82 results about "Network strategy" patented technology

The invention belongs to the technical field of information technology and intelligent health service, and discloses a community old-age care service resource optimal configuration method based on edge computing, which comprises the following steps: collecting physiological data, environmental data and activity data of old people to form original heterogeneous data, uploading the original heterogeneous data to a data warehouse through an urban and rural community network adaptation mechanism, generating a space-time tagging multi-modal data set; constructing a dynamic digital twinborn model, generating a space thermodynamic diagram, identifying different level priority requirements, and generating a time period allocation scheme through a conflict priority scoring mechanism; an MILP model is constructed to dynamically optimize edge node computing power and functional area space resources, and a resource optimization report and a block chain transaction list are generated; desensitizing the original data of the old people, creating a digital identity card, generating an encrypted service log, and synchronously recording migration behaviors of the old people; and triggering network strategy switching and priority threshold self-adaption, and carrying out resource scheduling and strategy closed-loop optimization.

The invention discloses a nature-based recreation infrastructure network construction method and device and electronic equipment, and relates to the technical field of geographic planning, and the method comprises the steps: recognizing a recreation resource center of a research area according to natural reserve data, park green land data, scenic area data, characteristic town data and river system data; constructing a comprehensive resistance surface of the research area according to the resistance factors; identifying a target corridor of the research area and a network strategy point of the target corridor according to the recreation resource center and the comprehensive resistance surface; and correcting and adjusting the network structure of the research area according to the recreation resource center, the target corridor and the network strategy point. According to the method, the recreation resource center meeting ecological functions and human recreation requirements is screened out according to the recreation resources, and the target corridor and the network strategy point are identified, so that the network structure of the research area is corrected and adjusted, and a recreation infrastructure network based on nature under recreation guidance is formed; based on the network, the experience of people in recreation can be improved.

The invention discloses a building composite phase change material intelligent matching and optimizing method based on a large language model, and belongs to the technical field of building energy saving and intelligent material design. Basic thermophysical property data, building environment parameter data and user demand data are collected; a deep reinforcement learning technology is utilized to construct an intelligent model based on a deep Q network strategy, and generated data samples are integrated into a thermophysical property database; outputting a candidate material combination recommendation scheme by adopting a large language model; evaluating the candidate material combination recommendation scheme by using the semantic tag vector and a sorting engine to obtain a performance evaluation result; generating a performance evaluation report according to the simulation model; a multi-agent negotiation algorithm is adopted, cross-regional thermal control performance is optimized, a multi-dimensional performance comparison diagram and tuning suggestions are generated, correction information of designers is recorded, optimization is carried out, and feedback is provided in a self-adaptive mode. According to the method, the building composite phase change material combination is screened, cross-regional thermal control is optimized, adaptive schemes and suggestions are output, and the self-adaptive optimization capability of the system is improved.

The embodiment of the invention provides an underground tool face dynamic control method and system based on reinforcement learning, and belongs to the technical field of directional drilling. The method comprises the steps of collecting drilling state data of a target well, performing data preprocessing on the drilling state data, and constructing a corresponding state vector based on the preprocessed drilling state data; calling an action decision model obtained by training based on a DDPG algorithm based on the state vector to obtain an action instruction; wherein the network strategy parameter of the action decision model is obtained by correcting the previous network strategy parameter based on the deviation between the current drilling state data and a pre-constructed reward function; analyzing the action instruction to determine a target physical action; and executing top drive torsional pendulum based on the target physical action, and recycling an execution result until the execution result indicates that the tool face angle meets the expectation. According to the scheme, the stability and the real-time control effect under the working conditions of complex well sections and severe disturbance are improved.

The invention discloses a robot control optimization method based on deep reinforcement learning, and the method comprises the steps: firstly initializing a control system, constructing an experience playback buffer pool, and setting an Actor network strategy function, a Critic network weight, a hyper-parameter, and a target network parameter; then, the robot generates an action according to the Actor network, collects environment feedback after executing the action, and stores the state transition tuple into a buffer pool; next, a small batch of data is sampled, a time difference error is calculated, and a network parameter is updated accordingly to minimize a value estimation deviation, synchronously optimize the network parameter, maximize an expected value of a state-action value function, and improve control performance. According to the method, a multi-thread architecture and a deep reinforcement learning technology are fused, the real-time performance, stability and generalization ability of robot control are improved, the problem of algorithm convergence in a non-stationary environment is effectively solved, the cost of adjustment and calculation is reduced, and the robustness of long-term stable operation and deployment feasibility in a complex scene are enhanced.

The invention provides a periodical knowledge graph construction method, a periodical knowledge question-answering method and a periodical knowledge graph construction device. The periodical knowledge graph construction method comprises the following steps: performing sequence labeling method knowledge extraction on an obtained periodical text to be processed to obtain a knowledge labeling sequence; performing vocabulary network strategy entity alignment on the knowledge labeling sequence to obtain an aligned knowledge sequence, performing priority strategy entity disambiguation on the aligned knowledge sequence to obtain a disambiguation knowledge sequence, and performing knowledge reasoning completion on the disambiguation knowledge sequence to obtain integrated journal knowledge; and performing knowledge graph construction according to the integrated periodical knowledge to obtain a periodical knowledge graph. Primary knowledge extraction is carried out through a sequence labeling method, knowledge entities of different data sources are aligned through a vocabulary network strategy, periodical knowledge of different sources can be effectively integrated, ambiguous content between entities is eliminated through priority strategy entity disambiguation, missing values and blank values are complemented through knowledge reasoning, the knowledge content can be effectively verified, and the accuracy of the periodical knowledge is improved. Therefore, the data quality of the periodical knowledge graph is improved.

The invention provides an SDN (Software Defined Network) dynamic control system based on a cloud native architecture, and belongs to the technical field of SDN and cloud native. The system adopts a three-layer decoupling architecture of a service layer, a control layer and a data layer, wherein the service layer consists of a core network, a cloud platform and edge nodes and is used for issuing service requirements and executing a joint resource scheduling strategy; the control layer comprises four core modules, namely a micro-servitization SDN control module, a cross-domain collaborative engine module, an end-to-end service quality guarantee module and an edge collaborative optimization module, and is responsible for unified analysis, decision and scheduling to realize dynamic updating of a network strategy and multi-cluster collaborative optimization; the data layer is composed of an SDN switch and a router, and is used for executing a flow table rule and collecting network situation data. Through deep fusion of the SDN and the cloud native technology, the problems of upgrade interruption, low cross-domain collaboration efficiency and edge resource limitation of a traditional network architecture are solved, and the method can be widely applied to hybrid cloud management, industrial Internet of Things and intelligent city network scenes.

Devices, systems, methods, and processes for recommendation and update of network policies. Existing network policy update solutions rely on human intervention in monitoring and analyzing traffic patterns in a network, checking for policy compliance, detecting any policy violations, and even updating new policies in the network. However, manual processes are prone to human error, introduce significant delays, and lack scalability and objectivity. To address these issues, an automated system is provided that monitors traffic across a network (in real-time or near real-time) and detects violations in a set of network policies associated with the network. The system utilizes one or more recommendation models to process network flow data and network inventory data, and generate one or more policy update recommendations to resolve the detected policy violations. The system further enforces the one or more policy update recommendations on various network devices within the network to resolve the detected policy violations.

The invention relates to the technical field of numerical control machine tool precision compensation and intelligent control, and discloses a numerical control machining center spindle dynamic compensation control method and system.The numerical control machining center spindle dynamic compensation control method comprises the steps that surface topography point cloud data of an online visual sensor, spindle motor current data and feed shaft position encoder feedback data are obtained, and a multi-source state monitoring data set is generated; a regularization method is adopted to solve reverse mapping from the surface low-frequency error component to radial runout and axial movement of the main shaft, and a main shaft rigidity state index and a tool abrasion index are output; describing an interaction relationship between the tool wear and the main shaft rigidity based on a coupling degradation kinetic equation, and calculating contribution weights of the tool wear and the main shaft rigidity to the machining precision; training a deep Q network strategy by using the deduced reward function, and outputting an optimal action; and executing spindle dynamic compensation parameter adjustment or generating a replacement instruction according to the optimal action type. According to the method, the factor of lacking a continuous learning improvement mechanism from historical data is overcome.

The invention discloses a virtual network configuration management method and system based on a block chain smart contract, and belongs to the field of virtual network management and block chains, the virtual network configuration management system comprises a block chain smart contract module, a kernel protocol stack module, an address generation and management module and a strategy storage module, the intelligent contract management module is used for realizing full-life-cycle management of a virtual network, a network element, a network strategy, a DNS strategy and a situation awareness strategy through an intelligent contract; according to the method and the system, the problems of centralization risk, insufficient multi-tenant isolation, potential safety hazard of strategy management, low authority verification efficiency and the like in the traditional virtual network management are solved. And the decentralized management of the virtual network resources is realized by utilizing the characteristics of the block chain smart contract, the efficient multi-tenant isolation is realized in combination with the optimization of the kernel protocol stack, and the security of the strategy and the accuracy and efficiency of the authority verification are ensured at the same time.

The invention provides an A / B link performance comparison and strategy optimization method and system based on service flow, and belongs to the field of network performance monitoring and network strategy verification. The method comprises the following steps: firstly, performing bypass collection on real service traffic to obtain original data; copying the same copy of original data, injecting the original data into the link A and the link B at the same time, and then respectively collecting service flows of the link A and the link B; establishing a session alignment model, and judging whether the service traffic of the link A and the link B belongs to homologous traffic or not; after time drift compensation is carried out on sending timestamps of the homologous flow in the two paths, visible time sequence fields of homologous events are extracted respectively, differential indexes of the homologous events of the two paths on key time sequence parameters are calculated, and different percentile values of all the differential indexes under each parameter type in a preset time window are counted; and link performance comparison and strategy verification are carried out according to the difference index and the percentile value. According to the invention, the authenticity and efficiency of performance comparison and strategy optimization of different paths are improved.

The invention relates to the technical field of communication networks, in particular to an intention-driven satellite-ground convergence network on-demand arrangement system and method, and the method comprises an intention input layer which is used for receiving a multi-mode intention of a user; the intention analysis layer is used for analyzing the intention of the user to generate a recognizable network strategy; the arrangement control layer comprises a design state and an operation state; wherein the design state is used for dynamically generating a resource arrangement strategy based on an identifiable network strategy, and verifying the feasibility of the resource arrangement strategy in the digital twin network; the running state is used for generating an executable scheme based on the verified resource arrangement strategy; and the infrastructure layer comprises a satellite-ground unified atomic power abstraction module which is used for abstracting heterogeneous resources into standardized atomic power and executing an executable scheme. The scheme aims to provide a brand-new intention-driven satellite-ground fusion network arrangement architecture with dynamic generation, thorough decoupling and active prediction capabilities, so that the technical problem that complexity and dynamics of a 6G network cannot be handled at present is solved.

Disclosed in the present application are a network policy management method and apparatus, and a device and a storage medium. The method comprises: acquiring at least one target policy instance for network management; on the basis of policy application knowledge, performing policy relationship analysis on the at least one target policy instance and an existing policy instance, so as to obtain a policy relationship analysis result; and on the basis of the policy relationship analysis result, performing optimization processing on the at least one target policy instance and the existing policy instance. The method can realize systematic management of network policies, can satisfy hierarchical and cross-field systematic universal management of operation and maintenance management policies for various network fields, and has the advantages of strong universality, high levels of flexibility and on-demand expandability.

The invention relates to the technical field of software quality assurance, and discloses an internet-based software engineering automation test system, which comprises a test environment perception module, a test intelligent arrangement module and a test execution and optimization module, a server resource state, a network delay parameter and a code structure feature are collected in real time through an environment dynamic sensing module, a multi-dimensional environment adaptation degree evaluation model is constructed, and when it is detected that the environment adaptation degree is lower than a threshold value, container resource expansion and network strategy adjustment are automatically triggered; establishing a mapping relation between multi-level defect features and code modules through a defect root positioning system, searching an optimal diagnosis scheme in a strategy space by applying an improved particle swarm algorithm, and accurately positioning a fault point to a source code line number; finally, closed-loop control of test environment self-adaption, test path self-correction and defect positioning self-optimization is realized, and the accuracy of a test result and the fault repair efficiency are improved.

The invention relates to the technical field of frozen soil monitoring, in particular to an InSAR frozen soil deformation monitoring method based on U test and a multilevel network construction strategy, which comprises the following steps: for a target frozen soil region, selecting PS candidate points through an amplitude deviation index and acquiring interference phases of the PS candidate points; u inspection is adopted to identify a homogeneous pixel set, an EMI method is adopted to carry out time sequence phase optimization processing, goodness of fit is adopted to measure the quality of an optimal phase estimation result, and DS candidate points and interference phases thereof are obtained; a multi-level network construction strategy is adopted, high-quality PS candidate points and DS candidate points construct a first-level resolving network, and the remaining candidate points gradually construct a second-level resolving network for parameter resolving; and separating frozen soil deformation from the unwrapping phase of each measurement point obtained from the multi-level network construction strategy by adopting joint parameter modeling. According to the invention, the precision of the deformation parameter calculation result of the frozen soil region and the spatial density of the measurement points are obviously improved, so that the frozen soil deformation monitoring quality is obviously improved.

The invention relates to an ocean transportation network system toughness evaluation and monitoring early warning method. The method comprises the following steps: S1, identifying key nodes and vulnerabilities of a network model; s2, analyzing short-term or long-term influence of robustness and fault tolerance evaluation results and node or edge change on network performance; s3, analyzing a flow direction prediction result of the bulk cargos; s4, analyzing the adaptation mechanism of the network to the dynamic change; s5, analyzing a network toughness evaluation result, and optimizing a network strategy based on the network toughness evaluation result; and S6, outputting a prediction result by the prediction model, and outputting a monitoring and early warning result based on the ship trajectory semantic model and the real-time AIS data. Compared with the prior art, the method has the advantages of more effectively identifying modes, trends and potential risk points and the like.

The present application belongs to the field of military command control and combat simulation, and relates to a local rapid reconstruction method for a killing network based on a complementary network strategy. The method acquires current state information of the killing network and detects failed nodes or edges, constructs a local repair area centered on the failed nodes, establishes a multi-objective optimization model in the repair area aiming to maximize task recovery benefits, minimize resource use costs and minimize task completion delays, solves to obtain a Pareto optimal solution set by using a TM-NSGA-III algorithm guided by a conversion matrix, and finally screens and executes a reconstruction scheme according to a battlefield situation. The present application reduces the search space by localized modeling, combines the conversion matrix to inherit the effective information of the original network, realizes rapid and high-quality reconstruction of the killing network, and improves the resilience and self-healing ability of the killing network in a dynamic battlefield environment.

The present invention discloses a method for translating the intent of multiple scenarios, multiple users and multiple services in a satellite communication network. The method solves the problem of low resource utilization due to large restrictions on user input and a wide variety of types. The implementation includes: constructing an intent recognition module; inputting and preprocessing the service intent of the front-end user; identifying the scenario, user and service to obtain a classification result; extracting the intent information extraction result; storing the original service intent, and dynamically learning the model; implementing the intention translation of the front-end user; and implementing the intention translation of multiple scenarios, multiple users and multiple services in a satellite communication network. The present invention adds an intent classification model to the intent recognition module, and can provide network strategies that meet different service quality requirements for front-end users through coarse-grained and fine-grained network strategies, thereby improving user satisfaction and experience. The coarse-grained fills the default value of the fine-grained strategy to ensure the stability and reliability of the intent translation. It is used for the intent translation of multiple scenarios, multiple users and multiple services in a satellite communication network.

The application discloses a kind of multi-agent centralized decision-making method, system and storage medium based on hierarchical representation.The method is realized based on reinforcement learning policy network, specifically includes: the situation information perceived by multi-agent is input into policy network, the first module of policy network is based on the hierarchical feature extraction of the data type of situation information, each layer situation information data is processed respectively and then feature fusion is carried out, and the processed situation information is output;The second module of policy network further processes the processed situation information;The third module of policy network carries out hierarchical decision-making according to action parameter type, and each action parameter is output by processing in turn, to obtain the joint action of final output.The high-dimensional situation information and action information brought by multi-agent centralized decision-making are reduced in dimension by adopting hierarchical method based on type, and the search efficiency in high-dimensional space is effectively improved.

The embodiment of the invention provides a data transmission method and device, equipment, a readable storage medium and a program product, and relates to the fields of cloud technologies, maps and the like, and application scenes include but are not limited to business data transmission scenes. The method is executed by a client on a terminal, and comprises the following steps: acquiring a service request and a preset initial network strategy; based on the initial network strategy, determining a data transmission state between the client and a gateway corresponding to the service request; if the data transmission state characterizes that abnormity exists, obtaining use condition data of an application program interface corresponding to the service request, and performing strategy adjustment on the initial network strategy based on the use condition data to obtain a target network strategy; based on the target network strategy, performing parameter adjustment processing on service related parameters in the service request to obtain an adjusted service request; based on a preset target coding and decoding protocol, obtaining a business request after coding conversion; and sending the business request after code conversion to a business server through the gateway.

The application provides a container-level network resource efficient management method for a deterministic distributed computing scene, and mainly solves the problems that the prior art cannot effectively manage network resources at the container level, the real-time performance of task scheduling is low, the success rate is low, and resources are wasted and the like. For the deterministic distributed computing scene, efficient network resource management at the container level is needed, first, the network resource controller needs to be extended, and a Service service is configured so that the container can communicate with the external cloud server; then, the use of network resources by the container is controlled by configuring a network strategy; further, the container tasks are aggregated according to the real-time performance and determinacy requirements of the tasks, and the transmission efficiency of the container tasks is improved; finally, the aggregated tasks are stored and transmitted. The method takes the container virtualization technology as the implementation means, aggregates the container tasks in multiple time slots based on the dynamic programming algorithm, and continuously optimizes the algorithm parameters in combination with the artificial intelligence technology, which not only guarantees the real-time performance and success rate of the tasks, but also significantly improves the resource utilization.

Embodiments of the present application relate to the technical field of communications, and provide a communication method and apparatus. The communication method comprises: a network function service consumer entity sends a first request to a network data analytics function (NWDAF) network element, wherein the first request comprises priority indication information, the first request is used for requesting the NWDAF network element to feed back a plurality of analytics results of a network policy on the basis of the priority indication information, and the priority indication information is used for indicating feedback priorities of the plurality of analytics results of the network policy; and the NWDAF network element sends a first response to the network function service consumer entity, wherein the first response comprises at least one analytics result, and the at least one analytics result is determined from among the plurality of analytics results on the basis of the feedback priorities of the plurality of analytics results. In this way, the NWDAF network element can selectively feed back at least one analytics result to the network function service consumer entity on the basis of the priority indication information, thereby improving the value of recommendation of the NWDAF network element.

The invention discloses an intelligent determination method and device of a network strategy and a storage medium, relates to the field of communication, is used for accurately matching the network strategy for network equipment, and comprises the following steps: receiving strategy request information; the strategy request information comprises resource demand information; determining at least one candidate network strategy from a strategy knowledge base based on the strategy request information and a similarity model; the strategy knowledge base comprises a plurality of network strategies, and the similarity between each candidate network strategy in the at least one candidate network strategy and the strategy request information is greater than a first threshold value; determining a policy score of each candidate network policy in the at least one candidate network policy based on the policy request information, the at least one candidate network policy and a reordering model; based on the strategy score of each candidate network strategy, determining a target network strategy meeting a matching degree requirement from the at least one candidate network strategy; the target network strategy comprises a strategy name and API information. The method and the device are applied to a network policy matching process.

The invention discloses a user behavior analysis method based on E-SIM, and particularly relates to the technical field of user behavior analysis. The method comprises the following steps: constructing a unified time sequence data set by collecting an E-SIM configuration switching log, a network strategy parameter and a user behavior event sequence; determining a configuration switching trigger window based on the unified time sequence data set, analyzing the correlation degree between network environment migration and user behavior change, and obtaining candidate drift fragments; respectively carrying out factor deconstruction on the equipment side and the network side, and outputting pseudo migration scores of the equipment side and the network side; performing comprehensive judgment of identity cognition drift based on the pseudo migration score, and calculating a behavior correction vector; and reconstructing a user behavior sequence by using the behavior correction vector, correcting an abnormal behavior tag, and outputting a corrected user behavior analysis result. And the accuracy and reliability of user behavior analysis are improved.

The invention discloses a network policy approval method and apparatus, and an electronic device. The method comprises the following steps: acquiring application work order information; network strategies in the application work order information are pre-screened according to a white list rule and a black list rule, a first examination and approval result is obtained, the white list rule comprises a predefined matching incidence relation of service attributes of a source IP and a target IP, and the black list rule comprises a predefined prohibition incidence relation of the service attributes or network segments of the source IP and the target IP; splitting a target network strategy which does not conform to a white list rule or a black list rule in the application work order information according to the system and the approval level to generate a corresponding approval process; and examining and approving the target network strategy according to the examination and approval process to obtain a second examination and approval result. According to the method and the device, the technical problems of missing pre-screening, disordered approval responsibility and insufficient later maintenance existing in network policy approval in related technologies are solved.

Embodiments of the present application relate to the technical field of communications, and provide a communication method and apparatus. The communication method comprises: a network function service consumer entity sends a first request to a network data analytics function (NWDAF) network element, wherein the first request comprises priority indication information, the first request is used for requesting the NWDAF network element to feed back a plurality of analytics results of a network policy on the basis of the priority indication information, and the priority indication information is used for indicating feedback priorities of the plurality of analytics results of the network policy; and the NWDAF network element sends a first response to the network function service consumer entity, wherein the first response comprises at least one analytics result, and the at least one analytics result is determined from among the plurality of analytics results on the basis of the feedback priorities of the plurality of analytics results. In this way, the NWDAF network element can selectively feed back at least one analytics result to the network function service consumer entity on the basis of the priority indication information, thereby improving the value of recommendation of the NWDAF network element.

The invention provides a multi-train dynamic scheduling method and system based on an MAPPO algorithm, and relates to the technical field of multi-train dynamic scheduling, and the method comprises the steps: obtaining multi-train operation state information, and recognizing the state information according to a pre-trained neural network strategy model to generate a discrete action decision; generating an adjusting instruction according to the discrete action decision, calculating new running state information and composite reward feedback of the current decision step according to the adjusting instruction, and updating train environment state information to enter the next decision step until the current round is finished; and judging whether the scheduling strategy corresponding to the current turn accords with a preset scheduling strategy or not according to all the rewards of the current turn, and if not, returning to execute the step of acquiring the running state information of the multiple trains until the preset scheduling strategy is met so as to dynamically and collaboratively optimize the scheduling of the multiple trains. The technical problem that a train scheduling method in the prior art is still limited to a single train decision or local competition paradigm and lacks collaborative global scheduling capability is solved.

This paper proposes a method for reconstructing the faces of curling athletes based on broadcast videos. This method optimizes the facial features of curling athletes in broadcast videos and utilizes deep learning techniques combined with a mask network strategy to overcome the limitations of traditional methods in processing reflective and weakly textured areas. This method provides more realistic and vivid digital human images for applications such as the metaverse, digital humans, and sports broadcasts.

The present invention discloses a method for intelligent selection and optimization of building composite phase-change materials based on a large language model, belonging to the technical field of building energy conservation and intelligent material design. The method collects basic thermal property data, building environmental parameter data, and user demand data; utilizes deep reinforcement learning technology to construct an intelligent model based on a deep Q-network strategy, and integrates the generated data samples into a thermal property database; uses the large language model to output candidate material combination recommendations; uses semantic label vectors, and a ranking engine evaluates the candidate material combination recommendations to obtain performance evaluation results; generates a performance evaluation report based on the simulation model; and uses a multi-agent negotiation algorithm to optimize cross-region thermal control performance, generate multi-dimensional performance comparison charts and tuning suggestions, and record designer correction information, optimizing and adaptively providing feedback. The present invention screens building composite phase-change material combinations, optimizes cross-region thermal control, outputs adaptation solutions and suggestions, and enhances the system's adaptive optimization capabilities.

The invention discloses an application program aware and identity aware network policy conversion system, which comprises a user / application program input plane, a policy conversion plane and an execution plane, the user / application input plane reads a plurality of descriptive parameters from a post-set data input; the strategy conversion plane converts the descriptive parameters into a plurality of strategy parameters; the execution plane applies the policy parameters to a plurality of data packets associated with the metadata input. Therefore, according to the invention, corresponding strategy processing can be carried out on the data packet in the network according to the application related information and the identity related information.