34 results about "Direct computation" patented technology

The invention discloses a rapid forward modeling method for a gravity field of a rectangular coordinate system based on an isoparametric transformation global discrete grid, and belongs to the technical field of geophysical exploration. According to the method, a measuring network composed of dggrid grids is manufactured to cover the whole measuring area range by selecting the measuring network forming form and the side length, the exploration depth is selected as D, the dggrid grids extend to the ground by an n < z + 1 > layer to form dggrid units of an n < z > layer, and the corresponding thickness of the dggrid units is D / n < z >. And calculating the abnormal response of all the dggrid units at each observation point by utilizing isoparametric transformation based on a dggrid unit interpolation type function, an integral point and an integral weight coefficient obtained by a hexagonal prism integral formula. Compared with a square grid, the dggrid grid has excellent angular resolution, the discrete distance measurement of the dggrid grid is better close to the Cartesian distance, and the dggrid grid also has the advantage of displaying uniform and clear adjacency. Compared with conventional direct calculation, the rectangular coordinate system gravitational field forward modeling based on the isoparametric transformation global discrete grid has extremely high acceleration potential.

The application provides a Petri net supervisory control method and a controller applied to a complex manufacturing system, and the method comprises the following steps: obtaining system information of the complex manufacturing system, and constructing a Petri net model of the complex manufacturing system according to the system information; constructing a reachable graph of the Petri net model according to the Petri net model; taking the current state and resources of each device as input, and predicting transition enabling values of each device in all reachable states through a trained neural network model; and performing supervisory control on the complex manufacturing system according to the transition enabling values of each device in all reachable states. For the complex manufacturing system, the application can avoid the state explosion problem caused by the calculation of the reachable graph of the complex manufacturing system, greatly reduce the time complexity and calculation complexity caused by the calculation of the reachable graph, and directly realize the deadlock control of the complex Petri net and has high behavior permissibility.

The invention discloses a method for analyzing time-frequency characteristics of collision vibration by using a segmented B-spline wavelet collocation method, which comprises the following steps of: (1) establishing a dynamic equation for wavelet discretization, discretizing a dynamic control equation of a collision-vibrator system by using a 4-order B-spline wavelet basis function, and establishing a nonlinear algebraic equation set about a wavelet coefficient; (2) carrying out segmented iterative solution on the time domain, dividing the whole time domain into a plurality of sub-time periods which are connected in sequence, and carrying out iterative solution on the nonlinear algebraic equation set in each sub-time period to obtain a global wavelet coefficient; and (3) analyzing the time-frequency characteristics, and directly calculating energy distribution based on the wavelet coefficient to realize time-frequency joint characterization. The invention aims to provide a method which can fuse solution of system response and extraction of time-frequency characteristics in a unified mathematical framework, and realizes high-precision time-frequency joint characterization by directly solving wavelet coefficients, thereby overcoming inherent defects of an existing two-step method.

The embodiment of the invention provides a training method for a machine learning model, and the method comprises the steps: constructing the machine learning model which comprises an input layer, a hidden layer, and an output layer; the hidden layer comprises at least one mixed feature extraction module; the mixed feature extraction module comprises a first transmission path for non-parametric feature extraction; the first transmission path directly calculates a non-parameterized feature based on the first input feature, and transmits the non-parameterized feature backwards as a first output feature; constructing an index mapping relationship between the first output feature and the first input feature; obtaining training set data to perform multiple iterations on the machine learning model to complete training of the machine learning model; and in the back propagation process of each iteration, the first transmission path maps the gradient parameter of the first output feature to the first input feature based on the index mapping relationship, and the gradient parameter is used as the gradient parameter of the first input feature, so that back propagation of the non-parameterized feature is realized. Through combination of mathematical calculation and trainable parameters of deep learning, the flexibility of a network architecture is promoted, the model calculation efficiency and the feature extraction precision are improved, and the training stability of the model is enhanced.

The invention discloses a five-axis linkage machine tool RTCP parameter measuring method and device, and provides a universal geometric mathematical model, a rotating shaft is deflected by the same angle in the forward direction and the reverse direction, the relative height of a workpiece tray surface is changed, an execution point falls on the workpiece tray surface, and the RTCP parameter is measured. The method comprises the following steps of: calculating the height change of an execution point relative to a reference plane in the process, and directly calculating the distance r of the tail end of a laser / cutter deviating from a rotating center in a five-axis machine tool by utilizing a formula, and the core advantage of the model is that parameters which are difficult to directly measure are solved through parameters which can be directly measured by utilizing mathematical model analysis; according to the method, the height error introduced by manual judgment is counteracted in a formula skillfully through a symmetric measurement design, so that the authenticity and reliability of a measurement result are greatly improved, micron-level-precision RTCP parameter calibration is realized, and the five-axis machining precision is remarkably improved; the same set of core mathematical model and process is suitable for two different types of five-axis machine tools, namely a cutter and a laser, and the universality is high.

The present application relates to the field of scientific computing and GPU technology, and particularly relates to a GPU scan path optimization method.The method comprises: constructing a three-dimensional Stencil calculation GPU framework based on an optimal scan traversal path; based on the three-dimensional Stencil calculation GPU framework, constructing a scan traversal path mathematical model, abstracting the scan traversal path into an integer programming problem, taking the total memory access amount as an optimization target, and optimizing to obtain an optimal two-dimensional window scan traversal path.Compared with the traditional direct calculation, the method of the present application can significantly reduce the data transfer in the global memory, improve the utilization rate of the accessed data, i.e., improve the data reuse rate, reduce the invalid waiting caused by the data blocking and high memory delay of the calculation unit, and thus improve the calculation efficiency.

This invention discloses a lithography simulation method and system based on circular sampling function decomposition and superimposed pupil shift matrix. The method includes: sampling the shape function and pupil function of the extended light source to obtain the corresponding superimposed pupil shift matrix; processing the superimposed pupil shift matrix, the frequency domain circular sampling function matrix of the extended light source and the pupil plane to obtain the frequency domain cross-transmission coefficient matrix and the projection matrix; performing singular value decomposition on the projection matrix to obtain the singular value decomposition result; processing the spatial domain circular sampling function matrix based on the singular vectors to obtain the convolution kernel matrix; constructing a spatial domain image simulation model based on the singular value decomposition result, the convolution kernel matrix and the initial Hopkins model; and obtaining the spatial domain image simulation result based on the spatial domain image simulation model. This invention utilizes the superimposed pupil shift matrix method and analytical circular sampling function decomposition, which can reduce the direct calculation and storage of the TCC matrix and significantly reduce the computational cost required to obtain the kernel.

The application provides a regular wave slamming construction platform stress analysis method and system and a computer readable storage medium thereof. According to platform basic dimensions and wave basic parameters, a stress condition of the regular wave slamming construction platform is directly, quickly and efficiently calculated based on a large amount of test measurement data. The defects of the direct calculation complexity, measurement cost and the like of the slamming pressure load prediction in the existing theoretical calculation and model test are solved.

The application provides an irregular wave slamming construction platform stress analysis method and system and a computer readable storage medium thereof. According to platform basic dimensions and wave basic parameters, a stress condition of the irregular wave slamming construction platform is directly, quickly and efficiently calculated based on a large amount of test measurement data. The defects of the direct calculation complexity, measurement cost and the like of the slamming pressure load prediction in the existing theoretical calculation and model test are solved.

A light-color computation chip, belonging to the field of data computation technology, includes: an instruction scheduling unit, a decoding unit, an optical transmitting unit, an optical receiving unit, a register buffer unit, a pre-stored code library unit, and a computation result integration unit. The optical transmitting unit, optical receiving unit, register buffer unit, and pre-stored code library unit corresponding to the same data bit collectively constitute a single-data-bit computation unit. The instruction scheduling unit receives external instructions and distributes them to the decoding unit, and receives the final computation result and outputs it uniformly. The decoding unit converts the electrical signal instructions issued by the instruction scheduling unit into corresponding bit-specific light-color codes. The optical transmitting unit outputs data light signals of corresponding colors or wavelengths according to the light-color codes. Advantages: It adopts a novel architecture combining direct wavelength encoding and lookup table operations, achieving a simplified, low-latency, and modularly expandable direct computation architecture.

This invention relates to the field of police information technology, specifically disclosing a police information recommendation method and system based on facial recognition. After successful facial recognition, multi-dimensional security policy parameters, including the operating entity, task type, and environmental tags, are collected. These parameters are then combined with a target identifier to construct a joint arbitration request and homomorphically encrypted. The encrypted request is distributed to at least two independent data access control nodes. Each node performs direct computation on the ciphertext based on its local private rules, generating and returning an encrypted partial arbitration result. A secure arbitration service aggregates all encrypted results to form a final encrypted arbitration result. This result is decrypted to obtain the plaintext arbitration conclusion, which is then used to filter and output a customized information list matching the current scenario from the associated database. This invention achieves secure collaborative adjudication while fully protecting the private policies and data of all parties, ensuring the accuracy and security of information recommendations.

The application discloses a kind of hard real-time Gamma implementation method and medium for machine vision based on approximate compensation method, and the implementation method is transformed by Gamma algorithm basic formula, and the exponential operation is transformed into multiplication and shift operation suitable for processor such as FPGA implementation, the precision lost in transformation process is compensated using Taylor expansion, and lookup table is used to assist calculation for some fixed subitems not suitable for direct calculation, a kind of gamma coefficient real-time variable and change range unlimited Gamma algorithm implementation mode is provided.The RAM and LE resource consumption of the algorithm are moderate, the lookup table used in the algorithm does not change with input bit width, and it is an implementation mode based on current gamma coefficient real-time calculation result to solve the problems existing in the prior art Gamma algorithm, and provide an effective new implementation means of Gamma algorithm for machine vision field data processing and data display.

The invention belongs to the technical field of operation and control of a grid-following converter, and discloses a method, a system and equipment for determining a transient synchronous stability boundary of the grid-following converter, and a medium, so as to solve the problems that an existing analysis method is limited by specific working conditions and is insufficient in universality and the like. The method comprises the following steps: a transient simulation step; an energy calculation step; an energy margin calculation step; a sensitivity calculation step; a critical value searching step; and determining a stable boundary. Aiming at the problem that an existing method is limited by specific working conditions and is poor in accuracy due to model simplification, the method comprises the following steps: establishing an accurate transient simulation model, introducing a mechanism for adaptively calculating the maximum potential energy, dynamically and intelligently selecting direct calculation, disturbance iteration or voltage adjustment paths according to a power angle track, and calculating the maximum potential energy according to the power angle track. It is ensured that the method can be applied to different power grid structures, faults and operation modes, the real stability boundary is accurately captured, and accurate evaluation of transient synchronization stability is achieved.

The application discloses a kind of MMC-BESS low-computing amount extended level model predictive control methods, adopt hierarchical architecture: output current control layer is determined theoretical optimal level by minimizing current envelope area, and take its adjacent two integer levels as candidate, only evaluate 2 candidates can obtain optimal output level, constant 2 with the number of sub-modules, irrelevant;Based on the result, the circulating current control layer obtains the optimal bridge arm input combination by direct calculation and rounding, without exhaustive search, the amount of calculation is zero.Simultaneously set bridge arm input combination boundary constraint mechanism to ensure physical realizability.This method completely eliminates the tuning problem of weight factor in traditional model predictive control, the total amount of calculation is extremely low and does not increase with the number of sub-modules, with excellent output current quality, circulating current suppression ability and sub-module state of charge balancing performance, especially suitable for large-scale modular multilevel energy storage system.

The application discloses a complex robot system robust invariant set constraint sparsification method, and belongs to the technical field of robot control.The method comprises the following steps: clustering and dividing constraints according to the constraint distribution of a convex set; and simplifying redundant constraints and obtaining a simplified approximate convex set, since the convex set vertexes of a sub-group in a group are gathered in one region, and the description of the convex set vertexes to the constraints is redundant; and limiting the error between the simplified approximate convex set and the original convex set within a preset range, and obtaining a constraint set which can be directly calculated.The method can accurately describe the space geometric characteristics of the robust invariant set by means of geometric analysis and sparsification processing with a small amount of key constraints, and significantly reduces the calculation complexity, so that the robust control algorithm can meet the real-time control requirements of the complex robot system.

This invention belongs to the field of communication equipment security protection technology, and discloses a method for characterizing the static safety margin of power systems based on the truncated polynomial approximation method. This method first maps the load growth factor to complex-domain embedded variables, establishing a fully embedded power flow model; then, it expands the system state variables into high-order truncated polynomials, transforming nonlinear terms into linear convolutions between coefficients through Cauchy products, and solving for the polynomial coefficients using linear recursion; subsequently, it constructs Padé approximation rational functions to achieve analytical extension, directly calculating the high-load operating condition solution exceeding the original series convergence radius; finally, through a multi-stage continuity tracking strategy, it adaptively approximates the voltage collapse critical point, synthesizing the system's static safety margin. This invention abandons the reliance of traditional iterative algorithms on Jacobian matrix inversion and initial value guessing, fundamentally avoiding numerical ill-conditioned problems near the critical point, and achieving rapid characterization of static safety margins with large step size, high precision, and global robustness.

The present invention provides a method for predicting remaining useful life of bearings based on a gated recurrent neural network, comprising the following steps: S1. obtaining full life cycle vibration signals of bearings, extracting the vibration distribution features and creating a training set of gated recurrent neural network; S2. constructing a gated recurrent neural network model, and introducing an attention mechanism that directly calculates weights to improve the integrity of extracting temporal information; S3. adding a Bayesian layer to construct a nonlinear mapping relationship between temporal information and remaining useful life; S4. taking vibration signals of a test bearing as input, the output result of the gated recurrent neural network model is the remaining useful life of the test bearing at the current time. The present invention does not need to add additional neural network layers, which avoids the problem of increasing the complexity of the model. The integrity of extracted information is improved through weighted fusion of temporal information extracted at different time. Moreover, by adding a Bayesian layer, the traditional point prediction results are converted into interval predictions, to consider the prediction uncertainty of remaining useful life of bearings.

The invention relates to the technical field of grease extraction, and discloses a grease extraction process parameter optimization method and system, and the method comprises the steps: representing a product order as a target quality vector, and constructing a quality function based on the target quality vector; based on the quality function, constructing a reverse mapping model from the target quality vector to the process parameters, and outputting the optimal process parameters by the reverse mapping model; based on the optimal process parameters, constructing a multi-constraint model to obtain optimized process parameters; based on the optimized process parameters, constructing a dynamic control model, and dynamically adjusting the process parameters according to actual parameters; a traditional forward test method is changed by establishing a reverse mapping model from terminal product quality specifications to grease extraction process parameters. Through the combination of the deep neural network and the genetic algorithm, the optimal process parameter combination can be directly calculated according to the specific quality requirements of customer orders, the production time is shortened, and the production cost is reduced.

This invention discloses a method and device for measuring RTCP parameters of a five-axis CNC machine tool. It proposes a universal geometrical mathematical model that, by rotating the axis forward and backward by the same angle, changes the relative height of the workpiece pallet surface so that the execution point falls on the workpiece pallet surface. The change in height of the execution point relative to a reference plane during this process is measured, and the distance *r* between the laser / tool ​​tip and the rotation center in the five-axis machine tool is directly calculated using a formula. The core advantage of this model lies in its use of mathematical model analysis to solve for parameters that are difficult to measure directly using directly measurable parameters. Its ingenious symmetrical measurement design cancels out height errors introduced by human judgment in the formula, thereby significantly improving the authenticity and reliability of the measurement results. This achieves micron-level precision in RTCP parameter calibration, significantly improving the accuracy of five-axis machining. The same core mathematical model and process are applicable to two different types of five-axis machine tools: those using cutting tools and those using lasers, demonstrating strong versatility.

This invention relates to the field of motion control technology for multi-axis CNC machine tools, specifically to a six-axis CNC machine tool control method based on dual-link decoupling of the tool and workpiece. During machining, the method functionally decouples the tool-side kinematic chain from the workpiece-side kinematic chain, uses control points and dual control directions to describe the machining posture, establishes kinematic equations between the tool and workpiece in a unified base coordinate system, and solves the workpiece-side rotation axis variables, workpiece-side translation axis variables, and tool-side translation axis variables step by step using analytical methods. This enables the direct calculation and issuance of six-axis linkage control commands, thereby solving the problems of severe variable coupling, reliance on numerical iteration, low computational efficiency, and insufficient stability commonly found in traditional six-axis CNC machine tool inverse kinematics solutions. This improves the control accuracy, real-time performance, and engineering reliability in multi-axis linkage machining processes.

The invention belongs to the technical field of communication equipment safety protection, and discloses a power system static safety margin description method based on a truncated polynomial approximation method.The method comprises the steps that firstly, a load growth factor is mapped into a complex field embedded variable, and a full-pure embedded power flow model is established; expanding a system state variable into a high-order truncated polynomial, converting a nonlinear term into linear convolution among coefficients through a Cauchy product, and solving polynomial coefficients by using linear recursion; then, a Pade approximation rational function is constructed, analysis continuation is achieved, and a high-load working condition solution exceeding the convergence radius of the original series is directly calculated; and finally, adaptively approaching a voltage collapse critical point through a multi-stage continuous tracking strategy, and synthesizing the static safety margin of the system. According to the method, the dependence of a traditional iterative algorithm on Jacobian matrix inversion and initial value guess is abandoned, the numerical ill-conditioned problem near a critical point is fundamentally avoided, and large-step-length, high-precision and global robust static safety margin rapid description is realized.

The disclosed memory architecture eliminates the need for the conventional queue-based work request model by allowing direct computation within memory modules in response to data writes. The system is designed to automatically update computed values, such as hashes, within a designated computational memory region in response to a write to a corresponding data set region, without explicit instructions from the host. The computation happens according to a defined policy, which may include computing a new result immediately after a write to a dataset segment, computing the result if no writes are detected to a dataset segment within a specified period of time, computing the result after a host reads an invalid compute validity bit, or the like.

The invention discloses a ship structure ice level strengthening method based on artificial intelligence, which comprises the following steps of: acquiring basic parameter information of various types of ships, and performing finite element simulation modeling and calculation to obtain stress distribution, displacement and total strain of a ship body structure under the combination of various ship types and ice conditions; performing linear strength direct calculation or non-linear strength direct calculation on the obtained data, analyzing a potential failure mode of the ship, and forming a structure ice-resistant grade database in combination with evaluation of the structure ice-resistant grade; an artificial intelligence model is established, and the structural ice-resistant grade database is used for training; basic parameters of a target ship in actual navigation are input into the trained artificial intelligence model, and an ice-level strengthening scheme matched with the target ship is generated through feature extraction, rule reasoning and decision output. According to the method, automation and intellectualization of ice level strengthening design under different ship types and ice area levels are achieved, and the safety, economical efficiency and efficiency of polar navigation ship structure design are improved.

The present application belongs to the technical field of grid-following converter operation and control, and discloses a method, system, device and medium for determining the transient synchronous stability boundary of a grid-following converter, to solve the problem of insufficient universality of existing analysis methods or being limited to specific operating conditions. The method comprises: a transient simulation step; an energy calculation step; an energy margin calculation step; a sensitivity calculation step; a critical value searching step; and a stability boundary determination step. To solve the problem of poor accuracy caused by existing methods being limited to specific operating conditions and model simplification, the present application establishes an accurate transient simulation model, introduces a mechanism for adaptively calculating the maximum potential energy, intelligently selects a direct calculation, disturbance iteration or voltage adjustment path according to the power angle trajectory dynamics, ensures that the method is applicable under different power grid structures, faults and operating modes, accurately captures the real stability boundary, and realizes accurate evaluation of transient synchronous stability.

The application provides an open office work efficiency attenuation prediction method, device, equipment and medium, acquires sound quality single value evaluation parameters and plane size parameters of an open office, first establishes a relationship function of a language transmission index and a sound source to a receiving point distance, converts discrete point-to-point measurement into a continuous distance function expression, then combines a language intelligibility and a relationship function of individual work efficiency attenuation to establish a relationship function of individual work efficiency attenuation and distance, and finally utilizes the plane size parameters to perform area integration on all sound source and receiving point position combinations in the office to directly calculate an overall work efficiency attenuation, so that it is not necessary to measure the language transmission index of each position combination one by one, and fast prediction from the sound quality single value evaluation parameters to the overall work efficiency attenuation is realized.

The invention discloses a traffic target course angle optimization calculation method and device and computer equipment. The method comprises the following steps: setting relevant parameters; creating a data structure for each tracking target to store a historical track point sequence, and obtaining latest position, speed, acceleration, classification and perception uncertainty information in real time to obtain target real-time data; dynamically adjusting the window length according to the target real-time data in combination with related parameters to obtain an adaptive window distance; removing the head point from the historical track point sequence based on the adaptive window distance until the residual track length does not exceed the adaptive window distance, and determining a sliding window starting point and a sliding window ending point; calculating an optimized course angle by using the coordinates of the sliding window starting point and the sliding window ending point; and outputting the course angle and the state information. By implementing the method, target characteristics and motion states can be intelligently adapted, the positioning noise influence is fundamentally reduced, and the course angle which is more stable and conforms to the physical law can be directly calculated from the original track point sequence.

A function approximation unit includes a programmable function approximation circuit configured to directly compute a piecewise function according to a pre-stored coefficient; one or more dedicated function circuits configured to process at least one specific function among a reciprocal, a reciprocal square root, and a negative exponential function through a fixed hard-wired operation pipeline; and control logic configured to selectively activate either the programmable function approximation circuit or the dedicated function circuit according to the type of the nonlinear function to be processed.

The general problem of pattern change discovery between high-dimensional data sets is addressed by considering the notion of the principal angles between the subspaces is introduced to measure the subspace difference between two high-dimensional data sets. Current methods either mainly focus on magnitude change detection of low-dimensional data sets or are under supervised frameworks. Principal angles bear a property to isolate subspace change from the magnitude change. To address the challenge of directly computing the principal angles, matrix factorization is used to serve as a statistical framework and develop the principle of the dominant subspace mapping to transfer the principal angle based detection to a matrix factorization problem. Matrix factorization can be naturally embedded into the likelihood ratio test based on the linear models. The method may be unsupervised and addresses the statistical significance of the pattern changes between high-dimensional data sets.

The invention discloses an adaptive variational mode noise reduction method and system based on bit plane decomposition, and belongs to the field of signal processing. According to the technology, binary conversion is carried out on a time domain signal through an innovative bit plane decomposition method, the signal is decomposed into time domain components under different bit resolutions, and the optimal decomposition parameter of VMD is adaptively determined based on component characteristics. The method comprises the following steps: firstly, carrying out bit plane decomposition on a noisy signal to obtain time domain components of different bits; calculating an autocorrelation test value of each component, and distinguishing a signal component and a noise component; dynamically calculating the modal number and penalty factor of the VMD according to the number of signal components and the energy entropy; and finally, performing VMD decomposition and reconstructing a noise reduction signal. Transient features are reserved through bit plane decomposition, and the feature retention rate is remarkably improved; vMD parameters are directly calculated, iterative optimization is avoided, and the processing speed is greatly increased; hardware resource occupation is greatly reduced, and power consumption is greatly reduced; and in a strong noise environment, the accuracy is greatly improved.