavelength. This invention eliminates the tedious operation of independently optimizing for each incident angle in traditional designs, avoids the inefficiency and accuracy problems caused by the difficulty in accurately setting the optimization range in manual optimization, and significantly improves the calculation efficiency, automation, and repeatability of the thin film thickness distribution on the surface of an ellipsoidal reflector.

The application discloses a robot morphology directional evolution method based on environment perception and a related device, and relates to the technical field of robot morphology evolution. The method comprises the following steps: initializing a morphology gene vector and a control strategy of a robot; sampling and executing an action strategy according to the control strategy and environment perception information, and then calculating a system benefit value; judging whether the morphology gene vector of the robot is adapted to a current environment according to the environment perception information and the system benefit value; when the judgment result is yes, performing a mutation operation on the morphology gene vector of the robot based on a morphology mutation matrix; otherwise, jumping back to the step of sampling and executing the action strategy until a maximum iteration number is reached. The application quantifies the cumulative stimulation of environmental changes on the robot through an environment perception function to determine whether the robot needs to perform morphology evolution, and guides the direction of morphology evolution of the robot in different environments through the morphology mutation matrix, thereby accelerating the morphology evolution process of the robot.

This application relates to a common-mode voltage suppression method based on cascaded observers, specifically for high-precision motor drives. The aim is to address the shortcomings of existing CMV suppression methods, which rely heavily on experience and suffer from low robustness. This method utilizes cascaded observers to observe and compensate for the total disturbance in the motor control system, calculating a reference voltage vector. A discrete virtual vector set is constructed, and the vector plane formed by this set is divided into multiple continuous and non-overlapping sub-regions, ensuring that each sub-region uniquely corresponds to a virtual vector in the discrete virtual vector set. The sub-region to which the reference voltage vector belongs in the vector plane is determined, and the virtual vector corresponding to that sub-region is used as the target vector. A PWM signal is generated based on the target vector to control the motor, achieving common-mode voltage suppression. This application demonstrates significant suppression effects on the first, second, and sixth harmonics, reducing the common-mode voltage to one-third of that achieved by traditional methods, while effectively reducing computation time.

The application relates to a long-span bridge nonlinear aerodynamic damping self-supervised identification method based on a hardened non-Gaussian response probability density constraint, which comprises the following steps: S1, acquiring displacement response data of a structure under side wind excitation; S2, pre-processing the displacement response data and constructing a target displacement probability density function; S3, establishing a motion equation related to first-order modal response containing nonlinear aerodynamic damping; S4, deducing an analytical displacement probability density function based on a Fokker-Planck-Kolmogorov equation; S5, constructing a probability density function information embedded self-supervised neural network PDF-ThetaNet; S6, constructing a composite probability distribution loss function and training the PDF-ThetaNet; and S7, outputting nonlinear aerodynamic damping parameters and buffeting excitation strength of a long-span bridge structure. Through construction of a nonlinear aerodynamic damping and unknown buffeting force spectrum strength identification model and based on vertical displacement, a self-supervised neural network based on a probability density function constraint is used to realize identification of nonlinear aerodynamic damping and buffeting force power spectrum strength.

The application provides a continuous casting billet online temperature field prediction method based on CPU+GPU heterogeneous parallelism, and relates to the technical field of metallurgical continuous casting. The method firstly determines the arrangement positions of rollers and nozzles on a continuous casting machine; records the cooling water supply data of each position displayed by a two-cooling water table; collects the crystallizer thermocouple temperature measurement data under actual working conditions; collects the components and physical property parameters of a steel grade to be produced; then arranges a database on a server side, establishes multiple data tables in the database according to the uses, and inputs the collected data into corresponding data tables; then performs mathematical modeling on the heat transfer and solidification model of the continuous casting billet on the server side, and performs CPU+GPU heterogeneous parallel programming relying on the C++ / CUDA architecture, so that the heat transfer and solidification model can be solved in parallel on the GPU; finally, a client interface is established on the client side in a B / S structure, which is used for realizing continuous casting billet production process parameter display, temperature curve drawing and temperature cloud chart drawing.