14 results about "Lyapunov optimization" patented technology

The application discloses a server energy efficiency dynamic optimization method based on workload prediction and a deep learning model, relates to the cross field of server energy efficiency optimization and deep learning technology, and comprises the following steps: collecting server multi-source data, performing space-time alignment on the server multi-source data, extracting causal characteristics, constructing a standardized data set, constructing a time series graph convolution network, combining a multi-head attention mechanism, outputting a probabilistic load prediction interval, constructing an energy efficiency objective function based on a Lyapunov optimization framework, quantifying performance loss and temperature drift, and outputting multi-agent optimization constraints, defining a multi-reinforcement learning agent, outputting a collaborative adjustment instruction through a counterfactual baseline algorithm, constructing a digital twin shadow model to deduce energy efficiency, combining a meta-learning fine-tuning model, forming a closed-loop optimization of physical and digital double verification, realizing dynamic and accurate optimization of server energy efficiency, and adapting to multiple computing power scenes.

The application discloses an edge intelligent body flexible scheduling method based on Lyapunov optimization and Stackelberg game. The application constructs a cloud-edge-end three-layer edge intelligent body federated learning network architecture to complete distributed collaborative training under the premise of protecting data privacy. In view of the problems of heterogeneous edge intelligent body network nodes, non-independent and identically distributed data and limited communication resources, a long-term joint optimization model of client selection and bandwidth allocation is established, and model accuracy, time delay and energy consumption are considered. Lyapunov optimization is used to decouple long-term random optimization into real-time decision-making per time slot, and to convert constraints into virtual queue stability control. Dynamic client selection is realized based on Stackelberg game, and high-quality nodes are adaptively selected. Adaptive bandwidth allocation is completed through an evolutionary algorithm, and communication requirements of key clients are preferentially guaranteed. The application can significantly improve model accuracy, reduce time delay and energy consumption, guarantee long-term stability of the system, and is suitable for high-dynamic edge intelligent body federated learning scenes.

This invention discloses a federated learning method for intelligent IoT, comprising: dividing the global model into continuous fragments on the server side, configuring lightweight auxiliary head and tail segments to simulate contextual features; introducing an event-triggered resource monitoring and Lyapunov optimization-driven dynamic fragment adjustment mechanism; employing fragment-level knowledge distillation to achieve cross-fragment knowledge transfer, combined with split gradient descent optimization. This invention solves the problems of heavy communication and computational burdens and poor adaptability in resource-constrained scenarios of existing federated learning and fragmented federated learning technologies. This invention can reduce device storage, computation, and communication overhead, adapt to dynamic resource changes, improve model convergence speed and consistency, and performs excellently under both IID and Non-IID data distributions, making it suitable for distributed training scenarios of heterogeneous AIoT devices.

This invention provides a task collaborative reasoning method, apparatus, electronic device, and medium, relating to the field of computer technology. The method includes: dividing a task arriving on a mobile device into multiple subtasks and constructing an offloading strategy for each subtask to characterize execution on the mobile device or MEC server; constructing an average overhead minimization problem for all mobile devices within a time slot for each mobile device; reconstructing the average overhead minimization problem using Lyapunov optimization theory to obtain an independent time slot policy optimization problem corresponding to the average overhead minimization problem; transforming the independent time slot policy optimization problem into a Markov decision process and solving the Markov decision process using a deep reinforcement learning algorithm to obtain the optimal offloading strategy, optimal mobile device transmission power, and optimal number of threads allocated to the MEC server for each mobile device. The invention effectively utilizes distributed computing resources and significantly improves reasoning efficiency.

This invention discloses a client selection method and system for multi-task federated learning. Addressing the shortcomings of client selection and insufficient consideration of task urgency in multi-task dynamic federated learning scenarios, this invention first constructs a multi-task federated learning system model, defining a utility function that includes learning quality and penalty terms. Second, it establishes fairness constraints and introduces a fairness queue to transform the problem into a queue stability problem. Then, based on Lyapunov optimization theory, it constructs a drift-plus-utility function, transforming a long-term stochastic optimization problem into a deterministic optimization problem for each round of communication by minimizing its upper bound. Finally, it constructs an auxiliary bipartite graph to transform client selection into a minimum-weight bipartite graph matching problem. This invention, by jointly optimizing fairness, learning quality, and task urgency through the Lyapunov framework, transforms long-term constraints into a solvable problem for each round, reducing computational complexity and achieving efficient and fair dynamic client selection.

The application discloses a multi-modal scheduling method based on early exit under edge-limited equipment and relates to the field of resource offloading.The method comprises the following steps: establishing a multi-modal scheduling framework, including an edge equipment end and a server end; obtaining a plurality of early exit nodes by using an early exit mechanism; under the constraint of maximum delay, establishing a multi-modal offloading scheduling system model based on Lyapunov optimization according to network environment information and the early exit nodes, and taking Lyapunov drift penalty minimization as an optimization objective; generating a modal offloading strategy by using the multi-modal offloading scheduling system model; generating a first modal recognition result at the edge equipment end according to the modal offloading strategy, and transmitting the second spectral data to the server end after adjusting the resolution of the second spectral data, so that the server end generates a second modal recognition result; and performing probability integration on the first modal recognition result and the second modal recognition result at the edge equipment end, so as to obtain a prediction result.Compared with the prior art, the application can guarantee system stability and model processing accuracy and can improve the processing speed of the system.

The application discloses a kind of multilayer edge cache network cooperative optimization method and system, it is related to edge computing and wireless communication technical field, this method considers the freshness (information age) of content, cache update cost and the dynamic change of user request simultaneously in content cache and update process, propose a new cache timeliness reward model, build a collaborative caching mechanism integrated with global optimization and local adaptive adjustment.The optimal update frequency of content is determined based on long-term popularity prediction on the MBS side to reduce update redundancy and ensure the timeliness of content;SBS side uses a dynamic adjustment strategy combining Lyapunov optimization and popularity prediction to maintain system stability while optimizing cache content in real time, improving the accuracy and timeliness of user content access.This method effectively improves the service quality of content distribution in edge network, reduces the cache update cost, and is suitable for vehicle networking, smart city and other scenarios with high information timeliness requirements.

This application discloses a collaborative control method and system for intelligent edge gateways based on dynamic weights, relating to the fields of Industrial Internet of Things (IIoT) and edge computing. The method includes: collecting multimodal data from heterogeneous devices; fusing the multimodal data to obtain fused data; uploading the fused data to a cloud platform based on a Lyapunov optimization algorithm; receiving control commands from the cloud platform and performing security verification on the control commands; parsing the verified control commands and performing collaborative decision-making using a state prediction model based on an encoder-decoder architecture; converting the control commands into control frames of a target device's dedicated communication protocol based on a dynamically configurable protocol description matrix; and sending the control frames to the target device; receiving the response from the target device and monitoring the execution status of the target device. This invention achieves optimal efficiency, reliability, and energy efficiency in data processing, transmission, and control in IIoT scenarios.

The application relates to a kind of unmanned aerial vehicle auxiliary-based vehicle task unloading method, its steps are: first, the task queue model, calculation model, communication model of vehicle, roadside unit and unmanned aerial vehicle and the energy consumption and collection model of unmanned aerial vehicle are constructed;Second, under the constraint conditions such as meeting task queue stability, long-term energy sustainability of unmanned aerial vehicle, the optimization problem with the minimum long-term average total task processing delay of system as target is constructed, and the long-term optimization problem is converted into deterministic single time slot optimization problem using Lyapunov optimization;Finally, in each time slot, according to the current system state, the deterministic optimization problem is solved using genetic algorithm, so that the task unloading and unmanned aerial vehicle path planning scheme of current time slot are obtained.The method can effectively improve the task processing efficiency and guarantee the system queue stability, reduce the task processing delay, and thus improve the overall performance of the vehicle edge computing network in the dynamic uncertain environment combined with the Lyapunov optimization framework.

The application discloses a kind of low-carbon dam area intelligent energy management and optimization method and device based on energy carbon double control, it is related to energy management and carbon emission control field, the method includes based on the real-time acquisition of each type sensor deployed dam area's energy consumption and carbon emission related data, and the data collected are preprocessed;Energy-carbon coupling model is constructed in dam area and is combined with the data collected, and the energy consumption accounting of dam area full life cycle, dam area full life cycle carbon emission accounting, energy consumption and carbon emission law accounting are carried out;According to the processing result of energy-carbon coupling model in dam area, through the hybrid algorithm constructed by combining NSGA-II and Lyapunov optimization, energy-carbon collaborative optimization strategy is formulated.The application can realize the collaborative management of energy consumption and carbon emission.

This invention discloses a low-carbon economic scheduling method for virtual computing power centers based on Lyapunov optimization, belonging to the field of collaborative optimization of power and computing resources. The method first integrates heterogeneous computing power nodes through a distributed computing power resource aggregation management platform, transforming them into equivalent CPU computing power to form a computing power cluster. It then combines energy storage systems with grid power purchase and sale to construct a comprehensive energy management system, establishing a net benefit maximization optimization objective and related constraints. Next, it introduces task backlog and virtual carbon deficit queues to handle coupled constraints. Finally, based on Lyapunov drift-reward theory, the long-term stochastic optimization problem is decoupled into two real-time solvable subproblems: computing power allocation and energy storage-carbon arbitrage, generating optimal scheduling instructions. This invention requires no predictive information, achieving low-carbon economic scheduling of computing power resources while meeting long-term carbon constraints and service quality requirements, thus improving the utilization efficiency and overall net benefit of computing power resources.

The present application relates to the field of information and communication technology, and discloses a kind of rural scenic spot-oriented resilience edge guide system and method.The basic principle of the method is that: the dynamic organization of tourist terminal is delay tolerant network node to realize data opportunity relay transmission under the environment without public network, while the lightweight artificial intelligence model is deployed on the local edge server to understand the semantics and knowledge reasoning of the request, and the model calculation accuracy and network communication power consumption are jointly regulated based on Lyapunov optimization theory. Therefore, the present application realizes the core technical effect that intelligent guide service can still run stably and continuously and provide depth associated narrative under the harsh conditions of rural scenic area network and resource double restriction.

This invention discloses an online optimization scheduling method for low-carbon multi-energy coupled systems based on Lyapunov optimization, belonging to the field of online optimization scheduling technology for low-carbon energy systems. The method involves constructing a low-carbon multi-energy coupled system architecture; building a virtual energy storage queue to reflect the current operating state of the system based on the dynamic changes in electrical energy, hydrogen energy, and carbon dioxide during system operation; constructing a Lyapunov function based on the virtual energy storage queue to reflect the dynamic changes in the system's energy state; and building a cyclic hydrogen economic operation model based on the Lyapunov drift-penalty function according to the system operating cost index. Under the constraints of system power balance and mass conservation, the low-carbon multi-energy coupled system is continuously optimized online. This invention can achieve coordinated optimization of economic benefits and carbon emission reduction effects, improving the stability, real-time performance, and sustainability of low-carbon integrated energy system operation.

The application provides an online task offloading and resource allocation joint optimization method for wireless energy supply edge computing, comprising the following steps: S1, establishing a basic framework of a wireless energy supply assisted mobile edge computing model under a random task data arrival and time-varying channel scenario; S2, according to the basic framework of the wireless energy supply assisted mobile edge computing model, performing model establishment on mobile edge computing resource allocation to obtain a mathematical model; S3, using Lyapunov optimization combined with deep reinforcement learning based on a convolutional neural network and a convex optimization method to maximize the weighted sum of the computing rates of all wireless devices and jointly optimize offloading decisions, local computing frequencies of the wireless devices, offloading transmission time allocation and offloading energy. The application solves the problem of wireless energy supply edge computing online task offloading under the conditions of a time-varying channel and random task arrival, and guarantees long-term offloading benefits of the wireless devices and long-term stability of the system through the system.