33 results about "Partition matrix" patented technology

The invention relates to the technical field of bridge safety monitoring, and comprises a bridge safety monitoring analysis system based on big data, and the system comprises a data collection processing module, a manifold feature dimension reduction module, an entropy change partition evaluation module, a health state dynamic analysis module, and a bridge safety early warning module. According to the method, a bridge measuring point topological relation matrix is established, measuring point spatial distribution characteristics are analyzed, spatial relevance of a bridge structure is reflected, measuring point local curvatures are calculated, a local curvature matrix is established, a neighborhood similarity matrix is combined, low-dimensional projection transformation is executed, local topological consistency of data is kept in the dimensionality reduction process, an entropy change partition matrix is established, and the spatial relevance of the bridge structure is reflected. The health states of different structural parts of the bridge are subjected to differential analysis based on mechanical characteristics, the entropy change trend and damage probability of a measuring point are calculated, and the overall safety state level of the bridge is judged through a damage risk threshold, so that the local damage severity of the bridge can be comprehensively considered in safety assessment, and the stability and adaptability of safety state judgment are improved.

The invention relates to a high-order analysis power flow and carbon flow tracking method based on full-pure embedding, and the method comprises the specific steps: introducing a complex embedding parameter s, constructing a node voltage vector, an injection power vector and a tracking matrix in a power grid power flow equation into an analysis power series and an analysis power series of the complex embedding parameter s based on the analysis continuation characteristic of full-pure embedding, solving a node voltage initial value when the power grid is in a basic operation state by adopting a Newton-Raphson method; calculating a zero-order line active power vector based on the node voltage initial value, and constructing a zero-order distribution matrix representing a power distribution relation in a basic operation state of the power grid; solving a zero-order power flow tracking matrix based on the zero-order line active power vector and the zero-order power distribution matrix; calculating node indirect total carbon emission based on the zero-order power flow tracking matrix; and replacing the power distribution matrix, the node outflow power and the power flow direction matrix in the zero-order power distribution matrix with power series expansion of corresponding variables.

The invention relates to the technical field of data processing, and discloses a high-rise building elevator partition intelligent distribution method and system and a storage medium. The method comprises the steps that floor people flow data and elevator calling frequency are collected, and a floor weight incidence matrix is constructed in combination with functional attributes; optimizing the partition boundary through a partition boundary game algorithm; load distribution is monitored, the unbalanced state is recognized, and an elevator-partition distribution matrix is constructed; the unbalanced partition pairs are screened to execute boundary floor transfer operation; and scheme verification is carried out based on the reconstruction cost and the energy-saving income constraint, and an elevator partition intelligent distribution scheme is generated. The technical problems that in the prior art, a static partition strategy cannot adapt to dynamic people flow changes, partition loads are unbalanced, and multi-target comprehensive optimization is lacked are solved. And the energy consumption control level and the load balancing performance of the elevator system are improved.

A load balancing scheduling method, system and device relate to the technical field of resource scheduling, and the method comprises the following steps: setting a node label for each working node in a cluster to label a hardware resource type, and obtaining a load index of each working node; receiving a task request, obtaining a task stage and a task type, and forwarding the request to the Service corresponding to the task stage and the task type; task resource requirements, Pod topological constraints and resource utilization rates of scheduling nodes are obtained to construct a QUBO model, a task-to-node allocation matrix is obtained through a quantum annealing algorithm, and target nodes are determined; and starting the pod at the target node through the pre-deployed KubeRay, and when a preset elastic expansion and contraction condition is triggered, performing elastic expansion and contraction. According to the method, the hardware resource type is labeled through the node label, and the task demand and the node resource are dynamically matched in combination with the QUBO model and the quantum annealing algorithm, so that the resource utilization rate is effectively improved.

The application relates to the technical field of digital site selection planning, and particularly provides a reservoir resettlement area site selection method and system. The method comprises the following steps: constructing a reservoir resettlement area planning model under multi-target optimization, generating an initial site selection planning scheme; performing topological representation on the initial site selection planning scheme to obtain a current resident gathering point-resettlement area distribution matrix, and constructing a solution space; estimating an initial resettlement area quantity; realizing global search by using a simulated annealing algorithm, avoiding local optimization by probabilistically accepting a poor solution, combining the current resident gathering point-resettlement area distribution matrix and an adaptive multi-step neighborhood generation algorithm meeting the resettlement area quantity constraint, iteratively optimizing and adaptively adjusting a search strategy under the feasibility constraint, and converging to obtain an optimal resettlement area site selection scheme. The application organically combines a policy gradient method in reinforcement learning and a simulated annealing framework, and solves the problem of insufficient intelligence of reservoir resettlement area site selection.

A neural processing device and method for pruning thereof are provided. The neural processing device includes a processing unit configured to perform calculations, an L0 memory configured to store input and output data of the processing unit, wherein the input and output data include a two-dimensional weight matrix and a weight manipulator configured to receive the two-dimensional weight matrix and partition it into preset sizes to thereby generate partitioned matrices, to generate a pruning matrix by pruning the partitioned matrix, and to transmit the pruning matrix to the processing unit.

The application discloses a multi-view clustering method and system based on self-step learning and view weighting, and belongs to the technical field of multi-view data processing. The method comprises the following steps: normalizing a multi-view data set, splicing the multi-view data, initializing each view clustering kernel and distribution matrix by using a kmeans algorithm, and calculating each view weight; each view sample weight matrix, each view clustering kernel and distribution matrix are iteratively updated in sequence through a target function; and when an iteration end condition is met, a final clustering kernel and distribution matrix are output. The clustering system comprises an acquisition module, a preprocessing module, a construction module, an optimization module and a clustering output module. The self-step learning model is used to sequentially learn clustering data and finally obtain a clustering result. Through view weighting, the model can selectively learn information of different views, thereby effectively improving clustering accuracy. The application can be applied to retrieval of an image database, a text database and the like.

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 provides a project scale and progress prediction method, electronic equipment and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: obtaining target enterprise project information, carrying out the multi-layer nested analysis of a project according to a plurality of project modules, and constructing a multi-layer nested project structure; on the basis of a multi-layer nested project structure, probability transformation is carried out on the membership relation between the nodes and the clusters, and a soft distribution matrix set is obtained; obtaining a plurality of project module structure entropy values; respectively analyzing the plurality of real-time acquisition data stream sets and the soft distribution matrix sets based on a dual-channel sequential network to obtain a plurality of prediction results of the plurality of optimization project modules; and obtaining a target project scale and progress prediction result. The technical problems that in the prior art, project scale and progress prediction is analyzed from a single dimension, the prediction result deviates from reality, and reliability is low are solved. The technical effects of considering hierarchical dependence between the nodes and improving the prediction accuracy are achieved.

The invention relates to a single-cell RNA (Ribonucleic Acid) sequencing data clustering method, device and equipment based on graph structure information fusion and a medium, and the method comprises the following steps: respectively calculating soft distribution matrixes corresponding to cell attribute representation, cell structure representation and consensus embedding representation, and deriving target distribution based on the soft distribution matrixes of the cell consensus embedding representation, all parameters of a zero-expansion negative binomial autoencoder, a graph autoencoder and a cross-modal fusion module are jointly optimized by minimizing the KL divergence between target distribution and each soft distribution matrix, iterative training is performed until the model reaches a preset convergence condition, and optimized cell consensus embedding representation is determined; and carrying out clustering analysis on the optimized cell consensus embedded representation by adopting a preset K-means clustering algorithm to obtain a cell type tag corresponding to the gene expression data of single cell RNA sequencing. According to the method, the problems of zero expansion, sparsity and over-dispersion characteristics of single-cell RNA sequencing data can be solved.

The application discloses a kind of dynamic blockchain sharding method and system based on composite clustering algorithm, the method includes: based on blockchain sharding system, determine transaction frequency matrix and sharding allocation matrix, construct the target optimization transaction model of blockchain sharding system;Based on the target optimization transaction model of blockchain sharding system, preliminary global division processing is carried out through agglomerative hierarchical clustering algorithm;The preliminary blockchain sharding result is divided and processed by DBSCAN clustering algorithm, and the blockchain sharding result after secondary division is obtained;Based on the blockchain sharding result after secondary division, carry out noise account division processing, obtain the final blockchain sharding result.The application can preferentially aggregate high-frequency associated accounts and classify low-frequency accounts, minimizing the proportion of cross-shard transactions and achieving load balancing.The application is a kind of dynamic blockchain sharding method and system based on composite clustering algorithm, which can be widely applied in the field of blockchain transaction technology.

The application relates to a subarray partitioning method based on rotational symmetry. The method comprises the following steps: according to a subarray type and an antenna array surface, a dictionary matrix is constructed, each array element on the antenna array surface is labeled according to an arrangement order to obtain an initial array element order, each array element on the antenna array surface is rotated for multiple times according to the symmetry, and the array element order after each rotation is recorded; then a row of candidate subarrays is randomly selected from the dictionary matrix, corresponding multiple rows of candidate subarrays are selected from the dictionary matrix according to the relationship between the initial array element order and the array element order after multiple rotations, the multiple rows of candidate subarrays are added to a subarray partitioning matrix, the multiple rows of candidate subarrays are deleted from the dictionary matrix, the rows related to the multiple rows of candidate subarrays are deleted from the dictionary matrix to form a new dictionary matrix, the deleting operation is continuously performed on the new dictionary matrix, and finally the subarray partitioning of the antenna array surface is realized. The method can improve the speed of subarray partitioning.

The application relates to the technical field of building three-dimensional modeling, and discloses a three-dimensional modeling information matching method in building operation based on AI, which comprises the following steps: extracting a native level bounding box tree for accelerated rendering in a three-dimensional modeling engine, mapping the hierarchical structure of the tree into a spatial matching potential field; driving a dynamic semantic node to complete gradient descent flow along the hierarchical bounding box tree, and determining a distribution matrix of the dynamic node to an initial grid model; calculating a Laplace differential coordinate array of a grid vertex to lock native high-frequency curvature features, combining position anchor point constraints to construct and solve a sparse linear equation set, and reconstructing an updated three-dimensional model; the application locks the curvature details of a model surface by using differential geometric features, makes the three-dimensional model maintain original geometric textures when responding to displacement anomalies, solves the problem of topological tearing caused in a dynamic updating process, and enhances the fidelity of spatial mapping.

The application relates to a communication-efficient decentralized federated learning method based on subgraph division, comprising the following steps: constructing a physical network topology graph of a decentralized federated learning environment; dividing the physical network topology graph into non-conflict subgraphs, and setting a mixing matrix for each non-conflict subgraph; constructing a problem constraint model with the minimum total iteration time as the target; optimizing a bandwidth allocation matrix with the delay value of the link with the maximum communication delay in the current non-conflict subgraph as the optimization target; optimizing the mixing matrix with the minimum expected communication delay as the optimization target; optimizing the non-conflict subgraph sampling probability based on the optimized mixing matrix with the minimum expected communication delay as the optimization target; and performing global updating of the decentralized federated learning model based on the optimized non-conflict subgraph sampling probability. The application can significantly reduce the global model convergence delay.

The present application relates to the technical field of bridge safety monitoring, comprising a bridge safety monitoring and analysis system based on big data, which comprises a data acquisition and processing module, a manifold feature dimension reduction module, an entropy change partition evaluation module, a health state dynamic analysis module and a bridge safety early warning module.In the present application, a bridge measurement point topology relationship matrix is established, the spatial distribution characteristics of the measurement points are analyzed, the spatial correlation of the bridge structure is reflected, the local curvature of the measurement points is calculated and a local curvature matrix is established, the neighborhood similarity matrix is combined, and low-dimensional projection transformation is performed, so that the data maintains local topological consistency during dimension reduction, an entropy change partition matrix is established, the health states of different structural parts of the bridge are differentiated based on mechanical properties, the entropy change trend and damage probability of the measurement points are calculated, and the overall safety state grade of the bridge is judged through a damage risk threshold, so that the safety evaluation can comprehensively consider the severity of local damage of the bridge, and the stability and adaptability of safety state determination are improved.

This disclosure provides for devices, methods, and systems for immobilizing partitions of a partition matrix, where partitions are rapidly generated from a sample, and for immobilizing a set of targets within a matrix, where examples of the matrix are optically clear. Embodiments of methods involve partition generation within a collecting container at a high rate (e.g., of at least 1 million partitions per minute, etc.), each of the partitions including an aqueous mixture. Embodiments of methods also involve stabilizing positions of targets within a matrix within a collecting container and performing reactions within the matrix at a temperature below the melting temperature of the matrix. Upon immobilization, readout of signals from targets within such partitions and / or the matrix can still be achieved in accordance with various assays.

The present application belongs to the technical field of nondestructive testing of concrete structures, and particularly relates to a method for identifying the topology of internal defects in concrete based on a graph neural network, comprising: obtaining three-dimensional detection data of a concrete test piece for voxel reconstruction and extracting a three-dimensional defect candidate; constructing an initial graph based on spatial adjacency relationships and extracting node features and edge features; recalibrating the adjacency relationships and edge weights based on the local graph structure topology index of the initial graph to obtain a topology-constrained graph, and performing message passing on the topology-constrained graph while updating the node embedding in combination with adaptive information aggregation weight; constructing a fitness function, optimizing the allocation matrix of nodes to clusters to determine graph coarsening mapping, and generating a multi-layer defect skeleton subgraph; extracting a cross-layer topology description vector from the multi-layer defect skeleton subgraph and outputting the classification and identification result of the defect topology structure. The present application can accurately classify and identify the topology of complex internal defects in concrete.

The application provides a pairing method and device for non-fixed additional pseudo codes, wherein the method comprises the following steps: setting pairing indexes, the first index being the aligned cross-correlation value absolute value, and the second index being the absolute value of the difference between the leading and lagging correlation values; determining a target function and a cost matrix based on the first index and the second index; establishing an additional pseudo code allocation model based on the cost matrix and an allocation matrix; dividing the additional pseudo codes into two parts, determining an initial population based on the division result, and solving the model by using a JVC algorithm to determine the initial pairing scheme corresponding to the individuals in the initial population; performing iterative updating on the population based on a meta-heuristic algorithm or an improved meta-heuristic algorithm, and performing iterative updating on the pairing scheme corresponding to the individuals based on the JVC algorithm until a preset maximum number of iterations is reached, and determining the optimal pairing scheme obtained in the last iteration process as the optimal pairing scheme of the non-fixed additional pseudo codes; wherein the optimal pairing scheme obtained in the iteration process is determined based on the target function.

A MIMO radar supporting overlapping detection of reflected signals from objects is disclosed. A system and method for processing reflected received radar signals are presented. A range partition matrix is ​​generated from a range-Doppler antenna cube generated using reflections of multiple transmitted radar signals. A first range partition array comprising multiple values ​​associated with different Doppler partitions is determined, and an offset range partition array comprising the multiple values ​​of the first range partition array offset by a certain number of Doppler partitions is generated. First Doppler partitions in the first range partition array and the offset range partition array, each comprising non-zero values, are determined, and the values ​​associated with the first Doppler partitions in the first range partition array are set to zero to generate a corrected range partition array. The radar system uses the corrected range partition array to determine an estimated direction of arrival.

The application discloses a kind of data processing method, system and storage medium of intelligent construction, it is related to data processing technical field, comprising the following steps: obtaining the communication path parameter between the multiple cooperative operation equipment of construction site, constructs first constraint condition, and using clustering algorithm to filter the first path set satisfying first constraint condition;Cooperative operation data flow in the first path set is obtained, and multi-resolution feature extraction is carried out using DWT, and layered data exchange protocol stack is obtained;According to layered data exchange protocol stack, construct bandwidth pre-allocation matrix, and carry out bandwidth preemption in combination with preset bandwidth preemption algorithm;Bandwidth preemption result is evaluated based on bayesian decision model, and equipment moving path switching is carried out according to evaluation result, solve the problem that traditional method is difficult to deal with the burst demand of high concurrency data stream, and high priority task delay is caused by bandwidth competition, seriously threatens construction safety.

The application relates to a late-stage fused object image clustering method, device and computer equipment. The method comprises the following steps: in response to an image clustering request, acquiring object image data to be clustered; determining a basic partition matrix, an initial rotation disturbance matrix and an initial weight coefficient of the object image data under different views, and an initial consistent clustering partition matrix corresponding to all views; constructing an object image clustering objective function according to the basic partition matrix, the initial rotation disturbance matrix, the initial weight coefficient and the initial consistent clustering partition matrix; alternately optimizing the parameters to be optimized in the object image clustering objective function, and obtaining a target consistent clustering partition matrix of a basic partition matrix fused with each view when the function value of the object image clustering objective function is a preset function value; and performing clustering processing on the target consistent clustering partition matrix to obtain a clustering result of the object image data. The method can improve the clustering accuracy of images.

The invention belongs to the technical field of machine learning, and relates to a heterogeneous federal map learning method and system. The method comprises the following steps: in each client, constructing a globally shared symbiotic space, and generating a unified target semantic prototype through a label propagation mechanism; generating a prototype distribution matrix, and uploading the prototype distribution matrix to a server side; generating a global prototype by aggregating the prototype distribution matrix of each client, and distributing the global prototype to each client; embedding and mapping the nodes into a hash bucket by using a hash function, generating aligned local hash codes and local anchor point embedding, and uploading the local hash codes and the local anchor point embedding to a server side; aggregating the local hash codes and the local anchor point embedding to obtain global hash codes and global anchor point embedding; global hash coding and global anchor point embedding are optimized through a graph auto-encoder and consistency constraint, and alignment of the concentrated graph is optimized. According to the invention, the flexibility and efficiency of federal map learning are improved, the data privacy and security are ensured, and the communication overhead is reduced.

The application relates to the technical field of data processing, and discloses a high-rise building elevator partition intelligent distribution method and system and a storage medium. The method comprises the following steps: collecting floor passenger flow data and call frequency, combining a function attribute to construct a floor weight correlation matrix; optimizing a partition boundary through a partition boundary game algorithm; monitoring a load distribution to identify an unbalanced state, constructing an elevator-partition distribution matrix; screening an unbalanced partition to perform a boundary floor transfer operation; verifying a scheme based on a reconstruction cost and energy saving benefit constraint to generate an elevator partition intelligent distribution scheme. The application solves the technical problems that a static partition strategy in the prior art cannot adapt to dynamic passenger flow changes, partition load is unbalanced, and multi-target comprehensive optimization is lacking. The energy consumption control level and load balancing performance of the elevator system are improved.

The application discloses a real-time dynamic task allocation and scheduling method for a multi-vehicle cluster, relates to the technical field of task scheduling, and comprises the following steps: acquiring real-time physical running state sequences of each vehicle node in the multi-vehicle cluster and global task distribution characteristic sequences of a task set to be allocated; constructing a space-time topology tensor with dynamic edge weights; performing node feature aggregation on the space-time topology tensor; calculating a state divergence degree between real-time local physical states of the vehicle nodes and expected states corresponding to an initial task allocation correlation matrix; extracting a sub-cluster topology tensor within the local reconstruction boundary; inputting the sub-cluster topology tensor into the task scheduling network for local deduction to generate a local correction allocation matrix; and updating a task execution sequence of the target vehicle node and associated vehicle nodes within the local reconstruction boundary by using the local correction allocation matrix. The application improves the stability and adaptability of cluster task scheduling.

The invention relates to the technical field of artificial intelligence and data mining, and discloses a sparse graph clustering pooling method based on reconstruction regularization, and the method comprises the following steps: employing a double-branch graph convolutional network architecture as a basic encoder, respectively inputting a node feature matrix and an adjacent matrix corresponding to the input graph structure data into the two graph convolutional networks, obtaining node embedding through the first graph convolutional network, and obtaining an initial distribution matrix through the second graph convolutional network; gumbel noise is introduced into an initial distribution matrix, a Top-K differentiable sampling mechanism based on Gumbel-Softmax is adopted, and the hierarchical representation process and the feature retention mechanism of graph structure data are optimized by combining differentiable sparsification, multi-scale reconstruction and double entropy constraint technologies. Under the synergistic effect of Gumbel-Softmax sampling and a Top-K selection mechanism, the balance between the sparsity and the differentiability of the distribution matrix is realized, and the recognition capability of the model on the key structure and the community mode in the graph is enhanced.

The invention belongs to the technical field of radar signal processing, and particularly relates to a non-uniform sea clutter partitioning method and device based on an M-order moment feature set. The method comprises the following steps: S1, carrying out distance and azimuth grid division on sea clutter echoes to form K grids, and determining a distance center R (k) and an azimuth angle center Az (k) of the kth grid; s2, calculating an M-order moment feature of the kth grid; s3, constructing a sea clutter partition feature vector set based on the distance center R (k), the azimuth angle center Az (k) and M-order moment features of the kth grid; s4, the sea clutter partition feature vector set is clustered, an L * K partition matrix U is obtained, and L is an expected partition number; and S5, dividing the grids corresponding to the columns where the same row elements with the same maximum value as each column are located in the partition matrix U into the same partition. According to the invention, the accuracy of parameter estimation of a subsequent detection algorithm is improved.

The application discloses a kind of partition filtering denoising methods of fringe image, it is suitable for the fringe image with gray periodicity characteristics, especially suitable for electronic speckle pattern interferometry fringe image (wrapped phase image) denoising processing.This method includes: obtaining and preprocessing discrete gray fringe image;Setting partition parameter and filter parameter;In the ring gray space of modulo, the boundary of each gray partition is calculated and the partition matrix is generated;Perform window filtering based on mode on partition matrix to obtain smooth partition matrix;Based on the number of partitions, generate gray cycle offset, perform "gray cycle offset-space filtering-gray cycle inverse offset" on input image to obtain a set of multi-channel inverse offset gray matrix corresponding to partition;Finally, according to the smooth partition matrix, select and combine from the multi-channel inverse offset gray matrix pixel by pixel, and output the denoising result.The application makes full use of the ring structure of gray in modulo sense, which can reduce the filtering distortion at the gray wrap-around, and at the same time, through the smoothing of partition matrix, it reduces the splicing marks caused by partition fragmentation, and realizes the differential noise suppression and detail preservation of different regions of noise characteristics and fringe structure.

The invention relates to a communication efficient decentration federated learning method based on subgraph division. The method comprises the following steps: constructing a physical network topological graph of a decentration federated learning environment; dividing the physical network topological graph into conflict-free sub-graphs, and setting a hybrid matrix for each conflict-free sub-graph; constructing a problem constraint model by taking minimization of the total iteration time as a target; optimizing the bandwidth allocation matrix by taking minimization of the delay value of the link with the maximum communication delay in the current conflict-free sub-graph as an optimization target; optimizing the hybrid matrix by taking the minimum expected communication delay as an optimization target; on the basis of the optimized hybrid matrix, optimizing a conflict-free sub-graph sampling probability by taking minimization of expected communication delay as an optimization target; and based on the optimized conflict-free sub-graph sampling probability, performing global updating of the decentralized federated learning model. According to the method, the global model convergence delay can be remarkably reduced.

The present invention relates to a Bernoulli sampling-based interpretable CNN training method and apparatus, and a medium. The method comprises the following steps: inputting an image into a CNN to obtain response feature maps of filters; for the response feature maps, performing Bernoulli sampling to obtain a binarized assignment matrix; calculating average weight matrices of the filters for image categories on the basis of the binarized assignment matrix, and calculating the sum of pairwise differences; calculating a Hadamard product of an assignment vector of the binarized assignment matrix and the response feature maps to obtain masked feature maps; separately inputting the response feature maps and the masked feature maps into a CNN fully-connected layer to respectively obtain classification prediction probability vectors, and respectively calculating cross entropy losses between the prediction results and a ground-truth label; and on the basis of the sum of the pairwise differences, and the cross entropy losses, using a stochastic gradient descent method to implement network training to obtain an interpretable CNN for image classification. Compared with the prior art, the present invention has the advantages such as high adaptability and high interpretability.