271 results about "Network intelligence" patented technology

The invention provides a network topology intelligent generation method and system based on deep learning and topology analysis, and relates to the technical network intelligence field, and the method comprises the steps: obtaining historical topology data, carrying out the feature extraction based on time sequence division, constructing a graph neural network model, training, and evaluating the evolution trend and stability of a network topology structure. And constructing a deep reinforcement learning model to generate an optimization strategy, and carrying out iterative optimization until a network topology structure meeting requirements is generated. The network structure can be adaptively optimized, the network performance is improved, the operation and maintenance cost is reduced, and the network stability is enhanced.

The invention discloses an intelligent operation and maintenance management and control system for an information communication network, and belongs to the technical field of communication. The system comprises the following modules: an intelligent sensing and data acquisition module which is responsible for real-time acquisition of multi-source network data, dynamic monitoring of cross-domain equipment states and conversion of heterogeneous data into a unified standard; the network intelligent analysis module integrates network performance prediction, fault risk assessment, abnormal behavior identification and security threat monitoring, and provides comprehensive network operation and maintenance insight; the intelligent resource scheduling module is used for realizing dynamic allocation of cross-domain network resources and ensuring efficient and safe utilization of the resources through multi-dimensional optimization and a real-time load balancing strategy; the intelligent decision support module is used for continuously optimizing an operation and maintenance strategy based on machine learning, analyzing a long-term operation trend and providing suggestions for management decisions; and the open integration and collaboration module provides integration of an open API interface and a third-party system, supports cross-domain collaboration management, and improves the flexibility and expansibility of the system.

The invention relates to the field of water quality pollution traceability, in particular to a water quality pollution traceability analysis method and system based on an intelligent unmanned ship. The method comprises the following steps: acquiring a multi-mode network monitoring parameter of a real-time position based on a multi-mode communication module, performing a dynamic multi-mode network intelligent switching decision, and constructing an intelligent multi-mode network switching strategy; real-time water area multi-dimensional water quality monitoring parameters of the intelligent unmanned ship are obtained according to the multi-dimensional water quality sensor, abnormal pollution parameter change detection is carried out, and abnormal change pollution parameters are marked; performing reverse water quality spatial change analysis and gradient descent iterative traceability calculation on the abnormal change pollution parameters to generate a potential pollution diffusion path; and performing intelligent traceability cruise according to the potential pollution diffusion path, and tracking and positioning the polluted water source to obtain a polluted water source point. According to the invention, efficient and accurate water quality pollution traceability is realized.

The invention relates to the technical field of 5G networks, in particular to a 5G network intelligent optimization method and system, and the method comprises the steps: obtaining 5G network signal data, recognizing an abnormal power spectral density region, carrying out the filtering extraction of the abnormal power spectral density region, and separating interference signals, interference types including narrowband interference, broadband interference and directional interference; extracting features from the obtained interference signals, and performing interference type identification according to a random forest algorithm; starting a corresponding anti-interference means according to the interference type, and generating operation state data in real time; a bee colony algorithm is adopted to simulate bee behaviors for iterative search, and a local optimal resource scheduling strategy is determined; and executing the resource scheduling strategy, feeding back an execution effect, and restarting the bee colony algorithm to determine a new resource scheduling strategy if the execution effect does not reach a set expectation. Therefore, the problems of lack of dynamic adaptive adjustment, single anti-interference means, lack of cross-base station collaboration and the like in the anti-interference aspect in the prior art are solved.

The invention relates to a distributed large two-layer network intelligent routing method and system based on edge cloud nodes, and belongs to the technical field of computer networks. Aiming at the problems of slow routing decision response and low fault recovery efficiency of the existing network in a dynamic environment, the invention provides a method for periodically collecting link bandwidth utilization rate, transmission delay and other state information through an edge cloud node, uploading the state information to a central cloud platform to generate a global routing strategy and issuing the global routing strategy to the edge node, and triggering fault switching in combination with local dynamic threshold monitoring. According to the technical scheme, bidirectional communication between edge nodes and a central platform, a neural network model of spatial-temporal feature fusion and a path priority generation mechanism of multi-objective optimization are included. The method and the system are suitable for a large-scale distributed network environment, and network resource utilization rate and service continuity can be improved.

The invention relates to the technical field of power network resource management, and discloses a power network resource intelligent management method and system based on 6G and deep learning. The method comprises the following steps: acquiring node data through a 6G technology to obtain a topological mapping table, preprocessing the data to obtain a feature tensor, training a model cluster by adopting a space-time diagram convolution and long-short-term memory network hybrid architecture, analyzing a network state based on a model, generating a scheduling scheme, and issuing a control instruction through 6G network fragmentation to realize intelligent management of a power network. According to the invention, through the combination of the 6G communication positioning integration technology and the space-time diagram convolutional network and long-short term memory network mixed architecture, the real-time data acquisition precision and intelligent analysis capability of the power network are improved. Based on a collaborative optimization mechanism of multi-source data fusion and a deep learning model cluster, the comprehensiveness of power grid state evaluation and the accuracy of resource scheduling are enhanced.

The invention relates to the technical field of wireless communication, in particular to a wireless communication network system and method for intelligently fusing a multi-mode heterogeneous network. Comprising a multi-mode access unit; a protocol compatibility and conversion unit; the heterogeneous network intelligent fusion unit completes cross-network dynamic flow collaboration and resource optimization decision through cross-layer feature association mining, service-network two-dimensional weight calibration and a double-closed-loop feedback optimization mechanism, generates a resource scheduling instruction and transmits the resource scheduling instruction to the intelligent scheduling and resource control unit; an intelligent scheduling and resource control unit; and a system control and cooperation unit. According to the invention, the service-network two-dimensional dynamic fusion weight is constructed through the intelligent fusion unit of the heterogeneous network, and bandwidth, frequency spectrum and power resources are allocated according to the priority and margin balance principle in combination with the intelligent scheduling unit, so that the problem of insufficient two-dimensional coordination of resource scheduling is solved, the resource scheduling is matched with the service demand and the network load, and the resource scheduling efficiency is improved. And resource waste or incapability of meeting business requirements is avoided.

The invention relates to the field of legal service management, and discloses a legal service management system based on a block chain, and the system comprises the following modules: a block chain network, an intelligent contract module, a data storage module, a user interface module, a feedback and monitoring module and a user management module, the non-tampering property and the transparency are ensured; the intelligent contract module automatically executes contract terms and supports intelligent management; the data storage module adopts a hierarchical structure, and on-chain data and off-chain data are stored in a distinguished manner, so that the storage and access efficiency is optimized; and the user interface module provides user-friendly operation experience through a visual tool. By using the block chain technology, all contract execution and compliance audit data are encrypted and stored in a distributed mode, it is ensured that tampering is avoided and whole-course traceability is achieved, and a user and related mechanisms can verify the authenticity and integrity of the contract data at any time.

The invention relates to the technical field of network resource scheduling, and provides a heterogeneous network intelligent slice resource scheduling method and device, electronic equipment and a medium. The method comprises the following steps: performing three-dimensional joint modeling on heterogeneous network physical resources to obtain a dynamic resource state tensor, and performing a dynamic resource allocation decision by combining a deep reinforcement learning model with a service demand matrix to obtain an initial slice resource allocation matrix; and performing mixed integer linear programming through a preemption decision model in combination with the URLLC service request queue to obtain a resource allocation sequence, and performing occupancy rate updating on the dynamic resource state tensor according to the resource allocation sequence to obtain ground network resource data, and carrying out resource fusion in combination with the satellite beam capacity data to obtain a space-ground integrated resource pool tensor, thereby carrying out traffic prediction in combination with historical traffic data through a long-short memory network to obtain a bandwidth pre-adjustment strategy. Through organic integration of technologies such as mixed integer linear programming and traffic prediction, the overall network resource utilization rate is improved.

The invention discloses an urban energy network intelligent allocation method and system, and the method comprises the steps: obtaining building energy consumption data and energy supply network topology information, building a supply and demand matching reference, recognizing a load peak value dense time period and a peak clipping and valley filling potential region, and exporting transferable load data to form a regional supply and demand distribution diagram; generating a peak shifting adjustment space in combination with the user response delay duration and the equipment start-stop period; performing gap matching analysis on the regional supply and demand distribution map and the peak shifting adjustment space, creating a hierarchical regulation instruction set, and differentially generating an instant response instruction and a delay response instruction; a regulation and control coordination parameter is formed through coordination configuration of the peak shifting revenue coefficient and the instant response instruction; finally, a regulation and control execution range is determined, cost benefit evaluation is implemented, an urban-level energy scheduling execution scheme is formed, accurate matching and efficient scheduling of urban energy supply and demand are achieved, and comprehensive and efficient scheduling decision support can be provided for application scenes such as an intelligent power grid, regional heat supply and comprehensive energy service.

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 a data processing method and device for information data traceability analysis. The method comprises the following steps: acquiring basic network information data information; the basic network intelligence data information comprises H pieces of first network intelligence data information; performing classification processing on the basic network intelligence data information to obtain target classification intelligence information; the target classification intelligence information comprises M pieces of target network intelligence data information; each piece of target network intelligence data information comprises N pieces of target category intelligence information; each piece of target category intelligence information comprises L pieces of target intelligence data information; the target intelligence data information corresponds to the first network intelligence data information; l is an integer not less than 0; and analyzing and processing the target classified intelligence information to obtain target processing intelligence information.

The invention discloses a large model fine tuning training method, device and equipment, a storage medium and a product, and relates to the technical field of network intelligence, and the method comprises the steps: inputting a label data set into a large model through end side equipment for forward propagation calculation, and transmitting an output activation value between each Trm layer to auxiliary computing power node equipment; and the auxiliary computing power node equipment fuses the output activation value with the output of each layer in the bypass network adapter to execute forward propagation calculation, and the end side equipment fuses the target output with the output of the last Trm layer to obtain model output. And determining a loss value according to the model output and the local tag data set, so that the auxiliary computing power node equipment performs back propagation parameter updating according to the loss value. Therefore, a backbone network passing through the end-side device is avoided, the total memory overhead in the training process is reduced, the memory pressure of the end-side device is effectively migrated to the auxiliary computing power node, and the end-side device with the limited memory efficiently executes a large-model fine tuning task.

The invention discloses a 6G network intelligent collaborative dynamic flow identifier distribution method and system, belongs to the technical field of mobile communication, and aims to solve the technical problem of how to realize intelligent collaborative dynamic flow identifier distribution. Comprising the following steps: customizing a flow identification feature dimension through a management layer; an identification template and an identification strategy are self-defined through the management layer, an identification template instance is created, and the identification strategy and the identification template instance are issued to the decision-making layer; the method comprises the following steps: collecting network situation data through a decision layer, selecting related data from the network situation data based on a selected flow identifier feature dimension to carry out network overall evaluation to obtain a network evaluation result, reading each part field of an identifier from an identifier template instance based on the network evaluation result and an identifier strategy, dynamically generating a flow unique identifier, and carrying out flow overall evaluation on the flow unique identifier. Issuing the generated flow identifier to an execution layer; the flow identifier is analyzed through the execution layer, mapping from the identifier to the physical resource is realized, and resource configuration is dynamically adjusted.

The invention discloses a federated learning-based satellite communication and edge network intelligent sensing method, which comprises the following steps of: 1, acquiring network edge environment, target and communication data, and preprocessing the data; 2, edge federated learning model training: performing fine tuning on pre-trained global model parameters at edge nodes by using local data, and transmitting the parameters to a base station through satellite or wireless communication for aggregation to obtain a new version; and 3, a non-orthogonal transmission and air computing technology design scheme is used for model parameter transmission of air computing. And 4, unloading a part of tasks to a base station, an edge server or a satellite according to the perception task complexity and time delay, realizing multi-dimensional calculation in the same resource block, improving the resource utilization rate and supporting satellite-ground cooperation. And 5, designing a joint beam forming algorithm to coordinate communication, perceive and calculate interference. And 6, based on network and service characteristics, the edge control center intelligently arranges services and coordinates ground and satellite network resources. And comprehensive acquisition of environment information, target states and communication data is realized.

The invention discloses a method and a device in a node used for wireless communication and artificial intelligence. A node receives first configuration information, the first configuration information indicating a first RS resource group, the first RS resource group comprising at least one uplink RS resource; then sending first report information; measuring the at least one uplink RS resource; calculating a first parameter; the calculation of the first parameter depends on the measurement for the uplink RS resource and depends on the first reporting information; the first report information is obtained through reasoning, and the first configuration information is associated to the reasoning. According to the invention, the performance of the AI / ML model related to the first configuration information is monitored, so that the reliability of the AI / ML model is ensured, and the network intelligence level is improved.

The invention discloses an electric power safety monitoring system, and relates to the technical field of safety monitoring, and the system comprises a multi-dimensional data collection module which is used for collecting various types of operation data in an electric power system, obtaining equipment identification information, and constructing an electric power operation basic network based on the operation data and the equipment identification information; and the intelligent analysis and modeling module is connected with the multi-dimensional data acquisition module, and is used for analyzing and processing the acquired operation data, constructing a virtual analysis model of the power system, establishing and dynamically adjusting a mapping relation between the virtual analysis model and the power operation basic network, and performing trend prediction and fault identification at the same time. According to the system and the method, various types of intelligent sensors are deployed in the multi-dimensional data acquisition module, the operation data of each node of the power system are comprehensively acquired and are bound with the equipment identification information, the problems of single traditional data acquisition means and incomplete data are solved, and a rich and accurate data basis is provided for subsequent in-depth analysis.

The invention provides an intelligent agile networking method and device for a large emergency wireless communication network. The method comprises the following steps: designing an agent learning environment Env containing wireless communication network node initial position distribution, environment constraint and target constraint based on orthogonal reward multi-agent depth deterministic policy gradient reinforcement learning (OTR-MADDPG) of topology perception and road network optimization; designing an improved action space based on road network search for the MOVBS intelligent agent, and designing an updating mode of an improved strategy network pi based on state input of neighbor nodes in a connected branch; designing an access / rejection action space based on an LEO satellite for the LEO intelligent agent, and designing an updating mode of a strategy network # imgabs0 # based on an MOVBS coordinate-overhead satellite state pair; a reward function # imgabs1 # is designed; a learning environment Env, an action generation mode of an intelligent agent, a strategy network updating mode and a reward function R are integrated into an existing framework of MADDPG. According to the invention, the problems of long topology construction time and poor node collaboration of the emergency communication network are effectively solved.

A method performed by an E2 node is provided. The method includes receiving, from a Radio Access Network (RAN) Intelligent Controller (RIC), an RIC subscription request message through an E2 interface. The RIC subscription request message may include information indicating a network interface type.

The invention provides a computer network intelligent security protection system based on big data, which relates to the field of network security protection and comprises a data acquisition layer, a data storage layer, an analysis processing layer, a decision response layer and a visual display layer. The data acquisition layer is electrically connected with the data storage layer; the data storage layer is electrically connected with the analysis processing layer; the analysis processing layer is electrically connected with the decision response layer; the decision response layer is electrically connected with the visual display layer; an execution result of the decision response layer can be transmitted back to the data storage layer to form iterative optimization, response effect data is transmitted back to the data storage layer through a closed-loop feedback mechanism of the decision execution result, a machine learning model and a threat detection rule are continuously optimized, and a detection-response-optimization iterative closed loop is formed; for novel variant attacks, the system can quickly complete feature extraction and strategy updating, and the problem that significant limitation exists when unknown threats are dealt with is solved.

According to the multi-source threat detection method based on the hybrid expert model, real-time collection and structured processing of network flow, system logs and user behavior data are achieved through a multi-mode intelligent collection engine, and high-quality multi-source input is provided for upper-layer analysis; the double-branch feature extractor carries out deep analysis on the network flow time sequence mode and the log semantic context to generate fine-grained feature vectors; the hybrid expert reasoning framework is based on expert models in three fields of a dynamic routing gating network, intelligent scheduling network behaviors, log semantics and user portraits, combines space-time alignment features through a cross-modal attention mechanism, and constructs an interpretable attack evidence chain in combination with a causal reasoning engine. Finally, a full-link closed loop from multi-modal data acquisition, feature collaborative extraction and intelligent threat reasoning is realized, and while millisecond-level real-time response is ensured, the complex internal threat detection accuracy is obviously improved.

The invention discloses a carbon emission checking method and system based on a multi-mode inversion model, and relates to the technical field of carbon emission checking. Air-based monitoring, foundation monitoring, mobile monitoring and maneuvering monitoring are adopted to form a three-dimensional monitoring network; the intelligent inversion calculation layer fuses global background concentration and regional real-time monitoring data, and adopts a GSI 4DVar algorithm to optimize a three-dimensional concentration field; in the first inversion stage, random disturbance is applied to the prior emission list, multiple emission sets are generated, each emission set corresponds to a grid area of a three-dimensional concentration field, and concentration distribution is simulated through a WRF-CMAQ model; in the second inversion stage, an EnSRF algorithm is adopted to assimilate measured data, and emission parameters are iteratively optimized; according to the invention, various monitoring means, advanced algorithms and data analysis methods are organically integrated, and automation and intelligentization of the whole process from data acquisition, processing and analysis to abnormity determination are realized. The method provides efficient and scientific technical support for carbon emission supervision, and is helpful for promoting the realization of carbon emission fine management and energy conservation and emission reduction targets.

A system for implementing an open cloud (O-Cloud) optimization policy by an application hosted in a near real-time radio access network Intelligent Controller (nRT-RIC) of a telecommunications network. The system includes a memory storing instructions; and at least one processor configured to implement the nRT-RIC within an open radio access network (O-RAN) architecture, the at least one processor configured to execute the instructions to: receive the O-Cloud optimization policy from a non-real-time radio access network Intelligent Controller (NRT-RIC) within a Service Management and Orchestration (SMO) framework of the telecommunications network; control to implement the O-Cloud optimization policy in the O-Cloud computing environment within the O-RAN.

The invention provides a digital twin enabling air-ground integrated dual power supply network supply-demand game method and system, and belongs to the technical field of intelligent management and control of energy and communication convergence networks. Comprising the following steps: constructing a digital twin system of an energy-communication fusion network, and accessing and mapping dynamic data from an energy network and a communication network in real time; based on the dynamic data, an energy side observation space and a communication side observation space are defined in the digital twin system, and the comprehensive supply and demand imbalance degree of the system is calculated; the energy side intelligent agent and the communication side intelligent agent output control instructions in a continuous action space according to respective observation spaces, and respectively aim at maximizing own long-term accumulated benefits; a final solution of the coordinated game framework is an optimal strategy combination meeting a Nash equilibrium condition; carrying out centralized training and distributed execution on the coordinated game framework by adopting a multi-agent depth deterministic strategy gradient algorithm based on supply and demand perception; and the intelligent agent carries out collaborative decision making based on the global supply-demand imbalance degree and local observation.

The invention discloses a wireless communication network intelligent optimization method and system based on neuron collaboration, and relates to the technical field of wireless communication. The method comprises the following steps: mapping a communication node into a bottom layer sensing neuron and constructing a neural state variable set; constructing a node neural situation function based on the variables; when the local threshold value is exceeded, excitation pulses are generated and uploaded to middle-layer convergence neurons; the middle layer carries out pulse space aggregation and extreme value search, and outputs a selection strategy and an adjustment strategy; and reporting to a top layer to execute whole network neural synchronization index analysis, and optimizing the wireless communication network. The technical problems of low spectrum resource utilization rate and unstable network performance caused by the fact that a traditional wireless communication network cannot realize high-efficiency spectrum allocation and dynamic topology reconstruction under user mobility change are solved, and the purposes of realizing local quick response and global collaborative optimization by constructing a layered neural network and improving the network performance are achieved. And the spectrum resource utilization rate is improved, and the network dynamic adaptive capability is enhanced, so that the user service quality is guaranteed.

The described technology is generally directed towards adaptive spectrum as a service, in which spectrum can be dynamically allocated to adapt to demand for wireless capacity. The demand for wireless capacity can be based on monitoring system state, and / or proactively predicted based on other system state such as time of day. Reallocated spectrum can be monitored for performance, to converge spectrum allocation to a more optimal state. Allocated spectrum can be relocated, increased or decreased, including by the use of citizens band radio service spectrum or other spectrum. Currently allocated spectrum can be adapted into modified allocated spectrum by an application program (xApp) coupled to a radio access network intelligent controller (RIC), a citizens broadband radio service device, a domain proxy service, and / or a user device. A user device can determine and request bandwidth, with spectrum allocated in response.

The invention discloses an optical fiber network intelligent regular checking and fault closed-loop processing system, method and device, and belongs to the technical field of optical fiber communication network intelligent operation and maintenance. The method comprises the following steps: collecting and fusing multi-source heterogeneous data such as resource management, fault network management, distributed sensing and geographic information; a multi-modal model based on a convolutional neural network and a graph neural network is utilized to analyze fusion data, meter-scale accurate positioning of faults is achieved, and a regular inspection plan is dynamically optimized by adopting a long and short-term memory network model; and based on an analysis decision result, a fault list, a mode list and a maintenance list are automatically created, circulated and associated, and a whole-process closed loop from diagnosis list sending to repair verification is realized. According to the scheme, the problems of data dispersion, single analysis dimension and lack of closed loop in the process in the prior art are solved, and the accuracy, efficiency and automation level of optical fiber network operation and maintenance are remarkably improved.

The invention provides a communication network fault prediction method and system based on deep learning, and relates to the field of communication network intelligent operation and maintaining.The method comprises the steps that equipment state parameters, a traffic matrix, environment sensor data and topological relation data of a communication network are obtained in real time, graph neural network interpolation is carried out based on a topological graph, and a fault prediction result is obtained; filtering abnormal values by adopting a long-short-term memory auto-encoder, and aligning heterogeneous data time sequences through dynamic time warping to obtain cleaned communication network data; inputting the cleaned communication network data into a space-time double-flow deep learning model, extracting topological correlation characteristics and time sequence evolution characteristics of equipment, and generating a fault probability matrix through a gating fusion unit; and calculating a node risk value based on a network topological graph and the fault probability matrix, and identifying a high-risk node and a fault propagation path in combination with a preset dynamic threshold to obtain a communication network fault prediction result, so that the accuracy and timeliness of network fault prediction can be effectively improved.

The invention provides a collaborative management method and system for a digital twinning and optical transport network management and control system, and the system comprises a multidirectional interface architecture of a physical layer southbound interface gateway, an application layer northbound interface gateway and a digital twinning east-west interface gateway, and supports data interaction and instruction transmission among a physical layer, an application layer and a digital twinning system. The method covers acquisition and processing of real-time data streams, full life cycle management of digital twin, simulation deduction and decision optimization, asynchronous communication and identity verification and authority management. According to the method, closed-loop control from user intention definition, simulation deduction, configuration issuing, real-time monitoring to optimization adjustment is realized through a multidirectional interface coordination mechanism, the coordination efficiency and control precision between systems are remarkably improved, the requirements of an optical transport network for real-time, dynamic and high-concurrency data processing are met, and the method is suitable for popularization and application. Cooperative operation in a multi-manufacturer and multi-device environment is supported, and an efficient and flexible solution is provided for intelligent management and optimization of the optical transmission network.

The present invention relates to the field of computer networks, and provides a generalized network intelligent optimization method and a generalized network intelligent optimization terminal. The generalized network intelligent optimization method comprises: acquiring a domain name access request and a web request initiated by a user network device; on the basis of a target domain name, a custom upstream host, and a pre-established DNS set, determining an optimal DNS server, and analyzing the target domain name by means of the optimal DNS server to obtain a target IP address; acquiring performance parameters of each network address translation (NAT) server in a NAT server cluster; on the basis of the performance parameters of each NAT server, determining an optimal NAT server, and, by means of the optimal NAT server and on the basis of the target IP address, responding to the web request by using full-cone NAT technology; and controlling the transmission rate of the user network device by using a BBR algorithm. According to the present invention, domain name resolution speed is increased, and network transmission efficiency is improved.