32 results about "Network strategy" patented technology

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.

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 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 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 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 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.

The invention belongs to the technical field of wireless communication, and discloses a resource allocation method for an eMBB and URLLC mixed service, and the method comprises the steps: 1, setting input variables: a round number T, a learning rate, an experience playback pool, a target network update frequency C, a state weight factor beta, a service weight factor sum, and a discount factor; 2, performing variable initialization: initializing a training network and a target network by using parameters selected in a randomized manner, wherein the parameters are selected in a randomized manner; step 3, randomly selecting power and a resource allocation strategy to initialize a network state in each turn (T in total); 4, in each round, selecting an action through a network strategy selection module, configuring a strategy in the network, then recording a network state and a corresponding return function after the action is executed, and finally storing an obtained parameter set in an experience playback pool; step 5, training; step 6, updating parameters of the target network; and the reliability is high.

The embodiment of the invention relates to the field of network security, and provides a network attack detection method and device based on deep reinforcement learning and topology reconstruction, and the method comprises the steps: collecting network flow data, node state information and security logs in real time, carrying out the dynamic modeling of a connection relation and communication behaviors between nodes, and generating a dynamic attack graph; analyzing the current network state of the dynamic attack graph through a deep reinforcement learning agent to obtain an optimal defense action; generating a strategy instruction based on the optimal defense action, and sending the strategy instruction to a network strategy execution point; by calling an application programming interface of a strategy execution point, blocking of malicious IPs, isolation of infected nodes or redirection of malicious traffic is dynamically executed, network topology reconstruction is triggered to rapidly repair or isolate fault nodes, and instant detection and interception of attack behaviors are achieved. According to the embodiment of the invention, active perception and intervention are carried out on attack behaviors, and intelligent decision and dynamic self-healing of network topology are realized through deep reinforcement learning.

The application discloses a large model and knowledge graph enabled intention driving network design method, comprising the following steps: S101, a user inputs a user intention through a front-end natural language interaction interface, and performs a pretreatment operation on the user intention; S102, a pretreated user intention is classified based on a few-shot learning method, and a large model cooperation method extracts key information of the user intention and constructs an intention knowledge graph based on the key information; S103, a network state knowledge graph is constructed; S104, an application program interface provided by an underlying layer is called by using intention understanding and reasoning capability of the large model, so that mapping of the user intention to an underlying network strategy and issuing are realized; and S105, the large model performs dynamic real-time adjustment based on network state knowledge graph information. The application realizes intention semantic mining, adaptation of intention demand and underlying resource capability, so as to bridge a semantic gap between the user intention, strategy management and the underlying network, and improve network operation efficiency.

The invention relates to the field of network communication, and provides a network strategy optimization method and system based on user behaviors and preference learning. The method comprises the following steps: collecting strategy operation behaviors and network quality sensing data of a user to obtain a user behavior data set; inputting the user behavior data set into a machine learning model for training so as to learn an association relationship between user behavior characteristics and network quality indexes, and obtaining a user preference weight vector; obtaining a quality parameter and available network strategies of the current network environment, and performing quantitative evaluation on the plurality of available network strategies according to the user preference weight vector to obtain a comprehensive score of the plurality of available network strategies; and performing priority ranking on the available network strategies based on the comprehensive score, and selecting a target network strategy according to a strategy availability state and a score result. According to the method, the personalized preference of the user on the network quality index can be learned, intelligent evaluation and dynamic switching of the network strategy are realized, and the user experience is improved.

The invention discloses a scheduling method and device, a storage medium, a program product and computer equipment, and the method comprises the steps: carrying out the processing of a collection end, generating merged data matched with user equipment, and generating network strategy information corresponding to the user equipment through a strategy management end according to the merged data through a strategy generation model, according to the method, the network strategy can be generated timely and intelligently, the adaptability between the network strategy and the user equipment is improved, then the target platform can respond to the network strategy information to generate the corresponding scheduling strategy information and send the strategy instruction to the second network element, and the second network element executes the scheduling operation. Therefore, the network policy scheduling of the user equipment can be completed, so that the flexibility, intelligence and timeliness of the network policy scheduling can be improved, and the real-time network service requirements of the terminal can be responded more timely, accurately and reliably; and the model can be generated according to the feedback data optimization strategy corresponding to the scheduling operation so as to improve the prediction accuracy and reliability of the model.

The application relates to a cross-network message pushing method based on a strategy model and belongs to the technical field of computer networks. The application establishes node management, engine management, route management and application management aiming at a cross-network strategy model, establishes a cross-network route model in combination with cross-network, cross-center and cross-system application scenes, shields cross-network link construction in complex scenes only by configuration of the strategy model, solves the interconnection and interworking of various service systems in service and a system application difficulty, and achieves out-of-box use. The application can realize end-to-end message sending only by simple registration and configuration in scenes where different networks, different centers and different systems cannot directly interact.

In one embodiment, a method herein may comprise: causing, for a quality-of-experience evaluation session, one or more network impairments to be injected according to a set of predefined scenarios on application traffic for a plurality of feedback sources that are using a particular application in a computer network; obtaining experience-based feedback from the plurality of feedback sources for the quality-of-experience evaluation session; correlating the experience-based feedback with the one or more network impairments to produce an evaluation result for the quality-of-experience evaluation session; and generating a quality-of-experience-based network policy recommendation for the particular application based on the evaluation result.

The invention relates to the field of communication, in particular to a communication method and a communication device. In order to meet task requirements, a terminal device may need a plurality of networks in a heterogeneous network to execute tasks, but due to the fact that different networks have differences in resource scheduling modes or processes and the like, task execution of the heterogeneous network often needs to be based on complex processes and cross-network strategies, and the task execution efficiency is low. In the application, a first task credential is information associated with a first task, and each subnet independently determines a resource scheduling strategy for executing the first task based on an own operation condition and the first task credential; and the terminal equipment calls resources of each subnet based on the first task credential to execute the first task. As the resource scheduling strategy for executing the first task is independently determined by each network, a cross-network strategy does not need to be formulated for executing the first task, so that the efficiency of executing the task by the terminal equipment through the heterogeneous network can be improved.

The embodiment of the invention relates to the technical field of cluster management, in particular to a multi-cluster network strategy management method and device. The method comprises the following steps: collecting network policy data in each K8s cluster; performing conflict identification of a global strategy based on the network strategy data, and determining a conflict strategy; performing execution priority matching on the conflict strategy based on a preset priority judgment matrix, and determining a conflict rule with the highest matrix level obtained by matching as an effective execution rule; generating a multi-cluster dynamic network strategy according to the effective execution rule; and issuing the multi-cluster dynamic network strategy to each K8s cluster to carry out strategy analysis and execution. According to the method, global unified management and control and dynamic collaboration of the multi-cluster network strategy can be realized.

The invention discloses a method for measuring a Kubernetes network strategy, which is characterized in that a system for realizing the method adopts a multi-layer technical architecture, and the omnibearing perception and interception of a network configuration change event are realized by deeply integrating the native AdmissionWebhook and Operator mechanism of Kubernetes; the core of the system comprises an access interception layer, a configuration monitoring layer, a measurement calculation layer and a verification service layer, and closed-loop management from event capture and data processing to credible verification is completed through mutual cooperation. The AdmissionWebhook mechanism is used for intercepting and pre-checking before network strategy resources are applied to a cluster, and it is ensured that each strategy change is verified by a trusted measurement process; the Operator mechanism is responsible for continuously monitoring the change of a CNI plug-in configuration file, and immediately triggering a measurement and verification process once configuration modification is detected; the change of the iptables rule is monitored in real time on each node through a monitoring Agent deployed by Daemon Set, and complete monitoring coverage from an application layer to a system layer is formed.

This application discloses a network strategy optimization method and apparatus, and a computer-readable storage medium, comprising: acquiring network parameters and service test indicators of a first network and a second network of competitors within each competitor network region; determining the superiority index value of the first network based on the comparison results of the superiority and inferiority of the same type of service test indicators of the first network and the second network; determining network strategy classification samples and corresponding strategy superiority and inferiority labels based on the network parameters and service test indicators of the first network and the second network, as well as the superiority index value of the first network, and training a network strategy classification model for classifying the network strategy of the first network as superior or inferior; classifying the network strategy of the target network to be optimized within the target competitor network region based on the network strategy classification model; and optimizing the network parameters of the target network to be optimized based on the network parameters of the competitor networks within the target competitor network region when the network strategy classification result is inferior.

The application relates to the technical field of screen projection management, and discloses a screen projection control method, device and equipment and a computer readable storage medium, the method comprising the following steps: acquiring current network information of a target terminal device; when the current network information and target network information are not in the same local area network, establishing a connection with the target terminal device through a peer-to-peer network strategy; receiving a control instruction sent by the target terminal device; and projecting to-be-projected content through the control instruction to realize control of screen projection based on the terminal device. Since the application judges whether the target terminal device and the server are in the same local area network through the current network information and the target network information, if yes, a connection is established with the target terminal device through the peer-to-peer network strategy, then when the connection is successful, the to-be-projected content is projected through the control instruction, the screen projection efficiency can be effectively improved, and the terminal device can remotely control screen projection.

The invention discloses a full life cycle management method, system and device of an SD-WAN access unit, and relates to the technical field of communication, and the method comprises the steps: creating an atomic AU, a combined AU and a client AU, and building a DAG dependence topology; when the version is updated, parallel gray migration of the double-version service domain name set is executed, a new-version atomic AU is synchronized step by step according to a downstream influence range, the service domain name access success rate is monitored, and when a preset condition is not met, automatic rollback is carried out before the version is updated; and detecting idle AUs in the database by combining the DAG dependent topology, the reference count corresponding to each AU and the operation flow access data, and after the operation and maintenance approval is passed, sequentially executing the cleaning processes of unbinding an association network strategy, filing and automatically deleting and releasing resources. According to the method, the AU creation efficiency can be improved, the dependency relationship between the AUs can be visually tracked, the out-of-control change influence is avoided, version upgrading can be safely and efficiently completed, idle resources are automatically cleaned, and the storage utilization rate is improved.

The invention relates to an intelligent fresh air and internal circulation cooperative control method for a motor home air conditioner, and aims to improve the automation and interpretability of air conditioner mode switching. According to the scheme, temperature and humidity, CO2 concentration, equipment states and user operation data of different areas of the motor home are collected and subjected to space-time alignment, a multi-source sensing sequence with position tags is generated, and semantic discretization and dynamic clustering technologies are adopted to automatically generate high-level situation tags. A main channel inputs a deep Q network based on a situation label to output an action priority, an auxiliary channel generates explainable behavior rules through rule mining, and key factor attribution and traceability of a control strategy are realized through multi-head attention fusion. The system supports self-iterative optimization of an online user feedback driving rule, and optimizes a network strategy through knowledge distillation, so that control optimization and transparent explanation are integrated. According to the method, the intelligence of a control decision, the behavior traceability and the system self-adaptive capability are improved.

The invention discloses a network management system based on a multi-source protocol network architecture. The network management system comprises a data acquisition module, a data fusion module, a semantic reasoning module, an unknown topology prediction module and a strategy optimization module, the data acquisition module is used for acquiring network state information from different data sources, wherein the network state information comprises structured data and unstructured data; the semantic reasoning module is used for performing semantic reasoning on the non-structural data to generate structured network exception information; the data fusion module is used for performing standardization processing on the structured data acquired by the data acquisition module and the structured network abnormal information extracted by the semantic reasoning module to construct a network cognitive map; the unknown topology prediction module predicts the network state change based on the network cognitive map; and the strategy optimization module is used for determining a network optimization strategy according to the predicted network state change and issuing the network optimization strategy to the equipment for execution. Second-level sensing of the network state, intelligent fault positioning and autonomous optimization of a network strategy are realized by constructing a network cognitive map.

The invention discloses an intelligent flow table generation, management and control method and system for a giant satellite base satellite network, and belongs to the technical field of satellite communication and network control. In order to solve the problems that dynamic region division is rigid and low in management efficiency, topological evolution prediction lacks logical reasoning ability, and flow table generation is insufficient in automation and accuracy, satellite original state data is collected and converted into structured text description; determining a region boundary and a master control node based on the text description, and constructing a link prediction context; predicting a future link connection state and potential network congestion nodes; and generating a network strategy flow table according to the prediction result, performing compliance verification, and issuing for execution. According to the method, the automation, accuracy and management efficiency of satellite network flow table generation can be improved, network resource allocation is optimized, and network stability and service quality are guaranteed.

The invention discloses a network security policy optimization system and method based on artificial intelligence, and relates to the technical field of network security, and the method comprises the steps: obtaining network multi-source data, and building an event data model and a dynamic behavior relation graph; obtaining an attack evidence accumulation score of the suspicious security behavior for the network subject; obtaining a potential behavior attack chain, and identifying a key path and a key cut point of the development of the potential behavior attack chain; establishing a multi-level security policy set, and forming different policy candidate sets for the attack chain; and determining an optimal strategy combination and executing the optimal strategy combination. According to the method, the multi-source network data is adopted as initial data, data unification and structural processing are carried out, event behaviors are converted into structured evidences, and evidence accumulation is carried out, so that the attack evidence accumulation score is obtained, the problem that fragmented attacks and latent low abnormal behaviors are difficult to discover and defend by a network strategy is solved, and the security of the network strategy is improved. And the network security defense effect is improved.

Security, access and the way organizations communicate with their employees, contractors and customers is evolving faster than ever, and as the world is becoming more hybrid, security policies, monitoring and control must become collaborative and interoperable. The techniques described herein provide meaningful correlation and analytics of data coming from multiple sources in the network, access, security and identity, thereby improving troubleshooting, optimizations, threat forensics and analysis, as well as enabling network administrators more control over network policies.

The invention discloses a network access processing method, a wireless broadband terminal and a system, and particularly relates to the technical field of network access, and the method comprises the following steps: a multi-dimensional perception and information collection step: collecting and reporting multi-dimensional context information of equipment; an intention analysis and strategy binding step: inferring a service intention of the equipment based on the information and automatically generating and issuing a fine-grained network strategy; a safety evaluation and strategy correction step: continuously monitoring the safety state of the equipment and automatically triggering strategy correction according to the dynamic score; a flow self-learning and elastic scheduling step: analyzing and predicting a device flow mode, and dynamically adjusting the network bandwidth resource quota; and a session termination and resource cleaning step: automatically cleaning related strategies and recycling resources after the equipment is offline. According to the invention, by constructing an intelligent closed loop, the fundamental transformation from static configuration to dynamic scheduling based on intention and context is realized, and the automation, security and resource utilization efficiency of network access are remarkably improved.

The invention relates to an intelligent routing system and method based on a digital twin network and a neural network, and the system comprises a digital twin scene engine module which is used for constructing topologies corresponding to real networks in a one-to-one manner, and supporting the path planning of an initial path, cutover drilling, business migration, and a new business scene; the output end of the digital twin scene engine module is connected with the data acquisition module, and the data acquisition module is used for acquiring link information and forming graph structure features during the scene operation period; the output end of the data acquisition module is connected with a neural network strategy module, and the neural network strategy module is used for realizing path planning by adopting a graph neural network based on the graph structure characteristics and by taking a Qos service index as an optimization target; the output end of the neural network strategy module is connected with the service scene implementation module, and the service scene implementation module is used for implementing a predetermined service scene. According to the method, the optimal path in different operation and maintenance scenes such as cutover drilling, business migration and new business can be quickly planned.

Techniques are disclosed for a network policy validation system that can obtain a set of connection paths of a containerized environment, each of which indicates a connection between a pair of containers. A first container and a second container of the pair of containers are identified based at least in part on the connection paths. A network policy corresponding to the connection paths and indicating an expected outcome of the particular connection is determined. A connection between the two containers can be initiated. A result can be presented to a user device based at least in part on a determination that the outcome differs from the expected outcome indicated by the network policy corresponding to the connection paths.

The invention relates to a network strategy automatic generation system and method based on a large language model in an SDN environment, and the method comprises the steps: analyzing a natural language demand based on the large language model, and extracting a structured network intention; the network intention is converted into an OpenFlow flow table rule set; detecting the conflict between the new strategy and the existing strategy, and generating a solution; adjusting the priority of the flow table based on performance feedback, and combining redundancy rules; the strategy effect is simulated in the test environment, and deployment is performed after correctness is verified; and storing network topology, historical strategies, performance data and optimization experience.