2325 results about "Manet routing" patented technology

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

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

The invention provides an outbound routing method and system based on multi-dimensional index coupling and large model decision, and the method comprises the steps: carrying out the preprocessing of the historical data of each line provider, predicting the future performance index through an LSTM neural network, and optimizing the prediction result through a Kalman filter, and eliminating the noise. In the route decision-making link, the system calculates the Q value of each line provider based on the deep Q learning network, comprehensively considers the call completing rate, average cost, negative feedback rate and other factors of each line provider, and intelligently selects the optimal line provider in combination with the user portrait, scene recognition and real-time load state. The system also constructs a complete feedback closed loop, continuously optimizes the model by collecting call result data, and automatically adjusts decision parameters. The call completing rate and the service quality of the outbound service are improved, and the communication cost is reduced.

A routing method for supporting differential service requirements in a satellite internet comprises the following steps: step 1, constructing multi-dimensional network state perception as a base layer which is responsible for collecting and quantifying physical states and logic states of a satellite network in real time, and obtaining objective network states and subjective QoS (Quality of Service) requirements of services; 2, generating a nonlinear adaptive weight driven by a service as a strategy layer, mapping an objective network state and a subjective QoS (Quality of Service) demand of the service, dynamically adjusting the value orientation of routing judgment, and generating a dynamic weight; and step 3, taking the dynamic cost routing decision as an execution layer, calculating the comprehensive cost based on the dynamic weight, and executing next hop selection and path maintenance, and through cooperative work of the dynamic cost routing decision, the comprehensive cost and the next hop selection and path maintenance, realizing intelligent path selection and high-reliability transmission for different service flows in a high-dynamic and resource-limited environment of the low-orbit satellite. According to the invention, while the routing computation overhead is effectively reduced, multiple requirements of differentiated services on low time delay and high reliability can be considered.

A system is provided for dynamic analysis and verification of smart contracts. The system includes an autoencoder configured to preprocess smart contract code to reduce noise and highlight critical features; a capsule network configured to analyze the preprocessed smart contract code, capturing hierarchical relationships and dependencies within the code; a generative adversarial network (GAN) configured to generate optimal routing coefficients for the capsule network, enhancing the efficiency and accuracy of the analysis; and a blockchain-based platform for deploying and executing smart contracts, wherein the platform utilizes the capsule network to continuously monitor the smart contracts for anomalies during execution.

The invention relates to an underwater wireless sensor network path sensing routing method based on deep reinforcement learning, which comprises the following steps that: firstly, a node constructs and periodically updates a transmission preference model based on local and neighbor node interaction information; secondly, deploying a deep reinforcement learning model at each underwater sensor node to perform distributed routing strategy learning; and finally, generating a global guide vector by the sink node according to the routing path information of the received data packet, reversely spreading the global guide vector to the source node, fusing the global guide vector with a local transmission preference vector of the node to generate a guide reward, optimizing the deep reinforcement learning model, and updating a routing strategy. According to the method, the problems of difference and complexity of underwater transmission tasks can be solved, and the network data transmission efficiency and the overall service quality are improved in combination with local preference and global guidance while the node online learning is kept to adapt to the dynamic underwater environment.

The invention discloses a multi-task dexterous hand operation method based on self-adaptive two-way distillation, which comprises the following steps of: constructing a plurality of single-task expert strategies based on vision-touch pre-training, and collecting state-action tracks of a high-degree-of-freedom dexterous manipulator on various operation tasks; on-line distillation is utilized to distill multi-task expert data into a unified diffusion strategy sharing perception characterization and task coding, and modeling action multi-modal distribution is generated through diffusion type actions; a self-adaptive routing network is introduced, supervision weights of all experts are dynamically distributed according to the current observation state, and weighted distillation of the mixed experts is achieved; and after the performance of the unified strategy is superior to that of experts, performing reverse distillation on the unified diffusion strategy into expert strategies of all tasks by adopting action-reward combined constraint, and performing two-way iterative distillation circulation. Through forward and reverse knowledge migration between a unified strategy and a task expert, the problems of observation distribution offset and task imbalance in multi-task dexterous operation can be effectively relieved.

The invention discloses an attention-based context-aware sparse hybrid expert model routing method, which comprises the following steps of: encoding prompt information input by a user to obtain context vector representation of the prompt information, and introducing a multi-head attention mechanism to obtain multi-scale semantic interaction information; further, constructing a gating network based on attention output, and dynamically selecting Top-K expert networks for reasoning; by introducing a self-adaptive neighbor attention weight and a fusion gating mechanism, expert dispatching and weight fusion of a token level are realized; combining with the sub-output of each expert network, and aggregating according to the weight to obtain the final model output; according to the method, the understanding ability of the model for different semantic contexts is enhanced through a multi-expert structure and a dynamic routing mechanism, and the method is adaptive to multiple rounds of token generation processes, so that the accuracy and diversity of generated texts can be improved.

The invention discloses a dynamic routing parameter efficient fine tuning method and system based on LoRA-MoE, and relates to the technical field of large model fine tuning. The method comprises the following steps: firstly, constructing a heterogeneous expert architecture-based LoRA module pool based on a multi-field data set; and secondly, coding the hidden layer features of the task through a dynamic gating network, realizing continuous differentiable expert activation, and improving the balance of expert allocation by adopting temperature attenuation and entropy regularization constraint. And then, dynamically selecting and carrying out weighted fusion on a plurality of LoRA parameter increments according to task semantics in a reasoning stage, so as to realize low-cost model adaptive updating. Finally, the module pool is continuously optimized through the confusion degree and manual evaluation feedback, low-efficiency modules are automatically eliminated, and a new LoRA module is generated to maintain task coverage. According to the method, the accuracy and generalization ability of the model in a complex scene can be remarkably improved on the premise of ensuring light weight, and rapid adaptation and dynamic optimization of a large model under a low-resource condition are realized.

The invention provides a data center interconnection link fault self-healing and path switching method, which comprises the following steps of: acquiring multi-dimensional link performance monitoring data, establishing a standardized time sequence database, realizing link health degree multi-index trend prediction by combining an LSTM-Attention model, and determining the link health degree according to the LSTM-Attention model. Risk-aware dynamic path selection and switching for multiple business scenes are realized by combining business service sensitivity modeling, path inherent stability quantification, nonlinear adaptive switching criteria and a debounce switching process, the response accuracy and the business adaptation degree of path switching are improved, false triggering and service interruption risks can be remarkably reduced, and the path switching efficiency is improved. And the high availability and robustness of routing in a complex network environment are enhanced.

The invention relates to the technical field of network slicing, and discloses a wireless network multi-link communication method based on dynamic link configuration. The method comprises the following steps: collecting state data of heterogeneous links such as cellular links, Wi-Fi links and satellites in real time, and constructing a global network state view; analyzing the network slice SLA and converting the network slice SLA into a quantitative performance constraint; predicting the future performance of the link by using a gating circulation unit neural network; generating a global optimal data flow routing and distribution strategy meeting the QoS (Quality of Service) requirements of the slices in the central controller based on reinforcement learning; and compiling the strategy into a configuration instruction and issuing the configuration instruction to the terminal and the access point for execution. The system comprises a state sensing module, a strategy analysis module, a prediction modeling module, a strategy generation module and a configuration execution module. By means of predictive planning and intelligent decision making, dynamic, fine and on-demand scheduling of heterogeneous resources is achieved, and the network resource utilization rate and the multi-service service quality guarantee capacity are remarkably improved.

The invention discloses a liquid drop routing method under fluid constraint in a digital micro-fluidic biochip based on reinforcement learning and imitation learning, and aims to solve the technical problems of low planning efficiency, low success rate and weak generalization ability of a traditional liquid drop routing algorithm under fluid constraint. The method adopts a strategy combining imitation learning and reinforcement learning and comprises the following steps: firstly, generating expert data by utilizing an expert algorithm meeting fluid constraint after transformation, and pre-training a network model through imitation learning to enable the network model to quickly grasp a basic routing strategy; the model and environment interaction are autonomously optimized through reinforcement learning, and the adaptability to the dynamic environment is improved. And finally fusing the weights to form a final model. Through the hybrid training framework, the success rate and planning efficiency of droplet routing are remarkably improved, and the generalization ability of the model to chip environments with different sizes is enhanced.

The invention discloses an intelligent authentication system based on a gateway, and relates to the technical field of intelligent authentication. The system comprises a strategy integration module, an intelligent analysis module, a strategy decision module, a dynamic signature module and an authentication execution module. According to the invention, heterogeneous authentication strategies are uniformly managed through the strategy integration module, the intelligent analysis module carries out real-time risk scoring on access requests, the strategy decision module dynamically generates authentication strategy identifiers according to the risk scoring, the client is driven to generate signatures containing algorithm identifiers through the dynamic signature module, and finally the authentication execution module verifies the signatures and routes according to the signatures. According to the method, the adaptive matching of the authentication logic and the risk context is realized, and the problems that the adaptive capacity of an authentication mechanism in a dynamic risk environment is insufficient and intelligent and gradient authentication response cannot be realized in the prior art are solved.

The invention relates to the technical field of software development, in particular to a zero-code protocol analysis and full-link visualization commissioning and testing method for a heterogeneous terminal, which comprises the following steps of: acquiring protocol configuration information of the heterogeneous terminal input by a user through a visual interface, analyzing communication parameters and message rules in the protocol configuration information, and performing full-link visualization commissioning and testing on the protocol configuration information. Constructing a standardized protocol model instance; and initializing a communication server according to the communication parameters in the protocol model instance, and establishing a one-to-one mapping binding relationship between the network monitoring port and the protocol model instance. According to the method, the data flow of a network monitoring port is monitored, the corresponding protocol model instance is called based on the protocol routing rule to execute bidirectional data automatic conversion and full-link adjustment and test, a transparent test channel which is running as configuration is constructed, and the black box operation defect that rule definition and effect verification are separated in the past is overcome; and fusion and integration of massive stock terminals to a unified service platform are realized.

Techniques are described for enforcing the flow of traffic between VNICs using ZPR policy. A method includes accessing a ZPR policy that specifies how a flow of traffic is enforced between endpoints within the one or more networks, wherein the policy includes one or more layer 4 rules and one or more layer 7 rules; identifying from the ZPR policy, a ZPR statement that specifies a connection between a first virtual network interface card (VNIC) and an endpoint; generating, based on the ZPR statement, one or more network security group (NSG) rules; and distributing at least one of the one or more NSG rules to a first NSG associated with the first VNIC.

The invention belongs to the field of urban traffic management, and provides a hierarchical dynamic routing system and method for a computing power-energy fusion network, and the method comprises the steps: dividing a fusion network graph into a plurality of region sub-graphs, and dividing a path table and a connection point path table based on each region sub-graph; monitoring the running states of the road, the edge computing node and the charging station in real time, and predicting the road congestion intensity, the edge computing node queuing estimation information and the charging constraint; and determining a candidate path set in the partition path table and the connection point path table according to an online request of the vehicle, screening each candidate path in the candidate path set based on the road congestion intensity, the edge computing node queuing estimation information and the charging constraint, and determining an optimal path. According to the method, online retrieval is converted into rapid combination and evaluation of small candidate sets from full-graph traversal; traffic, energy and computing power are incorporated into the same decision framework, and systematicness deviation caused by evaluation of the future at present is avoided.

The invention discloses a multipath relay transmission method and system for underground confined space construction data, and belongs to the technical field of wireless communication network routing, and the method comprises the steps: obtaining a signal quality parameter of a communication link and a distribution density parameter of an ad hoc network node, and combining the load state of the node and the information of a neighbor node, decision calculation is executed on a plurality of relay nodes in parallel, relay decision vectors corresponding to the relay decision vectors are generated, a plurality of candidate transmission paths are formed through evaluation, construction data blocks to be transmitted are fragmented and distributed to the corresponding optimal candidate transmission paths to be sent in parallel, all received data fragmented sub-blocks are verified and recombined, and the construction data blocks to be transmitted are obtained. And recovering a complete construction data block. A distributed relay decision based on network state feature fusion, data adaptive fragmentation based on path stability and a multipath parallel transmission and feedback optimization mechanism are adopted, so that the data transmission reliability in an underground complex environment can be improved, the transmission delay is reduced, and the dynamic optimization of network performance is realized.

A system comprises a memory communicatively coupled to at least one processor. The at least one processor is configured to receive a request for data sharding operations from a user device, determine a node comprising multiple network resources configured to create a response to the request, receive sharding feedback from the node, execute the machine learning algorithm to determine whether the performance of the network resources match a predefined performance threshold, generate a proposed sharding strategy to increase the network resources at the node in response to determining that the performance of the network resources does not match the predefined performance threshold, provide the proposed sharding strategy to the node for implementation, and generate multiple sharding commands configured to control routing of data between the first node and the user device in response to receiving the access command; and route the request to the node using the sharding commands.

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 relates to the technical field of wireless communication, and particularly discloses a satellite link-based network outage emergency communication guarantee system, which is characterized in that each distributed communication node periodically acquires local state information such as channel quality, service load and self energy of adjacent nodes, and constructs a dynamically updated local network state matrix; the nodes generate connection decision parameters updated in real time through depth feature extraction and multi-target parallel evaluation calculation based on the matrix; each node autonomously executes the connection relationship adjustment with the adjacent node according to the parameters, and realizes the dynamic reconstruction of the network topology through distributed state synchronization; a reconfigured network forms a multi-hop forwarding path through distributed routing discovery taking a satellite gateway as a root, nodes perform aggregation scheduling and forwarding according to data priorities, and trigger local path re-optimization under continuous link quality monitoring, so that an emergency communication link leading to a satellite link is autonomously and reliably formed and maintained.

A system can execute a containerized application that comprises a microservice in a decentralized service mesh architecture, and a sidecar. The system can intercept, by the containerized application, a call from the microservice that is directed to a remote endpoint, and direct the call to the sidecar. The system can communicate, by the sidecar to an identity manager, service account credentials associated with the microservice, resulting in receiving an identity token associated with the microservice. The system can determine, by the sidecar, connectivity information of the remote endpoint based on a virtual address of the remote endpoint identified in the call. The system can communicate, by the sidecar to a token exchanger, the identity token and the connectivity information, resulting in receiving an access token and a network route to the remote endpoint. The system can relay, by the sidecar, network traffic between the microservice and the remote endpoint.

The invention relates to the technical field of network security and auditing, and discloses an auditing workflow of an auditing network security agent and a construction method of the auditing workflow. The method comprises the following steps: receiving a security task request, generating a unique identifier and creating a session context containing multiple types of policy rules; analyzing the task, and generating a tool calling sequence through tool routing matching; performing risk level evaluation on each tool call in the sequence, and triggering an approval process when a threshold value is exceeded; dynamically selecting an isolation environment and executing a tool sequence according to an approval result and a sandbox strategy; capturing output and a system state in real time in an execution process, recording a state difference through a difference tracking algorithm, and generating an audit event flow; and finally, integrating all events, approval records and results to form a structured audit log, and carrying out associative storage. According to the invention, dynamic approval triggering based on real-time risks and fine-grained auditing of influence on a tool operating system level are realized.

The invention discloses a reinforcement learning routing optimization method considering a dynamic underwater sound environment and node mobility. The method comprises the following steps: performing parameter initialization on an underwater sensor network; monitoring and updating underwater environment parameters and node positions in real time, and dynamically updating a network topology connection state; during data transmission, constructing a multi-dimensional state vector, selecting a routing action by adopting an epsilon-greedy strategy, and generating a routing decision; if the data packet transmission fails when the routing action is executed, ending the iteration, otherwise, calculating a multi-target reward value; updating the Q value table to optimize the routing decision; and monitoring network performance indexes in real time, adaptively adjusting reinforcement learning parameters of the intelligent agent for next iteration, and outputting an adaptive routing selection model until an iteration termination condition is met. According to the method, time-varying channel and node mobility challenges can be intelligently dealt with, and multi-objective optimization balance of energy consumption, delay and stability is realized while the data delivery rate is remarkably improved.

The invention provides an NoC low-delay data transmission method based on a dynamic routing algorithm, relates to the technical field of data transmission, and aims to solve the technical problems that an existing NoC routing algorithm cannot adapt to a link dynamic load, is lack of congestion trend prejudgment, is slow in path search convergence and is insufficient in QoS differential scheduling. The method comprises the following steps: constructing a dynamic sensing module containing a double-branch LSTM time sequence prediction model, collecting states such as link bandwidth and queue length in real time, and predicting a congestion trend in 50-100ms in the future; constructing a multi-objective evaluation function taking delay as a core, and dynamically adjusting the weight according to the QoS level; solving an optimal path by adopting an improved ant colony algorithm which introduces a congestion penalty and pruning strategy; during transmission, dynamic reselection is triggered through a pre-calculation + fast matching mechanism; and iteratively optimizing the prediction model based on incremental learning. According to the method, congestion is avoided in advance, the path search efficiency and the transmission reliability are improved, the delay is remarkably reduced compared with a traditional algorithm, and the method is adaptive to delay sensitive scenes such as a high-performance processor and an AI chip.

The micro-service processing system provided by the embodiment of the invention comprises an event identification module which is used for defining a business domain event type and registering an event attribute structure; the event bus module constructs a distributed communication network containing a dual-channel message queue, and the event bus module implements partition routing according to event types; the event processing module is composed of an event producer and a consumer which are executed asynchronously, the event producer packages service actions into a standardized event data packet, and the consumer processes subscription events through a thread pool; the event storage module adopts a hierarchical log database for persistence of an event sequence and supports reestablishment of a service state according to a timestamp; and the security control module is used for implementing service authentication and event content encryption on a transmission layer. Through event bus dual-channel design, sender box transaction binding and hierarchical storage optimization, millisecond-level event processing delay is realized while the reliability of distributed transactions is ensured, and second-level state recovery can be realized based on a complete event log when a service fault occurs.

A route calculation method, device, and system. The route calculation method includes: receiving, by a first device, a service request message, where the service request message is used to request the first device to determine a transmission path of a borne service requested by the service request message to be established; and determining, by the first device, a transmission path of the service based on the service request message and first bearing capability information, where the first bearing capability information is used to indicate an actual bearing capability of each link on the transmission path. According to the foregoing solution, the system can calculate a route and configure the service based on an actual service bearing capability of each link of a network.

The application discloses a kind of virtual-real fusion registration satellite internet digital twin deduction evaluation methods, comprising: S1.Satellite internet global network data is acquired, network data is collected and time synchronization interpolation is carried out, and network equipment data matrix is constructed;S2.based on network equipment data matrix, establish satellite network basic routing topology graph;S3.based on satellite network basic routing topology graph, construct by user satellite network dynamic service demand vector, establish satellite network dynamic routing model;S4.based on network dynamic routing model, establish satellite internet digital twin deduction model;S5.based on internet digital twin deduction model, introduce real data and carry out pseudo-real correction, realize the virtual-real fusion digital twin dynamic deduction of all stages;S6.multiple dimensions index and evaluation function are established, and score is calculated.The application can overcome virtual-real mapping distortion, evaluation dimension single and dynamic adaptability insufficient and other problems, complete accurate deduction evaluation to satellite internet.

The invention discloses a software and hardware cooperation system and method for satellite network routing simulation, and mainly solves the problem that an existing simulation scheme is difficult to consider high fidelity of protocol behaviors and high performance of data forwarding in a high dynamic satellite network. The system comprises a software control plane unit, a hardware data plane unit, a satellite network simulation upper computer and a switch, and the switch is bidirectionally connected with the software control plane unit, the hardware data plane unit, the satellite network simulation upper computer and the switch and serves as a data exchange platform of the system. The satellite network simulation upper computer is used for system configuration management, operation state monitoring and result visualization; the software control plane is used for executing simulation calculation and routing decision of a satellite network routing protocol and generating a standardized forwarding table item; and the hardware data plane unit performs high-speed and deterministic table lookup forwarding according to the standardized forwarding table item, and simulates a satellite resource constraint environment. According to the method, the integrity of protocol simulation details is guaranteed, meanwhile, high-efficiency forwarding performance and non-interruption updating in a high-dynamic environment are achieved, and the method can be used for routing protocol verification, network performance testing and satellite-borne algorithm evaluation of large-scale low-orbit satellite constellations.

The invention particularly relates to an incremental scheduling method for deterministic services of a time-sensitive network, and belongs to the technical field of scheduling of the time-sensitive network. Aiming at the problems of high conflict detection overhead under dynamic access, large search scale of a long-short period mixed scene, reduced schedulability caused by time slot fragments and the like, the method comprises the following steps: constructing a scheduling model, and performing multi-rule sorting and route pre-selection on to-be-accessed services; time granularity scaling and periodic modular domain projection technologies are introduced, multi-link and multi-period conflict detection is converted into bitmap operation in a single period, and a modular domain taboo table is rapidly generated; a conflict-free sending phase is efficiently searched based on bitmap operation, a comprehensive cost function integrating fragment sensing, time delay and load balancing is adopted for multi-dimensional evaluation, and an optimal scheduling scheme is selected. According to the method, the efficiency and the real-time performance of online incremental scheduling can be remarkably improved, the search overhead is reduced, time slot fragments are reduced, and the service scheduling success rate in a high-load scene is improved.

The invention discloses a cross-virtual private cloud network scheduling method based on a cloud computing infrastructure, a cross-virtual private cloud network scheduling system based on the cloud computing infrastructure, electronic equipment and a readable storage medium, and the method comprises the steps: obtaining network configuration information of a virtual private cloud; establishing a cross-virtual private cloud access channel according to the network configuration information, wherein the access channel comprises a peer-to-peer connection channel and a terminal node channel; configuring an address translation rule and a routing rule for the access channel; and carrying out forwarding and access control on the network access flow among the applications and / or the computing resources in the virtual private cloud according to an address conversion rule and a routing rule. Uniform configuration and dynamic management of the network access path are realized while independence of each virtual private cloud is kept, so that the access relationship across the virtual private cloud is clearer, configuration is more concentrated, operation and maintenance are more simplified, and interconnection flexibility and manageability in a cloud multi-network environment are improved.