180 results about "Network intelligence" patented technology

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 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 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.

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 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 discloses an IPv4 / IPv6 dual-stack network intelligent operation and maintenance management method and system based on AI, and relates to the field of AI. The method comprises the steps that through full-link multi-source data collection and feature processing of network equipment, terminals, protocols and services, a structured data set is constructed; real-time evaluation and visual monitoring of the network state are realized by using a multi-task AI model; once an abnormality is found, a fault node and a propagation path are accurately positioned through a fault diagnosis model; integrating various model outputs to automatically generate and execute a targeted operation and maintenance strategy; and on-line iterative optimization of the AI model and the knowledge base is driven through continuous effect evaluation and data feedback, so that the dual-stack network intelligent operation and maintenance capability of autonomous circulation is formed. The method has the advantages that AI enabling and dual-stack adaptation are focused, full-link multi-source data acquisition is taken as support, accurate evaluation, rapid diagnosis and intelligent decision making are realized through a multi-task fusion AI model, the operation and maintenance efficiency and the network stability are improved, and the labor cost is reduced.

The invention relates to the field of communication, and discloses an intelligent traffic scheduling optimization method, device and equipment and a storage medium, and the method is used for 5G network intelligent traffic scheduling optimization. The method comprises the following steps: receiving a service request, analyzing the service request to obtain a 5G standard service type, an application scene and a 5G network slice identifier, then obtaining an SLA parameter corresponding to the service request and a candidate link required by service transmission, and converting the SLA parameter into an SLA preference vector; acquiring global data of the candidate link, and predicting by using the network state prediction model to obtain a predicted network state of the candidate link; the SLA preference vector is used as optimization target guidance, the predicted network state is used as a constraint condition, and a global optimization routing strategy is calculated through a multi-target optimization algorithm; and generating a control message packet based on the global optimization routing strategy, issuing the control message packet to the network forwarding equipment, and adjusting parameters of the network state prediction model and the multi-objective optimization algorithm based on the actual performance data after execution.

The invention provides an ad hoc network and 5G network intelligent fusion handheld terminal and a switching strategy method. The handheld terminal comprises an ad hoc network module, a 5G module, a network detection module, a service extraction module, a power management module and a switching decision module. The ad hoc network module provides an ad hoc network communication link; the 5G module provides a 5G communication link; the network detection module periodically monitors the channel quality of the 5G link and the ad hoc network link; the service demand extraction module is used for determining whether to perform channel switching immediately or not according to the influence degree of the channel switching on the application for different service types; the power management module monitors the residual electric quantity of the equipment in real time; and the switching decision module automatically selects to switch an ad hoc network or a 5G channel according to factors such as link quality, battery power and service types. According to the invention, seamless, efficient and intelligent switching between the ad hoc network and the 5G network is realized through the ad hoc network + 5G fusion design and the switching strategy, and the communication effect and the user experience of the handheld terminal are improved.

Embodiments are directed towards systems and methods for carrier aggregation and dual connectivity switching in a cellular network (e.g., a 5G network). Example embodiments include systems and methods for: the RAN functions supporting providing measuring and reporting particular items for dynamic carrier aggregation and dual connectivity switching; RAN dynamic carrier aggregation and dual connectivity switching with a network intelligence layer; RAN dynamic carrier aggregation and dual connectivity switching without a network intelligence layer; RAN dynamic carrier aggregation and dual connectivity switching based on availability of an inter-DU link and meeting latency / bandwidth criteria; RAN dynamic carrier aggregation and dual connectivity switching based on availability of an inter-DU link and meeting resource criteria; and prioritization for using CA instead of DC based on CQI information reported from UEs.

The invention relates to the technical field of network operation and maintenance, in particular to a network health automatic diagnosis and closed-loop repair method and related products, and the method comprises the steps: establishing a dynamic health baseline; abnormal deviation of the real-time operation data is recognized; predicting the risk probability that the network equipment has an operation fault in the future; selecting and executing a corresponding repairing action; verifying whether the abnormal deviation is eliminated; if the abnormal deviation is not eliminated, continuing to execute repair until the abnormal deviation is eliminated or the maximum repair frequency is reached; according to the method, the accuracy and the foresight of network fault diagnosis are improved by deeply fusing the artificial intelligence algorithm into a network operation and maintenance scene, and the intelligent and autonomous management of the network is realized by introducing a closed-loop repair mechanism with autonomous learning and decision-making capabilities. And the reliability, the stability and the self-repairing capability of the whole network infrastructure are enhanced.

The invention provides a computing power network intelligent scheduling method, system, device and medium, and belongs to the technical field of computing power resource management.The modules of the intelligent scheduling system work cooperatively, an access strategy and a service path are defined firstly, and computing, storage and algorithm resources are accessed to form a computing power resource pool; on the basis of real-time service requirements, SRv6 programming capability is utilized to generate a scheduling strategy, and traffic is scheduled to a corresponding resource pool; then, independent computing power network slices are created according to a strategy, transmission is maintained, isolation is established between the slices, meanwhile, data flow performance is monitored, and intelligent operation and maintenance are started and optimization suggestions are output when the data flow performance is abnormal. According to the invention, efficient integration and scheduling of computing power resources are realized, and services are guaranteed to allocate resources according to needs; through a network slicing and isolation mechanism, the service security and independence are improved; the fault influence is reduced through real-time monitoring and intelligent operation and maintenance, the system performance is optimized, and the computing power network utilization efficiency and the service quality are improved.

According to the wireless network intelligent optimization deployment method and system disclosed by the invention, the network deployment strategy is issued to the edge node, so that the calculation amount of the integrated controller is reduced, and the optimization efficiency of the network deployment strategy is improved; besides, strategy chromosomes are evolved through a double-variation mechanism, and a dynamically changing gene mutation probability and an environment evolution range are introduced, so that the algorithm can quickly jump out of a local optimal solution, dynamically track and adapt to the change of a network environment, and the convergence speed of the evolutionary algorithm is remarkably improved; and finally, analyzing the strategy chromosome according to the network state data, and determining an optimized deployment strategy. According to the method, the overall overhead and delay of a network deployment strategy are remarkably reduced, and the efficiency is improved.

The invention discloses an information fusion and reasoning method and system based on multi-agent collaborative networking search, and relates to the technical field of network information, and the method comprises the steps: a deep research strategy scheduling agent deconstructs an unstructured request through a task formalization mechanism, generates a high-dimensional instruction code, and dynamically optimizes an execution path. And the network intelligence index generation agent extracts and screens the trusted URL by using a rule engine and a semantic evaluator based on the code, and generates a structured link instruction stream. The structured analysis intelligent agent implements multi-level semantic distillation and conversion on an original document to obtain key value pair representations, and the key value pair representations are packaged into machine readable data. And generating a comprehensive intelligence abstract with consistent logic through the information fusion analysis agent. And the natural language generation engine converts the information into a professional report, and returns the professional report to the scheduling module through a feedback interface to realize cognitive closed-loop iterative optimization. The technical problem that in the prior art, it is difficult for geologists to obtain accurate, comprehensive and credible professional conclusions is solved.

The invention is suitable for the field of fire extinguishing and automatic control, and provides a digital twin-driven dynamic topology network intelligent fire-fighting cooperation method and system, and the method comprises the steps: building a digital twin model of a fire-fighting monitoring site, carrying out the real-time monitoring of data through various devices, and enabling the model to be dynamically updated; establishing a dynamic topology network, deploying communication nodes and optimizing topology; and then analyzing a monitoring scene, defining task priorities in combination with resource and environment information, allocating tasks and scheduling communication nodes, and finally visually outputting a result. According to the system, task allocation, resource scheduling and communication optimization are incorporated into a unified mathematical framework, the defect that a traditional fire-fighting decision is split is overcome, and the rescue efficiency is improved.

The invention belongs to the field of network decision optimization, relates to a data analysis technology, and particularly relates to a network intelligent decision optimization system based on dynamic feature extraction, which is used for solving the problem that stage correlation analysis cannot be carried out according to a single performance parameter in a dynamic network in the prior art. Comprising a network test module, a feature extraction module, a performance monitoring module and a decision analysis module, and the network test module, the feature extraction module and the performance monitoring module are all in communication connection with a database; according to the method and the device, dynamic association analysis of the network performance parameters and the operation parameters is realized, and the system can accurately identify key factors causing performance abnormity in a complex and changeable network environment. Compared with a traditional static threshold judgment method, the dynamic feature extraction mechanism of the invention significantly improves the accuracy of anomaly diagnosis, and through the separate design of the test period and the monitoring period, the system can perform comprehensive feature analysis while ensuring the real-time monitoring efficiency.

The invention discloses a 6G-oriented cross-domain knowledge-driven network intelligent scheduling system and a 6G-oriented cross-domain knowledge-driven network intelligent scheduling method. The architecture comprises a cross-domain knowledge collaborative evolution module, a network closed-loop management module and a multi-normal-form learning decision module, the cross-domain knowledge collaborative evolution module is used for disassembling a network state into an environment domain, a network domain and a user behavior domain, and online incremental fusion of three-domain knowledge is realized through a graph neural network; dynamically updating the global knowledge base based on knowledge distillation; the network closed-loop management module is used for constructing a function closed loop of perception-reasoning-knowledge generation-decision issuing-verification optimization-memory retrieval; and the multi-normal-form learning decision module is used for fusing meta learning, reinforcement learning, federal learning and self-supervised learning to realize rapid migration and continuous evolution of the strategy. According to the method, the network data throughput, the fault recovery speed, the flow prediction precision, the resource fairness, the path efficiency and the task success rate are improved, and the method is suitable for complex 6G scenes such as air-ground integration and low-altitude traffic control.

A communication method and a communication apparatus, the communication method comprising: a first agent receiving capability information of a second agent, and, according to the capability information and task description information, determining to send to the second agent related information of first data and configuration information of sensing modules, wherein the capability information comprises a processing function and / or modal information of multi-modal data that the second agent supports for processing, the related information of the first data comprises the content of the first data, modal information of the first data, and processing priority information of the first data, and the configuration information of the sensing modules comprises identification information of the sensing modules and / or a first key. The method uses the interaction between the first agent and the second agent to enable the first agent to be capable of analyzing multi-modal data on the basis of the task description information and the capability information of the second agent, and indicating to the second agent the related information of multi-modal data to be processed and the configuration information of the sensing modules, thereby achieving multi-modal data processing by agents, and improving network intelligence.

The invention relates to the technical field of unmanned aerial vehicle path planning, and discloses an unmanned aerial vehicle path planning method based on deep reinforcement learning, and the method comprises the steps: firstly collecting point cloud data of a tunnel environment through a three-dimensional laser radar, carrying out the complementation processing, and carrying out the dynamic slicing in the main extension direction of a tunnel, the slice thickness is adaptively adjusted according to the local point cloud density, each slice is projected to generate a two-dimensional section grid map, a two-dimensional map sequence is used as a training environment, a deep Q network agent is adopted for exploration, a safe candidate path point set is generated, and finally, the candidate point set is evaluated and screened, so that the path point of the path is obtained. According to the method, the optimal path point of each slice is obtained, the discrete optimal point sequence is smoothed, the continuous and safe optimal flight path is finally formed, and simulation experiments verify that the method can effectively solve the problem that in a complex tunnel point cloud map, path planning is difficult to guarantee path safety, motion smoothness and calculation real-time performance at the same time.

The invention provides an air-space-ground-sea multi-domain network intelligent cooperation system and method, and belongs to the technical field of communication networks. The system collects heterogeneous network data through a multi-domain sensing layer, and constructs a digital twinborn body by using quantum reinforcement learning to realize network state prediction; semantic analysis is adopted to convert user intentions into a resource demand matrix, and non-orthogonal multiple access and dynamic spectrum sharing technologies are combined to realize joint optimal allocation of satellite-air-sea-ground resources. Compared with the prior art, the method has three advantages that 1) intention-driven intelligent resource scheduling is supported, and the service matching precision is improved by 40%; 2) cross-domain spectrum sharing is realized through federal learning, and the spectrum utilization rate reaches 92%; and 3) the transmission reliability in an anti-interference mode reaches 99.99%, and the system is suitable for emergency communication, ocean monitoring and other scenes.

The invention belongs to the technical field of base station intelligent monitoring, and provides a base station out-of-service prediction method and system based on big data analysis and a knowledge graph, and the method comprises the steps: obtaining first historical data information of a base station group, and carrying out the preprocessing of the first historical data information, and obtaining the preprocessed second historical data information; constructing a dynamic knowledge graph based on the second historical data information, and extracting multi-scale causal feature information according to the second historical data information and the dynamic knowledge graph; training an out-of-service prediction model based on the multi-scale causal feature information to obtain a trained target out-of-service prediction model; and acquiring real-time data information of the base station, and predicting the real-time data information by using the target out-of-service prediction model to obtain out-of-service prediction information of the base station. According to the invention, the prediction precision, timeliness and adaptability are remarkably improved, the engineering practicability and operation and maintenance operability are also considered, and a technical scheme with high accuracy, high interpretability and self-evolution capability is provided for intelligent operation and maintenance of the communication network.

The invention discloses an AI-based wireless communication network intelligent resource scheduling system, which belongs to the technical field of resource scheduling of wireless communication networks, and comprises the following steps: establishing a region prediction model; analyzing the region material data according to the region prediction model to obtain unit prediction data of each unit region in the prediction time in the prediction time period; generating a unit prediction map of prediction time according to the unit prediction data; establishing a digital twinborn model, and performing resource scheduling simulation on the unit prediction map according to the digital twinborn model to obtain a basic scheduling scheme; the device end collects user information in real time, and generates a unit area map according to the user information in the unit area; whether the basic scheduling scheme needs to be adjusted or not is evaluated according to the unit area graph; when the evaluation does not need to be adjusted, marking the basic scheduling scheme as a target scheduling scheme; and when the evaluation needs to be adjusted, optimizing and adjusting the basic scheduling scheme to obtain a target scheduling scheme.

The invention discloses a building supply chain transparent communication system and method based on a block chain, and relates to the field of block chain technology and building supply chain management, and the system comprises the following components: a block chain underlying network, an intelligent contract module, a terminal collection node, an authority management module and a communication interaction module. According to the invention, by deploying the intelligent contract module, the business rules of automatic payment, performance verification and dispute early warning are realized, and the application of a dynamic acceptance weight verification algorithm and a dynamic threshold dispute early warning mechanism enables the payment trigger logic to better meet the actual demand of a project, improves the payment accuracy by 27%, reduces disputes caused by fuzzy acceptance standards, and improves the payment efficiency. Meanwhile, the dispute early warning mechanism monitors on-chain performance data in real time, automatically triggers early warning when the data deviates from a preset threshold value, synchronously pushes early warning information to related nodes, and generates an on-chain early warning certificate.

The invention discloses a salinized frozen soil roadbed multi-parameter coupling monitoring method, and relates to the field of salinized frozen soil area roadbed monitoring, and the method comprises the steps: executing field investigation and demand analysis, outputting a field investigation and monitoring demand analysis report, executing a sensor network intelligent dynamic topology design, and outputting an intelligent dynamic sensor network layout detailed drawing. According to an intelligent dynamic sensor network layout detailed drawing, sensor preparation, verification and drilling channel construction are executed, and drilling holes and channels are output; cooperative arrangement, installation and fixation of a multi-parameter sensor are executed, an on-site sensing network is output, cables are led out and connected to an integrated data acquisition box in a tandem mode, cable tandem is executed, system integration and acquisition box installation and debugging are carried out, and an integrated on-site monitoring station is output. According to the method, the ultimate targets of comprehensive, synchronous and accurate monitoring and intelligent early warning of the roadbed water-salt-thermal coupling migration process are achieved, and the fundamental problems of data splitting and analysis lagging of a traditional method are effectively solved.

The invention provides an unmanned aerial vehicle multi-hop network intelligent resource allocation method based on deep reinforcement learning, and the method comprises the steps: abstracting task input into a virtual network, abstracting an unmanned aerial vehicle cluster into a physical network, and regarding a resource allocation process as embedding from the virtual network to the physical network. And processing a single node in the virtual network every time by taking the network state information as input, and updating the physical network resource information according to an output result. And a return value is obtained by counting the embedding success rate, evaluating the resource cost-effectiveness ratio and the network QoS (Quality of Service) performance. And calculating a dominant function expectation corresponding to the action by using the intelligent resource allocation network, and updating the strategy under constraint based on a strategy gradient optimization method until the network converges. Finally, task input is converted into a virtual network request, the virtual network request is input into the trained resource allocation network, an optimal embedding strategy is obtained, and efficient allocation of resources is achieved.

The application discloses a kind of hearing-impaired person intelligent terminal adaptive feedback method, intelligent terminal and system, it is related to smart home and barrier-free interaction technical field.The present application is aimed at the defects of single feedback mode, lack of intelligent scheduling and no adaptive capacity, constructs an end-to-end intelligent feedback scheme.First, the user situation characteristics are collected in real time by multidimensional sensor, and the situation characteristic vector is constructed;At the same time, the importance score of visitor is calculated by extracting the multi-modal features of visitor.Secondly, the situation characteristics and visitor characteristics are input into the deep Q network, and the optimal multi-modal feedback device combination and feedback intensity level are intelligently decided.Then, the graded response strategy is executed, and the reminder intensity is dynamically adjusted based on the user response.Finally, the decision model is continuously optimized through online learning mechanism.The present application realizes the leap from passive fixed reminder to active intelligent perception feedback, significantly improves the use experience and efficiency of hearing-impaired person intelligent terminal.