1354 results about "Subnetwork" patented technology

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 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.

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.

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.

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 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 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.

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.

The invention discloses a network security multi-target monitoring system and monitoring method based on monitoring data, and aims to solve the problems that the existing network security monitoring technology is insufficient in single-point monitoring, lacks multi-target comprehensive evaluation, is difficult to adapt to a dynamic network environment, lacks an effective early warning mechanism and the like. The invention provides a network security multi-target monitoring method and system based on monitoring data. The method comprises the following steps: dividing a target monitoring network into a plurality of sub-network domains, arranging a network traffic feature acquisition module in each sub-network domain, acquiring first and second traffic data, and extracting abnormal features in combination with an attack frequency; cloud data is used to calculate the security margin and the security margin index of each sub-network domain, and then the security uniformity and the security change trend of the target monitoring network are evaluated; and performing early warning according to the historical data and the current trend.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output sequence from an input sequence. In one aspect, one of the systems includes an encoder neural network configured to receive the input sequence and generate encoded representations of the network inputs, the encoder neural network comprising a sequence of one or more encoder subnetworks, each encoder subnetwork configured to receive a respective encoder subnetwork input for each of the input positions and to generate a respective subnetwork output for each of the input positions, and each encoder subnetwork comprising: an encoder self-attention sub-layer that is configured to receive the subnetwork input for each of the input positions and, for each particular input position in the input order: apply an attention mechanism over the encoder subnetwork inputs using one or more queries derived from the encoder subnetwork input at the particular input position.

An embodiment includes detecting by a system a request, responsive to the detecting of the request, computing by a Compute component of the system a plurality of internet protocol addresses in a subnet. The embodiment includes computing by an Analysis component of the system a threat metric for each of the plurality of internet protocol addresses. The embodiment includes determining by the Reputation component of the system a threat score for the subnet where the threat score is based on the threat metric. The embodiment also includes sending by the system the threat score representative of a vulnerability of the subnet as a source of a malicious attack.

The invention belongs to the field of artificial intelligence, particularly relates to a power plant infrastructure multi-target intelligent scheduling optimization method, and aims to solve the problems of static weight imbalance, disturbance response hysteresis and process coupling effect modeling insufficiency of traditional scheduling. According to the method, a multi-dimensional space-time semantic model is constructed, BIM, sensor and environment data are integrated, construction period, cost, resource and safety four-dimensional target weights are dynamically set, and an improved non-dominated sorting genetic algorithm is adopted to generate an initial Pareto optimal schedule; and then inferring an inter-process nonlinear coupling delay factor through a graph neural network, embedding a disturbance response module, starting local rolling re-optimization when a progress deviation or an external event is detected, limiting an influence subnet and freezing a stable region. According to the scheme, stage self-adaptive target focusing, chain risk pre-buffering and minute-level robust adjustment are achieved, the stability of a critical path is improved by 45%, the secondary optimization frequency is reduced by 60%, the calculation efficiency and the execution toughness are both considered, and efficient and accurate landing of a large power plant infrastructure project is supported.

The invention relates to the technical field of network communication and intelligent optimization, in particular to a multi-mode network global routing optimization method and device, electronic equipment and a medium. Comprising the following steps: constructing a data plane and a control plane according to a hierarchical structure of the software defined network, performing multi-modal analysis on an actual network topology of the data plane according to a modal type supported by a switch, and dividing the actual network topology into logic subnets of different modals based on a multi-modal analysis result; generating a set of N paths satisfying a preset condition between the source switch and the destination switch by using a preset path searching algorithm based on a network topology relationship between the logic subnets of different modalities; and based on the N path sets and the global controller of the control plane, generating a global route of the multi-modal network by using a preset global strategy model. Therefore, the problems that a traditional network optimization method is insufficient in collaboration and low in global characteristic utilization rate in a multi-domain data environment are solved, and the network transmission efficiency and the cross-domain collaboration capability are improved.

The invention provides an air-space heterogeneous network cross-domain link selection and message distribution method, and belongs to the field of communication ad hoc networks. According to the method disclosed by the invention, the cross-domain path capability and the flow condition of each subnet are summarized through a heterogeneous network situation awareness technology, and the obtained network situation information provides a basis for subsequent link planning; carrying out capability evaluation on each path through a heterogeneous network cross-domain link selection technology, and selecting an optimal cross-domain path by comprehensively considering link time delay, packet loss and network load so as to meet the communication requirements of services and realize network load balancing; through a segment routing technology, protocol format conversion of cross-domain data packets is realized by routing nodes, and cross-domain message distribution is realized. According to the invention, interconnection and intercommunication of space and air heterogeneous networks can be realized, network overhead of message forwarding among the heterogeneous networks is reduced, meanwhile, limited network resources are fully utilized, load balancing is achieved, and an effective networking technical solution is provided for the fields of emergency rescue and the like.

The invention discloses a multi-sub-network collaborative modeling method for unsupervised solution of a multi-domain electromagnetic field, and the method comprises the steps: carrying out the partitioning of a computational domain in a solving region according to medium parameters and field source information, and dividing the whole region into a plurality of physical sub-regions; a neural network model is independently deployed in each sub-region and is used for fitting electromagnetic field distribution in the region, so that the learning ability and modeling precision of the network for local physical characteristic changes are improved; a cross-network continuity coupling mechanism is constructed between sub-regions, and a continuity loss function of a magnetic field tangential component corresponding to a vector magnetic potential value continuity constraint and a space derivative thereof is introduced at an interface of adjacent regions, so that physical coupling between sub-networks is realized; and the boundary continuity of the electromagnetic field solution among different regions and the consistency of the overall solution are ensured. The method has the advantages of being high in universality, high in capacity of adapting to complex boundary conditions and the like, and is suitable for modeling and simulation calculation of multi-region, multi-material and multi-source coupling field problems.

The invention discloses a wireless sensor network node fault diagnosis system based on a multi-scale Bayesian network, and relates to the technical field of wireless sensor networks. The system comprises the following components: a data acquisition and preprocessing module which adopts sensor nodes distributed in a monitoring area to acquire environmental parameters according to a preset frequency, wirelessly transmits the environmental parameters to a data processing unit, performs preprocessing in a self-adaptive mode, and stores the processed data into a database; according to the method, a micro-scale Bayesian network model, a node-scale Bayesian network model and a sub-network three-scale Bayesian network model are constructed, so that cross-scale fault analysis from a component level to a network level is realized, the micro-scale captures fault association of internal components, the node scale evaluates the overall sensor performance, and the sub-network scale analyzes a cooperation state between nodes. The multilevel information interaction and fusion mechanism can comprehensively consider node internal fault propagation, external communication interference and subnet topology influence, and significantly improves the integrity and accuracy of fault diagnosis.

A distributed cloud computing system is disclosed that includes a controller configured to deploy network constructs including any of transit gateways, spoke gateways, subnets, or private networks and logic that, upon execution by one or more processors, causes performance of operations including: causing rendering of a graphical user interface that includes a display panel configured to display progress of a build process for a network topology graph, receiving first user input through the graphical user interface indicating selection of a first cloud service provider, a first access account, and a first cloud region, receiving second user input through the graphical user interface indicating selection of one or more of the network constructs to be deployed in the first cloud region, instructing the controller to deploy the one or more of the network constructs in the first cloud region according to the first user input and the second user input.

The invention discloses a thermal power plant operation risk intelligent early warning method based on deep learning. The method comprises the following steps: S1, collecting operation data to construct a multi-modal time sequence; s2, respectively inputting modal sub-networks after standardization processing; s3, setting a reversible residual structure for each sub-network and introducing a dynamic scaling mechanism; s4, introducing a time gating mechanism to adjust residual response; s5, fusing the modal features to form fusion representation; s6, performing forward transformation, combining with intermediate state reverse reconstruction, and calculating a residual error; s7, a sliding window aggregates residual errors to generate a scoring sequence, and risk levels are output; and S8, generating a key factor sequence based on residual attribution analysis features. According to the invention, operation abnormity in-advance identification and risk source accurate positioning under complex working conditions are realized.

The invention discloses a generative adversarial network architecture search method and system and an image generation method, and belongs to the technical field of network architecture search. The searching method comprises the following steps: performing single-path sampling on a pre-constructed generator super network according to a parameter quantity constraint range to obtain an effective subnetwork; training the generator super-net by adopting a complexity adaptive learning rate optimization strategy to obtain a pre-trained generator super-net; adversarial training is carried out on the generator hypernet and the discriminator to obtain a pre-trained discriminator; generating a generator super-network candidate architecture through a genetic algorithm in the early stage of the evolution stage and through a covariance matrix self-adaptive evolution strategy in the later stage of the evolution stage; and performing multi-target non-dominated sorting on the generator super-network candidate architecture, updating an effective sub-network and keeping a Pareto optimal solution to obtain a searched optimal generator architecture and further obtain a searched optimal generative adversarial network architecture. The method not only ensures the search quality, but also improves the calculation efficiency.

The present application discloses a method, apparatus, device and storage medium for cross-correlated harmonic control in power grids. The method includes: performing wavelet packet decomposition on subnet electrical signals to obtain the harmonic components of the subnet in each frequency band; calculating the harmonic phase difference between subnets based on the cross-correlation function and topological impedance matrix between the harmonic components of each subnet, and calculating the total harmonic distortion rate based on the harmonic components of the subnet in each frequency band; constructing a harmonic transfer equation based on the harmonic phase difference between subnets and the equivalent output impedance of the inverter to calculate the harmonic responsibility ratio of the subnet; solving a two-layer game model based on the total harmonic distortion rate and its reference value and the harmonic responsibility ratio of the subnet to obtain the output of the control equipment; inputting the output of the control equipment into the fuzzy controller to obtain the impedance adjustment amount of the subnet, and adjusting the virtual impedance according to the impedance adjustment amount to update the harmonic return path. The present application realizes the harmonic control between multiple subnets during high-penetration distributed grid connection, thereby improving the stability of power grid operation.