154 results about "Energy constraint" patented technology

The invention discloses a federated learning terminal selection and resource scheduling method based on a dynamic priority, and relates to the technical field of user selection and bandwidth allocation of federated learning, and the method comprises the steps: obtaining a model basic structure and resource information parameters exchanged between a federated learning central controller and a user; calculating a priority value of each user according to the resource information parameters, and sorting the users according to the priority values from large to small; selecting a user subset meeting a training delay requirement based on a user priority sequence; for the user subset, calculating the minimum bandwidth allocation proportion required for completing the training task, and calculating and considering the data volume, the calculation capability and the network condition of the user; bandwidth resources are allocated to the user subsets by adopting a dichotomy, and bandwidth allocation is adjusted through iteration by adopting the dichotomy; and judging whether the total energy loss of the user subset exceeds a given energy constraint threshold, and if so, deleting the user with the lowest priority from the user subset. The federal learning performance is improved to the maximum extent.

The invention provides a virtual power plant group resource scene adaptive scheduling method and system, and a storage medium, and the method comprises the steps: building a typical external feature model of a virtual power plant based on the resource characteristics and core parameters of different types of distributed resources; generating a feasible region of the single equipment based on power constraint, electric quantity constraint and climbing constraint of the single equipment in the virtual power plant, and aggregating the feasible region of the single equipment to form an aggregated feasible region of the virtual power plant; based on a typical external feature model of the virtual power plant and different service scene requirements, dynamically adjusting response capability index weights in different service scenes, and based on an aggregation feasible region of the virtual power plant, constructing a virtual power plant dynamic aggregation model adapted to multiple scenes; and solving the dynamic aggregation model of the virtual power plant by taking minimization of the power generation cost of the virtual power plant as a target to obtain an optimal scheduling scheme of the virtual power plant.

A distributed system and method for compressing and restoring data across edge computing devices and cloud infrastructure is disclosed. The system dynamically adjusts compression based on available computing resources, network conditions, and now energy constraints. Edge devices monitor power consumption and battery levels, optimizing compression parameters to extend battery life while maintaining data quality. A workload scheduler prioritizes tasks based on energy availability, offloading intensive processing to cloud infrastructure when necessary. The system utilizes an energy-aware coordination layer to balance workloads across multiple devices, ensuring efficient data flow and long-term operational stability. Homomorphic operations allow secure distributed processing on compressed data, while an adaptive neural upsampler enhances reconstructed outputs. By integrating energy optimization, the system improves performance and longevity of edge devices in power-limited environments.

The invention relates to the technical field of comprehensive energy optimization scheduling, in particular to a shared energy storage optimization scheduling method and system considering island operation. According to the shared energy storage optimization scheduling method, a double-layer optimization framework of a double-layer energy storage optimization model is adopted, and island operation constraints of regional integrated energy subsystems are introduced on the optimization basis of minimizing the operation cost of a multi-regional integrated energy system; a real-time shared energy storage scheduling scheme obtained by solving a double-layer energy storage optimization model not only can optimize the operation cost of the multi-region integrated energy system based on historical operation data and real-time operation data, but also can meet the island operation emergency energy constraint requirement of a region integrated energy subsystem; the power grid disconnection risk caused by the real-time load change of the system is handled, the power supply reliability of the system under the extreme working condition is improved, and then the robustness of the energy storage dynamic correction strategy of the multi-region integrated energy system is improved.

The invention discloses a flexible wire harness assembly force control method based on reinforcement learning, and aims to solve the problems of inaccurate phase switching, impact and semi-locking caused by strong nonlinear resistance of a connector sealing ring and a buckle. According to the method, a phase gating threshold value and energy budget are generated through multi-modal sensing and phase probability estimation, reinforcement learning, a trajectory is generated under energy guidance through condition diffusion of phase gating, a passive consistent trajectory is obtained through energy projection and passive projection, variable impedance force control parameters are synthesized, and online energy shaping and closed-loop updating are implemented. The technical effects that the impact peak value and the semi-locking rate are reduced, instantaneous power and accumulated injection energy constraints are met, and the in-place locking success rate and the assembly stability are improved are achieved.

The invention discloses a force control joint dynamic error compensation method based on Kalman filtering, and the method comprises the steps: collecting and preprocessing multi-source signal data, and forming a force control joint input data set; constructing an energy hierarchical Kalman filtering model, executing energy constraint correction and outputting state estimation; calculating a prediction residual error, carrying out time-frequency analysis, adjusting a compensation gain, and generating residual error information; torque and angular velocity signals are extracted, a semantic state is recognized, and semantic gating parameters are output; fusing the state estimation, the residual information and the semantic parameters to generate a dynamic compensation instruction signal; energy layer and residual information changes are monitored, self-calibration is triggered, and a feedback closed loop is formed. According to the invention, by introducing energy hierarchical Kalman filtering, time-frequency modulation compensation and a semantic gating feedback mechanism, dynamic error self-adaptive accurate compensation of the force control joint in a complex multi-disturbance environment is realized.

The invention provides an ocean satellite Internet of Things multi-modal data narrow-band transmission scheduling method and device, belongs to the technical field of ocean satellite Internet of Things, and innovatively provides a multi-modal data narrow-band transmission scheduling strategy for challenges of ocean observation equipment in an energy-limited and dynamic satellite channel environment. The core of the method comprises the following steps: constructing a multi-modal data dynamic transmission model, and carrying out data value evaluation based on space-time intelligence; designing a congestion perception and hierarchical congestion control strategy to guarantee data timeliness; the transmission fairness is optimized in combination with the data value weight factor, the PAoI and the Jain fairness index; an energy dynamic evolution model is constructed, and long-term energy revenue and expenditure balance of equipment is guaranteed; based on a deep Q network and imitation learning, in combination with a security reinforcement learning framework, information timeliness, energy consumption and congestion are jointly optimized. According to the method, the deep and far sea observation and detection data transmission time delay, energy consumption and the congestion occurrence rate are remarkably reduced, and the information timeliness and the multi-modal data transmission scheduling fairness are improved.

The invention discloses a transformer area-oriented mobile energy storage scheduling method, which comprises the following steps of: acquiring historical and real-time measurement data of each node based on a three-phase line topological structure and line parameters of a transformer area; respectively establishing a quantile time sequence prediction model for the load and the photovoltaic output of each node in the prediction time period, and obtaining a quantile prediction set based on the load and the photovoltaic output; generating scenes / samples according to the quantile prediction set, and load and photovoltaic samples of each node at each moment contained in each scene; establishing a three-phase unbalanced power flow model satisfying a branch balance and power flow relationship, and establishing a mobile energy storage model based on node selection and energy constraint; for a three-phase unbalanced power flow model and a mobile energy storage model, joint optimization of a rolling time domain is established, and the optimization takes expected operation cost minimization as a target; and the optimized three-phase unbalanced power flow model and the mobile energy storage model are used for scheduling, so that the problems of safe operation and efficient absorption of the high-permeability photovoltaic transformer area are solved.

The invention discloses a marginal device intelligent sensing method and system based on information freshness, and a Markov decision process model is constructed by analyzing key factors in the marginal device intelligent sensing system. And secondly, the system state is updated in real time through dynamic adjustment of the sensing state, and parameters of equipment are adjusted according to new environment changes, so that the system can quickly respond in a complex environment. In the aspect of joint optimization, joint optimization is carried out on information age and energy consumption by utilizing a Lagrange multiplier method, and the freshness of information is maximized while energy constraints are met. And finally, through the deep Q network, the system can continuously optimize a decision strategy through an experience playback mechanism, so that the edge equipment can minimize the information age under the condition of limited energy in a continuously changing environment. According to the invention, the timeliness of sensing information is improved.

The embodiment of the invention provides a multi-unmanned aerial vehicle data acquisition task planning method and device considering communication interruption. The method comprises the steps that a working scene is set, the working scene comprises a plurality of unmanned aerial vehicles and a plurality of to-be-collected data points (PoI), and the unmanned aerial vehicles need to plan routes to go to the PoI to execute data collection tasks; under the working scene, an unmanned aerial vehicle data acquisition model and an unmanned aerial vehicle communication interruption model are established, an optimization target of a multi-unmanned aerial vehicle data acquisition task planning problem considering communication interruption is set as data quality sum maximization, and corresponding constraint conditions are set as position constraint, energy constraint and communication interruption risk constraint. Based on this, a partially observable Markov game model of a multi-unmanned aerial vehicle data acquisition task planning problem considering communication interruption is established, and a multi-agent reinforcement learning algorithm is adopted to solve the partially observable Markov game model. In this way, multi-unmanned aerial vehicle data acquisition task planning can be performed in consideration of communication interruption.

The invention provides an energy distribution optimization method and device, electronic equipment and a storage medium, and can be applied to the technical field of artificial intelligence. The method comprises the following steps: acquiring energy purchase cost data and energy supply attribute data of an energy supplier in a target scheduling period and energy demand attribute data and energy constraint attribute data associated with each energy load area; the energy purchase cost data, the energy supply attribute data, the energy demand attribute data and the energy constraint attribute data are processed based on an energy distribution prediction model, energy distribution information is obtained, and the energy distribution prediction model comprises a first-stage distribution sub-model constructed based on a multi-objective optimization function and a constraint condition set; and determining a target energy distribution scheme corresponding to the energy load area according to the energy distribution information. Cooperative optimization of key targets of cost, reliability and timeliness is realized in a target scheduling period, and the overall robustness and feasibility of an energy distribution scheme in a complex and uncertain environment are remarkably improved.

The invention discloses a performance simulation calculation method for an aviation hybrid electric power system. The method comprises the following steps: firstly, constructing a sub-component calculation model covering an aerodynamic thermal component, a power generation subsystem, a power conversion and transmission subsystem, a battery subsystem, a motor and an aerodynamic system; establishing a reverse logic calculation framework driven by a load side demand, and realizing hierarchical reverse parameter solution from a load end to a power source end; and establishing a power balance equation at a bus end, carrying out iterative solution by adopting a Newton-Rafson algorithm, and realizing dynamic distribution and coordination control of multi-source energy in combination with an energy constraint module. According to the method, through a reverse solution and bus end balance strategy, the simulation calculation efficiency and convergence of the complex aviation hybrid electric power system are remarkably improved, and meanwhile, the adaptability and expandability of the model to different topological architectures and task profiles are enhanced; and an efficient and high-credibility calculation tool is provided for design, performance evaluation and energy management strategy optimization of the hybrid electric propulsion system of the aircraft.

The invention provides a decentration convex optimization method and system for enhancing AGV edge computing energy efficiency, and the method comprises the steps: building a task unloading model based on an edge computing network; based on a preset base station index set, an AGV index set and the calculation task of the AGV, obtaining a state space and an action space of the task unloading model; establishing a flow model, a calculation model and a communication model; integrating the models to obtain a joint optimization problem of task unloading and resource allocation; decomposing the joint optimization problem into a task unloading problem and a resource allocation problem by adopting a hierarchical decomposition method; a probability task unloading strategy is adopted, load prediction is carried out on a task unloading problem, a resource allocation problem is decomposed and convexly optimized, and a joint optimization problem is solved in combination with a load prediction result. The invention aims to realize calculation unloading satisfying energy constraint in the AGV edge calculation network so as to reduce the time delay of the edge calculation network and promote the development of the edge calculation technology.

The application discloses a base station intelligent energy coordination method and system, and belongs to the technical field of energy management, and the technical solution points comprise the following steps: collecting a data set of each base station in a regional base station cluster; calculating energy constraint data based on energy data and environment data to obtain an energy scheduling sequence of each base station; obtaining a load prediction curve of each base station through a preset LSTM network model based on load data to determine a first base station; and obtaining a corresponding second base station and a third base station according to the load prediction curve of the first base station. The application obtains the load prediction curve of each base station through the preset LSTM network model and determines the first base station, obtains the second base station through the analytic hierarchy process, and obtains the third base station through multi-target reinforcement learning, so that the energy scheduling of the regional base station cluster is realized, the clean energy utilization efficiency and the energy scheduling precision are improved, and the stability of base station operation is improved.

A distributed system and method for compressing and restoring data across edge computing devices and cloud infrastructure is disclosed. The system dynamically adjusts compression based on available computing resources, network conditions, and now energy constraints. Edge devices monitor power consumption and battery levels, optimizing compression parameters to extend battery life while maintaining data quality. A workload scheduler prioritizes tasks based on energy availability, offloading intensive processing to cloud infrastructure when necessary. The system utilizes an energy-aware coordination layer to balance workloads across multiple devices, ensuring efficient data flow and long-term operational stability. Homomorphic operations allow secure distributed processing on compressed data, while an adaptive neural upsampler enhances reconstructed outputs. By integrating energy optimization, the system improves performance and longevity of edge devices in power-limited environments.

The invention discloses a shared energy storage fine scheduling method considering multi-scene uncertainty and hierarchical decision, and the method comprises the steps: building system power-energy constraint modeling based on a typical scene set; modeling is carried out based on a typical scene set and system power-energy constraint, electricity purchasing and selling, energy storage efficiency and life degradation cost are comprehensively counted in multiple scenes, a multi-target scheduling model is formed, and opportunity constraint is introduced to ensure supply and demand reliability; based on a multi-target scheduling model, constructing a hierarchical decision-making model which takes a shared energy storage operator as a main part and takes a plurality of micro-grids / communities as an auxiliary part, and establishing a coupling constraint and power balance relation of energy storage charging and discharging-energy state-capacity leasing price; an energy storage dynamic rental price, capacity distribution and a time-sharing charging and discharging plan are obtained by adopting distributed iteration solution, and income allocation can be carried out in combination with an improved Shapley mechanism. According to the invention, the optimal decision of consumption improvement, energy consumption cost reduction and multi-agent coordinated operation under the condition of high-proportion access of new energy can be realized.

This invention relates to the field of multi-processor multi-task joint scheduling technology, specifically disclosing a typed task joint scheduling system and method based on a heterogeneous multi-core architecture. It introduces a joint scheduling mechanism to divide and sort tasks according to task size and priority. Based on this, for the time-limited characteristics of real-time tasks, an inertial weight coefficient particle swarm optimization method is used to iteratively update the load balancing strategy, minimizing the maximum response time of real-time tasks while meeting their schedulability requirements. Simultaneously, for the low-priority characteristics of non-real-time tasks, the problem-solving is simplified using a Lagrange-based convex optimization approach, and an energy-constrained binary search algorithm is employed to effectively reduce the average response time of non-real-time tasks, achieving optimal load distribution for the system.

The invention discloses an unmanned aerial vehicle flight control method and system based on a Mangbar and a dual-channel attention mechanism under wind field disturbance, and particularly relates to the technical field of unmanned aerial vehicle autonomous navigation and intelligent control. The method comprises the following steps: constructing a wind field disturbance model based on computational fluid mechanics, and simulating an unsteady wind field environment among urban building groups; designing a deep reinforcement learning network fusing a Mama framework and a double-channel attention mechanism, wherein the deep reinforcement learning network is used for realizing dynamic modeling of long-sequence wind field features and attention weighting of key spatial-temporal features; a dynamic road sign point guiding mechanism and a multi-stage attenuation greedy strategy are put forward, the sparse reward problem is relieved, and the local path optimization capability is improved; and a multi-target composite reward function including energy constraint, safety distance and path efficiency is constructed, and global path planning of energy consumption perception is realized. The method can improve the flight stability, path smoothness and energy efficiency of the unmanned aerial vehicle in a strong wind disturbance environment, and is suitable for unmanned aerial vehicle autonomous navigation tasks in an urban complex environment.

The invention discloses a new energy consumption collaborative optimization method and system based on a system new energy utilization rate. The method comprises the steps that the system new energy utilization rate is calculated according to a single equipment basic attenuation index, temperature efficiency, humidity corrosion efficiency and maintenance efficiency recovery amount; according to a real-time correlation mechanism of the carbon counteracting intensity and new energy output, new energy constraints are constructed, and the new energy constraints comprise a carbon emission intensity constraint and a multi-time scale coupling constraint; and under the carbon emission intensity constraint and the multi-time scale coupling constraint, establishing a collaborative optimization objective function according to the system new energy utilization rate, and solving the multi-objective collaborative optimization objective function to obtain a new energy consumption collaborative scheduling scheme. The new energy utilization rate of the system is effectively improved, the carbon emission intensity is reduced, internal optimization is carried out on a high-proportion new energy power system, and a scheduling strategy is determined.

The invention relates to the technical field of delay optimization, in particular to a multi-user cooperative reasoning delay optimization method and device based on the batch processing technology, and the method comprises the steps: obtaining reasoning tasks of a plurality of users, and dividing the reasoning tasks into a plurality of subtasks; traversing the segmentation points of the plurality of subtasks through a target iterative traversal algorithm to find an optimal task segmentation point of the plurality of subtasks, and dividing the plurality of subtasks into a server reasoning subtask and a local reasoning subtask based on the optimal task segmentation point; and calculating the minimum total completion time of the server reasoning sub-task and the local reasoning sub-task based on the actual broadband and the energy constraint condition, and generating a delay optimization strategy of the cooperative reasoning task of the multiple users. According to the method, the task processing delay can be reduced, the system performance of multi-user cooperative reasoning can be effectively improved, the energy consumption of the user equipment can be reduced, the method has relatively high applicability and robustness, and powerful support is provided for application of edge calculation in the fields of Internet of Things and artificial intelligence.

The invention provides a space refrigeration load regulation type virtual power plant regulation characteristic and boundary modeling method. The method comprises the following steps: constructing a space refrigeration load monomer discrete physical model; constructing a load polymer model by integrating a consistency control algorithm and the load monomer discrete physical model; using statistics to represent load polymer model parameters and curve parameters; calculating polymer model parameters based on the load equipment monomer parameter statistics; based on the difference between the basic model time scale and the actual scheduling time scale, updating the multi-time scale aggregation model parameters; and based on the updated multi-time scale aggregation model, calculating an adjustment boundary by comprehensively considering power constraint and energy constraint, and quantitatively evaluating the maximum up-regulation power and the maximum down-regulation power of the virtual power plant under the specified adjustment duration. According to the method, the adjustment characteristics and the adjustment boundary of the space refrigeration type load virtual power plant are accurately described, a parameter dynamic updating mechanism is designed for different scheduling cycles, and the time scale adaptability of the model is enhanced.

The invention provides a spectrum sensing internet of things communication fair access method and system, and the method comprises the steps: building a spectrum sensing internet of things communication system model which is a model integrating cognitive radio, energy collection and environment backscatter communication; according to the spectrum sensing-based internet-of-things communication system model, energy constraints, time resource constraints and QoS constraints of a main communication link are considered, and a fair access problem of throughput maximization of a spectrum sensing internet-of-things communication system is constructed; and converting the fair access problem into a convex optimization problem, and solving the convex optimization problem by using a convex optimization solver to obtain the fair access parameter of the Internet of Things communication. According to the Internet of Things communication system model based on spectrum sensing, the utilization rate of spectrum resources can be improved, and fair access of Internet of Things communication is achieved under the condition that multiple constraints are comprehensively considered.

The invention relates to a multi-unmanned aerial vehicle system cooperative task allocation method based on an overlapping alliance formation game, and the method comprises the steps: carrying out the modeling of a multi-unmanned aerial vehicle system task allocation problem under the constraint of communication energy as a system total utility maximization problem, and building an optimization problem model; establishing an overlapping alliance to form a game model; on the basis of an overlapping alliance forming game model, solving the optimization problem model by adopting an alliance forming and transmitting power joint optimization algorithm to obtain an optimal alliance structure and optimized unmanned aerial vehicle transmitting power; and according to the optimal alliance structure and the optimized transmitting power of the unmanned aerial vehicle, allocating a corresponding task to the unmanned aerial vehicle and setting the transmitting power. Compared with the prior art, the utilization rate and decision-making flexibility of the unmanned aerial vehicle can be improved, and the task execution efficiency and the system total utility are improved.

The invention provides a wireless power supply network throughput optimization method and system. The method comprises the following steps: constructing a wireless power supply communication system model with energy constraint and time delay constraint; constructing a throughput maximization problem P0; s3, on the basis of the throughput maximization problem P0, constructing a charging energy maximization sub-problem P1 under the constraints of total energy and energy collection duration, and solving the sub-problem P1 under the conditions that the total available energy of the wireless power supply base station is less and the total available energy of the wireless power supply base station is more; based on the throughput maximization problem P0, constructing a throughput maximization sub-problem P2 with wireless communication duration as a variable under the condition that an energy collection user gives an energy collection strategy, and solving the throughput maximization sub-problem P2; based on the optimal wireless energy transmission strategy and the optimal wireless communication duration strategy characteristics, the throughput maximization problem P0 is converted into a single-variable optimization problem P3, and the single-variable optimization problem P3 is solved. According to the method, the actual throughput performance of the wireless energy supply communication network is improved, and the efficient communication requirement in a complex dynamic network environment is met.

Certain aspects of the present disclosure provide a method for wireless communications. The method generally includes obtaining a sequence device of information bits, applying a shaper algorithm to the sequence of information bits to generate a sequence of shaped symbols, wherein the shaper algorithm involves at least one parameter that indicates a quantity of symbol sequences that satisfies an energy constraint and further wherein the application of the shaper algorithm comprises determining, for a given energy value and a symbol sequence length, whether to use an approximation or a stored value of the at least one parameter, and outputting the sequence of shaped symbols for transmission.

The invention relates to the technical field of data communication, and particularly discloses a communication anti-interference parameter cooperative regulation and control method and system, and the method comprises the steps: obtaining energy state information; adjusting the frequency and fineness of environment perception activities, and perceiving and collecting environment perception information; determining a report format and a sending frequency according to the energy state information and the communication parameter set, and generating cooperative information according to the energy state information and the environment perception information based on the report format; the cooperative information is uploaded to the sink node based on the sending frequency, so that the sink node generates a parameter adjustment instruction according to the cooperative information and issues the parameter adjustment instruction to the corresponding sensor node; receiving a parameter adjustment instruction, and updating the communication parameter set based on the parameter adjustment instruction; according to the method, an energy sensing type self-adaptive closed-loop control mechanism is formed, and the problems of energy waste and response lag caused by a traditional fixed or non-energy sensing regulation and control mode when the energy of the wireless sensor network is limited and the environment is dynamically changed are effectively solved.

This invention relates to the field of optical communication, providing a method, system, device, product, and medium for optimizing the energy efficiency of multi-access point optical communication. The method includes: calculating the link channel gain to obtain the composite channel gain; obtaining the received energy at the receiver; establishing a nonlinear received energy expression based on the received energy; constructing a battery dynamics model using the nonlinear received energy expression to obtain device energy constraints; establishing a arrival Markov matrix and a service Markov matrix based on the number of participating communication devices and their obstruction states; calculating the arrival martingale and service martingale; calculating the queue length change based on the arrival martingale and service martingale; establishing a delay default probability inequality through the queue length change to obtain an upper bound on the delay default probability; establishing communication optimization constraints; constructing a multivariate optimization equation; solving the multivariate optimization equation based on the communication optimization constraints to obtain communication optimization parameters; and optimizing the optical communication using these communication optimization parameters.

This application relates to a device capability description method, apparatus, computer device, and storage medium. The method includes: acquiring a capability description language document for a target device; the capability description language document includes device type information, operability status information, operation method information, physical constraint information, and feedback format information; the physical constraint information includes at least one or more of time constraints, energy constraints, safety constraints, lifetime constraints, and mutual exclusion constraints; upon receiving an intent instruction from an upper-layer application, parsing the intent instruction to extract one or more execution targets; and performing capability matching on the one or more execution targets according to the capability description language document to determine target operations from the target device that can achieve the execution targets. This method enables automatic capability adaptation between upper-layer applications and heterogeneous devices, reducing development costs and improving execution security.

This invention relates to the field of UAV communication technology, and particularly to a method for UAV environmental monitoring and data processing supported by edge computing. The method includes: acquiring time-series data streams collected by multi-source environmental sensing devices, compressing and processing them based on a linear projection compression model to obtain compressed feature vectors; calculating communication costs based on current heterogeneous network conditions and energy constraints using a preset communication cost function; and executing different data processing strategies based on different communication costs. This invention achieves efficient edge compression in embedded resource-constrained scenarios; realizes resource-aware data upload control through the communication cost function; and reconstructs the Kalman filter matrix structure based on a Bayesian state fusion method using compressed feature space, effectively supporting local state estimation and uncertainty control, and triggering immediate warnings when environmental indicators exceed limits, effectively enhancing the system's risk perception and response capabilities.

The application discloses an edge device intelligent sensing method and system based on information freshness, constructs a Markov decision process model by analyzing key factors in the edge device intelligent sensing system. Secondly, the system state is updated in real time through dynamic adjustment of the sensing state, the parameters of the device are adjusted according to the new environmental changes, so that the system can quickly respond in a complex environment. In the aspect of joint optimization, the Lagrange multiplier method is used to jointly optimize the information age and energy consumption, so as to maximize the freshness of information while meeting the energy constraint. Finally, through the deep Q network, the system can continuously optimize the decision strategy through the experience replay mechanism, so that the edge device can minimize the information age under the condition of energy limitation in the changing environment. The application improves the timeliness of the sensing information.