312 results about "Inter-domain" patented technology

The invention relates to the technical field of communication scheduling, discloses a communication scheduling network management intelligent optimization system and method based on AI dynamic decision, and solves the problems of insufficient scheduling dynamics, closed loop deficiency and poor edge adaptation in the prior art. Comprising a multi-dimensional data fusion acquisition module, a dynamic AI decision engine module, a cross-domain collaborative scheduling module and an intelligent closed-loop feedback optimization module. The dynamic AI decision engine module evaluates business value and resource pressure based on an edge-center collaborative architecture, predicts transmission quality and quantifies strategy income, the cross-domain collaborative scheduling module realizes intra-domain resource slicing and inter-domain strategy negotiation and path optimization, the intelligent closed-loop feedback optimization module constructs a data closed loop to iteratively optimize model parameters, and the dynamic AI decision engine module performs multi-domain collaborative scheduling on the basis of the edge-center collaborative architecture. Intelligent scheduling and autonomous optimization of network resources are realized, and the real-time performance, the reliability and the resource utilization rate of a communication network are improved.

Techniques disclosed in the present disclosure relate to communication networks and methods for supporting time-sensitive deterministic communication based on a time-sensitive network (TSN) mechanism. A communication network may include a plurality of network domains in the communication network connecting each other, and a plurality of schedulers in the plurality of network domains. A respective of the plurality of schedulers may compute a local schedule for a respective of the plurality of network domains, and a first scheduler and a second scheduler of the plurality of schedulers may iteratively exchange a first local schedule and a second local schedule. The first local schedule and the second local schedule may include different cycle times. The first network domain may transmit interdomain ethernet link information related to a link connecting the first network domain and the second network domain, and intradomain schedule information related to the first local schedule.

A system and method for cross-domain knowledge transfer in federated compression networks. The system enables efficient lossless data compression across diverse data types by intelligently sharing compression strategies between domains. A cross-domain knowledge transfer system identifies relationships between different data domains, adapts compression parameters accordingly, and optimizes learning processes to maximize knowledge reuse. The architecture may include a knowledge repository for storing domain features and compression patterns, domain mapping components that identify similarities, and transfer learning optimization that enables efficient adaptation with minimal examples. This approach significantly accelerates model training for new domains while improving compression performance. Applications include satellite telemetry systems where efficient compression is critical for transmitting large information sets between distant locations. The system may employ probability prediction driven arithmetic coding paired with long short-term memory networks, enhanced by cross-domain knowledge sharing that adapts successful compression strategies from one domain to another while preserving domain-specific optimization.

The invention provides an SDN (Software Defined Network) inter-domain traffic engineering method based on reinforcement learning, which comprises the following steps of: deploying a data traffic demand monitoring platform and a control system, and constructing a global network topological graph; calculating a short link identifier for the link in the network and distributing the short link identifier to each network device; flow judgment is carried out, upward notification is carried out according to requirements, and pre-operation of intelligent routing is cooperatively completed; deploying a reinforcement learning model in the total intelligent body, outputting an optimal cross-domain path strategy to the cooperative controller, disassembling the optimal cross-domain path strategy into flow table rules which can be executed by each domain, and issuing the flow table rules to local controllers of related domains; and each local controller pushes the flow table configuration to the domain switching equipment to complete the forwarding decision of the flow. According to the method, a complete closed-loop process of flow measurement, intelligent decision making, cross-domain control and path issuing is realized, feasible reference is provided for actual deployment of an intelligent network, and the method has good engineering popularization value and is suitable for intelligent scheduling scenes such as an operator backbone network, an industrial internet and metro edge cloud.

The invention discloses an adaptive evaluation method and system for transient stability of a power system based on dynamic adversarial migration, and the method comprises the steps: building a transient stability evaluation model based on Swin Transform, performing self-attention calculation on the electric power data characteristics in the fixed window and the movable window to realize global modeling between the electric power data characteristics and the transient stability of the electric power system; on the basis of dynamic adversarial adaptive transfer learning, self-optimization-approaching adjustment is carried out on a transient stability evaluation model based on Swin Transformer, and optimization-approaching evolution of an original model is realized after the operation condition of a power grid is changed; and evaluating the transient stability evaluation model of the power system based on dynamic adversarial adaptation. A dynamic adversarial adaptive transfer learning method is adopted, an implicit metric function of inter-domain differences is learned in an adversarial learning mode, and the relative importance of inter-domain edge distribution and conditional distribution is dynamically measured, so that self-optimization-approaching adjustment of a transient stability evaluation model is realized.

The invention belongs to the technical field of image processing, and particularly relates to a cross-domain medical image segmentation method based on feature decoupling and enhancement, and the method comprises the steps: inputting a plurality of collected medical images of different centers into a trained medical image segmentation model which comprises a feature decoupling module and an inter-domain cooperative reasoning network; after an input image is processed in the feature decoupling module, domain irrelevant features and domain specific features are output, and then the inter-domain collaborative reasoning network predicts and outputs a segmentation result after inter-domain information flow guidance and pseudo boundary perception enhancement are carried out on the domain irrelevant features and the domain specific features; an inter-domain information flow guiding module based on bidirectional information flow is adopted for inter-domain information flow guiding, the boundary resolution of domain-independent features is improved through imaging details of domain specific features, and meanwhile domain noise of the domain specific features is restrained through anatomical structures of the domain-independent features. According to the method, the domain irrelevant features are sharper at the anatomical boundary, the domain specific features are purer in imaging details, and a solid foundation is laid for subsequent segmentation.

The invention relates to an industrial part defect classification method capable of realizing inter-domain category self-adaption, which comprises the following steps of: acquiring a source domain data set with label information and a target domain data set without label information, and preprocessing the source domain data set and the target domain data set; inputting a to-be-detected target domain sample into the trained neural network detection model for defect detection; and removing a domain adaptation structure which is not needed in the domain adaptation detection network, and carrying out defect detection on the target domain scene. According to the industrial part defect classification method and device for realizing inter-domain category self-adaption, the processor and the computer readable storage medium, the domain invariant feature information aiming at the category is decoupled from the middle layer of the feature extraction network and is fused into the original feature so as to enhance the classification capability; an ELA attention mechanism is added to solve the key problems of small defect size and difficult feature extraction of part detection, and finally, class labels are taken as conditions during classification, inter-domain alignment is carried out for classes, and the cross-domain classification capability is further improved.

The invention relates to the technical field of lithology identification, and discloses a semi-supervised domain adaptive lithology model construction method and system, and a lithology prediction model construction and training process comprises the steps: taking labeled logging data of an explained well as source domain data, and taking part of labeled logging data and unlabeled logging data of a target well as target domain data; constructing a threshold dynamically adjusted semi-supervised domain adaptive lithology prediction model, wherein the lithology prediction model comprises a feature extractor, a domain discriminator, a class-level intra-domain discriminator designed for each lithology class, an independent discriminator and a label classifier; training the lithology prediction model through inter-domain confrontation, intra-domain confrontation, dynamic threshold adjustment and source domain data reweighting; according to the method, a remarkable alignment effect is realized in the feature space, the data distribution difference between different well positions is effectively reduced, and meanwhile, the identification accuracy of each lithology category is greatly improved.

The invention provides a cross-domain equipment fault diagnosis method and system based on large and small model cooperation, and relates to the technical field of equipment fault diagnosis. According to the method, the causal field generalization structure is introduced into the small model, explicit decomposition is carried out on the stable causal law and the field specific difference, and meanwhile, the causal field generalization structure is corrected by using the large model, so that the small model can automatically identify and retain the causal relationship which is universally applicable to each device and each field; therefore, the influence of inter-domain distribution difference is effectively eliminated. Theoretical analysis shows that the generalization error of the model mainly depends on the accuracy of the stable causal item, and the structure can minimize error drift caused by distribution drift. Therefore, the robustness of health state evaluation and fault prediction can be remarkably improved in a cross-domain scene, and the fault diagnosis model can still keep the prediction capability close to the training domain level under the condition of no target domain annotation data.

The invention discloses a wind power prediction method based on multi-source domain deep transfer learning, and relates to the field of new energy power prediction.The method comprises the steps that the data distribution difference between a multi-source domain and a target domain is reduced through an Euclidean alignment method, and the maximum mean value difference between the domains after optimization is obtained; setting a migration weight factor for each source domain based on the maximum mean value difference, and constructing a weighted migration training set; constructing a multi-source domain deep migration learning model, and carrying out migration training on the model based on a weighted migration training set; and finely tuning the pre-training model according to a small amount of data of the target domain to obtain a target domain wind power prediction model. The wind power data distribution difference between the multi-source domain and the target domain is reduced through the Euclidean alignment method, the knowledge migration efficiency is improved, and the negative migration risk is reduced; setting a migration weight factor for each source domain based on inter-domain MMD, so that the model preferentially learns high-correlation source domain wind power characteristics; and the multi-source domain deep migration learning model is fused into a dynamic weight module, so that low-efficiency migration is avoided.

The invention discloses a large-scale low-orbit satellite network domain-division intelligent routing method, which belongs to the technical field of satellite communication, and comprises the following steps: constructing a network model based on multi-dimensional resource characteristics, and providing resource constraint conditions and space-time correlation characteristics for routing decision by combining with a regional flow prediction algorithm of an improved Transform architecture; virtual nodes and an autonomous domain are divided based on a geographic area, and a low-orbit satellite network routing process is decoupled into an intra-domain part and an inter-domain part; a multi-agent deep Q network is adopted in a domain, a sum tree mechanism-based deep Q network is adopted between domains, a routing algorithm and a resource scheduling algorithm are respectively designed, and a domain intelligent routing system with flexibility and expandability is constructed through hierarchical routing and resource decoupling scheduling. According to the method, the service capability of the low earth orbit satellite network in extreme scenes such as topology high-frequency change, strict resource limitation and traffic space-time mutation is remarkably enhanced, and a high-reliability and low-delay routing solution is provided for space-ground integrated communication, emergency disaster early warning and global real-time data transmission.

The invention provides a low earth orbit satellite network dynamic routing optimization method and system based on domain division routing, and the method comprises the steps: dividing a satellite network into a plurality of regions, and distributing a unique IP address for each satellite node; in each region, synchronizing intra-domain topology information through a Hello data packet and a link state notification, and generating an intra-domain routing table; announcing and synchronizing inter-domain topology information through a boundary gateway and a summarized link state, and generating an inter-domain routing table; and forwarding the data packet according to the intra-domain and inter-domain routing tables. According to the invention, the signaling overhead can be reduced, the routing calculation efficiency is improved, the network stability is enhanced, and the network resource utilization is optimized.

The invention relates to a cross-domain space-time diagram micro-expression recognition method based on adversarial domain self-adaption. The method comprises the following steps: A, preprocessing a micro-expression video sequence to be processed; b, further processing the micro-expression optical flow sequence, and constructing a facial key point graph structure as first input; c, the micro-expression video optical flow sequence serves as input, and global features are obtained through a global feature extraction network and serve as second input; meanwhile, second-layer features are extracted through a pyramid feature extraction module in the global feature extraction network and serve as third input; d, inputting the first input, the second input and the third input into the cross-domain space-time diagram embedding layer to obtain processed features; and E, inputting the processed features into an adversarial network for adversarial training, and respectively obtaining a domain label and a predicted emotion category label. The distribution difference between the source domain and the target domain is further reduced, and the feature consistency of the same time step among different domains is optimized.

The invention discloses a multi-modal emotion recognition method and system based on field generalization and a graph neural network, and relates to the technical field of emotion recognition. According to the technical key points, the method comprises the following steps: acquiring a data set, wherein the data set comprises three types of modal data: a text modal, a voice modal and a visual modal; performing feature extraction on the three types of modal data to obtain text features, voice features and image features; performing field generalization by taking each mode as a field, namely dividing features extracted by each mode into two parts, one part is used for extracting intra-domain invariant features, and the other part is used for extracting inter-domain invariant features; fusing the intra-domain invariant features and the inter-domain invariant features by using a graph neural network to obtain global information and local context information at the same time; inputting the fused features into a classification model for training; inputting the to-be-detected data into the trained classification model for classification, and obtaining an emotion recognition result. According to the method, the emotion recognition performance and robustness are improved.

The invention discloses a multi-modal unsupervised cross-domain sleep staging method, which adopts adversarial learning and designs a multi-modal convolution feature extractor module, a domain generalization feature enhancement module and a domain attention module. The method comprises the following steps: firstly, designing a multi-mode convolution feature extractor for physiological signals of two modes of electroencephalogram and electro-oculogram; for the electroencephalogram signals, convolution kernels of different scales are adopted to extract multi-scale features; for the electro-oculogram signal, firstly, the electro-oculogram signal is converted into a two-dimensional frequency spectrum through Fourier transform, and then feature extraction is carried out through two-dimensional convolution, so that unique physiological information of the electro-oculogram signal is fully captured. Thirdly, adaptively adjusting data distribution of a source domain and a target domain by using a domain generalization feature enhancement module, reducing inter-domain differences, and adaptively enhancing high-discrimination-force features; the domain attention module reserves key domain specific features in the adversarial learning process, the classification precision and generalization ability of the model are remarkably improved, and the method shows excellent performance in an unsupervised cross-domain sleep staging task.

The invention discloses a domain generalization method for a remote sensing image segmentation task, which relates to the technical field of image processing, and comprises the following steps of: firstly, extracting a feature map from a remote sensing image sample by utilizing a basic remote sensing image segmentation model, and decomposing the features into a domain specific component and a domain invariant component by adopting a feature decoupler; therefore, differences and generality among different domains can be captured more accurately. Thirdly, model parameters are updated based on the calculated cost loss, a meta-learning framework is used for optimizing a feature decoupler, and independence and semantic attributes of decoupling components are ensured; through the technical means of continuous value space modeling, vector orthogonal decoupling, generated data maintenance and the like, the remote sensing image segmentation accuracy is improved, and the generalization ability of the model in an unknown domain is remarkably enhanced.

The invention provides a red and black isolation system and method for realizing encryption algorithm dynamic switching based on an FPAG dynamic configuration technology, and aims to solve the problems of rigid isolation strategy, high switching response delay, encryption algorithm solidification and the like of a traditional red and black isolation architecture. The system comprises a red area CPU unit, a black area CPU unit and an isolation area FPGA access control unit, and automatic switching of protection strategies is achieved through FPGA local dynamic configuration. According to the invention, the encryption algorithm can be rapidly and dynamically switched according to the identified network threat level, and efficient and secure transmission of data among different security domains is ensured. The method has the characteristics of file encryption storage and integrity verification, key security processing, dynamic region isolation, multi-algorithm support and the like, is suitable for cross-security domain information interaction scenes needing to ensure information security, and has good social benefits and wide application prospects.

The invention belongs to the technical field of adversarial samples. The invention provides a robust generalization-oriented few-sample continuous confrontation defense method. According to the embodiment of the invention, edge distance loss is resisted, in the pre-training stage, by maximizing the distance between the clean sample and the model decision boundary, the discrimination capability of the model for easily confused samples near the boundary is explicitly improved, the robustness of the model for the subsequent few-sample adversarial adaptation stage is enhanced, and the generalization capability of the clean sample and the adversarial sample is improved. Gaussian mixture model prototype playback is also provided, modeling is carried out on historical adversarial domain feature distribution by using the Gaussian mixture model, pseudo features are generated for knowledge playback, original adversarial samples do not need to be stored, and the robustness of the model in new and old adversarial domains is improved. Besides, by designing multi-domain balance loss, in multi-domain continuous adversarial training, updating is facilitated for most historical domains by constraining a model updating direction, inter-domain loss variance is reduced, and cross-domain balance optimization is realized.

The invention discloses an unsupervised domain self-adaptive remote sensing semantic segmentation method based on RetNet. The unsupervised domain self-adaptive remote sensing semantic segmentation method is used for solving the problem of data distribution difference between a source domain and a target domain caused by illumination and seasonal changes in an urban road scene. According to the method, an attention mechanism with a spatial attenuation matrix is introduced into a RetNet segmentation network, and the attention weight between pixels is dynamically adjusted; cross-domain data alternate mixing is achieved by newly adding an intermediate domain and adopting a TokenMix method, and dynamic mixing proportion and feature consistency constraint are combined; and meanwhile, a complete model is constructed by utilizing self-training and a teacher network. Experimental results show that the method significantly improves the multi-scale feature utilization efficiency, effectively reduces the inter-domain distribution difference influence, obtains 58.49% of mIoU and 70.25% of mF1 score on a Potsdam-Vaihingen data set, and verifies that the method has higher precision and generalization ability in a remote sensing image semantic segmentation task.

The invention discloses an efficient graph comparison learning method for social recommendation. As an emerging self-supervised learning normal form, graph contrast learning is excellent in response to data sparseness and cold start due to the fact that the graph contrast learning can effectively capture similarity and heterogeneity characteristics in a graph structure, although the learning normal form achieves a good effect in a recommendation system, the graph contrast learning can be used for solving the problems of data sparseness and cold start. However, the method still faces three defects: (1) average neighbor aggregation and a non-adaptive representation reading mechanism are adopted in a message propagation process, and high-quality node representation is difficult to learn; (2) a visual angle is enhanced by depending on a random disturbance generation graph during intervention of comparative learning, which may destroy the inherent structure of graph data and further weaken the accuracy of the model; and (3) equally treating all observation samples during parameter optimization, and neglecting the difference influence of positive samples in different training stages. Specifically, aiming at the problems, the invention provides an efficient graph contrast learning method (EGCL for short). The method comprises the following steps: firstly, designing a graph adaptive propagation module, improving an information propagation rule of a graph neural network by referring to a thermonuclear thought and an attention mechanism, and realizing differentiated aggregation of neighbor nodes by adopting a learnable weight distribution strategy; secondly, designing a double contrast learning normal form which does not need graph enhancement, and realizing mutual promotion of node characterization through intra-domain contrast learning (inter-CL) and inter-domain contrast learning (inter-CL); and finally, introducing a sample weight adaptive efficient optimization algorithm, converting the training process into a double-layer optimization problem, and adaptively adjusting the contribution degree of each sample to model optimization in different stages.

The invention relates to an intelligent mechanical fault diagnosis method based on a progressive transfer learning network, and belongs to the technical field of mechanical part monitoring and fault diagnosis. The method comprises the following steps: respectively acquiring original signal data in a laboratory environment and a real industrial environment, and preprocessing the data; establishing a fault identification model based on a progressive transfer learning network architecture, and obtaining an optimal network parameter by combining a loss function in a propagation process and by taking a classification error of minimizing a source domain sample and a target domain sample and an inter-domain difference of minimizing sample distribution as targets; and collecting real-time signal data in a real industrial environment, inputting the real-time signal data into the optimized fault identification model based on the progressive transfer learning network architecture, and outputting a fault type by the fault identification model. According to the method, the sample migration success rate and the fault identification accuracy are improved by gradually constraining feature mapping in the sample migration learning process.

The invention relates to the technical field of intelligent automobiles, in particular to an intelligent cabin multi-AI Agent cross-domain collaborative super-brain system and an interaction method, and the system comprises a sensor cluster, a central decision AI Agent, a five-domain AI Agent and a cross-domain synchronization mechanism. The sensor cluster is used for collecting multi-dimensional data and transmitting the multi-dimensional data to the central decision AI Agent; the central decision AI Agent is used for processing the data, including analyzing fused data to establish a mapping relation corresponding to scenes and services, performing cross-domain priority arbitration and realizing inter-domain data synchronous scheduling; the five-domain AI Agent comprises a cabin domain AI Agent, a power domain AI Agent, a vehicle body domain AI Agent, a chassis domain AI Agent and an automatic driving domain AI Agent and is used for executing specific functions. According to the invention, the problem of function islands of a traditional intelligent cabin is effectively solved, deep coupling and dynamic linkage of cross-domain functions are realized, the function utilization rate is increased from less than 20% in the prior art to more than 78%, and the utilization rate of system resources is remarkably improved.

The invention relates to the technical field of computer vision, in particular to an adaptation method and system for continuous testing for differences between construction scene domains. According to the method, the similarity between Gram matrixes between adjacent domains is calculated, an elastic adjustment factor is set, the elastic adjustment factor is utilized, different weights are given to strong data enhancement and weak data enhancement, an elastic data enhancement strategy is provided, and an enhanced target domain image data set is input into a teacher model; updating the pseudo-tag by combining the elasticity regulation factor to obtain an elastic pseudo-tag; and inputting the target domain image data set into the student model to obtain a prediction result, constructing a global elastic symmetric cross entropy loss function based on the elastic adjustment factor, the cross entropy loss and the reverse cross entropy loss, updating student model parameters through the loss function, updating teacher model parameters, and finally obtaining a target model. According to the method, the construction scene monitoring model can adapt to complex domain changes when continuously learning test data, and the prediction result precision of the model in different environments is improved.

The invention provides a federal domain generalization abnormal traffic detection method based on dynamic feature alignment. In a training starting stage, a server distributes model parameters to all clients, the clients use self-encoders to perform joint training with a local model, and the similarity between the local and global feature encoders is calculated as an additional loss function item. And the client uploads parameters to the server, the server sends all collected models back to each client, and each client carries out prediction on a local data set by utilizing the models of different clients and then feeds back a result to the server. And the server calculates the difference between the domains based on the prediction result, dynamically adjusts the weight coefficient of feature alignment loss of each client, aggregates the model weight and updates the global model. Through iteration, the weight of a feature alignment loss item is dynamically adjusted in the federated learning process, and finally a global model with good generalization in a known domain and an unknown domain is obtained.

The invention discloses a data transmission method and device between security domains, a storage medium and electronic equipment. Relates to the field of data transmission, and the method comprises: in a front-end application of a first security domain, in response to an interaction request triggered by a user, generating a request message containing a unique identifier, and issuing the request message to a message queue service in the first security domain, the message queue service being used for a large model processing service in a second security domain, consuming the request message according to a pre-configured security access strategy, and writing response data of the large model into a shared cache service of a first security domain; and initiating a query request to the shared cache service to obtain response data corresponding to the interaction request from the shared cache service, and displaying the response data to the user through a user interface of the front-end application. The problem that data transmission efficiency is low when data transmission between security domains in a one-way network isolation environment is realized by depending on a manual or semi-automatic off-line ferry mode in the prior art is solved.

The invention belongs to the technical field of pipeline safety assessment, and discloses a multi-dimensional information interconnection and autonomous evolution collaborative pipeline full-state safety assessment method. And capturing a high-order relationship of data through double hypergraph reasoning of the instance-level hypergraph and the modal-level hypergraph to realize efficient interconnection. According to the method, mode-level and instance-level hypergraph information features are extracted through hypergraph information propagation, high-order correlation is mined through double-graph information aggregation, cross-mode and cross-instance consistency information and exclusive information are output after feature recombination, multi-dimensional data deep fusion is promoted, and high-quality data support is provided for follow-up pipeline full-state safety assessment. A two-stage autonomous evolution mechanism of intra-class progressive calibration and inter-class knowledge migration is respectively adapted to slight fluctuation and significant change scenes of the deep sea environment: precise adaptation of environment perturbation is realized through dual-branch feature extraction and dynamic weight adjustment in a domain; model parameter dynamic optimization is completed between domains through spatial-temporal feature clustering and cross-domain knowledge migration, and dynamic environment self-adaption can be achieved without manual intervention.

The invention discloses an Internet of Vehicles cross-domain authentication method based on a block chain and a self-sovereignty identity, and the method provides a set of cross-domain authentication service that a high-speed moving vehicle carries out an application service request among different security domains, and can achieve the distributed sharing of vehicle identity authentication information among different security domains. The cross-domain authentication time delay is effectively reduced, meanwhile, the vehicle completely has the control right of identity information, and necessary information of cross-domain authentication can be selectively disclosed. A distributed identity information management architecture is established based on an alliance chain, identity credential information of a Merkel tree structure is stored in a dynamic accumulator on the alliance chain, a sparse Merkel tree is adopted to construct a vehicle access credential, and privacy information leakage in a cross-domain authentication process is avoided; the message is signed by using a conditional privacy anonymous method, integrity and credibility verification of request information transmission is realized through pseudonyms, and batch verification of signed messages and identity revocation of malicious vehicles are supported. According to the invention, the system parameters and storage space are small, the verification time delay is small, and the cross-domain authentication requirement of the vehicle in the dynamically changing vehicle networking environment can be met.

The invention relates to the technical field of network communication, and provides a hierarchical reinforcement learning scheduling and routing method for a multi-domain TSN, and the technical scheme comprises the steps: collecting the global state information of the multi-domain TSN, and carrying out the coding processing of the global state information to generate comprehensive state representation; performing a cross-domain routing decision, and outputting an inter-domain path and a time delay budget of a cross-domain flow; executing intra-domain scheduling, determining a sending sequence and a specific path of a domain flow, and generating gating list configuration; through hierarchical coordination and strategy optimization, a cross-domain routing decision and intra-domain scheduling are updated based on reward feedback of an intra-domain scheduling result; and deploying the finally updated intra-domain scheduling to a switch of the multi-domain TSN network for execution, thereby realizing deterministic transmission of the time-triggered flow. According to the invention, through a layered agent architecture, a hybrid neural network coding mechanism and a cross-domain collaborative optimization strategy, challenges of complexity, expandability, dynamic adaptability and the like of a joint routing and scheduling problem in a multi-domain TSN environment are effectively solved.

Methods and apparatus relating to scalable access control checking for cross-address-space data movement are described. In an embodiment, a memory stores an Inter-Domain Permissions Table (IDPT) having a plurality of entries. At least one entry of the IDPT provides a relationship between a target address space identifier and a plurality of requester address space identifiers. A hardware accelerator device allows access to a target address space, corresponding to the target address space identifier, by one or more of requesters, corresponding to the plurality of requester address space identifiers, respectively, based at least in part on the relationship provided by the at least one entry of the IDPT. Other embodiments are also disclosed and claimed.

The invention provides a multi-source domain invariant acoustic feature extraction method and system for an equipment operation state, and belongs to the technical field of equipment maintenance, and the method comprises the steps: constructing a multi-source domain invariant acoustic feature extraction network based on a DANN model, and the network comprises a feature extractor, a classifier, a domain discriminator and a multi-domain acoustic feature class boundary constraint module; the feature extractor extracts high-dimensional features of sound signals, the classifier carries out fault mode recognition, and the domain discriminator realizes domain prediction. The multi-domain constraint module generates an embedding space, and calculates the maximum mean value difference, the local maximum mean value difference and the Euclidean distance among different source domain features. The network constructs a loss function by taking minimization of inter-domain difference and classification loss and maximization of inter-domain distance and domain discrimination loss as targets, and carries out adversarial training through multi-source tagged acoustic data. According to the method, domain invariant features are extracted by using adversarial learning and hidden space constraint alignment of multi-source domain acoustic features, so that the influence of feature offset under a cross-working-condition condition is effectively reduced, and the fault mode recognition accuracy is improved.