1365 results about "Message delivery" patented technology

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 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 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.

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 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 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 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 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 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.

A system and method for privacy-preserving communication validation and multi-attribute trust scoring is disclosed. The system analyzes communication metadata to determine pattern legitimacy by comparing current communication patterns against relationship fingerprints without accessing communication content. The system validates relationship context between communicating parties using interaction graph analysis and historical communication data. Cross-network intelligence correlation compares current patterns against aggregated patterns across voice, email, and messaging services, creating a self-strengthening security framework that recognizes emerging threat patterns while validating legitimate communication behaviors. The system generates comprehensive multi-attribute trust assessments comprising individual trust attribute scores including engagement rate, reliability index, channel preference, temporal pattern, and behavioral pattern, combined into overall trust levels. Trust context is displayed through a user interface presenting simplified, intuitive, and actionable information with progressive disclosure capabilities, enabling informed user decisions while preserving privacy. Communication processing actions provide users with appropriate engagement options tailored to specific trust assessment results.

The invention discloses an unbiased scene graph generation method for relieving long-tail distribution. The method comprises the following steps: S1, constructing a model; s2, data preprocessing; s3, object feature extraction; s4, constructing a graph learning structure (GLS); s5, a regional message passing network (RMPN); s6, generating a pseudo label; s7, defining a loss function; s8, performing model training; s9, generating a pseudo tag and a triple; and S10, carrying out iterative training and optimization. According to the unbiased scene graph generation method for relieving long-tail distribution, the correlation between entities is calculated by utilizing GLS, the relation graph is optimized, the relation representation of head and tail categories is enhanced, meanwhile, the RMPN improves the semantic representation of objects and relations through an information transmission mechanism, pseudo labels are generated on the basis of unlabeled relations in a training set through a pseudo label generation mechanism, and the robustness of the unbiased scene graph generation method for relieving long-tail distribution is improved. And in combination with a high-confidence screening mechanism, generating a learning sample of a pseudo-triple enhanced tail category.

The invention discloses a retrieval enhancement generation method and system based on a sparse graph neural network, and aims to solve the problems of large-scale knowledge graph retrieval enhancement generation and a multi-hop reasoning task. The method comprises the following steps: embedding a query vector, receiving a user query, and encoding the user query into a query embedded vector through a sentence embedding model; performing sub-graph retrieval, performing hash processing on the query embedded vector by using a hierarchical hash index to generate a composite hash key, and matching entities in the knowledge graph layer by layer according to the queried hash key to construct a sub-graph; dynamic sparse message transmission: executing dynamic sparse message transmission update entity embedding on the constructed subgraph; document retrieval sorting is carried out, document correlation scores are calculated according to final entity embedding, and first K documents are selected to form a retrieval result document set; and answer generation: inputting the query and retrieval result document set into a generation model, and outputting a final answer. The method is suitable for large-scale knowledge graph application, and an efficient retrieval enhancement generation solution is provided.

Systems and methods for predicting recommendations using graph relationships are disclosed. According to an embodiment, a method may include: (1) monitoring, by a computer program, a messaging interface for transactions; (2) updating, by the computer program, a heterogeneous graph with data from the transactions, wherein the heterogeneous graph identifies a plurality of assets and a plurality of clients; (3) training, by the computer program, a graph model with the heterogeneous graph; (4) querying, by the computer program, the graph model with one of the plurality of assets, wherein the graph model returns a recommendation that identifies a subset of the plurality of clients for the asset; and (5) outputting, by the computer program, the recommendation.

A core network server for defining authentication credentials and authenticating a wireless communication device according to WIFI communication protocols includes a central processing unit (CPU) and a non-transitory memory comprising executable instructions that when executed by the CPU, causes the core network server to receive an encrypted authentication request from a wireless communication device; send the encrypted authentication request to an authentication server based on one or more attributes in the encrypted authentication request; receive an indicator of a specialized network slice associated with the wireless communication device based on sending the encrypted authentication request; communicate authentication messages to the wireless communication device according to one or more network functions of the specialized network slice; and authenticate the wireless communication device according to the specialized network slice responsive to communicating the authentication messages.

The invention belongs to the technical field of information and communication, and relates to an active user detection and data decoding method of an asynchronous massive machine type communication system. Aiming at the problem that the detection and estimation performance is reduced due to unknown user time delay in asynchronous transmission, the method comprises the following steps: initial estimation and preprocessing: detecting initial active users and transmission time delay thereof through correlation peaks, and whitening received signals; the iterative joint detection and channel estimation module is used for iteratively updating channel information and data symbol information for multiple rounds based on a factor graph and a message passing algorithm; the data decoding module is used for decoding data based on a soft decision device; the detection and decoding performance is improved by transmitting external information among the modules; and the time delay parameter updating adopts a greedy search algorithm to obtain the optimal time delay. According to the method, the active user detection rate, the channel estimation precision and the data decoding accuracy under the condition that the receiving and transmitting ends are completely asynchronous are improved.

A real-time anomaly detection framework for embedded BI systems (100) using LLMs, consisting of: (a) a data collection and pre-processing module configured to collect, cleanse, normalise and encrypt structured and unstructured data from multiple enterprise data sources in real time; b) a contextual embedding and feature extraction module configured to transform processed data into semantically rich, domain-aware embeddings that capture temporal, correlational, and contextual features; c) an LLM-based anomaly detection engine configured to analyze said embeddings using fine-tuned large language models to identify anomalies based on semantic inference, historical trends, and adaptive learning; d) a real-time stream processing and low-latency pipeline configured to perform high-throughput parallel in-memory computations to deliver anomaly detection results with minimal latency; (e) a root cause analysis and interpretability module configured to identify probable causes for detected anomalies and generate human-readable explanations, confidence scores and causal narratives; (f) an alerting and visualisation module configured to deliver real-time notifications via embedded BI dashboards, messaging platforms and graphical representations such as heat maps and trend charts; and g) a continuous learning and feedback optimization module configured to refine the accuracy of anomaly detection by incorporating user feedback, updated historical data, and evolving business rules.

This disclosure is generally directed to wireless communication systems and methods and relates particularly to a mechanism for implementing an artificial intelligence framework for adaptively configure the over-the-air communication interfaces of the wireless communication systems. AI network configuration functions may be provided as services for AI-assisted network configuration (AI configuration services, or AICS). Such AICS may be requested and configured via various messaging and signaling mechanisms. The AI model life cycle management including training, delivery, activation, inference, deactivation, switching, performance evaluation, and the like may be configured, triggered, and otherwise provisioned via control and data messages and signaling communicated between the various network elements in the wireless communication system.

The invention relates to the technical field of traffic data analysis, and provides a cold-chain transportation route optimization method and system based on deep learning, and the method comprises the steps: firstly collecting a target data set containing traffic state information, cargo characteristic data and vehicle scheduling information in a cold-chain transportation scene; then carrying out association mapping processing based on the cargo characteristic data and the vehicle scheduling information, and generating a cold chain transportation line network comprising a plurality of transportation nodes and line sides; identifying the traffic condition of the transportation node by using the traffic state information, and inferring and analyzing by combining the influence relationship between the nodes to obtain the congestion distribution characteristics of the line nodes and the cold chain transportation delay label; and finally, in response to an instant transportation demand instruction, processing the original traffic area node network in combination with the above characteristics and a message passing mechanism of the pre-training graph neural network, updating node state information and connection weight, and generating a cold chain transportation optimization line matching the instant transportation demand, and the method can improve the cold chain transportation efficiency and reliability.

The invention discloses an intelligent manufacturing system deadlock prediction method based on Petri-Net-GCN. The method comprises the following steps: dividing an intelligent manufacturing system into a production equipment module, a production process module and a production resource module; carrying out formalized modeling on the module on the basis of a Petri network; a Petri-Net-GCN deadlock prediction model is constructed, and the Petri-Net-GCN deadlock prediction model comprises a house-to-house transition message transmission module and a transition-to-house message transmission module; performing bidirectional aggregation updating on features among nodes in the Petri net graph by using a graph neural network structure; collecting initial Tokenn distribution, an incidence matrix and a deadlock label of the intelligent manufacturing system to construct a data set, and inputting the data set into a model for training; predicting a deadlock risk in an unknown system state by using the trained model; according to the method, rapid prediction and judgment are carried out in the current state of the intelligent manufacturing system, and prior early warning information is provided for production scheduling and resource configuration.

The invention discloses a traditional Chinese medicine adverse reaction risk prediction method based on a heterogeneous graph neural network, and the method comprises the following steps: obtaining data, obtaining a traditional Chinese medicine-target relation file from an ETCM database, and obtaining an adverse reaction-target relation file from an ADReCS database; data preprocessing: performing data cleaning on the obtained relation file to obtain a preprocessed file; constructing an isomeric graph, taking the traditional Chinese medicine herb, the target spot and the adverse reaction adverse as three types of nodes, and constructing the isomeric graph by utilizing the pre-processing file; node features are initialized, initial feature vectors are constructed for each type of nodes herb, target and adverse, and initial embedding of all the nodes is mapped to the same dimension space; carrying out HAPM feature fusion, inputting the node features into an HAPM heterogeneous graph attention network to carry out message passing and aggregation, and outputting a representation vector of a node level; predicting and outputting; and performing verification and feedback iteration. The invention further provides a system adopting the method. The method and the system can accurately predict the adverse reaction risk of the traditional Chinese medicine.

The invention provides an intelligent logistics management system based on a large language model, and relates to the field of intelligent logistics management. According to the method, a low-rank adaptation technology general large language model is adopted in advance for fine tuning, and an external LLM service obtained through fine tuning is innovatively and deeply fused into a task decision process, so that deep understanding of user intentions and accurate analysis of complex environment situations can be realized, and an optimal distribution scheme is autonomously generated based on the deep understanding of the user intentions and the accurate analysis of the complex environment situations. Besides, the urban environment management module, the logistics management scheduling module, the simulation engine module and the large language model interface module included in the system do not work independently, but closely cooperate through an event-driven and message-passing mechanism, so that a complete unmanned aerial vehicle logistics management closed loop from environment perception to decision making and execution to event feedback is formed. The architecture can reflect and control the distribution task of the unmanned aerial vehicle in real time, and carries out self-correction on a set scheme, thereby finally remarkably improving the intelligent level and operation efficiency of logistics management.

A method, computer system, and computer program product are provided for dynamic user personalization using large language models. A conversation history is obtained comprising a plurality of messages associated with a user’s activity in a messaging system. The conversation history and a request to identify interests of the user, based on the conversation history, are provided to a large language model. A natural language output is received from the large language model comprising one or more identified interests of the user. A text sample, the natural language output, and a request to summarize the text sample based on the natural language output are provided to the large language model. A summary of the text sample that is personalized based on the one or more identified interests of the user is received from the large language model.

A method performed at a data broker comprises receiving from a subscriber client a request for a subscription for sensor data transmitted by a sensor client, the request in accordance with a publish / subscribe messaging protocol, receiving sensor data from the sensor client, the sensor data published by the sensor client in accordance with the messaging protocol, and determining, in dependence on the sensor data and a current state of a state machine associated with the sensor client, whether the sensor data is to be subject to subscription processing. The method further comprises determining, in dependence on the receipt of the sensor data and the current state of the state machine, an updated state for the state machine, updating the state machine to the updated state, and if the data is to be subject to subscription processing, forwarding the data to the subscriber client in accordance with the messaging protocol.