406 results about "Mix network" patented technology

The invention provides a multi-private-domain visitor portrait sharing and privacy protection routing method based on federal learning, and relates to the technical field of data sharing and privacy protection, and the method comprises the steps: calculating the feature sensitivity and distributing a differential privacy budget, and adding mixed noise to the original data of a visitor portrait for privacy protection, according to the method, feature vectors are encrypted by adopting fully homomorphic encryption based on ideal lattices, threshold password segmentation is performed by utilizing a Viterbi algorithm, sub-key components are distributed to private domain environments, a hybrid network structure is constructed to extract features, federal optimization is performed based on data quality and historical contribution degree, and a feature extraction network is updated. The method comprises the following steps: carrying out feature dimension reduction by using a dual auto-encoder, extracting key features and generating user feature fingerprints, carrying out user grouping by calculating the similarity of the feature fingerprints, and realizing safe sharing of user similarity information and visitor portrait original data in combination with multi-party safe calculation and a hierarchical data access strategy.

The invention discloses a rolling bearing fault diagnosis method based on an SE-ResNeXt and Informer hybrid network model, and relates to the field of industrial process control, spatial features of time series data are extracted through a plurality of convolution modules in the SE-ResNeXt, an SE module is added for adaptively adjusting the response intensity of each channel, and a fault diagnosis result is obtained. The design of a residual network is combined, so that the feature extraction process is more efficient, and fault diagnosis analysis is performed through an Informer model. In the training process of the model, cross entropy is adopted as a loss function, and optimization is carried out through an Adam optimizer, so that the prediction accuracy and stability are improved. Experimental results show that the method shows excellent fault diagnosis capability on a public data set CWRU, has good robustness and prediction precision, is significantly superior to a traditional deep learning model, and provides a new thought for state monitoring and fault prediction under complex working conditions.

The invention discloses an industrial internet of things-oriented equipment digital twin operation and maintenance management system, and relates to the technical field of equipment management. The system comprises a data acquisition and preprocessing module, a digital twin model construction module, a data transmission and storage module, a state monitoring and fault diagnosis module, an operation and maintenance decision and optimization module and a visual interaction module. The data acquisition module adaptively acquires data through a sensor and preprocesses the data; the model construction adopts multi-scale and multi-model fusion; a hybrid network architecture and an encryption technology are used for transmission and storage; performing feature fusion and transfer learning for monitoring diagnosis; reinforcement learning and multi-agent collaboration are used for decision optimization; visual interaction supports VR / AR fusion. According to the invention, multiple modules work cooperatively, accurate acquisition, efficient transmission and storage of data are guaranteed, and accurate fault diagnosis and scientific operation and maintenance decision are realized through an advanced algorithm; the operation experience is improved through visual interaction; the method also has energy consumption optimization and supply chain cooperation capabilities, and can improve the operation and maintenance efficiency of industrial equipment and enterprise benefits.

A system and method including a vehicle having a first network zone and a second network zone of a different type than the first network zone; a converged network device (CND) interposed between the first network zone and the second network zone, the CND including a policy manager circuit structured to interpret a policy comprising a network regulation description; a configuration circuit structured to configure a first network interface circuit in response to the network regulation description; and where the first network interface circuit is structured to regulate communications between end points of the first network zone and end points of the second network zone.

The invention discloses an electrocardiosignal open-set identity recognition method based on a hybrid network and a cross attention mechanism, and belongs to the technical field of identity recognition. The electrocardiosignal open-set identity recognition method comprises the steps that electrocardiosignals are preprocessed to obtain standardized input; constructing a hybrid network model to extract a long-time dependency relationship between multi-scale local space information and a time sequence; fusing different types of information by using a cross attention mechanism; and establishing an Openmax classification layer to realize identity tag identification in an open set environment. By means of the method, the known individual identity can be effectively recognized, unfamiliar persons are rejected, and the accuracy of electrocardiosignal identity recognition and the applicability in the practical application scene are improved.

A system and method, including a vehicle having a first network zone and a second network zone; a converged network device (CND) interposed between the first network zone and the second network zone, the CND comprising: a policy manager circuit structured to interpret a policy comprising a network regulation description, wherein the policy comprises a default policy value; a configuration circuit structured to configure at least one network interface circuit in response to the external data routing description and the external data service description; the at least one network interface circuit, wherein the at least one network interface circuit is structured to regulate communications between end points of the first network zone and end points of the second network zone.

A system and method for interpreting a policy comprising a network usage permission description; configuring a first network interface circuit in response to the network usage permission description; and regulating communications, using the first network interface circuit, between end points of a first network zone of a vehicle and end points of a second network zone of the vehicle.

The invention provides an electric energy load real-time data acquisition and analysis method based on a low-cost scheme, and the method comprises the following steps: constructing a distributed data acquisition network, employing a combined hardware architecture of a current / voltage sensor and a low-power-consumption MCU, and achieving the data acquisition optimization through a dynamic sampling rate adjustment mechanism, when the load fluctuation exceeds a set threshold value, the sampling rate is automatically increased, when the load is stable, the sampling rate is reduced to a calibration sampling rate, and a Kalman filtering algorithm is adopted to carry out real-time data preprocessing; a three-level data transmission system is established, the three-level data transmission system comprises an edge acquisition node layer, a convergence gateway layer and a cloud processing layer, an optimized AODV routing protocol is adopted among nodes to construct a star-shaped and net-shaped hybrid network topology, and an optimal transmission path is dynamically selected according to the signal strength and the network congestion condition; a differentiated QoS transmission strategy is designed, and data is divided into three priorities of fault alarm data, real-time monitoring data and historical data.

The invention relates to an unmanned aerial vehicle cluster topology reconstruction method under an emergency rescue task, belongs to the technical field of unmanned aerial vehicle communication, and solves the problem of low communication efficiency after existing topology reconstruction. Comprising the steps that a topology reconstruction network is constructed and trained, and the topology reconstruction network comprises a global hybrid network and local hybrid networks and proxy networks corresponding to all unmanned aerial vehicles; the proxy network receives local state information of the corresponding unmanned aerial vehicle at each moment and outputs an action function value of an action at each moment, the local hybrid network receives the action function value of the corresponding unmanned aerial vehicle and outputs a local joint action value, and the global hybrid network receives the local joint action values of all the unmanned aerial vehicles and outputs a cluster joint action value; when the unmanned aerial vehicle breaks down, local state information of the undamaged unmanned aerial vehicle and actions of the undamaged unmanned aerial vehicle at the previous moment are obtained and transmitted into the corresponding trained proxy network, and the corresponding actions are selected according to an epsilon-greedy strategy for topology reconstruction. And topology reconstruction with high communication efficiency is realized.

The invention discloses an industrial equipment defect detection method based on multi-modal feature fusion and dynamic optimization, and belongs to the technical field of computer vision, and the method comprises the steps: 1, multi-modal industrial data collection: deploying multiple sensors in a production line, collecting data in multiple periods, constructing a defect-free and multi-type defect sample library, and carrying out multi-modal industrial data collection; a time-space aligned multi-modal label is marked; step 2, data enhancement and defect synthesis; step 3, multi-modal hybrid model training: constructing a hybrid network, and performing pre-training and fine tuning by using a dynamic loss function; step 4, edge end dynamic optimization and deployment: edge end reasoning is realized through dynamic knowledge distillation, and model fine tuning is automatically triggered when false detection and missing detection are found; and step 5, intelligent labeling and result visualization: a front-end interface displays a detection result in real time. The problem that a current target detection framework is not high in small target recognition accuracy and low in efficiency is solved, and the reliability of industrial equipment defect detection is improved.

A method of distributed training of a machine learning model is provided. The method includes inputting a first set of observations and a first set of actions for a primary agent to generate a first Q function for the primary agent, inputting a second set of observations and a second set of actions for a set of secondary agents to generate a set of Q functions for the set of secondary agents, and generating a Qtot function from the first Q function and the set of Q functions by a mixing network for the primary agent, the Qtot function to generate actions or predictions to configure a first node to operate in a telecommunication network.

The invention discloses a 5G + TSN fusion network asynchronous flow optimization scheduling method with high schedulability, which comprises the following steps: S1, in a 5G and TSN fusion network, firstly, a controller of a center collects overall information of the network through a network control protocol, the information comprises channel conditions and frequency resources in the 5G network, and the channel conditions and the frequency resources in the 5G network are transmitted to a server; the communication requirements of the network topology, the link rate and the flow of the TSN network are met; and S2, running an asynchronous traffic optimization scheduling model of the 5G + TSN hybrid network through the central controller, performing routing decision and transmission scheduling on the traffic collected in the previous step in the hybrid network to obtain a scheduling result, and minimizing the consumption of network resources while meeting the transmission requirement of hard real-time traffic. According to the invention, the schedulability of the TSN gateway is obviously improved, the application range of an asynchronous deterministic access mechanism is expanded, and end-to-end traffic scheduling is realized.

The invention relates to the technical field of remote monitoring, and discloses an intelligent AI-based fault detection method and system for a high-low voltage power distribution cabinet, and the method comprises the steps: constructing an LSTM-Attention hybrid network of a multi-modal data flow, extracting the time sequence characteristics of the multi-modal data flow, extracting the non-stationary signal frequency domain characteristics of the multi-modal data flow, and carrying out the fault detection of the multi-modal data flow. The method comprises the following steps: constructing a fault knowledge graph of a high-low voltage power distribution cabinet, analyzing a topological correlation feature vector of the high-low voltage power distribution cabinet, training a pre-constructed power distribution cabinet fault global model to obtain a trained power distribution cabinet fault global model, and analyzing a fault probability matrix of the high-low voltage power distribution cabinet by using the trained power distribution cabinet fault global model. And establishing a digital twinborn body of the high-low voltage power distribution cabinet, analyzing a fault evolution path of the high-low voltage power distribution cabinet by using the digital twinborn body, and analyzing a fault detection report of the high-low voltage power distribution cabinet. The maintenance efficiency of the high-low voltage power distribution cabinet can be improved.

The invention discloses a remote sensing image ship segmentation method based on a U-KAN network model. The method comprises the following steps: constructing the U-KAN network model; the U-KAN network model is a hybrid network architecture fusing a U-Net network and a KAN network; wherein the U-Net network comprises an encoder and a decoder which are in jump connection; a plurality of KAN layers are nested in the KAN network; u-KAN network model training is carried out in combination with the remote sensing image data set, and an optimized U-KAN network model is obtained; and inputting a remote sensing image to be processed into the optimized U-KAN network model to obtain a corresponding ship segmentation result. The method not only can effectively capture ship targets with different scales and complexities in the remote sensing image, but also can realize effective fusion between shallow detail features and deep semantic features, thereby overcoming the limitation in processing complex backgrounds and diversified ship forms in the prior art.

The invention discloses an interconnected micro-grid group optimization scheduling method based on multi-agent deep learning, and the method comprises the steps: building an optimization scheduling network model of an interconnected micro-grid group system, the interconnected micro-grid group system comprises a main power grid and n micro-grids, and the optimization scheduling network model comprises n agents and M hybrid network modules, and the micro-grids are in one-to-one correspondence with the intelligent agents. According to the method, the strategy action of each micro-grid is dynamically adjusted under the continuously changing environment information through the optimization scheduling model, more efficient and more collaborative energy management is realized, and the optimization scheduling model is composed of a plurality of intelligent agents and a hybrid network module, so that the energy management efficiency is improved. In the training process, the total target Q value and the total estimation Q value are adopted to calculate the loss function of each module, the overhigh deviation in Q value estimation is effectively reduced, the strategy is prevented from being excessively optimistic, and the scheduling strategy is more stable and reliable in practical application.

The invention discloses a task unloading and resource allocation collaborative optimization method for an industrial hybrid network, and belongs to the technical field of industrial communication networks. The method comprises the following steps of: firstly, acquiring multi-dimensional attributes of an industrial field terminal task and a network node state in all directions through intelligent super-sensitive sensing and quantum-level monitoring means; thirdly, constructing a dual-objective function joint optimization model dominated by task total execution time delay minimization and assisted by resource utilization rate maximization, integrating factors such as local queuing time delay and transmission and calculation time delay under the influence of a quantum state, and setting strict constraint conditions; then, an improved quantum heuristic distributed co-evolution algorithm is adopted for solving, and a fine operation and self-adaptive quantum dynamic parameter adjustment mechanism is designed to approach an optimal solution; and finally, allocating tasks and resources according to an optimization result, establishing a continuous monitoring system, and rapidly restarting an optimization process in case of an abnormality.

The invention provides a distributed large-scale anti-traceability elastic network intelligent routing method and system, belongs to the field of network communication and network security, and is suitable for intelligent routing decision optimization in a dynamic network environment. The method is based on a multi-agent reinforcement learning framework, network nodes are mapped into independent agents, and path traceability risks are blocked through local information constraints; dynamic feature aggregation of a neighbor link state is realized by adopting a lightweight graph attention network, and the local sensing efficiency of a large-scale network is improved; a QMIX algorithm is introduced, and network parameters are optimized by nonlinear fusion of a local Q value and global graph state representation through a hybrid network; and in combination with a self-adaptive exploration mechanism driven by action entropy, the sudden change scene strategy response capability is enhanced. According to the system, in military anonymous communication, dark network data transmission and cross-border sensitive services, the anti-traceability and transmission efficiency balance can be remarkably improved, the characteristics of high concealment, high elasticity and low resource consumption are achieved, and a systematic routing solution is provided for a dynamic network environment.

The invention provides a UWB channel environment state identification method under limited mark data. The method comprises the following steps of: selecting UWB (Ultra Wide Band, Ultra Wide Band) CIR (Channel Impulse Response) data fragments, and converting UWB channel impulse response data into a two-dimensional image through continuous wavelet transform and Gramer angle field transform; the UWB channel state identification method comprises the following steps: constructing a hybrid network fusing a Vision Transform, a Wide Residual Network, a Wide ResNet and a channel attention mechanism compression and excitation (SE) module, inputting a two-dimensional image into the hybrid network, and combining the hybrid network with a semi-supervised learning algorithm SoftMatch to obtain a UWB channel state classification identification result under limited tag data. According to the method, the semi-supervised learning algorithm is applied to the UWB CIR signal-based channel state recognition task, so that the accuracy of channel state recognition can be remarkably improved under the condition of scarcity of marked data, and meanwhile, the time and economic cost required by manual marking are greatly reduced.

The invention discloses a building mixed purpose classification method and system fused with multi-modal data, and belongs to the technical field of remote sensing image data processing, and the method comprises the steps: obtaining the multi-modal data of a research region, and carrying out the preprocessing of the multi-modal data; constructing a hybrid network model based on the convolutional neural network, wherein the hybrid network model comprises a parallel branch network, a buffer area module and a predictor which are connected in sequence; and inputting the preprocessed multi-modal data into the hybrid network model, extracting a multi-scale feature map of the multi-modal data through the parallel branch network, and extracting semantic information of a main environment feature region around a building in the multi-scale feature map through the buffer region module, and outputting the probability that the building belongs to each category through the predictor. According to the invention, the method achieves the recognition of the mixed purpose of the building based on the multi-modal data, can make up for the defects existing in the classification research of urban buildings only depending on single-modal data, and improves the recognition accuracy.

The invention relates to the technical field of radio frequency power matching, and discloses a direct-current radio frequency hybrid high-power network matching optimization method. The method comprises the following steps: collecting current fluctuation data of a direct-current power supply module and power reflection data of a radio-frequency emission module, and generating a hybrid network impedance characteristic set; calling a power grid topology analysis engine to analyze the set to obtain an impedance track characteristic matrix; performing dynamic trajectory optimization on the matrix based on a preset frequency domain stability constraint rule to generate corrected impedance trajectory distribution; fusing the corrected track distribution and the radio frequency load dynamic response data, and constructing multi-band matching topological feature representation; performing parameter decoupling on the topological feature representation through a nonlinear matching mapping engine to generate an optimal matching parameter configuration table; and finally adjusting the voltage stabilization coefficient of the DC power supply module and the tuning capacitance value of the radio frequency emission module according to the configuration table. According to the method, efficient matching optimization of the hybrid network is realized, and the stability and adaptability of system operation are improved.

The invention discloses a stainless steel thin-wall pipe bending forming production line control method and system. The method comprises the steps that state feature vectors are generated through data collection; establishing a hybrid network model, and optimizing hyper-parameters of the hybrid network model by using an OOA eagle optimization algorithm to obtain an improved hybrid network model; inputting the state feature vector into an improved hybrid network model, and outputting predicted values of the springback value, the wrinkling probability and the ovality which are about to occur in the current bending section; inputting the predicted value into a multi-objective optimization algorithm, and performing back calculation in real time to obtain an optimal compensation parameter set by taking minimization of springback, wrinkling risk and ovality as optimization objectives; and the compensation parameter set is transmitted to a physical execution unit for production line control including bending control, core rod control, clamping control and feeding control. And the first-pass yield of bending forming and the production control efficiency are improved.

The invention discloses a vehicle-mounted video transmission hybrid topology network, and relates to the technical field of vehicle-mounted video communication. The vehicle-mounted video transmission hybrid topology network comprises a plurality of transmission nodes, each transmission node comprises at least one input port and at least one output port, and the plurality of transmission nodes form a hybrid network of a tree topology structure and a daisy chain topology structure; wherein the tree topology structure in the hybrid network comprises a root node and a plurality of subordinate sub-nodes, the daisy chain topology structure is a sub-branch of the tree topology structure, and the hybrid network comprises a main link and a standby link which are used for signal transmission. Through innovative designs such as hybrid topology, dynamic bandwidth allocation, data retransmission, redundant channels, routing address query and the like, high bandwidth, low delay and high reliability in the field of vehicle-mounted video transmission are realized.

The invention discloses a grinding wheel online state monitoring system and method based on multi-sensor fusion. The monitoring system is composed of an acquisition module, a data preprocessing module and a man-machine interaction module. Firstly, multi-source information collection is carried out through a sensor, then time-frequency domain analysis and wavelet transformation are carried out on the collected information in a targeted mode, and data feature values are extracted; according to the method, image characteristic values such as a Hilbert spectrum are obtained through Hilbert-Huang transform and fast Fourier transform, high-dimensional data are subjected to importance evaluation through a reelief algorithm to obtain multi-modal data with high correlation, a ResNet-DenseNet-LSTM hybrid network model is built, accurate monitoring of the online state of the grinding wheel is achieved through data driving, and the accuracy of the online state of the grinding wheel is improved. And finally, integrating a monitoring algorithm in an upper computer interface. According to the invention, sufficient monitoring precision and accuracy can be ensured, online monitoring is realized, and a complicated process of offline data processing is avoided.

The invention discloses a power load prediction method and system. The method comprises the following steps: acquiring historical power attribute data and constructing a time sequence; carrying out standardization processing on the sequence; performing multi-scale decomposition and reconstruction on the standardized sequence by using a wavelet transform convolution module, and extracting a reconstructed sequence fused with multi-scale features; and inputting the reconstructed sequence into an xLSTM-Informer hybrid network, capturing time sequence dependence characteristics through xLSTM, and outputting a load prediction result through introducing an Informer model of a probability sparse attention mechanism. According to the method, the problems of insufficient long sequence dependence capture, single multi-scale feature extraction and low calculation efficiency are effectively solved, and the precision and stability of long-time prediction are improved.

The invention relates to a lightweight low-delay continuous authentication method and system based on a linear attention hybrid network, and belongs to the technical field of information. The method specifically comprises the following steps: S1, a registration stage: collecting behavior data of a legal user and preprocessing the behavior data; training a lightweight hybrid feature extraction model, and establishing and storing a user behavior contour; s2, an authentication stage: collecting user behavior data in real time and preprocessing the user behavior data, extracting behavior characteristics and matching the behavior characteristics with stored behavior contours, and executing access control operation according to a matching result. According to the method, a global attention mechanism and a space-time attention mechanism are introduced, the long-term dependency relationship in a behavior biological feature sequence is fully captured, and the robustness of the model to noise and abnormal values is enhanced; through effective fusion of multi-sensor data and global features of different time points, high-precision legality verification of a user identity in a small authentication window is finally realized.

The invention discloses a lightweight hybrid Transform-Mama network-TransMama for super resolution, which is a lightweight hybrid Transform-Mama network, and the calculation cost brought by a self-attention mechanism is reduced by alternately using a Transform module and a Mama module. In order to balance extraction of local and global information, a Transform aggregation block (TAB) and a Mama aggregation block (MAB) are designed to enhance feature representation. In addition, a re-parameterization space gating feedforward network (RepSGFN) is provided, and the feature extraction capability of the model is further improved. A large number of experiments show that the most advanced performance (SOTA) is achieved in models of the same scale, and meanwhile an efficient model architecture is kept.

The invention discloses an intelligent pipe network leakage active prediction and early warning system based on multi-technology fusion, and the system comprises a multi-source heterogeneous data fusion collection module, a spatial-temporal feature depth extraction module, a degradation trend prediction and residual life evaluation module, and a multi-stage early warning and decision generation module. The multi-source heterogeneous data fusion acquisition module acquires and fuses ultrasonic guided wave signals, pressure flow time sequence data and environmental factor data; the spatial-temporal feature depth extraction module extracts spatial-temporal fusion features through continuous wavelet transform and a CNN-LSTM hybrid network; the degradation trend prediction and residual life evaluation module determines a degradation level, predicts residual life and quantifies a pipe explosion risk probability; the multi-stage early warning and decision generation module generates graded early warning signals and maintenance strategy suggestions, the technology crossing from post-event detection to pre-event prediction is realized, and the scientificity and refinement level of operation and maintenance management of a pipe network are effectively improved.

The invention discloses a port container automatic scheduling method based on multi-agent reinforcement learning, and the method comprises the steps: S1, building a corresponding relation between equipment and agents, and constructing a task set; s2, collecting operation state data, and constructing global and local state vectors; s3, generating a scheduling constraint vector, and cutting actions according to the resource, storage yard and path state to form a feasible action set; s4, on the basis of an improved QPLEX algorithm, constructing an individual value network containing a dump structure, and calculating an individual action value; s5, constructing a joint action value hybrid network, and mixing individual values according to the global state vector to form joint action values; s6, constructing a training sample, differentiating and aggregating instant and delayed return, and updating network parameters; and S7, during online scheduling, selecting an optimal action combination according to the combined action value, and generating and issuing a scheduling instruction. According to the invention, automatic collaborative scheduling of port container operation is realized.

The invention discloses a virtual power plant load regulation method and system based on a deep learning scheduling strategy, and relates to the technical field of power plant load regulation, and the method comprises the steps: obtaining multi-source heterogeneous data, and obtaining a feature tensor through preprocessing; the feature tensor is input into an LSTM-Transform hybrid network model, and a context code is output; inputting the context code into the time sequence convolutional network model, outputting an ultra-short-term prediction result, and further obtaining a short-term prediction result; constructing a multi-objective optimization model, and modeling constraint conditions; solving the multi-objective optimization model through a preset layered architecture to obtain an optimal scheduling strategy, and generating a scheduling instruction; and issuing the scheduling instruction to distributed resources in the virtual power plant, and executing and converting the scheduling instruction into an equipment action. The method solves the problems that in the prior art, the optimization target is single, the dynamic adaptive capacity is lacked, and the real-time fluctuation response speed of the power grid is limited to a certain extent.

The invention relates to a flow detection filtering mimicry distribution decision method based on deep learning, which is characterized in that a message analysis and preprocessing concept is added on the basis of a distribution component, a decision component and an executor component in a general mimicry defense structure, and comprises the following steps of: firstly, carrying out network message high-speed detection of an application environment based on a network driver; the network message high-speed detection comprises data acquisition, data preprocessing and data analysis; according to the part, CNN and LSTM are combined by using a deep learning method, a mimicry architecture is combined with a CNN-LSTM model, a CNN-LSTM-based network message detection method is proposed, and time and space features of network traffic data are extracted by using a hybrid network of CNN and LSTM, so that the efficiency and accuracy of network anomaly detection are improved. According to the method, the problems of low efficiency and low accuracy of untrusted message flow detection in a network are solved, the efficiency and the safety of the whole method are improved, the anti-interference attack capability of a system is effectively improved, and a better effect of abnormal message flow detection and classification is achieved.