81 results about "Hadamard product" patented technology

The invention relates to the technical field of information resource fusion management, and particularly discloses a fusion and management system for multi-source heterogeneous science and technology information resources. Analyzing an equipment fault chain from an unstructured text of a historical operation and maintenance log, extracting a rated parameter constraint from a structured table of an equipment manual, and collecting an operation feature vector from a real-time sensing data stream to generate a knowledge graph containing N entity relationships; based on an entity attribute constraint rule of the knowledge graph, designing a bidirectional attention mapping network to calculate semantic similarity weights of multi-source data and knowledge nodes, and generating a graph embedding vector set with weight marks through Hadamard product operation; according to the method, the embedded vector set is input into the pre-trained graph neural network model, and the root cause equipment set causing feature offset is positioned, so that efficient fault diagnosis and positioning are realized, decision support is provided for a subsequent preventive maintenance strategy, and the reliability and the operation and maintenance efficiency of the system are improved.

The invention relates to the technical field of electrical digital data processing, and discloses a train ice melting simulation optimization method of an electromagnetic thermal coupling model fused with a deep learning method, which comprises the following steps of: inputting a geometric representation tensor and a physical working condition parameter vector containing an electromagnetic excitation frequency and a reference environment temperature into a feature mapping neural network; outputting a dual-channel space source item tensor containing basic heat source power density and a heat source to temperature change sensitivity distribution matrix through nonlinear convolution operation; constructing a heat conduction discrete numerical value evolution operator configured with an active item linear correction interface; time stepping operation is executed according to the heat conduction time scale, a basic heat source is corrected in real time through the Hadamard product of a sensitivity distribution matrix and temperature deviation, and an operator is substituted for solution. On the premise that electromagnetic-thermal nonlinear coupling characteristics are reserved, decoupling of the time scale is achieved, and the calculation efficiency in high-frequency physical field simulation is effectively improved.

The invention belongs to the field of sensors and signal processing, and particularly relates to a wavelet kernel scale sensitivity oriented abrasive particle induced voltage signal noise reduction method, which comprises the following steps: acquiring an abrasive particle induced voltage signal, and carrying out harmonic elimination on the abrasive particle induced voltage signal to obtain a preprocessed signal; constructing a wavelet kernel function, and calculating the wavelet kernel function and the preprocessed signal to obtain a kernel scale guiding spectrum; constructing a sparse joint noise reduction model based on the kernel scale guiding spectrum; processing the sparse joint noise reduction model to obtain a convex optimization objective function; solving the convex optimization objective function by combining a self-adaptive step gradient descent method and a self-adaptive iterative shrinkage threshold method to obtain a weight vector representing the distribution of the abrasive particle characteristic signals; carrying out binarization processing on the weight vector representing the distribution of the abrasive particle characteristic signals to obtain a characteristic indication vector; carrying out Hadamard product on the feature indication vector and the preprocessed signal, and then carrying out low-pass filtering to obtain a noise reduction signal; according to the method, the abrasive particle characteristic signals can be self-adaptively subjected to non-destructive enhancement and noise reduction processing in a strong interference environment.

The invention provides an MIG fragment rearrangement method and system based on topology awareness and a double-layer agent, and belongs to the technical field of artificial intelligence. The method comprises the steps of collecting calculation of each partition and video memory occupation and remaining execution time, generating partition embedding through Kronecker multiplication, Hadamard product and PCIe neighborhood aggregation, constructing an address adjacency matrix, generating a global condition vector through Sigmoid gating and fusion pooling, and inputting a diffusion network to generate a fragment evolution trajectory and confidence. And calculating continuous splicable capacity based on confidence, constructing a graph structure, evaluating candidate migration actions by a lightweight graph strategy network, and selecting an optimal sequence through risk tensor and Bayesian calibration iterative search. And in the execution stage, the migration duration is controlled through an NVML lock table, RDMA straight pulling and a sliding window, and the device tree is updated after the migration duration is completed. Real evolution is continuously collected on line, deviation is monitored through divergence, the model is updated through distillation, and the optimal action and pause duration are recorded through an experience pool.

The invention discloses a feature enhancement-based infrared and visible light image fusion method. The method comprises the following steps of: preprocessing data; performing shallow feature extraction on the two types of modal images by using a double-branch encoder, and performing decomposition to obtain low-frequency and high-frequency features; constraining the low-frequency and high-frequency features; the low-frequency features are superposed and recombined, and cross-modal difference mining is carried out after the high-frequency features are superposed; refining the fusion features, performing Hadamard product operation on the fusion features and corresponding modal fusion features to obtain self-refining features, and performing cross-modal feature combination reconstruction to generate complementary refining features; and reconstructing to obtain a fused image. According to the embodiment of the invention, preliminary integration of cross-modal information is realized through feature superposition and difference mining, and core features of each modal are reserved; through refining and complementary enhancement processing, collaborative expression of cross-modal structure features and detail information can be effectively enhanced, it is ensured that a fused image has stable performance in the aspects of texture fidelity and saliency target presentation, network conciseness is kept, and the quality of a fusion result is high.

The invention relates to the field of deep learning, and discloses a multi-agent collaborative task state embedding method based on a multi-scale hypergraph and multi-dimensional aggregation. In order to solve the problems that an existing graph neural network is difficult to capture a high-order interaction relation, poor in dynamic environment adaptability and limited in communication, the method comprises the steps of constructing a state observation graph; calculating an adjacent matrix according to explicit states such as position and speed; generating a latent layer feature adjacency matrix through nonlinear conversion and similarity calculation of a graph convolutional network, and fusing the latent layer feature adjacency matrix with the Hadamard product of the interactive graph; constructing a multi-scale hypergraph (containing S scales) according to the latent layer matrix, and searching a high-density sub-matrix to form hyperedges; building a two-stage information aggregation model: integrating multi-dimensional features and calculating association degree and interaction types in a hyperedge aggregation stage, and updating node features by using a graph attention network GAT in a node aggregation stage; a multi-agent soft behavior-commentator algorithm MASAC is fused, a behavior and reward function is designed, and an MHGNN-MASAC model is formed; the method is applied to cooperative control task decision.

The invention relates to the technical field of wireless communication signal big data analysis, and discloses a LoRa unmanned aerial vehicle communication signal-oriented identification and analysis method, which comprises the steps of obtaining radio signal discrete data in a monitoring area, executing short-time Fourier transform to generate a time-frequency two-dimensional spectrogram, and identifying and intercepting a LoRa signal noisy IQ time sequence by using a convolutional neural network; mapping the sequence into a high-dimensional feature tensor, inputting the high-dimensional feature tensor into a slope statistical attention generative adversarial network, calculating a first-order differential tensor by using a generator, carrying out kernel convolution on the first-order differential tensor and preset linearity statistics to generate an attention mask, and reconstructing a de-noised IQ time sequence by using a bidirectional long-short-term memory network after Hadamard product gating; according to the method, the tensor gating operator based on the physical statistical law is embedded in the neural network, so that the common problem that physical semantic drift is easily generated by a generative model at an extremely low signal-to-noise ratio is solved.

The invention discloses a compilation optimization method and device for accelerating Attention calculation, and belongs to the field of compilation optimizing.The method comprises the following steps that multiple scheduling strategies are generated according to the number, shape and Attention type of input matrixes and a target hardware platform; defining a cost function, evaluating time and space overhead of each scheduling strategy, and selecting an optimal strategy based on hardware platform characteristics and user requirements; the method comprises the following steps of: performing multi-core parallel division on input data in sequence length and Head dimension, and reducing delay through asynchronous communication overlapping calculation and communication; in a training stage, a random floating point matrix generated by hardware and an attention weight are used for carrying out Hadamard product operation, and a Dropout function is realized. According to the method, the characteristics of different hardware platforms can be flexibly adapted through the generated Attention operator template, excellent performance can be achieved under various shapes, and efficient data division is achieved by optimizing balance between calculation and communication.

The invention relates to the technical field of image semantic segmentation, in particular to an intelligent landslide identification method based on multi-source remote sensing image fusion. Comprising the following steps: calculating a gradient matrix and a water flow convergence index based on a digital elevation model, searching and determining a physical prior interval of cohesive force and an internal friction angle based on geological lithology data, and constructing an input tensor; extracting a surface semantic feature map of the optical image by using visual perception branches through a double-branch coupling network; based on a physical prior interval, mapping branches by using physical parameters, and carrying out nonlinear mapping to obtain a limited rock-soil mechanical parameter diagram; and calling a micro infinite slope stability layer, executing forward derivable calculation based on a limit equilibrium equation, generating a slope stability coefficient field, constructing physical attention gating, executing Hadamard product operation, and generating a semantic feature tensor. Through deep coupling integration and collaborative optimization of the visual perception branch and the physical parameter mapping branch, the true effectiveness of the obtained position data is ensured.

The invention discloses a heterogeneous graph-based multi-modal teaching video abstract generation method. The method comprises the steps of obtaining a plurality of video samples to form a training set; establishing a multi-modal abstract generation model, training by using a training set, and executing the following operations by the model: respectively inputting a video frame sequence and a sentence sequence into a visual feature extraction model and a language model to obtain a visual feature vector set and a text feature vector set to form multi-modal feature representation; initializing an adjacent matrix; performing Hadamard product on the intra-modal constraint matrix, the inter-modal constraint matrix and the adjacent matrix to obtain an optimized heterogeneous graph; executing a double-stage fusion strategy; and screening a key video frame node set and a key sentence node set by using multi-modal unified representation output by the trained multi-modal abstract generation model, and correspondingly reserving a connection relationship in the optimized heterogeneous graph as a sub-adjacency matrix to obtain a multi-modal abstract graph. According to the method, teaching video abstracts with consistent semantics and rich contents can be generated, and the generalization ability is high.

The invention relates to an aerospace target radar image data generation method based on low illumination enhancement correction. The method comprises the following steps: designing a content delivery decomposition network, decomposing low light characteristics into illumination-independent reflection components and adaptive illumination components through Hadamard product constraint in a submerged space, proposing a learnable intensity compression function, and dynamically generating a submerged space mask through gradient consistency constraint and sparse regularization; embedding a mask into a reverse denoising process to realize directional repair of a degraded region, and combining the generation capability of a diffusion model with the guidance of physical prior; a diffusion path is reconstructed based on a bidirectional diffusion principle, physical consistency of a generation process is constrained through end point binding, a diffusion step length is adaptively adjusted, a cyclic implicit iteration mechanism is introduced, a generation result is gradually refined through a cyclic neural network module, and accurate optical-ISAR image translation can be realized.

The invention belongs to the technical field of audio coding, and particularly discloses an air traffic control audio coding method and system based on dynamic acoustic masking, and the method comprises the steps: collecting an air traffic control audio signal, and carrying out the complex short-time Fourier transform to generate an audio time-frequency diagram; inputting the time-frequency graph into a personalized auditory feature extraction model, extracting a physiological feature graph and a dynamic weight matrix, and fusing the physiological feature graph and the dynamic weight matrix to generate a final masking matrix; performing Hadamard product on the masking matrix and the time-frequency map to obtain a perception saliency map; extracting audio features based on the perceptual saliency map, constructing a basic codebook and a tree residual codebook to perform hierarchical vector quantization compression, and outputting compression representation; and converting the compressed representation into a coding triple consisting of a basic index, a path index and a termination flag bit, and taking the coding triple as a final coding result. The method adapts to individual hearing differences, improves instruction intelligibility and compression efficiency, and is suitable for high-noise aviation communication scenes.

The invention discloses a traffic flow prediction system and method based on a space-time dynamic incidence matrix, and belongs to the technical field of intelligent traffic. The system comprises a data preprocessing module, a space-time dynamic association network (STDCN), a space attention module, a Chebyshev graph convolution module and a time sequence convolution prediction module. The method comprises the following steps: preprocessing original traffic flow data to obtain a three-dimensional tensor; then Pearson and Spearman correlation coefficient matrixes and a JS dispersion matrix are calculated, and a space-time dynamic incidence matrix is generated through fusion; enhancing features through a multi-head attention mechanism and matrix Hadamard product operation; a Chebyshev graph is used for convolution extraction of spatial and temporal features; and finally, realizing prediction through time sequence convolution. According to the method, multiple matrixes are fused to construct dynamic association, an attention mechanism and graph convolution are combined, the traffic flow prediction precision is effectively improved, and the method is suitable for a flow prediction scene of an intelligent traffic system.

The invention provides a pathological image analysis method based on cross-scale spatial constraint fusion and local perception. The method comprises the steps of 1, data preprocessing and feature extraction; step 2, executing a two-way fusion strategy based on spatial constraint to obtain fused enhanced features; step 3, capturing high-order semantic information between scales by using a bilinear fusion mode based on a Hadamard product; 4, performing residual connection on the enhanced features and the fused features to serve as input of a 10-time branch and a 20-time branch; 5, remodeling the features after residual connection into a two-dimensional shape, and inputting three convolutional layers which are arranged in a layered manner and expanded layer by layer; and 6, further performing gating adaptive fusion on the global semantic features obtained after the 10-time branches and the 20-time branches are subjected to the step 5. On the basis of easy deployment, feature representation with high expression ability is obtained, information between branches of different scales is fully utilized, and better performance is obtained in downstream tasks.

The invention discloses a dynamic nerve radiation field real-time three-dimensional reconstruction method and device based on window attention and gradient equilibrium, and the method comprises the steps: carrying out the stratified sampling of rays, and obtaining sampling points; projecting the sampling points to six groups of two-dimensional feature planes; querying corresponding feature vectors from the six groups of feature planes through bilinear interpolation, and generating spatio-temporal features through Hadamard product fusion; inputting the fused spatial-temporal characteristics into a decoding network of a Swin Transform, and outputting the color and the volume density of a corresponding sampling point; and carrying out volume rendering integration along the ray of the camera to generate a composite image, and optimizing and updating the model according to a multi-target loss function between the composite image and a real image. The invention aims to solve the problems of large memory consumption, poor rendering quality, long training time and poor geometric consistency in the prior art, and realizes real-time three-dimensional reconstruction of a dynamic scene by using six-plane grid representation, Swin Transform feature generation and a gradient equilibrium mechanism.

The present application relates to the field of oil seismic exploration method, disclose a kind of oil seismic surface wave dispersion curve automatic extraction method, including prestack single shot data preprocessing, dispersion energy spectrum calculation, threshold transformation processing, mask image morphological dilation processing, mask small area object removal processing, mask morphological corrosion processing, mask and dispersion spectrum point multiplication (Hadamard product) operation, dispersion automatic picking.The present application uses image threshold transformation and morphological operation to process the dispersion energy spectrum of oil seismic surface wave, suppresses other types of wave field except surface wave in the energy spectrum of frequency-velocity domain, retains the dispersion band information of surface wave, and automatically identifies and extracts dispersion curve from the image containing only surface wave dispersion energy band, so as to realize the automatic measurement of dispersion curve.

The application relates to a skin lesion area rapid detection and segmentation method, which comprises the following steps: given an input X element R C×H×W , which becomes Y element R C×HW after remodeling operation, and 4 times expanded attention map Att element R 4C×HW is obtained by calculating the relevance of the query vector and the memory unit; given an input feature map X and a randomly initialized learnable tensor P; the size of the tensor P is adjusted by using bilinear interpolation to match the size of the X, and a depth separable convolution is used on the P; the feature map is uniformly cut into four parts X1, X2, X3 and X4 along the channel dimension. The application improves and introduces Hadamard product attention module to extract multi-angle pathological features of different shaft groupings of the feature map, fuses multi-scale context information, aggregates cross-dimension information, improves the representation ability of the model, designs a new boundary loss function, and puts the boundary information into the model learning process, encourages the model to pay attention to the boundary details, is excellent in various segmentation performance indexes, and can accurately extract a lesion area.

The application discloses a kind of proton exchange membrane fuel cell parameter identification method, belong to fuel cell technical field, comprising: definition is the parameter vector to be identified, with the error between the actual measured terminal voltage corresponding to reference true value working condition data set of predicted voltage as optimization goal, establish objective function, based on Hadamard product introduces multi-dimensional independent random vector to make follower approach adjacent better solution and current global optimal solution, introduce random disturbance term to make leader carry out big step search update, obtain global updated individual position;If the objective function value corresponding to global updated individual position has not occurred decline, then introduce inertia weight with dynamic exponential decay along with iteration number, in the neighborhood of current global updated individual position, carry out local depth search and position update to all individuals, obtain local updated individual position, retain the solution corresponding to smaller objective function value.The application solves the problem that existing technology extracts semi-empirical parameter is not accurate.

The invention relates to an interpretable CNN (Convolutional Neural Network) training method and device based on Bernoulli sampling and a medium, and the method comprises the following steps: inputting a picture into a CNN, and obtaining a response feature map of a filter; performing Bernoulli sampling on the response feature map to obtain a binary distribution matrix; calculating a filter average weight matrix of each picture category according to the binarization distribution matrix, and calculating the sum of pairwise differences; calculating the Hadamard product of the distribution vector of the binary distribution matrix and the response feature map to obtain a mask feature map; respectively inputting the response feature map and the mask feature map into a CNN full connection layer to respectively obtain classification prediction probability vectors, and respectively calculating cross entropy loss with a real label; and according to the sum of the pairwise differences and the cross entropy loss, using a stochastic gradient descent method to realize network training, and obtaining an interpretable CNN for image classification. Compared with the prior art, the method has the advantages of high adaptability, high interpretability and the like.

The invention discloses a sound signal periodic feature extraction method, a network model training method, a storage medium and equipment, and belongs to the technical field of sound event detection. The objective of the invention is to solve the problems of high sensing difficulty and poor decoupling effect of overlapped acoustic events in the current acoustic detection process. The method comprises the following steps: for a sound signal i, mapping the sound signal i to a low-dimensional space through two different linear layers to obtain p and g, and respectively carrying out expansion convolution operation on p and g to obtain pconv and gconv; for p and g, feature coding is carried out based on a Fourier basis function and a gating mechanism to obtain Fourier features, for pconv and gconv, Fourier features are obtained in the same mode, and Hadamard product is carried out on the pconv and the gconv to obtain representation of periodic features. And in the training process of the corresponding model, performing reconstruction error on the sum and the original signal i, respectively calculating two norms of the sum, and adding the two obtained two norms to obtain a Fourier series regular term for training the model.

Disclosed is a computing system comprising a processor; and a neural network, wherein the processor obtains a third block circulant matrix, which is the Hadamard product of a first block circulant matrix and a second block circulant matrix from each layer of the neural network, trains the neural network by utilizing the third block circulant matrix as weights, and fine-tunes the first block circulant matrix and the second block circulant matrix by pruning a plurality of first sub-block circulant matrices included in the learned first block circulant matrix and a plurality of second sub-block circulant matrices included in the learned second block circulant matrix, respectively, for an arbitrary layer among layers of the neural network.

This invention discloses a method and system for analyzing and outputting TCM acupuncture cases based on machine learning, belonging to the field of medical image processing technology. The method acquires facial image sequences from two pathological cycles of the subject, extracts spatiotemporal feature maps, and obtains local receptive field feature matrix pairs through sliding windowing. It calculates the cross-cycle joint entropy gradient and its matrix, and generates a two-dimensional deformation vector using optical flow. Using the coordinates of the first cycle as a reference, the deformation vector is set to zero when the joint entropy gradient does not exceed the rigidity threshold; otherwise, the joint entropy gradient matrix is ​​used as a weight matrix through a sigmoid activation function, and a Hadamard product is performed with the deformation vector to obtain a denoised deformation vector. The denoised vectors are superimposed to obtain corrected pixel coordinates, which are mapped to the target localization coordinate matrix, and the acupuncture case analysis results are output. This scheme achieves precise spatial decoupling of local tissue evolution, eliminates the contamination of rigid regions by global deformation, and significantly improves the robustness and accuracy of cross-cycle acupuncture target localization.

The present application belongs to the technical field of computer vision processing, and particularly relates to a kind of method and system for generating adversarial samples based on deep neural network, first, the original image in sample data is converted into saliency map;The saliency map is used to circle the salient region in the original image of sample data for adding disturbance, and the saliency mask is obtained by binary processing of saliency image pixel value;The original image in sample data is input into image classification model, and the gradient information in the reverse transmission process of Nadam optimization algorithm and convolutional neural network is used to iteratively generate global disturbance adversarial sample;The difference between adversarial sample and original image is obtained, and global adversarial noise is obtained;The Hadamard product of global adversarial noise and saliency mask is used to obtain adversarial noise in salient region, and the final output salient region adversarial sample is obtained by combining adversarial noise and original image.The present application can improve the quality of generated samples, facilitate testing and improve the security and robustness of image classification model.

The application relates to the technical field of storage and transportation equipment maintenance support, in particular to a storage and transportation system equipment maintenance support capability evaluation method, system, equipment and medium, which comprises the following steps: constructing an evaluation index system of the storage and transportation system equipment maintenance support capability; defining a homologous index group and constructing a pairwise comparison matrix for each homologous index group; constructing a judgment influence factor matrix for the pairwise comparison matrix according to the storage and transportation scene of an evaluation object, performing Hadamard product operation to obtain a scene adaptation judgment matrix, and then performing analytic hierarchy process weight calculation to obtain a final index scene weight matrix; calculating the initial evaluation value of each final index, calculating the comprehensive evaluation value of the storage and transportation system maintenance support capability according to the initial evaluation value and the final index scene weight matrix, and further evaluating the equipment maintenance support capability of the evaluation object. The application can make the weights of various indexes match the actual demand in real time, overcome the unexplainable defects, and provide transparent basis for maintenance decision.

This application provides a method, device, equipment, and storage medium for small-sample ultra-short-term wind power prediction of newly built wind farms, relating to the field of wind power prediction technology. The method includes: calculating the distribution similarity coefficient based on two-dimensional wind speed-power kernel density estimation and KL divergence to select the optimal base station; constructing an LSTM model with embedded dynamic similarity alignment gating, fusing temporal features with the gating output using Hadamard product to alleviate long-term prediction decay; employing an inner and outer loop update strategy of model-independent meta-learning, combined with elastic weight solidification regularization to suppress catastrophic forgetting; and achieving 1-4 hour ultra-short-term power prediction for newly built wind farms through base station pre-training, meta-task set training, and small-sample adaptive updating. At the 1-4 hour prediction scale, NRMSE and NMAE are reduced by 4.19% and 4.73%, respectively, and R... 2 It improves by an average of 5.36% and has excellent generalization performance across different sites.

The invention relates to a machining uncertainty calculation method, in particular to a machining uncertainty calculation method of a technological process composed of multiple machining procedures. The method is characterized in that the process processing uncertainty is the synthesis of the regeneration processing uncertainty and the genetic processing uncertainty. The machining uncertainty of each process is a machining uncertainty vector composed of one or more machining uncertainty components. The genetic processing uncertainty is a Hadamard product of the processing uncertainty of the previous process and a genetic coefficient. The regeneration processing uncertainty is the combination of the uncertainty of all error sources and the uncertainty of the Hadamard product of the corresponding sensitivity. The uncertainty of the error source is the uncertainty synthesis of all the uncertainty components of the error source and the Hadamard product of the corresponding sensitivity. The method has the advantages that before the process test is implemented, whether the workpiece precision level which can be achieved by the given process meets the design drawing requirement or not is quantitatively calculated and predicted, the process reasonability and effectiveness are evaluated, the process test risk and cost are reduced, and the first workpiece trial-manufacturing success rate is increased.

The invention relates to the technical field of pre-stack seismic inversion, and discloses a pre-stack seismic wide-angle AVO inversion method based on a second-order approximation, and the method comprises the steps: constructing an inversion objective function through a seismic plane wave second-order approximation on a solid / solid medium horizontal interface; and solving the inversion objective function by adopting an alternating direction multiplier method to realize high-resolution inversion of the longitudinal wave velocity, the transverse wave velocity and the density. According to the method, an inversion objective function is constructed by using a second-order approximation expression, the complex inversion objective function is converted into a plurality of single-parameter linear sub-problems easy to solve by using an alternating direction multiplier algorithm, and meanwhile, a Hadamard product operator is introduced to decompose and solve a high-order sub-problem. Model test and practical application show that the longitudinal wave velocity, the transverse wave velocity and the density resolution ratio inverted by the method are higher, the thin layer identification capability is stronger, the pseudo layer phenomenon can be greatly weakened, and a certain support can be provided for improving the prediction precision of the complex oil and gas reservoir.

This invention discloses a method for inverse kinematics modeling of a polyhedral mobile robot, comprising: S1, constructing a forward kinematics model of a polyhedral open-chain robot, defining a kinematic space with the edge lengths of a virtual polyhedron as the core, and establishing a mapping relationship between joint angles and the edge lengths of the virtual polyhedron; S2, constructing an optimization objective function based on the Hadamard product based on the error between the target edge length set and the actual edge length set of the virtual polyhedron; S3, generating an initial set of iteration points covering all quasi-convex regions in the joint space of the polyhedral open-chain robot; S4, solving the gradient of the objective function and iteratively updating the joint angles; S5, setting a convergence threshold and termination condition, performing convergence judgment on the iteration process, and selecting the optimal solution for inverse kinematics; This invention achieves accurate modeling of the inverse kinematics of a tetrahedral mobile robot by constructing an error optimization model based on the edge lengths of a virtual polyhedron and combining Monte Carlo sampling initialization and gradient descent iterative solution.

The invention discloses a computing engine of a convolutional neural network hardware accelerator. The system is characterized by comprising a data distribution unit which adopts a numerical feature distribution algorithm and a judgment arbitration mechanism, realizes dynamic data distribution by analyzing numerical features of an input feature map and weight data, keeps data integrity, and improves result accuracy and proximity; the heterogeneous convolution calculation unit integrates a multi-operator heterogeneous architecture to process data in a shunting manner, constructs a composite hardware architecture of fitting algorithms such as a waterfall array, a Hadamard product array, a shift addition chain and the like around img2col and Winograd algorithms, and improves the convolution speed and the hardware adaptability of the algorithms; and the data caching unit is used for designing a caching mechanism of ping-pong line buffering according to the feature map data, and realizing collaborative storage optimization of block multiplexing and parallel prefetching.

The embodiment of the invention provides a method for executing polynomial multiplication and acceleration hardware. The acceleration hardware comprises an input transformation module, a convolution kernel transformation module, a modular multiplication module, an output transformation module and a partial product processing module. The method comprises the following steps: respectively segmenting a first polynomial coefficient vector and a second polynomial coefficient vector to obtain T first sub-vectors and K second sub-vectors; the input transformation module and the convolution kernel transformation module transform the first sub-vector and the second sub-vector into an input square matrix and a convolution kernel square matrix respectively based on a two-dimensional Winograd input transformation matrix and a convolution kernel transformation matrix; and executing the first operation for K times. Any ith first operation comprises the steps that a modular multiplication module multiplies T input square matrixes and an ith convolution kernel square matrix element by element to obtain T Hadamard product square matrixes; the output transformation module transforms the Hadamard product matrix into partial product sub-vectors based on a two-dimensional Winograd output transformation matrix; a partial product processing module accumulates partial product sub-vectors to corresponding sub-vectors in the product polynomial coefficient vector in a specified manner.