1622 results about "Subnetwork" patented technology

The invention provides a micro-grid energy management method and system based on deep reinforcement learning, and relates to the technical field of power grids, and the method comprises the steps: constructing a dual-time scale deep reinforcement learning model which comprises a day-ahead scheduling sub-network and a real-time scheduling sub-network; the day-ahead scheduling sub-network predicts micro-grid operation strategies at a plurality of time points in the future based on the long short-term memory network; the real-time scheduling sub-network is based on a depth deterministic strategy gradient algorithm, real-time state data and a day-ahead scheduling prediction result are fused to construct an evaluation function, an instant reward value is calculated, and renewable energy power generation, energy storage charging and discharging and an external power grid electricity purchasing and selling power adjustment instruction are optimized online. According to the invention, through dual-time-scale collaborative optimization, the energy management efficiency and economic benefits of the micro-grid are improved, and the operation stability of the micro-grid is enhanced.

The invention belongs to the technical field of communication transmission, and discloses a PLC networking data acquisition system and method based on an edge gateway, and the system comprises a dynamic virtual isolation module which is used for identifying and classifying different PLC devices through the deployment of an SDN controller and the adoption of a deep packet inspection technology, automatically distributing the different PLC devices to a virtual subnet, and obtaining a virtual IP; each virtual subnet isolates the PLC equipment with the same IP address through a virtual switch and a virtual router; recording the virtual IP and actual physical network port information of the PLC equipment through a mapping table, and establishing a network topology view; the multi-protocol analysis module is used for connecting the edge gateway and the PLC equipment based on a network topology view, carrying out protocol analysis by integrating a PLC communication protocol library and adopting FPGA hardware acceleration, and reading PLC networking data from the PLC equipment in real time; and the flexibility and the expandability of data acquisition can be improved.

The invention relates to the technical field of traffic control, in particular to a traffic signal cooperative control method based on multi-agent reinforcement learning, and the method comprises the following steps: modeling each signal lamp intersection in a road network as an agent, and constructing a distributed multi-agent control environment; dividing the whole road network into a plurality of small subnets according to spatial correlation, and sharing and aggregating traffic information in the subnets through a neighborhood information sharing mechanism; utilizing a space-time diagram attention network to extract traffic state characteristics including a space-time dependency relationship in an intersection agent and a neighborhood thereof; and defining a traffic state, a traffic signal control strategy, a reward and punishment function, a network architecture and a target function required by training of the distributed intelligent agent, and carrying out joint training on the distributed intelligent agent until a training target is achieved. According to the invention, the real-time sensing capability of the signal control intelligent agent to the traffic flow dynamic state can be improved, and the collaborative decision-making capability among multiple intelligent agents is enhanced.

The invention discloses a defect diagnosis method and system based on multi-modal data cooperative training, and belongs to the technical field of defect diagnosis, and the method specifically comprises the steps: constructing a multi-modal cooperative diagnosis model comprising a feature extraction sub-network and a cross-modal attention module; after multi-modal data is collected and preprocessed, initial features are obtained through the feature extraction sub-network, attention weights are generated through the cross-modal attention module, weighted multi-modal features are obtained, multi-scale fusion features are obtained through the multi-scale feature extraction sub-network, and a preliminary diagnosis result is given through cascade processing. Meanwhile, the data integrity is detected, and a modal missing scene is coped with through cascade collaborative diagnosis; and finally, comparing the two types of diagnosis results with a defect labeling sample to obtain a multi-modal collaborative diagnosis model after training optimization, inputting to-be-diagnosed sample data, outputting a final diagnosis result and updating the defect labeling sample, and realizing efficient and accurate defect diagnosis.

The invention discloses an SAM2 multi-task perception binary segmentation method based on a hybrid expert adapter. The method is specifically implemented according to the following steps: step 1, constructing a data set and an encoder; step 2, constructing a hybrid expert adapter module; and step 3, constructing a task awareness gating module. According to the method, a pre-trained SAM2 is taken as a main network, on the basis of freezing the main body weight, a standard adapter and a MoE-Adapter are respectively deployed on odd and even layers of an encoder, and a lightweight expert sub-network and a dynamic gating strategy are combined, so that unified processing of multiple tasks such as salient target detection, camouflage target identification, marine animal segmentation and the like is realized.

The invention provides a knowledge graph-based security event automatic response method, which comprises the following steps of: acquiring multi-source security data, performing de-duplication and standardization processing, and generating a structured security data set; based on the network security ontology model, entities and relationships are extracted from the data set, and a security knowledge graph is constructed through entity alignment and conflict resolution; according to ATTamp; the CK framework divides network subnets, generates attack path diagrams of the subnets, and fuses the attack path diagrams into a global attack graph. Then, abnormal behavior nodes in the security knowledge graph are analyzed, and risk scores of the service layer, the host layer and the system layer are calculated in combination with the global attack graph; and matching a predefined response strategy library based on the risk scores, executing operations such as banning an IP (Internet Protocol), isolating a host or updating firewall rules and the like through an SOAR platform, generating a disposal report and updating a security knowledge graph. According to the invention, the network security event processing efficiency and accuracy can be improved, and the overall security protection capability of the system is enhanced.

The invention relates to the field of remote sensing image processing, and discloses a high-quality HRHS image generation method based on a double-subnetwork structure, and the method comprises the following steps: receiving an input low-resolution hyperspectral image and a panchromatic image, which are respectively used for providing spectral information and spatial information; performing noise filtering on the low-resolution hyperspectral image through an anti-noise pre-fusion sub-network, and generating a pre-fused hyperspectral image by using the correlation between the high spatial resolution information of the panchromatic image and the spectral characteristics of the low-resolution hyperspectral image; performing high-frequency detail reconstruction and enhancement on the pre-fused image based on a conditional diffusion model through a high-frequency enhancement sub-network; and fusing the low-frequency information of the pre-fused image and the high-frequency detail information generated by the high-frequency enhancement sub-network through wavelet inverse transformation, and outputting a final high-resolution hyperspectral image. According to the method, spectrum and space information can be effectively fused in various noise environments, and a high-resolution hyperspectral image with hyperspectral fidelity and space details is generated.

The invention relates to the technical field of data processing, and provides a network device discovery method, which can perform iterative retrieval under a target network according to an ARP table, provides a more comprehensive view, and is beneficial to monitoring the health condition and the network load condition of the target network. Legality detection of an IP address is carried out based on a rule base; legality detection of an MAC address is carried out based on a multi-level detection mechanism; uniqueness detection is carried out based on a device fingerprint; subnet suitability detection is carried out based on a subnet mask matching algorithm; a self-adaptive network equipment discovery algorithm combined with multi-dimensional data analysis can reduce redundant data, eliminate invalid information and improve the accuracy and management efficiency of equipment discovery; information generation is carried out based on the initial network equipment data to obtain the network equipment view, so that more accurate network equipment discovery is carried out.

A system comprises an encoder configured to compress and encode data for a three-dimensional dynamic mesh. The three-dimensional dynamic mesh is compressed and signaled using a base-mesh sub-bitstream and an atlas sub-bitstream (e.g., displacement and / or attribute sub-bitstream). The atlas sub-bitstream comprises displacement information to be applied at sub-division points of the base mesh to recreate the three-dimensional dynamic mesh at a given moment in time and may also include attribute values, such as colors, textures, etc. to be applied to the reconstructed mesh. Sub-mesh identifiers are included in tile headers of an atlas sub-bitstream that enable selective reconstruction of sub-meshes. A complimentary decoder decodes a compressed bitstream for the three-dimensional dynamic mesh, encoded such as by the encoder.

The invention belongs to the field of fault monitoring, and particularly relates to a fault self-healing method and system under an intelligent operation and maintenance global P2P architecture, and the method comprises the steps: constructing a global operation and maintenance monitoring double-layer node network which comprises a basic monitoring subnet and a collaborative management and control subnet; the basic monitoring subnet collects platform operation data in real time according to a preset inspection strategy, determines an abnormal root node and an association node through causal association analysis, generates an early warning signal and uploads the early warning signal to the collaborative management and control subnet; after the collaborative management and control subnet receives the early warning, a fault self-recovery strategy is started, resources are dynamically allocated, abnormal node data batch migration and recovery are executed, and a self-recovery result is fed back to the basic monitoring subnet to trigger loop check until abnormity is eliminated; according to the method, real-time monitoring and intelligent analysis of operation and maintenance data are realized through cooperative work of the double-layer node network, and disaster tolerance and load adaptive adjustment capability of the system are effectively improved in combination with an automatic fault self-healing mechanism and data difference migration.

A method performed by an optical network controller includes: obtaining a topology structure of a subnet of a cluster, where the topology structure records an address of compute nodes in the subnet; determining a wavelength division configuration based on the topology structure, where the wavelength division configuration is used to allocate different wavelengths to the compute nodes in the subnet; providing the wavelength division configuration for the optical network switching device. Then the optical network switching device establishes an optical path between the optical network switching device and the compute node in the subnet based on the wavelength division configuration.

The invention discloses a power grid cross-correlation harmonic suppression method, device and equipment and a storage medium, and the method comprises the steps: carrying out the wavelet packet decomposition of a subnet electric signal, and obtaining the harmonic component of a subnet at each frequency band; calculating the harmonic phase difference between the subnets according to the cross-correlation function between the harmonic components of the subnets and the topological impedance matrix, and calculating the total harmonic distortion rate according to the harmonic components of the subnets in the frequency bands; constructing a harmonic transfer equation according to the harmonic phase difference between the subnets and the equivalent output impedance of the inverter so as to calculate the harmonic responsibility proportion of the subnets; solving the double-layer game model based on the total harmonic distortion rate, the reference value of the total harmonic distortion rate and the harmonic responsibility proportion of the subnet, and obtaining the output of the governance equipment; and inputting the output of the treatment equipment into the fuzzy controller to obtain the impedance adjusting quantity of the subnet, and adjusting the virtual impedance according to the impedance adjusting quantity to update the harmonic backflow path. According to the invention, harmonic suppression among multiple subgrids during high-permeability distributed grid connection is realized, and the operation stability of the power grid is improved.

The invention discloses a lightweight natural language processing large model training method. Comprising the following steps: obtaining processing language data; a labeled enhanced data set is obtained; a sparse mechanism of the sub-network is dynamically activated through semantic complexity, and an activation mask is obtained through calculation; generating a quantization bit width by activating the parameter sensitivity of the mask; performing cross feedback adjustment on the mixing precision quantification strategy; and evaluating the trained student model. According to the method, through dynamic sparse activation, mixing precision quantification and collaborative optimization, the core problems of static strategy stiffness, quantification precision loss, low knowledge migration efficiency and the like in large model lightweight are solved; dynamic sparse activation replaces traditional static pruning, and semantic loss is reduced; in order to realize optimized feature extraction of the enhanced data set, fusion optimization features are adopted; the mixing precision quantification effectively reduces the complexity of calculation.

Methods, systems, and apparatuses, including computer programs encoded on computer storage media, for training a denoising neural network and, once the denoising neural network is trained, generating new images using the denoising neural network. In particular, the described techniques include obtaining data specifying a trained initial denoising neural network, obtaining training data, and training, using the training data and on a denoising objective, a target denoising neural network. The target denoising neural network includes the core-subnetwork of the trained initial denoising neural network, but also includes a target encoder and decoder neural networks that are not included in the trained initial denoising neural network. By training a target neural network that includes the core-subnetwork of a trained initial denoising neural network, the system achieves stable training of a large-scale target denoising neural networks that can be used to generate high resolution images.

The invention relates to the technical field related to automatic driving, in particular to a trajectory prediction method based on adaptive interaction and dynamic intention, which comprises the following steps: firstly, constructing a heterogeneous interaction map, dividing a traffic scene into a vehicle grid, an environment grid and a non-driving area grid, and embedding multi-dimensional dynamic features; then dynamically adjusting a region of interest based on the behavior intention of the target vehicle, and extracting a high-correlation interaction subnet; modeling an interaction relationship by adopting a heterogeneous graph convolutional network, and processing the motion characteristics of the target vehicle and the neighbor vehicle through a sub-channel coding strategy; further realizing dynamic intention perception through a double-branch parallel attention architecture, and fusing macroscopic intention and dynamic intention information; and finally, iteratively generating a future trajectory prediction result based on a decoding architecture of a message passing mechanism. The method can effectively improve the long-term prediction performance in a lane changing scene, adaptively captures a dynamic interaction relationship, and improves the adaptability of a prediction system to the behavior intention change of a driver.

A computer system connected within a local network ingests telemetry data received from devices or sensors located within a premises and connected to the network. A virtual measuring instrument module instantiates within the computer system and includes communication protocol interfaces and predefined data transformation tools provisioned for interaction with telemetry data generated by the devices or sensors. The application associates with a subnetwork of the local network to which the devices or sensors are collectively connected. The subnetwork uses a first communication protocol that is different a protocol used on the local network. The communication protocol interfaces include a first interface that uses the first communication protocol. The telemetry data is received within the application from the devices or sensors on the first subnetwork using the first communication protocol interface as received telemetry data. The predefined data transformation tools translate a format of the received telemetry data to a normalized format. A presentation of the normalized telemetry data is configured for output to a user device.

The invention discloses an industrial ERP dynamic scheduling optimization method based on deep reinforcement learning, and the method comprises the following steps: S1, obtaining to-be-scheduled industrial ERP data, and carrying out the preprocessing of the to-be-scheduled industrial ERP data; s2, constructing a heterogeneous graph structure, and setting node types and edge weight parameters in the graph; s3, performing multi-layer graph feature extraction on the initial scheduling state graph based on a graph attention mechanism to generate a node feature vector; s4, constructing a distributed reinforcement learning environment, and setting an independent strategy sub-network for each type of scheduling sub-tasks; s5, performing joint decision-making on the scheduling action to generate a scheduling scheme as a current round scheduling result; s6, calculating a reward value and feeding back the reward value to the corresponding strategy sub-network; and S7, repeatedly executing the steps S1 to S6, and generating a new scheduling action by using the updated strategy sub-network. According to the method, adaptive optimization of industrial ERP scheduling under multi-source heterogeneous data is realized, and the resource utilization rate and the order delivery efficiency are remarkably improved.

The invention relates to the technical field of network space security, and provides a DoH malicious tunnel traffic detection method and system based on feature fusion, and the method comprises the steps: carrying out the preprocessing of obtained to-be-detected DoH traffic data, carrying out the sequence segmentation processing, obtaining a Token sequence, and extracting features based on a byte sequence feature extractor; statistical features are extracted and standardized, features are extracted through a statistical feature extraction sub-network, and statistical feature vectors are obtained; fusing the byte sequence feature vector with the statistical feature vector to obtain a classification result; the byte sequence feature extractor and the statistical feature extraction sub-network extract features, and training is carried out by adopting a semi-supervised learning framework of dynamic pseudo-tag screening for non-tag training samples. According to the method, multi-modal features are fused, a semi-supervised learning mechanism is introduced, malicious DoH traffic generated by multiple DNS tunnel tools is effectively identified under a limited annotation data set, and the detection precision and generalization ability are improved.

The invention provides a method for efficiently detecting a fish target in a complex underwater environment based on a priori knowledge guide network, which comprises the following steps of: 1, acquiring an underwater image and preprocessing the underwater image; 2, establishing a recovery subnet module, a relation reasoning attention module and a self-adaptive feature fusion module, and performing complex underwater environment fish target detection; the recovery subnet module is used for guiding network learning to remove underwater turbid features through an underwater scattering model, generating a clear image through a water body recovery decoder WR, and reconstructing an underwater turbid image; the relation reasoning attention module is used for constructing a co-occurrence relation graph, performing relation reasoning by using a graph convolution network, and dynamically adjusting attention weight; and the adaptive feature fusion module is used for optimizing feature expression in combination with a Sigmoid function and a channel-by-channel weighting mechanism. The method can effectively cope with the conditions of sudden change of turbidity of a water body or complex background and the like, and can keep higher detection performance under different underwater conditions.

The invention relates to a terminal equipment risk grading method under a local area network in a hospital. Comprising the following steps: generating behavior fingerprints based on a communication rhythm, a protocol sequence and a started response sequence of equipment in a local area network; if a new online or abnormal behavior device cannot fit the historical rhythm, the new online or abnormal behavior device is marked as an identity-uncertain terminal; carrying out microscopic modeling on the access path, the request depth and the trigger resource change of the equipment before and after the risk trigger; constructing an equipment internal state change map and a network external stimulation event flow; analyzing a trigger cause behind the abnormality; modeling the continuity of the equipment risk state along with time evolution; a trend weighted curve mode is introduced, and the speed, stability and direction of risk increase are identified; the single risk is stabilized at a middle level and is not reduced for a long time; quickly raising the risk of single equipment; performing high-priority response; and adopting a strategy range convergence mechanism of local network stability: analyzing the states of other devices in the subnet / department local section where the device is located.

The invention relates to the field of gateway verification, in particular to a security authentication gateway system and method based on AI analysis, and the system comprises a log verification module, an AI analysis module, a solid-state hardware module, a quantitative decoding module and a condition latching module, the AI analysis module is used for positioning a target subnet and generating a signature function, the solid-state hardware module is used for constructing a physical communication link of the gateway, the quantization decoding module is used for confirming receiving and transmitting time and decoding data, and the condition latch module is used for processing response signals and adjusting data packet receiving and transmitting time. According to the method, the influence of attack modes such as malicious traffic, DDoS attack and SQL injection on gateway communication is prevented, and meanwhile, the communication speed of network communication is improved, so that the security and flexibility of the security verification gateway are ensured, and the network management cost is reduced.

The invention relates to an FTTR bandwidth management method based on a PON, and the method comprises the steps: dividing a to-be-managed region into a plurality of sub-regions according to the demands, distributing independent IP sub-networks, predicting the service of each IP sub-network, carrying out the pre-distribution, collecting the ONU flow data in real time, carrying out the feature extraction, obtaining the flow change features, dynamically adjusting the bandwidth distribution strategies of different slices, and constructing a double-layer dynamic weight model. Each network slice corresponds to an independent weight adjustment strategy, a dynamic time slot distribution field is inserted in an MPCP protocol, time slot distribution is carried out according to weight values of different network slices, burst traffic is preprocessed, demand prediction values are reported according to network slice classification, burst traffic information and traffic feature data are integrated, and the data are distributed according to the burst traffic information and the traffic feature data. And dynamically allocating bandwidth to each terminal device in the FTTR network. FTTR network bandwidth fine and dynamic management is achieved through multi-dimensional data processing, the network resource utilization rate and the user service quality are effectively improved, and the transmission requirements of diversified services are met.

The embodiment of the invention provides a hierarchical network topology layout method and device based on topology potential, and the method comprises the steps: calculating the topology potential value of a node according to the position information, connection relation and service attribute of a known network node, and positioning a key node based on the topology potential value; performing clustering layered layout on the network nodes according to the key nodes and the service areas where the key nodes are located to obtain a plurality of clustering points; and taking the plurality of clustering points as a first hierarchy of the network topology, visually displaying representative nodes of the first hierarchy, and performing hierarchical topology display on a subnet region where the representative nodes are located based on an interactive operation of a user on the clustering points.

The invention provides an advanced glioma grading method and device based on multi-mode completion and subregion segmentation, and the method comprises the following steps: obtaining a residual mode MRI image data set of the same patient, inputting the residual mode MRI image data set into a pre-trained completion subnetwork, and obtaining a full-mode MRI image data set; inputting the full-mode MRI data set into a pre-trained sub-region segmentation sub-network to obtain a sub-region segmentation result; and performing glioma grading based on the subregion segmentation result. According to the scheme, the full-modal data is generated according to the residual-modal MRI image data set by complementing the sub-network, a complete information basis is provided for subsequent analysis, weights are reasonably distributed for different modals through the sub-region segmentation sub-network, and glioma sub-region features are highlighted; fine segmentation of glioma subregions is realized, so that high-grade glioma can be graded accurately.

The invention discloses a network traffic anomaly detection method and system based on a multi-modal coupling Mamba model and a mixed expert. The method comprises the following steps: collecting network traffic; cleaning the network flow data, and standardizing the data format; shunting the cleaned network traffic data based on quintuple (a source IP, a destination IP, a source port, a destination port and a protocol type) information; extracting data packet length, transmission direction and load byte information, constructing a data packet length sequence and a load byte sequence based on a time sequence view angle and an interaction view angle, and constructing a layered data packet interaction graph; the multi-mode flow representation is input into a coupling Mamba model, deep coupling and dynamic interaction are carried out among different modes, and high-level feature representation with higher identification capacity is generated; dynamically selecting and activating a plurality of expert sub-networks through a gating network by utilizing a hybrid expert system; and a final flow detection result is generated in combination with a result output by the MoE classifier and the original features, and confidence feedback is provided.

The present application relates to devices and components including apparatus, systems, and methods to provide and / or implement user equipment-centric predictive mobility with subnetwork support in wireless communication systems.

The invention discloses systems and methods for privacy-preserving digital communications compliant with frameworks such as GDPR, DPDPA, HIPAA, and PSD2. A Virtual Identity (VI) is instantiated within a secure enclave and bound to one or more Compliance Jurisdiction Tokens (CJTs). Unlike conventional tokenisation limited to payment or aliasing, the invention enables multiple concurrent VIs (Multi-VI), each scoped to a declared purpose, jurisdiction, subnet, or session. Communication is permitted only if all bound CJTs validate inline, including Multi-CJT bindings where jurisdiction, purpose, and consent must all succeed, and Multi-Purpose CJTs (MCJTs) where multiple lawful purposes must be simultaneously satisfied. Each validation produces a Ledger-Anchored Validation Receipt (LAVR) containing pseudonymised metadata, anchored into tamper-evident ledgers for transparency. Regulators access receipts through a Regulator Query Interface (RQI), enabling filtered oversight without exposure of raw identifiers. The technical effect is to transform privacy and compliance into cryptographic enforcement across device, telecom, and network layers

The invention discloses an expert parallel computing method for a hybrid expert model and a computer program product, and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring a storage address of each input data block in a global sequence based on the input data block processed by each expert sub-network and an expert identification sequence corresponding to a plurality of expert sub-networks; based on the occurrence frequency of each expert sub-network in the expert identification sequence and the storage address corresponding to each input data block, obtaining input fragments processed by all expert sub-networks deployed on each computing card; distributing a target video memory space for each computing card based on the input fragment corresponding to each computing card; controlling each expert sub-network deployed on each computing card to read the input data block needing to be processed from the target video memory space corresponding to each computing card, and calculating the input data block needing to be processed to obtain sub-result data output by each computing card; and performing fusion processing on the at least one piece of sub-result data to obtain target result data output by the hybrid expert model.

The invention belongs to the technical field of defect detection, and relates to a PCB defect detection method based on image processing, which comprises the following steps: collecting defect-free PCB image data, and making a data set; loss functions are set for the reconstructed subnet and the segmented subnet respectively, the data set is used for training to obtain the trained reconstructed subnet and the segmented subnet, and the trained reconstructed subnet adopts a diffusion model to carry out noise removal on to-be-detected image data subjected to preprocessing and data enhancement step by step according to time steps to obtain a reconstructed image. And decomposing the reconstructed image into a low-frequency component and a high-frequency component through discrete wavelet transform, inputting the low-frequency component into the trained segmentation subnet, comparing the low-frequency component with the to-be-detected image data, and distinguishing a defect part. According to the method, the number of used samples is far smaller than that of other target detection model samples, discrete wavelet transform is utilized, high-frequency components are extracted for back diffusion denoising, the reasoning speed is increased, and the segmentation and defect detection precision is improved through the extracted low-frequency components.

Devices, networks, systems, methods, and processes for packet forwarding and routing are described herein. A leaf switch can be in communication with a host device. The leaf switch may configure a subnet comprising the host device. The leaf switch can assign a next hop interface as a system port of the leaf switch and generate subnet information indicative of the assignment. The leaf switch may distribute the subnet information to one or more network devices through one or more internal Border Gateway Protocol sessions. A data packet destined to the host device can be transmitted to the system port of the leaf switch when a host Media Access Control (MAC) address of the host device is not resolved. The leaf switch can resolve the host MAC address and transmit the data packet to the host device based on the host MAC address.