26 results about "Network decomposition" patented technology

The invention relates to the technical field of chip testing, and discloses an LED driving chip testing method, device and equipment and a storage medium, and the method comprises the steps: carrying out the multi-point temperature excitation and constant-current output synchronous collection of an LED driving chip, and obtaining electrothermal coupling test data; performing thermal resistance network decomposition on the temperature field in the chip to obtain temperature distribution characteristic parameters; performing nonlinear function fitting on the temperature-constant current coupling relation to obtain an electrothermal coupling transfer coefficient; performing four-dimensional coupling calculation on the temperature distribution characteristic parameter and the electrothermal coupling transfer coefficient to obtain four-dimensional coupling response characteristic data; and partial differential sensitivity calculation is carried out based on the four-dimensional coupling response characteristic data, and an electrothermal coupling sensitivity analysis result is obtained.According to the method, the problem of test errors caused by time desynchrony in a traditional separated temperature-current test is solved, quantitative sensitivity analysis of electrothermal coupling parameters is achieved, and the sensitivity of the electrothermal coupling parameters is improved. Therefore, the test accuracy of the LED driving chip is improved.

The invention discloses a personalized cross-domain recommendation method and system based on federal learning, and the method provides a personalized cross-domain recommendation service for a user on the premise that original data of interaction between the user and an article and user parameters are kept locally. The method comprises two stages of federated training: stage 1, intra-domain users cooperatively train a single-domain score prediction model by using a neural collaborative filtering method; in the second stage, overlapping users of the two domains cooperatively train a migration module based on a multi-layer neural network to capture a mapping relation represented by potential user features between the two domains; besides, each layer of network of the cross-domain recommendation model is decomposed into a base vector and a personalized vector which respectively represent common knowledge among different users and unique knowledge of the users, and a local model obtained by final training can provide personalized recommendation services for registered users in a target domain, so that the recommendation efficiency is improved, and the user experience is improved. Meanwhile, the global model obtained through training provides effective initial recommendation for new users in the target domain, and the cold start problem in a recommendation algorithm is effectively relieved.

The invention discloses a heterogeneous network embedding method based on dynamic heterogeneous network decomposition and a hierarchical space-time attention mechanism. The method comprises the following steps: dynamic network decomposition: decomposing a heterogeneous network sequence into independent subgraph sets according to edge types; multi-order node attention aggregation: for each sub-graph, fusing information of nodes and different edge types; semantic level attention is embedded, wherein global semantic embedding is generated in combination with global relation type weight and meta-path long-short-term memory network coding; time dynamic modeling: respectively calculating a time attention weight and a Monte Carlo sampling approximate Horkes intensity, and then aggregating and splicing results of the time attention weight and the Monte Carlo sampling approximate Horkes intensity to obtain a final embedding result. According to the method, through cross-granularity semantic modeling and efficient time sequence dependence learning, an innovative solution is provided for node representation of the dynamic heterogeneous network.

The invention discloses a moving target detection method based on low-rank full-connection tensor network decomposition and a total variation model, and belongs to the technical field of computer vision and real-time video analysis. Aiming at the problems of poor real-time performance, dynamic background misjudgment, serious noise interference and the like caused by neglect of space-time relevance, fixed rank constraint and high calculation complexity of a traditional method in a complex dynamic scene, the following technical scheme is provided: through full-connection tensor network (FCTN) decomposition, a common rank is utilized to realize accurate modeling of a dynamic background; interference from background leakage to foreground is reduced; incremental updating of time core parameters is carried out in combination with a sliding window strategy, and a historical space core is fixed to reduce calculation complexity; total variation (TV) regularization is combined to optimize a low-rank-sparse separation process, the continuity of a space gradient and a time gradient is restrained, the contour integrity of a moving target is enhanced, and noise is suppressed.

The invention discloses a time sequence and congestion collaborative optimization-oriented GPU (Graphics Processing Unit) parallel acceleration global wiring method, which comprises the following steps of: according to a given netlist, dividing a super-large network by adopting a Kruskal algorithm in combination with a lookup set, and constructing a time sequence propagation path of the super-large network; performing network decomposition based on the timing margin estimation of the pins; calculating the time sequence weight of the two-pin network by adopting a time sequence weight calculation method based on an Elmore delay model; performing path cost calculation by adopting a cost function which comprehensively considers the time sequence and the congestion cost; executing two-level GPU parallel kernel acceleration mode wiring; optimizing the network topology by adopting a delay-aware pin connection improvement technology; the invention relates to a congestion-driven GPU (Graphics Processing Unit) accelerated routing and routing strategy for executing non-critical networks. According to the method, a high-quality wiring result with balanced time sequence congestion can be quickly obtained, the time sequence performance is effectively improved, and the requirement of a current super-large-scale high-performance circuit design wiring stage can be met.

The invention discloses a flexible job shop scheduling method based on a generative adversarial training framework. The method comprises the following steps: establishing a Markov decision process model for flexible job shop scheduling, and completing the design of a state space, an action space and a reward function; collecting expert scheduling tracks through a plurality of algorithms, and carrying out data cleaning and standardization processing; constructing a state encoder based on a graph attention network, mapping a scheduling environment state into low-dimensional vector representation, and designing a hierarchical strategy network to decompose a scheduling decision task; constructing a value network to provide stable value estimation so as to accelerate a reinforcement learning process, and constructing a discriminator network to guide a strategy search direction by generating an imitation reward; and finally, hybrid training based on near-end strategy optimization and generative adversarial imitation learning is executed, and through adversarial training and strategy gradient optimization, an intelligent agent learns to obtain a high-performance scheduling strategy with expert empirical performance and environment adaptability.

The application discloses a kind of based on the transport hub maintenance scheduling efficiency optimization method of deconstruction of variable topology super network, comprising the following steps: step one, the maintenance element involved in the current real-time maintenance scheduling scene of transport hub and its interrelated relationship mapping modeling is modeled into supergraph network model;Step two, the basic uncertainty characteristics of supergraph network CNN topological structure are characterized;Step three, according to the supergraph network model, the evaluation value of measurement index is calculated;Step four, the three indexes calculated are compared with the reasonable value interval range of historical scheduling result, and the rationality of current scheduling state is judged;Step five, based on the measurement index, the maintenance scheduling efficiency of transport hub is improved.The beneficial effects of the present application are that the execution efficiency, resource collaboration degree and dynamic robustness of maintenance scheduling can be effectively improved through super network modeling and quantitative analysis, and bottleneck positioning and optimization are realized.

The application discloses a social network link prediction method and device, the method comprises the following steps: decomposing a social heterogeneous network into a plurality of account association sub-views through a social network meta-path, using a graph convolution network to represent the accounts in each account association sub-view, fusing the features of the multiple views through an attention mechanism to generate an account feature vector, using the account feature vector to construct a social network link prediction model, and calculating a link score to realize link prediction. By using the social network meta-path to extract the multi-dimensional association relationship between the accounts, more heterogeneous information between the accounts is considered, and the attention mechanism fuses the features under the multiple account association sub-views to automatically calculate the contribution of various relationships to the link prediction effect, thereby solving the problem that the utilization rate of the social network heterogeneous association relationship is low in the prior art, and the link prediction accuracy is not high.

The invention provides a parallel structured network subdivision system suitable for an FDTD algorithm, and belongs to the technical field of electromagnetism. Surface fitting is conducted on each triangular surface element through a surface fitting module, a triangular surface element complexity list is generated, the load process of each triangular surface element in the complexity list is dynamically allocated based on a greedy algorithm, and a global interface file containing surface data is generated; carrying out internal grid filling on the target object subjected to surface fitting through a medium filling module, creating a Cartesian structure for each grid plane process needing to be filled, and carrying out filling in a parallel mode to generate a grid model; and realizing cross-module data transmission through the global interface file, and outputting a grid file containing a grid model. According to the method, the problem of resource idleness caused by the difference of the calculated amount of the triangular surface elements is effectively solved, the parallel efficiency is remarkably improved, the surface fitting module can achieve the acceleration effect close to linearity, and the method is particularly suitable for large-scale geometric models with complex structures.

This invention relates to the field of off-grid power supply systems with multi-energy coupling, and discloses a hardware-in-the-loop (HIL) simulation method for an off-grid power supply system with DC coupling of wind, solar, and hydrogen storage. The method includes the following steps: building a real-time simulation model of the off-grid power supply system; designing and establishing communication links; developing hardware and software for equipment controllers; and building and testing a hardware-in-the-loop real-time simulation platform. This invention's HIL simulation method for off-grid power supply systems with DC coupling of wind, solar, and hydrogen storage improves modeling through model simplification, network decomposition, and electrical decoupling, achieving high-precision real-time simulation of the off-grid power supply system.

The invention discloses a distributed multi-agent self-adaptive optimization incremental power distribution network power voltage balancing method, which comprises the following specific steps: carrying out data monitoring by a voltage and current sensor, acquiring real-time voltage and current data of each feeder line of a system, calculating real-time power on each sub-network node by agents through the data, and calculating the real-time power of each sub-network node according to the real-time power of each sub-network node; calculating reference power by using real-time power data of the current converter and an adjacent agent, and realizing decoupling control of voltage by the current converter through the data; a radiation type system is considered, a virtual voltage source-sub-network decomposition model is constructed, the system is decomposed into a plurality of sub-networks, a network construction type power supply is decomposed into two virtual voltage sources, and each sub-network comprises the virtual voltage sources on the two sides, an internal load and a network following type power supply; and constructing a cost optimization function of each sub-network, carrying out iteration based on a spherical search analysis method to continuously obtain optimal voltage and power points, and carrying out multiple iterations to realize the optimal solution of the voltage and power of the system.

In one implementation, we model information content in a signal as a synthesis of global and local information. The global information is common and shared for all natural signals, and this can be learned from a big data set. However, the local information is specific for each signal. By means of the synthesis property of a neural network, the INR network is decomposed into a head layer and a tail layer, the head layer is responsible for global information, and the tail layer is responsible for local information. The weights of the header layer are approximated based on a dictionary, and the dictionary is learned from a big data set and known to both encoders and decoders. Therefore, we need to send only the weight of the tail layer plus some additional information about the head layer.

The invention discloses a line loss diagnosis method based on multi-source data, diagnosis equipment, a medium and a program product, and relates to the technical field of intelligent power distribution network digital operation and maintenance. According to the invention, accurate diagnosis and abnormal positioning of the line loss of the transformer area are realized. According to the topology calculation unit division, a complex transformer area network is decomposed into manageable basic units, so that the line loss analysis is more refined. The unit theoretical line loss-load characteristic curve model established by regression fitting provides a scientific reference for anomaly detection. The theoretical line loss rate is obtained by mapping a future period load curve obtained by the prediction model, and the theoretical line loss rate is compared with the real-time line loss rate to form an anomaly detection process from macroscopic to microscopic. And accurate positioning of the abnormal unit is completed through deviation feature recognition. Fine management from the whole transformer area to a specific unit is realized, line loss management is improved from post-event statistics to an active mode of pre-event prediction and in-event intervention, and the detection efficiency and accuracy of line loss abnormity are improved.

This invention discloses a method for decomposing complex networks based on graph contrastive learning and multi-hop aggregation, comprising the following steps: collecting the number of neighbor nodes, connecting edges, and average clustering coefficients of a complex traffic network to construct a network decomposition model; inputting the original graph into a role graph generation module to obtain a role graph; inputting the original graph and the role graph into a multi-view representation learning module to obtain multi-angle graph representations of the role graph and the original graph; obtaining an importance score for each node; calculating and optimizing the joint loss function to train the network decomposition model; decomposing the traffic network to obtain the importance values ​​of traffic nodes in the traffic network, and setting stronger security measures for traffic nodes with high importance and weaker security measures for nodes with low importance. This invention uses intra-graph contrastive learning and cross-contrast learning to improve graph representation performance and proposes a multi-hop aggregation mechanism to predict node importance by combining multi-hop neighbor information, achieving high-performance network decomposition.

The invention relates to the technical field of power grid risk assessment, and discloses a power grid risk assessment method based on integrated load flow calculation and topology analysis, and the method comprises the steps: collecting and preprocessing power grid operation data, and organizing the preprocessed data into a four-dimensional high-order tensor structure; constructing a power grid topological relation tensor network; constructing an optimization problem through an L1 norm form, and solving the optimization problem by using an augmented Lagrangian multiplier method; the complex coupling relation among time, space and parameters is revealed by analyzing the relation among different dimension factor matrixes; performing risk assessment on different granularity levels under a multi-scale tensor analysis framework, and coordinating analysis results of each level to form comprehensive assessment; designing a tensor completion estimation algorithm, constructing an optimization model by using low-rank hypothesis, and solving a complete data set through a tensor low-rank decomposition method; according to the method, the high-order tensor representation and tensor network decomposition technology is adopted, so that the dimension and complexity of data are effectively reduced, and the calculation complexity is reduced.

The application discloses a kind of roadbed pavement test and data acquisition system under mobile load, comprising: collecting high-precision road surface roughness data, and inhibiting noise by space-time convolution variation auto-encoding network decomposition feature.Based on normal stiffness and morphological gradient, dynamic grid division is used to adjust sensor density, and multi-modal data is fused using Bayesian weight. Adaptive signal details are extracted using multi-scale transformation, and filter is designed to enhance damage signal and optimize structural entropy. A multi-field coupled grid control equation is constructed to realize dynamic topology optimization reconstruction. A damage evolution state space is established, and a prediction model is trained through a multi-stage reinforcement learning framework combined with experience replay. A digital twin mapping model is constructed, integrating Bayesian optimization and reinforcement learning to establish a virtual-real feedback channel for dynamic decision optimization. The application not only provides innovative improvements to traditional test methods and data acquisition systems, but also accurately simulates and analyzes complex nonlinear dynamic behavior.

The invention relates to the technical field of unmanned aerial vehicle cluster control, and discloses a super network-based unmanned aerial vehicle cluster key point calculation method, which comprises the following steps of S1, constructing a super network of an unmanned aerial vehicle cluster; s2, constructing an unmanned aerial vehicle cluster sub-network based on the super network; s3, calculating a sub-network weight; and S4, calculating a key point deviation degree based on a key point deviation degree algorithm. According to the method, by introducing super-network modeling, function-driven sub-network decomposition and a multi-dimensional threat quantification mechanism, key point analysis scientificity and actual combat effectiveness are remarkably improved, unmanned aerial vehicle cluster task characteristics can be comprehensively considered, the relation between nodes in a network attack system is analyzed and explored, the key points of an unmanned aerial vehicle cluster are identified, and the risk of the unmanned aerial vehicle cluster is reduced. And the speed is higher, and the expandability is higher.

The invention discloses a sound velocity field reconstruction method based on full-connection tensor network decomposition and regularization, belongs to the technical field of ocean three-dimensional sound velocity field reconstruction, is used for ocean three-dimensional sound velocity field reconstruction, and comprises the following steps: obtaining a sparse three-dimensional sound velocity field observation value, inputting FCTN-T for processing, and obtaining a reconstructed ocean three-dimensional sound velocity field; a self-adaptive rank increasing strategy is added into the FCTN-T, and the self-adaptive rank increasing strategy comprises the steps of setting an initial value of a rank of an FCTN model, calculating a maximum rank of the FCTN model, iteratively updating parameters and obtaining a reconstructed ocean three-dimensional sound velocity field after a convergence condition is reached, so that the reconstruction efficiency is greatly improved. According to the method, the spatial correlation and the local smoothness characteristic of the three-dimensional sound velocity field are fully utilized, the best reconstruction precision is obtained in the aspects of overall and small-scale details, and particularly, a good reconstruction effect is achieved in a sparse sampling region; and by adopting a rank increasing strategy, the model is greatly improved in the aspect of operation efficiency, namely, the reconstruction precision and the reconstruction efficiency are considered at the same time.

The application discloses a heterogeneous network topology mapping method for power grid offline and online data, and has the characteristics that: according to the electrical characteristics of primary equipment of a power grid and inherent characteristics of the power grid, the primary equipment topology network of the power grid is decomposed into two-level networks; the first-level network is composed of power stations and lines, and forms a power station-level topology network; the topology network is homologous and isomorphic, and a subnet isomorphic algorithm of graph theory is applied to topology mapping; the second-level network is composed of primary equipment of each power station, and offline data and online data form topology networks according to the connection relationship between the primary equipment and the primary equipment; the first-level network and the second-level network expand the topology network of the power stations and the lines of the first-level network to the topology network of the primary equipment through the connection relationship of busbars and lines, realize the whole-network topology mapping of the offline and online data, and realize the primary equipment "node corresponding" mapping of the offline data and the online data to the whole-network "topology network" mapping.

The invention discloses a method for automatically generating a beam and a plate based on column structure decomposition and axis net connection. According to the method, geometric information of columns is extracted from a two-dimensional structure planar graph, through the processes of axis net decomposition, adjacent coupling beam pairing, degree sealing and surface domain tracking plate forming, a beam net frame is automatically generated according to the positions of the columns, a floor slab surface is finally generated, and therefore the complete topological relation among the columns, the beams and the slabs is established. According to the method, generation of wrong beam segments or wrong connections can be effectively avoided through a pairing strategy of pseudo interval filtering and outer contour priority. Therefore, the stable automatic modeling process of automatically generating the beam from the column and automatically generating the plate from the beam is realized. The method is used in a two-dimensional building structure plane graph, the arrangement of beams is automatically generated according to the positions and shapes of columns, and the floor outline is formed through closing. According to the method, manual participation can be reduced, manual omissions and errors are avoided, the modeling efficiency is greatly improved, and an accurate data basis is provided for engineering quantity calculation and cost analysis.

A decoding method is disclosed. Coefficients for a first set of layers of an (Implicit Neural Representation) network decomposed into a first set of layers and a second set of layers and parameters of the second set of layers are decoded. Parameters of the first set of layers are 5 determined as a linear combination of basis functions weighted by the coefficients, the basis functions being basis functions of a multiscale dictionary. An image or 3D scene is reconstructed based on the INR network using the parameters of the first set of layers and of the second set of layers.

The invention relates to a method for evaluating the awakening effect of a spinal cord electrical stimulation parameter on disturbance of consciousness. The method is technically characterized by comprising the following steps: respectively acquiring electroencephalogram signals of a patient with disturbance of consciousness in a resting state and a spinal cord electrical stimulation task state; the electroencephalogram signals are preprocessed; performing sliding window division on the preprocessed electroencephalogram signals, calculating an inter-channel weighted phase lag index in each sliding window, and constructing a dynamic brain network; performing hierarchical network decomposition on the dynamic brain network to obtain two main consciousness sub-networks; according to activation and interaction of the two main sub-networks, sub-network features are extracted, and the function of evaluating the awakening effect of the patient with disturbance of consciousness is achieved. The method is reasonable in design, effectively quantifies the treatment effect of spinal cord electrical stimulation on patients with disturbance of consciousness by adopting hierarchical network decomposition, a sliding window technology and an aggregation hierarchical clustering technology, improves the effectiveness of SCS stimulation parameters, has the characteristics of objectiveness, accuracy and the like, and can be widely applied to evaluation of the effectiveness of the stimulation parameters of the nerve stimulator.

The invention is suitable for the technical field of high-definition video wireless transmission, and provides a high-definition video wireless transmission control system, which comprises a video transmitting device, a video receiving device and video playing equipment, the video sending device comprises a video sending device, a video receiving device and video playing equipment; the video sending device comprises a content decomposition module, a bit grouping module, a network decomposition module, a field conversion module and a wireless sending module. The video receiving device comprises a wireless receiving module, a video decoding module, a decompression calculation module, a wireless transmission module, a wireless network module, a memory and a processing center, and the wireless receiving module, the video decoding module, the decompression calculation module, the wireless transmission module, the wireless network module and the memory are respectively connected with the processing center; the video playing device comprises a wireless communication module and a video playing module. According to the invention, the problems of time delay, lagging, low transmission image quality and the like during wireless video playing are solved.

The application relates to the technical field of power grid risk assessment, and discloses a power grid risk assessment method based on integrated power flow calculation and topology analysis, which comprises the following steps: collecting power grid operation data and preprocessing, organizing the preprocessed data into a four-dimensional high-order tensor structure; constructing a power grid topology relation tensor network; constructing an optimization problem in the form of L1 norm, and solving the optimization problem by using an augmented Lagrange multiplier method; analyzing the relationship between different dimension factor matrices to reveal the complex coupling relationship between time, space and parameters; performing risk assessment on different granularity levels under a multi-scale tensor analysis framework, and coordinating the analysis results of each level to form a comprehensive assessment; designing a tensor completion estimation algorithm, constructing an optimization model by using a low-rank assumption, and solving a complete data set by using a tensor low-rank decomposition method; by adopting high-order tensor representation and tensor network decomposition technology, the application effectively reduces the dimension and complexity of data, and the calculation complexity is reduced.

The present invention relates to the field of industrial equipment intelligent simulation technology, and discloses a simulation model construction method and system, comprising the following steps: Step 1, parsing the electrical control logic of industrial equipment and converting it into a mixed integer nonlinear programming problem containing discrete Boolean variables and continuous variables; Step 2, modeling the physical field based on the tensor network decomposition algorithm to generate a compressed low-rank tensor network model; Step 3, dynamically adjusting the parameters of the tensor network model using a hierarchical optimization strategy, wherein the hierarchical optimization strategy switches between global search and local optimization algorithms according to the error convergence state; Step 4, allocating and executing symbolic calculations, tensor network operations, and parameter optimization tasks through heterogeneous computing systems; Step 5, real-time collection of production line sensor data. The present invention dynamically activates different physical field sub-models through Boolean variables, significantly improving the accuracy of joint simulation in complex industrial scenarios.

This invention discloses a sound velocity field reconstruction method based on fully connected tensor network decomposition and regularization, belonging to the field of marine three-dimensional sound velocity field reconstruction technology. It is used for marine three-dimensional sound velocity field reconstruction, including obtaining sparse three-dimensional sound velocity field observations, inputting them into FCTN-T for processing, and obtaining the reconstructed marine three-dimensional sound velocity field. An adaptive rank-increasing strategy is added to FCTN-T, including setting the initial value of the rank of the FCTN model, calculating the maximum rank of the FCTN model, iteratively updating parameters, and obtaining the reconstructed marine three-dimensional sound velocity field after reaching the convergence condition, greatly accelerating the reconstruction efficiency. This invention fully utilizes the spatial correlation and local smoothness characteristics of the three-dimensional sound velocity field, achieving the best reconstruction accuracy in both overall and small-scale details, especially in sparsely sampled regions. The rank-increasing strategy also significantly improves the model's running efficiency, thus simultaneously balancing reconstruction accuracy and efficiency.