1749 results about "Message delivery" patented technology

The invention provides a Dores front-end node-based service proxy method and system, and relates to the technical field of database access request processing, and the method comprises the steps: collecting front-end node performance indexes, extracting node performance characteristics through employing multi-head recursive attention and causal convolution solution, extracting inter-node correlation characteristics through combining graph attention convolution, and obtaining a database access request; the method comprises the following steps: constructing a node state vector, optimizing a multi-layer connection pool parameter by utilizing an online learning algorithm, establishing a node incidence matrix based on comparative learning, constructing a node performance probability graph evolution model through variational reasoning and Monte Carlo sampling, predicting a node performance change trend, and receiving a database access request; and generating a query plan by adopting an edge attention message passing mechanism, carrying out resource evaluation in combination with performance trend prediction, carrying out scheduling decision making by utilizing multi-agent reinforcement learning and Monte Carlo tree search, carrying out fault detection and migration according to node performance deviation, and updating a probability graph evolution model.

The invention relates to a power edge cloud cooperative management method based on a swan OS, and the method comprises the following steps: S1, deploying intelligent edge nodes, setting an embedded AI acceleration unit, operating the swan OS on all the intelligent edge nodes, and achieving the seamless cooperation of equipment through the distributed characteristics of the swan OS; and S2, realizing self-organizing connection of edge devices by adopting a Mesh network technology, designing a direct communication path between the edge devices, and realizing rapid processing of local problems. S3, aiming at the real-time requirement of a power system, optimizing an MQTT / CoAP protocol stack, improving the efficiency and reliability of message transmission, and allocating special network resources for different types of power applications by utilizing the 5G network slice characteristics, S4, constructing a security architecture based on a zero-trust principle, enhancing the equipment access control and data transmission security, and S5, establishing a security architecture based on a zero-trust principle. And identity authentication and data integrity protection are performed by using a block chain technology, so that data tampering and counterfeiting are prevented. According to the method, the local distributed decision-making capability and the global cooperation capability are effectively improved.

The invention provides an intelligent short message scheduling method and device based on multi-dimensional dynamic optimization, and the method comprises the steps: obtaining the performance data of a plurality of short message channels, and calculating a channel health score based on a weight dynamic adjustment model; determining a scheduling strategy according to the priority identifier of the to-be-sent message, and performing channel screening and optimal matching; executing message sending and monitoring a sending state; terminal state detection is carried out on the failure message through operator base station signaling, and a decision tree model is applied to determine a retry strategy; and performing Huffman coding compression processing on the P2-level marketing messages which fail in retry, and performing batch sending in an idle window. According to the method, a comprehensive performance evaluation index and reward function model is also constructed, and parameter optimization is performed by applying a reinforcement learning algorithm. According to the invention, multi-dimensional dynamic channel scoring, intelligent retry decision making based on terminal state perception, batch processing with balanced cost-time efficiency and a closed-loop self-optimization system are realized, the short message delivery rate is obviously improved, and the invalid retry rate and the sending cost are reduced.

The invention provides a cache information processing method and device of a port platform and a medium, and belongs to the technical field of port distributed information processing. An update request is received, and authentication and flow limiting processing are performed on the request; obtaining a cache key prefix and a data structure type, routing a cache key to a fragment node based on a consumption write-back event, executing a write-in operation in a distributed cache service, and setting survival time and multi-copy synchronization; delivering the message body to the message middleware and obtaining delivery confirmation; the messages are pulled from the queue according to the configured concurrency, deduplication verification is carried out on idempotent identifiers of the messages, and data persistence operation is executed after the verification message version is higher than the current version of the database; index collection points are implanted into all modules of the whole link, and the request delay, the cache hit rate and the message accumulation depth index are visually displayed. The reasonable distribution and optimization of system resources are realized; the consistency and integrity of the data are guaranteed, and the reliability of message transmission is improved at the same time.

The invention provides a legal affair intelligent auxiliary processing method and a multi-agent system. The method comprises the following steps: constructing a dynamically updated legal affair knowledge system; according to the dynamically updated law knowledge system, constructing a case shunting agent Agent, a law application Agent, a class case retrieval Agent and a risk early warning Agent, and realizing a multi-Agent collaboration mechanism through a collaboration structure based on message transmission and state sharing; according to a multi-Agent cooperation mechanism, a preference mapping relation model is constructed through case complexity evaluation and quality evaluation, and multi-case relation perception preference mapping is realized; constructing a planning tree PLANTOR framework by combining a large language model and a knowledge management technology based on Prolog based on multi-case relationship perception preference mapping, generating a robot-oriented knowledge base and a planning program, and converting the robot-oriented knowledge base and the planning program into a behavior tree; according to the PLANTOR framework, multi-Agent inference chain alignment is achieved through joint optimization of transmission optimization and embedded optimization, and law decision assistance is completed. According to the scheme, the legal affair processing accuracy and efficiency are effectively improved.

The invention relates to the technical field of automatic driving, and discloses an automatic driving lane changing trajectory planning method based on deep learning, and the method comprises the steps: carrying out the data collection and preprocessing of a multi-modal sensor; performing spatial feature extraction and time sequence modeling on the preprocessed multi-modal data by adopting a CNN-LSTM hybrid architecture, performing feature fusion through an attention mechanism, and outputting a first feature extraction vector; taking the detected vehicles as graph nodes to construct a traffic graph, learning an interaction relationship between the vehicles through a graph attention network and a message passing mechanism, and calculating a scene urgency score and a safety score; generating a lane changing decision based on the deep Q network and the strategy gradient; and generating a trajectory based on the generative adversarial network. The technical problems that an existing lane changing track planning method cannot adapt to the dynamic traffic environment, lacks the ability of understanding complex multi-vehicle interaction and is difficult to balance safety and urgent conflict requirements are solved, and intelligent, safe and efficient automatic driving lane changing track planning is achieved.

The invention discloses an AI-based method and system for optimizing and supervising an internal control process of an administrative institution, and relates to the technical field of intelligent management. Time sequence message transmission coding is carried out on the benchmark process and the actual process to achieve deep semantic alignment of the benchmark path and the actual path, the process execution semantic offset coefficient between the benchmark process and the actual process after semantic alignment is further quantified, and the process execution semantic offset coefficient is compared with a preset threshold value. Determining whether a path offset early warning prompt is generated or not; according to the method and the device, the process deviation can be captured in real time, early warning can be triggered, the real-time performance and objectivity of supervision are enhanced, abnormal risks in process execution are effectively prevented through an intelligent early warning mechanism, and the dynamic management and control capability with flexibility and normativity is provided for administrative institutions.

Disclosed are systems, apparatuses, methods, and computer-readable media for use in a wireless network for facilitating radio access synchronization. The method includes receiving, from a user equipment (UE) a message having a media access control (MAC) control element (CE) format and comprising a MAC protocol data unit (PDU) structure, the message being formed by multiplexing one or more MAC service data units (SDUs). The method further includes generating a response message to the received message by multiplexing a MAC random access response (RAR) with one or more MAC SDUs. The method then transmits the response message to the UE.

A method includes sending, by a user equipment (UE), a registration request message to a mobile communication network, the message including a core network capability indication that the UE supports partial registration of at least one network slice that is not available within all of the tracking areas in a registration area. The method includes, receiving, from the mobile communication network, a mobility management message having parameters for (i) one or more allowed network slices supported by each tracking area of the registration area, and (ii) a partial network slice registration identifying at least one local network slice that is supported in at least one, but not supported within all of the tracking areas. The method includes configuring the UE to use the local network slice of the partial network slice registration when the UE is located in the tracking area supporting the local network slice.

The invention discloses a remote centralized control distributed data processing and analysis method based on a JMS, and the method comprises the steps: achieving the message routing and resource scheduling through the real-time monitoring of the state of a system, reinforcement of learning decision, deep learning optimization and multi-dimensional message distribution; dynamically adjusting message flow rate and queue length based on real-time network conditions, node load and flow fluctuation; through edge node intelligent task scheduling, distributed collaboration of calculation and storage is realized; adaptive compression algorithm selection is carried out by using deep learning, and the consumption of message transmission bandwidth is reduced; non-tampering storage of messages is realized through a block chain technology; based on a container arrangement technology, dynamic expansion and contraction of computing resources are realized; and dynamic resource scheduling and optimization decision are realized by utilizing reinforcement learning and multi-dimensional monitoring data. According to the invention, the bottleneck problem of the traditional message transmission system is overcome, and the system can efficiently and reliably process mass data in a high-concurrency and high-throughput scene.

The invention provides a hydrological trend prediction method based on big data analysis, and the method comprises the steps: building an initial graph structure which takes a monitoring station as a node and geographic distance and water system connectivity as an edge weight, carrying out the iterative aggregation of node features through a message passing mechanism, and capturing the space interaction of hydrological variables between stations; dynamically adjusting a water system connectivity parameter and an edge weight based on rainfall change, and generating an adaptive graph structure; in combination with the graph neural network, the dynamic Bayesian network and the space-time collaborative Kriging interpolation algorithm, hour-level hydrological dynamic transmission features are extracted, and a space-time coupling prediction model is formed; model parameter optimization and message passing mechanism adjustment are triggered through a prediction deviation threshold value, and a self-adaptive prediction process of'dynamic modeling-feature fusion-closed loop optimization 'is realized. According to the method, hydrological element space-time correlation can be accurately described, the analysis precision of a complex hydrological process is improved, and the adaptability to sudden hydrological events and basin environment changes is enhanced.

To solve the problems of fractured identity across multiple blockchains, these methods and systems allow for a single unified identity to be managed and resolved across multiple chains within an interop network. By leveraging message passing, identity records can exist securely across different chains. The system incorporates counterfactual blockchains, enabling trust-minimized name registration that reduces costs while maintaining decentralization and security. ENS name resolution is supported across both onchain and offchain environments, with verifiable proofs ensuring efficient resolution. By structuring message passing and utilizing decentralized indexers, these methods and systems provide a scalable and reliable framework for cross-chain identity.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical treatment and health and the like, and discloses a multi-modal event data processing and collaborative circulation method, device, equipment and medium. Generating a stereoscopic scene model based on the event data file and positioning a target area, performing multi-modal fusion by combining a positioning result and non-spatial data of the event data file to generate a fusion analysis result and form a processing report, and constructing an auditing and judging item set containing a conditional strategy and an execution action to execute auditing and generate an auditing and judging result. And generating a collaborative circulation instruction set based on the processing report and the audit judgment result, and triggering execution through distributed message transmission to obtain an execution result. According to the method, through multi-modal data processing, three-dimensional scene modeling, cross-modal fusion analysis, rule-driven auditing and distributed execution, full-process automatic processing is realized, and the processing accuracy and efficiency are improved.

Described herein are systems, devices, methods, and media for an emergency response data exchange system. The system provides an operations center emergency management application with a textual and / or graphical notification of emergency communications initiated on a premises managed by the operations center. The system exchanges operations center data (e.g., floorplans, video feeds, point of contact information, building access controls) with an emergency communications center (ECC) and / or with a first responder device to facilitate reducing emergency response times. The system selectively establishes communications (e.g., messaging windows) between the operations center, ECC, and / or first responder device to facilitate real-time information sharing between relevant parties of an emergency.

The invention discloses a clinical examination and detection item correlation analysis method based on multi-agent cooperation, and relates to the technical field of correlation analysis, and the method comprises the steps: obtaining the name and basic parameters of an examination item, and determining the technical field of the examination item through a classification system and an algorithm; related field parameters are extracted from the knowledge management module, and configuration parameters of the intelligent agent are set; generating a task instruction based on the basic parameters, transmitting the instruction through a structured message transmission mechanism, and obtaining an agent processing result; and performing verification analysis on the processing result by using a hypothesis production and verification engine to obtain a correlation analysis result of the project. According to the method, the hypothesis content can be evaluated from multiple angles, it is ensured that the obtained correlation analysis result has high scientificity and credibility, and powerful support and basis are provided for research and application of clinical examination and detection items.

The invention relates to the technical field of substation operation and maintenance management, in particular to a three-dimensional visual substation operation and maintenance management method and system based on digital twinning. The method comprises the following steps: acquiring real-time working condition data of equipment; acquiring equipment distribution data and space electric field intensity distribution; inputting the equipment distribution data and the historical operation and maintenance data into a physical information neural network model to obtain a health index and a residual service life prediction value; when the real-time working condition data is abnormal or the health index is lower than a preset threshold value, message transmission and node updating are carried out in the substation graph neural network model, a fault source is positioned, and the cascading fault probability is evaluated; and generating an operation and maintenance work order based on the fault source, the health index and the cascading fault probability, and planning a safe operation and maintenance path by using an A-star algorithm. According to the scheme, the limitation that traditional operation and maintenance depend on surface monitoring and experience judgment can be overcome, the depth and the breadth of fault diagnosis are improved, and the safety of field operation is enhanced.

The embodiment of the invention provides a network card equipment switching method and device based on a bonding mode, and relates to the technical field of network card equipment. The method comprises the following steps: in response to a switching operation of network card equipment based on a bonding mode, generating a switching operation message, and transmitting the switching operation message to a preset callback interface; calling a switching operation message from a preset callback interface based on the remote direct memory access driving module; and based on the remote direct memory access driving module, switching the current data information of the first network card device to the second network card device through the shared memory according to the switching operation message, and executing the current data information in the second network card device. By adopting the technical scheme, when one network card device has only one port, a communication mode of remote direct memory access with other devices can be carried out, so that the stability of data transmission is improved.

The invention discloses an emergency broadcast message scheduling system, a processing method and a broadcast terminal, and belongs to the technical field of message scheduling. Emergency event data are collected, public events and regional events are divided according to an initial influence range, and SIP labels are constructed to generate broadcast messages; constructing a dynamic list based on a terminal identity and a network state, starting multi-network integration to execute a differential transmission strategy, and ensuring message delivery; public event preemption, regional event dynamic polling and priority jump triggering during event diffusion are realized through a multi-stage scheduling queue; volume coverage mapping is established by using multi-dimensional information of the terminal, the volume is dynamically calibrated in combination with regional characteristics, formats are adapted, and non-sensitive regulation and control are realized. The objective of the invention is to solve the problems of rigid multi-event scheduling, unstable heterogeneous network transmission, insufficient terminal adaptation capability and the like of a traditional system, and to improve emergency scene message scheduling efficiency, transmission reliability and coverage accuracy.

The technology disclosed allows for leveraging decentralized credentials to achieve bidirectional authentication between two actors leveraging a messaging platform / system, such as email or another text-based system, verifiable credentials (VCs), and secure web endpoints. It empowers one party (“Sender”) to send a message enclosed with a Verifiable Presentation which allows another party (“Recipient”) to authenticate the message's provenance and the identity of the sender. Moreover, the message contains a link to a secure web endpoint, where the recipient can submit a response signed by their own Verifiable Presentation, allowing the Sender to authenticate the identity of the Recipient. In this way, both participants are able to authenticate each other's identities with an additional factor of authentication, with neither participant being required to share a single service.

The invention provides an unmanned aerial vehicle ad hoc network transmission and calculation integrated resource scheduling method based on task driving, and the method comprises the steps: building a multi-dimensional resource pool model which comprises the communication bandwidth, calculation resources and storage resources of an unmanned aerial vehicle, and collecting the resource state vector of each unmanned aerial vehicle node in real time; a dynamic topology sensing network is constructed, link duration is predicted through relative motion speed between unmanned aerial vehicle nodes, and a network structure chart with weights is generated; constructing a decision model based on a fusion architecture of a preset message passing neural network and a deep reinforcement learning network, and inputting the network topology features of the network structure chart and the resource state vector into the decision model; and outputting an optimal scheduling strategy including target node selection and multi-hop path planning through the decision model, and maximizing system benefits while meeting constraints of tasks on communication and computing resource quality. The problems that existing unmanned aerial vehicle networking communication is high in time delay, low in reliability and difficult to calculate and maximize utilization of resources are solved.

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 belongs to the technical field of semiconductors, and particularly discloses a photoetching hot spot detection method and system based on a graph neural network. The method comprises the following steps: decomposing each polygonal metal in a layout into rectangles by scanning in the horizontal direction, and taking the rectangles as nodes of the layout; adding an internal edge and an external edge between the rectangles according to a specific rule; the diagonal vertex coordinates of the rectangles are used as node features, the minimum distance between the rectangles represents edge features, and graph representation of the layout is completed; inputting the graph representation of the layout into a message passing layer of a GNN model to perform graph feature extraction, and performing global maximum pooling and global average pooling on each dimension of all node features to obtain global graph representation; and classifying the global graph representation into hotspots and non-hotspots. According to the invention, the accuracy and calculation efficiency of layout hot spot detection can be improved.

The invention relates to a security management knowledge graph construction and dynamic updating method based on association modeling, and the method comprises the steps: collecting multi-source heterogeneous data in the field of security management, carrying out the recognition of the data type, carrying out the data conversion, setting an active learning mechanism in a named entity recognition model, carrying out the training, and recognizing an entity from the data. The entity is extracted; constructing an attention-enhanced graph neural network to analyze image data, calculating an attention weight for each node and edge in a message transmission process, and updating node information to realize relation extraction; based on the entity and relational data, constructing a preliminary security management knowledge graph; reasoning and supplementing missing information and relations based on an external standard knowledge base; the data change increment is detected in real time to update the security management knowledge graph, nodes and relationships are newly added or updated in the security management knowledge graph, the security management knowledge graph is efficiently constructed and dynamically updated, and support is provided for cross-modal intelligent question answering and real-time security evaluation.

The invention belongs to the field of computer artificial intelligence and wireless communication networks, and provides an edge model scheduling method and system fusing graph nerves and a hybrid expert model, and the method comprises the steps: collecting multi-modality, hardware parameters and model performance data, constructing a graph nerve network structure according to the resource condition of edge equipment and task calculation demands, and carrying out the scheduling of the edge model. Therefore, the topological relation and conditional dependence among data, hardware and models are captured; according to the method, feature processing is carried out at the local where data is generated, so that the time delay of data transmission to a cloud or a central server is remarkably reduced, and the resource utilization and model processing efficiency of the whole system is improved; the graph neural network prediction model constructed based on the general features and the new message passing mechanism can effectively deal with the zero sample prediction problem in an unfamiliar hardware equipment environment, does not need to perform special training and optimization for each hardware equipment and generate diversified prediction results, and effectively reduces the risk of prediction result overfitting.

The invention relates to the technical field of logistics management, and discloses a multimodal transport cargo arrival time prediction method based on a graph neural network, and the method comprises the steps: receiving an arrival time prediction request for a target cargo; planning a candidate transport path according to the arrival time prediction request, and dynamically extracting a sub-graph from the global multimodal transport network; endowing each node and each directed edge with a feature vector, endowing each sub-graph with a global feature vector, and inputting the sub-graphs after feature initialization into a graph neural network model; the method comprises the following steps of: generating an initial state vector for each node through a graph neural network model; iteratively updating the state vector of the node through a multi-layer message transmission mechanism, and simulating the cascade propagation effect of the transportation event on the path network; and based on the final state vector of the destination node after iteration updating, regression is carried out to obtain a probability distribution parameter of the arrival time. According to the method, cascade propagation of transportation delay in the network can be simulated, probabilistic prediction containing uncertainty measurement is output, and the prediction precision is improved.

The invention provides a PCBA (Printed Circuit Board Assembly) multi-defect collaborative diagnosis method fusing hypergraph diffusion and cross-modal comparison, which comprises the following steps of: constructing a multi-layer hypergraph structure, and generating a multi-modal defect sample by adopting a condition hypergraph diffusion model based on the multi-layer hypergraph structure. Performing cross-modal feature alignment on the multi-modal defect sample by adopting a multi-layer alignment mechanism, and calculating a defect interaction matrix of aligned features; according to the defect interaction matrix, node features of each node in the node layer are updated by adopting a message passing mechanism of a graph neural network; and inputting the node features into a multi-task learning framework, and predicting the defect of each node to obtain a node defect prediction result. According to the method, complex correlation between PCBA defects can be effectively modeled, multi-mode sensor information is fully utilized, and the method has real-time collaborative diagnosis capability so as to meet the urgent requirements of the modern electronic manufacturing industry for high-precision, high-efficiency and high-reliability quality detection.

A method for secure onboarding to a closed mesh network in which a camera-equipped device scans a QR code embedded in a durable physical carrier (e.g., adhesive bandages, photographs, ID cards) to extract an access credential, configuration profile, or secure URL that automatically configures the device for network access, authenticates the device to the mesh without manual credential entry or transmission of plaintext credentials over insecure channels, and establishes end-to-end encrypted communications between network nodes; the method supports cryptographically random, single-use or time-limited credentials with periodic refresh to prevent replay, encoding of VPN / WireGuard or proprietary configuration profiles, substantially real-time onboarding (e.g., under five seconds), optional multi-factor verification (biometric or PIN), immediate initiation of secure messaging, voice / video or data sessions upon onboarding, audit logging for compliance and revocation based on detected unauthorized activity, and covert or overt embedding of QR codes in environment-resistant carriers for emergency, disaster response, first responder, or covert deployment, with the QR code rendered unreadable or deactivated after successful onboarding to prevent credential reuse.

The invention belongs to the technical field of aero-engine fault diagnosis, and discloses an aero-engine gas path fault diagnosis space-time network model of a self-adaptive graph structure advantage. Firstly, multi-source sensor data are processed through a time sequence factor extraction module, changes in time dimension are effectively captured, and the stability of a model is improved through a normalization layer. Thirdly, correcting improper spatial relationship representation in the time factor by adopting a random embedding method, constructing an initial graph structure by utilizing cosine similarity calculation, selecting hidden information in the graph structure again in combination with an attention mechanism, and then performing deep fusion on spatial features by using a message passing mechanism; and the node embedding is more accurate. According to the method, graph structure information in sensor data can be adaptively captured, the performance of the model in complex spatio-temporal information processing is remarkably improved, characteristic representation with higher robustness can be generated, and subsequent analysis and prediction are supported.

The invention discloses a multi-target track association method and system based on interactive attention map matching and a storage medium, and the method comprises the steps: preprocessing track data, and obtaining an input track; utilizing a track historical feature extraction network to obtain embedded features of each track; a multi-graph structure is utilized to model tracks, nodes represent one track, edges represent message passing between the tracks, and the number of the edges is limited through graph cutting; features of neighbor nodes are aggregated by using a self-attention mechanism and a cross attention mechanism, and finally embedded representation of the nodes is obtained; and constructing a score matrix, and performing a Sinkhorn algorithm on the matrix to obtain an association probability matrix. And track association is carried out by using the multi-target track association model based on interaction attention map matching, and an association task is completed. According to the method, the spatial-temporal characteristics of the track can be fully mined, the track association accuracy is improved, and the track association error rate is reduced.

An ecommerce messaging system described herein comprises one or more ecommerce extension apps that interact with a messaging app. These embodiments leverage the extension core to manage session apps and smart services for using tap-to-message and tap-to-update processes to generate, update, and manage order workflows and in-app stores through branded messages communicated between customer and seller session apps to share message transcripts on devices of the same user groups via the messaging host, empower ecommerce with action-key-based communication, serialize-deserialize processes, and dynamic update propagation, manage return-refund workflows with temporary tables until one-time entity data model updates can be performed for session apps, and configure multi-store shopping systems on sellers' multiple devices using a store-as-a-service model to enable distributors to distribute production copies of assigned stores to authorized publishers to add custom promotions, which will be published together as production copies to their subscribed user groups for multi-store shopping and tracking.