15 results about "Network decomposition" patented technology

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 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 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 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 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.

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.

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.