4410 results about "Manet routing" patented technology

A method is provided for enhanced data routing in neural networks using Self-Organizing Maps (SOM) integrated with Autoencoder-GAN. The method comprises training an autoencoder to encode input data into a latent space representation; applying a Self-Organizing Map (SOM) to organize the latent space representation into a topological map; refining the latent space representation using a Generative Adversarial Network (GAN), wherein the generator generates enhanced latent space representations and the discriminator evaluates their quality; using the refined latent space representations to update the SOM topology dynamically; generating routing coefficients based on the updated SOM topology to guide data routing in a capsule network; and dynamically adjusting routing within the capsule network using the generated routing coefficients to enhance performance based on the refined latent representations.

Systems, methods, and devices that relate to routing requests to large language models (LLMs) are disclosed. In one example aspect, the system receives session-specific data elements in response to a request to generate an output using LLMs. The system determines a hierarchy of operational constraints including privacy protocols and performance requirements. Weights for a multi-variable optimization are dynamically updated using the session-specific data elements. The system executes the multi-variable optimization across candidate LLMs that satisfy privacy constraints and optimize performance constraints. Based on the optimization, at least one candidate LLM is selected and the request is routed to it. In response to performance feedback, the system automatically selects a different LLM to improve one constraint, resulting in degradation of another constraint.

The invention provides a dynamic routing selection method and system, electronic equipment and a storage medium, and aims to solve the problem that a routing strategy is difficult to adapt to a dynamically changing network, the method comprises the following steps: a terminal layer collects the state of a terminal and network data, and performs lightweight feature extraction; the edge node layer receives the data of the terminal layer, carries out space-time-semantic feature aggregation, and generates a region-level resource scheduling and routing decision strategy based on fragmented reinforcement learning; the central cloud service layer gathers whole network data, generates a global optimization strategy and issues the global optimization strategy; the edge node layer fuses global optimization and a region-level strategy, and executes dynamic routing selection; and security and privacy protection are provided through the trusted chain layer. According to the invention, adaptive path selection can be realized, the network resource utilization rate is improved, and the network stability is improved.

The invention, which relates to the technical field of customer relationship management, discloses a multi-channel interactive customer relationship management system comprising a dynamic routing decision module, a multi-modal data fusion module and an intelligent feedback optimization module. The dynamic routing decision module evaluates channel load through a deep neural network, dynamically allocates client requests to an optimal node by utilizing reinforcement learning, and realizes load balancing and service continuity; the multi-modal data fusion module integrates text, voice and image data, constructs a space-time correlation graph, identifies a cross-channel behavior mode, and ensures data consistency through multi-dimensional verification; the intelligent feedback optimization module combines customer satisfaction evaluation and multi-modal sentiment analysis, optimizes a service strategy by using a genetic algorithm, and synchronizes the service strategy to a cross-channel knowledge graph to realize adaptive iteration; according to the method, the problems of unreasonable multi-channel load distribution, insufficient data fusion and consistency verification and inaccurate service strategy optimization are effectively solved, and the customer service quality and experience are improved.

The invention provides a signal optimization transmission system and method based on HPLC and HRF dual-mode communication, a signal acquisition and processing module of the signal optimization transmission system based on HPLC and HRF dual-mode communication is used for acquiring signals of two communication modes of HPLC and HRF in real time, preprocessing the signals and extracting key signal parameters; the dual-mode switching module is used for dynamically switching an HPLC mode and an HRF mode based on channel quality; the access module is used for constructing a longitudinal backbone network and a transverse Mesh network, forming a hybrid topology, optimizing a routing path and coordinating resource allocation and conflict avoidance of HPLC and HRF; the intelligent decision module is used for predicting future interference intensity according to the historical interference data and adjusting channel parameters; the central coordinator module is used for network initialization and resource allocation; according to the system and the method, the HPLC mode and the HRF mode are automatically switched according to the channel quality, the longitudinal HPLC backbone network is combined with the transverse HRF Mesh network to form a four-dimensional communication network, and the robustness in a complex scene is remarkably improved.

The invention relates to the technical field of communication, in particular to an underground power distribution room communication system and method based on heterogeneous network and multi-modal fusion, and the system comprises a three-dimensional heterogeneous network cooperation module, a cross-modal feature fusion module, a dynamic routing decision engine module, a self-adaptive interference suppression system and a cross-layer cooperation optimization module. The three-dimensional heterogeneous network cooperation module comprises a 4G / 5G wireless communication sub-layer, a power line carrier communication sub-layer and an edge computing management sub-layer; the cross-modal feature fusion module comprises a multi-modal encoder group, a shared feature projection layer, a twin network structure and a cross-modal attention fusion module; and the dynamic routing decision engine module comprises a state space monitoring unit, a routing optimization unit and a millisecond-level path switching strategy. Through the arrangement, the reliability, the real-time performance and the energy efficiency of communication of the underground power distribution room are systematically improved, and a high-robustness communication infrastructure is provided for an intelligent power grid.

The invention relates to the technical field of data processing, in particular to a backtracking analysis model construction method based on an attack chain, which comprises the following steps that: a kernel layer security agent acquires process, file and network behavior characteristics in a hardware isolation environment, and generates an event tuple; the tensor network pipeline performs three-dimensional decoupling mapping on the tuple into a behavior fingerprint vector, an orthogonalization noise feature and an asymmetric adjacent tensor, and compresses the behavior fingerprint vector, the orthogonalization noise feature and the asymmetric adjacent tensor into a space-time topology tensor block; the reinforcement learning controller constructs a directed acyclic graph based on the tensor blocks, calculates connectivity loss and outputs an event risk score; the dynamic routing engine constructs a decision tree model according to the risk mark, the burst frequency and the correlation entropy, and implements three-level shunting and a multiple simulation system to generate an anti-interference index; and when the deviation between the physical trajectory and the digital model exceeds the tolerance, the closed-loop feedback weight coefficient updates the loss function parameter and adjusts the channel resource weight. And the problem of threat discovery delay caused by attack chain breakage under massive events is solved.

An intention-driven low-orbit satellite network SRv6 routing management and control method comprises the following steps: step 1, receiving and analyzing a user service demand from an application layer, and generating a corresponding intention label according to a service type; step 2, collecting state information of a satellite network in real time, tracking and recording satellite node position change and link state conversion, and establishing a complete network state database; 3, calculating a routing path meeting the service quality requirement by adopting an improved breadth-first search algorithm; 4, monitoring the state change of the inter-satellite link in real time; step 5, continuously monitoring fault events in the network, immediately starting a pre-calculated standby path when a fault is detected, updating a corresponding SRv6SID list, and performing rapid path switching; and step 6, dynamically adjusting the network flow. The method not only can adapt to the dynamic characteristics of the low earth orbit satellite network, but also can effectively meet the differentiated service requirements, and has the rapid fault recovery and load balancing capability.

The invention provides a large model lightweight reasoning deployment method under limited hardware resources, and the method comprises the steps: quantifying the weight importance of a large model through a composite index of gradient sensitivity and activation frequency, and carrying out pruning operation in combination with an improved index weighted moving average strategy, thereby obtaining a structured sparse model; the sparse model is divided into sub-networks by adopting double rules, a routing decision network is trained, and an adaptive feature shunting architecture model is constructed; a multi-precision weight set is generated through a nested quantization technology, quantization bit width is dynamically adjusted, and edge equipment hardware parameters are adapted to complete reasoning environment initialization; after a reasoning request is received, an optimal sub-network is selected based on the trained routing decision network, corresponding weights are loaded in parallel, and a reasoning result is fused and output; and converting a reasoning result format, and dynamically optimizing a scheduling strategy based on a system real-time monitoring index. The method is compatible with a mainstream large model and a hardware platform, and an efficient and universal deployment scheme is provided for end-side AI engineering landing.

The invention discloses an adaptive modulation anti-multipath unmanned aerial vehicle swarm communication method and system based on MIMO-OFDM, and relates to the technical field of wireless communication, and the method comprises the steps: receiving a down-conversion baseband; synchronization and compensation are carried out; removing a cyclic prefix and carrying out fast Fourier transform; estimating a channel and noise on the training and pilot frequency, and correcting a common phase; performing frequency domain equalization and soft decision, forward error correction and cyclic redundancy check according to the estimation to obtain a control parameter, decoding a payload according to the control parameter, and performing statistics to form a link quality indication; modulating coding, bandwidth and spatial mode, configuring mapping, power and weight, and packaging physical layer protocol data; carrier sensing conflict avoiding sending, confirmation and retry are adopted to generate media access statistics; and the routing selects the next-hop multi-hop forwarding according to the transmission quality, and performs end-to-end statistics and recharge. Through cooperation of hierarchical synchronization and phase correction with cyclic prefix and FFT, crosstalk is suppressed, channel noise scale optimization soft decision is robust, data decoupling is controlled, and end-to-end availability is improved through multi-hop self-healing recharge.

The invention relates to the technical field of electric power communication, in particular to an electric power communication resource topological optimization method and system based on a graph database. The specific implementation process comprises the following steps: constructing an electric power communication resource topological graph comprising communication exchange nodes, data transmission links and attribute information through a graph database; analyzing and quantifying the received business type into a routing constraint condition according to a business service level protocol; performing dynamic weighting calculation on each candidate communication path meeting the routing constraint condition, evaluating comprehensive performance indexes and outputting an optimal communication path; and converting the optimal communication path into a topology configuration instruction capable of guiding network equipment to carry out data deployment, and carrying out dynamic optimization on the power communication resource topology. According to the method, the service quality requirements of different services are quantified into dynamically adjusted routing constraints and weights, so that reasonable allocation of power communication resources and effective optimization of network topology are realized, and the working efficiency of a power communication resource network is improved.

The invention discloses a multi-agent collaborative data exchange dynamic routing optimization method and system, and relates to the technical field of network communication, and the method comprises the steps: obtaining the real-time state information of each node in a network, and outputting a node state data set; constructing a historical state sequence of each node, calculating a load change trend, and outputting a routing adjustment signal when the load change trend reaches a load critical value; calculating a collaborative weight according to the network contribution degree of each agent, establishing a task allocation mapping relationship among the agents based on the collaborative weight, and outputting a collaborative scheduling result; determining a candidate path set, performing index evaluation on paths in the candidate path set, and outputting an optimal routing path; and executing data transmission, obtaining a quality difference between an actual transmission effect and an expected effect, and performing parameter correction. According to the invention, active optimization and multi-target balance of network routing are realized, and routing efficiency and system stability in a dynamic network environment are improved.

The embodiment of the invention discloses a multi-agent task collaboration method and device and a medium, and the method is characterized in that the method comprises the steps: registering functions corresponding to all agents to a dynamic service directory through an MCP protocol; disassembling the to-be-executed task to obtain a plurality of to-be-executed sub-tasks, matching each to-be-executed sub-task with the capability range of each agent in the dynamic service directory, and establishing a communication channel between each to-be-executed sub-task and the corresponding agent; performing dynamic routing distribution on the to-be-executed sub-tasks through the cooperative bus, and selecting an optimal execution node according to the real-time requirements of the sub-tasks and the node load state of each corresponding agent; according to a subtask dependency relationship defined by a directed acyclic graph, determining an execution mode of each subtask to be executed; and executing each to-be-executed sub-task at the optimal execution node based on the communication channel and the execution mode of each to-be-executed sub-task.

The invention discloses a multi-dimensional computing power dynamic perception routing decision-making method and system based on SRv6 driving. The method comprises the following steps: firstly, screening an optimal target computing power node based on a service demand and a node real-time load; and then, based on the dynamic network topological graph fusing the service sensitivity, calculating an optimal network path reaching the computing power node by using an improved SPFA algorithm, and realizing a global collaborative decision of selecting the most suitable computing point and searching the most efficient connection path. Meanwhile, based on an SRv6 driving service chain dynamic generation and closed loop execution method, a target computing power node and necessary network functions are abstracted into a programmable SID, an SID sequence (service chain) is dynamically constructed according to a double-stage decision result and is packaged in an SRH head, second-level path issuing and state monitoring are achieved through a programmable controller, and a real-time monitoring result is obtained. According to the method, the problems of poor real-time performance, single dimension, weak cooperative capability and the like in the prior art are solved, and efficient, stable and intelligent development of a future-oriented distributed intelligent computing network system can be promoted.

The invention discloses a large language model progressive field fine tuning and knowledge fusion method for shield engineering. The method comprises the following steps: constructing a layered shield training course containing a wide-area academic theory and a proprietary enterprise construction method; parallelly training a plurality of physically isolated parameter efficient adapters based on the frozen base; performing singular value decomposition on the adapter, extracting a geometric feature subspace representing knowledge distribution, and calculating a conflict correlation degree; based on this, a uniform adaptation mechanism of resource awareness is constructed. The mechanism not only can generate a static fusion model for conflict removal, but also can dynamically activate a specific rank slice of an adapter through a routing network based on real-time hardware resource budget (video memory / FLOPs) and geometry-resource signature. According to the method, multi-source knowledge is reserved, and adaptive dynamic scheduling of edge hardware resources by model reasoning is realized.

The systems and methods disclosed herein enable dynamic selection of a routing model for generation of an output in response to a provided input (e.g., a prompt for a large-language model). Based on the selected routing model, the data generation platform can evaluate the input and / or other suitable system parameters (e.g., system resource usage) to determine a suitable model for processing the provided input. For example, the routing model can determine a technical application associated with the input and dynamically determine to modify the input prior to generation of the output based on system resource measurement values and / or other suitable information, thereby conferring efficiency, security, and accuracy benefits while preserving system resilience.

The invention discloses a non-direct connection-oriented data routing method, which comprises the following steps of: S1, acquiring topological structures, link bandwidths, time delays, loads and energy consumption states of nodes in a network in real time, and constructing a time sequence dynamic matrix of the nodes and links; s2, on the basis of the dynamic matrix, adopting a neural network adaptive enhanced ant colony optimization method to generate alternative paths; s3, generating a grey wolf optimization algorithm initial population by using the alternative paths, and constructing a multi-dimensional composite fitness function; s4, according to the fitness function, driving the grey wolf optimization algorithm to perform multi-scale iteration to update the path; s5, topology and node state prediction is carried out based on the dominant path, and the prediction path is optimized in advance; and S6, issuing the optimal path and the alternative path at the same time, carrying out data parallel forwarding, and driving a neural enhanced ant colony optimization algorithm to update online. The method improves the network path selection efficiency and the resource utilization rate, and is suitable for data routing in a complex network environment.

Technologies for creating an optimized and accelerated network pipeline using a network pipeline abstraction layer (NPAL) for policy-based routing (PBR) over Service Function Chaining (SFC) are described. A DPU includes acceleration hardware engine to provide a single accelerated data plane. A processing device can generate a first virtual bridge and a second virtual bridge, the first virtual bridge to be controlled by a first network service hosted on the DPU and having a set of one or more network rules, and the second virtual bridge having a policy-based routing policy (PBR policy). The processing device can add the virtual port between the first virtual bridge and the second virtual bridge. The acceleration hardware engine, in the single accelerated data plane, can route network traffic data using the PBR policy and process the network traffic data using the set of one or more network rules.

The invention discloses an electric power communication multi-route intelligent planning method and device based on scene classification. The multi-route intelligent planning method in the electric power field comprises the steps that S1, multi-source heterogeneous data fusion processing is carried out; s2, classifying three-dimensional scene features; s3, dynamic routing modeling is carried out; s4, a hybrid intelligent optimization algorithm; and S5, real-time verification and feedback optimization are carried out. According to the multi-route intelligent planning method in the electric power field, the problems of static route planning, poor adaptability and insufficient multi-source data fusion capability in a traditional electric power communication network are solved, intelligent perception and dynamic adaptation to a complex communication environment are realized, and the multi-source data fusion capability is improved. And the network resource scheduling efficiency, the flexibility of the routing strategy and the overall service quality are improved.

The invention relates to a brain inspired data service system and a brain inspired data service method, which are characterized in that a layered architecture is constructed, the layered architecture comprises a data node cluster deployed in a mixed manner, the data node cluster specifically comprises a core node, an acquisition node and a gateway node, and the core node, the acquisition node and the gateway node are respectively used for processing core service data, real-time edge data and cross-domain connection; the small-world connection layer realizes same-domain efficient communication through high-clustering local connection, and breaks a data island through dynamic long-range cross-domain connection; the intelligent control layer integrates a DMN control center, a collaborative state monitoring engine, a prediction analysis module and a reinforcement learning driven resource scheduler. The system initializes a topological structure through a small-world network manager, and when the communication frequency of a cross-domain node exceeds a threshold value, long-range connection is dynamically inserted to ensure that the hop count of a cross-domain path is stably not higher than a preset value. The DMN control center realizes dual-mode switching based on a system load threshold; in an idle period, data pre-cleaning, knowledge graph construction, predictive pre-fetching and resource pre-allocation are executed; and optimizing routing in combination with a graph traversal algorithm in a task period.

The invention relates to the technical field of integrated circuits and computer networks, and particularly discloses a transmission control and intelligent scheduling system for an integrated chip, and the system sets a dual-mode decision and dynamic switching mechanism for each routing node. Calculating dynamic characteristic parameters including an instantaneous value, a first-order trend and a second-order acceleration; when the parameter is matched with a pre-stored abnormal feature set and the load exceeds a threshold value, the node is immediately atomized and switched to a predefined security scheduling strategy loaded from a shared storage area, and otherwise, the node generates a scheduling decision according to self-adaptive decision logic continuously optimized based on historical performance feedback; all the nodes carry out data packet forwarding control according to the current execution strategy; and the system also periodically realizes federated global knowledge evolution according to the quality evaluation result of the self-adaptive decision of each node.

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 relates to the technical field of adaptive routing optimization, and particularly discloses a topology self-recognition and routing optimization method and system for a concentrator and a collector, and the method comprises the steps: carrying out the topology modeling of a multi-layer heterogeneous network; executing high-speed link state monitoring based on the initial topological graph; global loop detection and link optimization are completed based on the link state matrix; a weighted robust multi-dimensional path selection method is adopted to configure a main path and a standby path for each node; and monitoring the path state in real time and dynamically switching. In the prior art, a static route, a single protocol path or a fixed main and standby path is mainly adopted, and especially in a cross-protocol, cross-level and dynamic multi-node environment in a smart city, rapid path switching and high-reliability route redundancy guarantee cannot be realized. Due to the fact that the multi-dimensional path selection method and the cross-level and cross-protocol self-adaptive switching strategy are adopted, the problems of routing interruption and performance reduction are avoided, and the reliability of the whole network is improved.

The invention discloses a transmission path selection method and device based on reinforcement learning and electronic equipment, and relates to the technical field of big data, and the method comprises the following steps: obtaining node state information and link state information transmitted by each node in a target network topology, generating a routing selection action based on the node state information and the link state information, and sending the routing selection action to the target network topology; based on the node state information, the link state information and the corresponding route selection action, updating a target Q value table by adopting a reinforcement learning agent center; after a new route transmission request is received, based on the updated target Q value table, the reinforcement learning proxy center is adopted to select a target data transmission path for the current network node, and the target data transmission path is used for completing route transmission of a data packet in the new route transmission request. The technical problem that a static routing algorithm in the prior art lacks flexibility and cannot be adjusted in real time according to network topology and flow change is solved.

The invention relates to the technical field of computers, and discloses a method and system for dynamically routing a back-end address by an API gateway, and the method comprises the steps: receiving an HTTP request, and analyzing a URL and a request header; extracting a tenant identifier, and supporting multi-mode identification of a request header, a sub-domain name or a path segment; loading an exclusive routing rule set based on the tenant identifier; sorting according to local priorities in the tenants, executing longest path prefix matching through a prefix tree, and determining an optimal back-end address; and finally forwarding the request. The system comprises a request analysis module, a tenant identification module, a rule loading module, a local sorting module, a longest matching module and a request forwarding module, and supports routing rule hot update and conflict detection. According to the method, cross-tenant interference is effectively eliminated through tenant context awareness and a two-stage matching mechanism, and the routing accuracy and the system isolation are improved.

The invention relates to the technical field of project management, in particular to a large-model batch reasoning and data flow optimization system oriented to an MOE architecture. According to the method, a collaborative architecture of the request access module, the environment sensing module, the expert routing engine, the resource scheduling module and the dynamic optimization control module is set, the text length and the subject type are extracted by using the request access module, a basis is provided for accurate routing, and the GPU video memory, the I / O bandwidth and the request queue depth are acquired in real time through the environment sensing module, so that the real-time routing is realized. The system load is comprehensively monitored, meanwhile, an expert sub-network is activated through an expert routing engine according to request features, invalid calculation is avoided, weight loading and resource allocation are managed through a resource scheduling module, the I / O bottleneck is reduced, and finally an optimization strategy is intelligently triggered through a dynamic optimization control module based on routing conflict factors. The problems of large reasoning delay fluctuation and unbalanced resource utilization rate mentioned in the background technology are solved, and stable low-delay response and resource collaborative optimization in a high-concurrency scene is realized.

The invention provides a hierarchical deep reinforcement learning routing protocol method based on a multi-link ad hoc network, relates to the technical field of communication routing, and solves the problem that the calculation efficiency in the current multi-link ad hoc network is degraded. The method comprises the following steps: firstly, constructing and dynamically updating a global network view for all nodes in a network, and further constructing a route selection model which is divided into a high-level controller and a low-level controller; a high-level controller plans a route from a global perspective and balances a global load, and a low-level controller selects a target node according to a planning result of the high-level controller and distributes an optimal communication link for the target node in combination with link state information; training and optimization of the network of each controller are carried out independently, external rewards are introduced to a high-level controller, and internal rewards are introduced to a low-level controller; the external reward is used for optimizing the overall performance of the high-level controller; the internal reward is used as an index value of the low-level controller for feeding back the link state in real time, and collaborative optimization of the high-level controller and the low-level controller is achieved.

The invention discloses a high-dynamic mobile ad hoc network communication and sensing integrated routing method, device and equipment and a storage medium, an OFDM (Orthogonal Frequency Division Multiplexing) sensing system is adopted to actively acquire feature information of neighbor nodes, and meanwhile, neighbor features passively acquired based on communication feedback are matched and fused, so that the sensing precision and efficiency in an ad hoc network are remarkably improved. In order to reduce noise interference of a wireless communication environment and realize prediction of a future state of a network, a Kalman filter is adopted to carry out dynamic filtering and state estimation on neighbor feature information. And on the basis of the processed sensing data, the node further calculates stability and effectiveness indexes of the link, inputs the stability and effectiveness indexes into a link quality comprehensive evaluation model constructed by fuzzy logic, and finally generates predictive link quality parameters to provide priori knowledge for following routing selection. And the routing efficiency and the resource utilization are optimized through an on-demand routing normal form and a multipath routing mechanism. And meanwhile, through a node early warning mechanism and a fault pre-detection mechanism, the control overhead is remarkably reduced, and the network stability is improved.

The invention discloses a source network load storage route adjusting method based on an AI control strategy, and relates to the technical field of power system control, and the method comprises the steps: collecting a source side signal, a network side signal, a load side signal, a storage side signal and an exogenous signal, and generating a unified system state of confidence weight and topology embedding; the state is used for digital twinning online calibration, a feasible range and a constraint set are extracted, and a projection operator and guaranteed operation curve set is generated; generating a unified energy routing vector in the feasible range by a hierarchical strategy; performing simulation checking calculation in the digital twinning and compiling into a final instruction and execution plan table; performing closed-loop issuing and receipt checking, and performing event write-back to update guardrails and strategies; the method realizes collaboration, improves on-site consumption of new energy, reduces comprehensive cost, enhances boundary compliance and traceability, is suitable for parks, micro-grids and the like, and is convenient for deployment, operation, maintenance, expansion and upgrading.

The invention discloses an MQTT message transmission optimization method and system. The method comprises the steps of analyzing a theme, extracting a device type, a data feature and a geographic position triple, calculating a hash value, and mapping a device to a specified Broker fragment cluster node to generate a fragment mapping table; processing the equipment data in the edge domain in the fragment mapping table through an edge calculation layer, removing invalid data according to a preset rule, merging the equipment data in the same fragment node, and embedding a fragment node ID for an aggregation message generated after merging; identifying a fragment node ID and routing to a target fragment node, positioning a corresponding shared memory pool, and writing the message into the shared memory pool; and responding to a direct access request of the client, so that the client directly accesses the data from the shared memory pool through the user mode network stack. According to the method, the problem of uneven load is effectively solved, multiple times of state switching in the data transmission process is avoided, and the MQTT message transmission efficiency is improved while the transmission cost is reduced.