146 results about "Energy consumption minimization" patented technology

The invention discloses a multi-unmanned aerial vehicle auxiliary edge computing resource allocation method based on task prediction. The method comprises the following steps of: firstly, modeling a communication model, a computing model and an energy loss model in an unmanned aerial vehicle auxiliary edge computing unloading scene; modeling a system total energy consumption minimization problem of the unmanned aerial vehicle auxiliary edge computing unloading network into task predictable process of terminal devices; obtaining prediction model parameters of different terminal devices by adopting centralized training through accessing historical data of the terminal devices; obtaining a prediction task set of the next time slot by utilizing the prediction model based on the task information of the current access terminal devices; and based on the prediction task set, decomposing an original problem into an unmanned aerial vehicle deployment problem and a task scheduling problem for joint optimization. The response time delay and completion time delay of the task can be effectively reduced through the deep learning algorithm, so that the calculation energy consumption is reduced; anevolutionary algorithm is introduced to solve the problem of joint unmanned aerial vehicle deployment and task scheduling optimization, the hovering energy consumption of the unmanned aerial vehicleis greatly reduced, and the utilization rate of computing resources is increased.

The invention discloses a routing method of a double-cluster head wireless sensor network. The method comprises the following steps: performing network initialization; performing temporary clustering, and collecting the positions and the energy information of intra-cluster ordinary nodes by selected temporary cluster head nodes; selecting out the optimal main cluster head node and the optimal secondary cluster head node by utilizing the particle swarm optimization algorithm according to the information of the intra-cluster ordinary nodes collected by the temporary cluster head nodes and the collection energy consumption and transmission energy consumption minimization criterion; selecting the next-hop node by every secondary cluster head node according to the distance from the secondary cluster head nodes to an aggregation node and the residual energy information at the data transmission stage, and transmitting data to the aggregation node by multiple hops. According to the routing method of the double-cluster head wireless sensor network disclosed by the invention, the problem that loads of cluster heads are overweight can be effectively relieved, the respective energy usage ratios of the two cluster heads are improved, the cluster heads are prevented from rapidly dying, the energy consumption of the network is balanced, and the life cycle of the whole network is prolonged.

The invention relates to the field of wireless communication, in particular to an energy efficiency optimization method for a wireless body area network. The energy efficiency optimization method for the wireless body area network comprises the steps of A analyzing the characteristics of the body area network and a path channel loss model and determining a work mode; B establishing an energy consumption minimization model and a network life cycle maximization model; C carrying out simulation and performance analysis on the energy consumption minimization model and the network life cycle maximization model. The energy efficiency optimization method for the wireless body area network has the advantages of establishing the network life cycle maximization model more according with practical application, verifying an optimal distribution method through simulated analysis, improving energy efficiency, and obtaining an optimal network life cycle.

The invention provides a random task queuing unloading optimization method based on edge computing and belongs to the technical field of wireless communication. The method comprises the following steps: firstly, the probability that a task generated by a user MDi is locally executed as the probability that the MDi unloads the task is represented through a macro base station and the probability that the task is unloaded through a small base station respectively, wherein the queuing model of the tasks processed locally is an M / M / 1 queue, and the queuing model of the tasks unloaded to the serverfor processing is an M / M / c queue. Secondly, a user-centered time delay and energy consumption minimization optimization target is established, and the decision probability is utilized to reflect the intention of the user to select different paths to execute tasks; the objective of the invention is to solve the problems of time delay and energy consumption minimization. A task allocation algorithmbased on a quasi-Newton interior point method is provided, the optimal solution of a target variable is regarded as a combination, the inverse of a target function Hessian matrix is replaced with an approximate matrix Dk by using a quasi-Newton condition, the Dk matrix is continuously updated through an iterative formula, the optimal search direction and the search step length are updated, and finally the optimal solution is approximated.

The invention discloses a cloud computing resource distributing method based on energy consumption optimization. The cloud computing resource distributing method includes the steps that a cloud computing experiment platform is deployed; cloud computing resource distribution describing and modeling are conducted; different energy consumption optimization factors are selected; simulation platform construction is conducted; a test experiment is conducted on performance; model correction is conducted; a performance simulation experiment is conducted; a performance analysis model and key factors are determined; an energy consumption optimizing model for resource distribution under the cloud computing environment is studied; a heuristic resource distribution algorithm for achieving load balancing and meeting energy consumption minimization is designed; a performance simulating and testing experiment is conducted. According to the cloud computing resource distributing method based on the energy consumption optimization, the practical situation of resources under the cloud computing environment can be accurately and truly reflected, the energy consumption optimizing model is provided, and the limitation that only user task time demands are considered in an existing research method is broken through. In addition, the heuristic resource distribution algorithm achieving resource load balancing and energy consumption optimization is provided, and the limitation that global and local search balancing cannot be ensured in an existing resource distribution algorithm is broken through.

The invention relates to a heterogeneous network combined computation unloading and resource allocation method, and belongs to the technical field of wireless communications. The method comprises thefollowing steps: step S1, modeling user computing task characteristics; step S2, modeling a user computing unloading decision variable and a qualification thereof; step S3, modeling user computing task local processing energy consumption and completion time; step S4, modeling user computing task unloading completion time; step S5, modeling user computing task unloading completion energy consumption; step S6, modeling system total energy consumption; step S7, modeling user computing task completion time and a qualification thereof; and step S8, determining a computation unloading and resource allocation optimization strategy based on system total energy consumption minimization. According to the heterogeneous network combined computation unloading and resource allocation method provided bythe invention, wireless resources can be reasonably utilized under the condition of satisfying the maximum delay tolerance of a user computing task, and the computation unloading and resource allocation strategy is optimally designed, so that the system total energy consumption minimization is realized.

The invention relates to the field of resource optimization in an Internet of Vehicles, in particular to an Internet-of-Vehicles resource optimization method based on a non-orthogonal multiple accesstechnology, which comprises the following steps of: when a vehicle task is processed in an NOMA-assisted vehicle edge computing system, taking minimization of total energy consumption of the vehicle edge computing system as a principle; determining an unloading and caching decision of the system, calculating and allocating caching resources, namely considering random flow arrival and queue stability of a vehicle user, and defining as a random optimization problem through joint optimization calculation of the unloading decision and the content caching decision and calculation and allocation ofthe caching resources; and utilizing a Lyapunov optimization theory to propose a dynamic joint calculation unloading, content caching and resource allocation algorithm for solving the problem, decoupling the algorithm into two independent sub-problems, and utilizing 0-1 integer programming and linear programming to solve the two sub-problems. According to the invention, the computing resources ofthe mobile edge computing server can be effectively processed, and the energy consumption of the system is reduced.

The invention relates to the technical field of unmanned aerial vehicle video relay, and particularly discloses an unmanned aerial vehicle video relay system and an energy consumption minimization method thereof. Under the condition that a first base station does not work, an unmanned aerial vehicle serves as a relay node to communicate a second base station and a user, and bandwidth is dynamically allocated between the second base station and the user; the method is based on the unmanned aerial vehicle video relay system, firstly, a dynamic resource allocation strategy is adopted to perform transmission bandwidth and power allocation, and the transmission bandwidth and power allocation is combined with the flight path of the unmanned aerial vehicle, so that a problem is modeled into a non-convex optimization problem; further the problem is decomposed into two sub-problems, namely a transmitting power and bandwidth allocation optimization sub-problem and an unmanned aerial vehicle trajectory optimization sub-problem; and finally, the two sub-problems are solved by using successive convex approximation and alternating optimization technologies to obtain a sub-optimization solution meeting a KKT condition. Experiments show that the energy consumption of the unmanned aerial vehicle can be significantly reduced by 30%, and meanwhile, the video experience quality requirements of users can be well met.

The invention discloses an unmanned aerial vehicle energy consumption minimization design method and device. The method comprises the following steps: calculating channel gains of an unmanned aerial vehicle and a sensor node; calculating energy consumed by communication of the sensor node according to the channel gain of the unmanned aerial vehicle and the sensor node; calculating energy collected by the sensor node according to the channel gain of the unmanned aerial vehicle and the sensor node; calculating energy consumed by the unmanned aerial vehicle; calculating a sensor information age AoI according to the energy consumed by communication of the sensor node and the remaining electric quantity condition of a battery of the sensor node; minimizing the energy consumption of the unmanned aerial vehicle by jointly optimizing the flight path and the flight time of the unmanned aerial vehicle and an information collection and energy collection strategy, and establishing an optimization problem model; establishing an unmanned aerial vehicle control framework based on the DQN; and according to the unmanned aerial vehicle control framework based on the DQN, planning an unmanned aerial vehicle flight strategy based on the DQN.

The invention discloses a method and device for allocating the capacity of a storage pool. The method includes: creating a storage pool of a magnetic disk pack and encapsulating the storage pool to be a virtual volume; monitoring the business data of the magnetic disk pack accessed by a business host and judging whether the business data is stored on an optimal storage layer; and conducting dynamic hierarchical processing on the storage pool if the business data is not stored on the optimal storage layer. The method and device for allocating the capacity of the storage pool can provide the maximum available capacity with the minimum space, improves the system performance through automatic data placement, and reduces the cost. All the storage resources are uniformly managed and allocated so that performance maximization and the power consumption minimization of the unit storage capacity can be realized and the storage and management can be faster and convenient.

The invention relates to a mobile edge computing network resource allocation method based on full duplex relay, and belongs to the field of mobile edges. The method comprises the following steps: constructing an uplink mobile edge computing network model assisted by a full duplex relay; analyzing transmission characteristics of the constructed system, jointly optimizing a user matching strategy, auser task unloading coefficient and transmission power when constraints such as user computing task delay, user outage probability, user maximum computing power and maximum transmitting power are met, and establishing a system total energy consumption minimization optimization problem; decomposing an original non-convex optimization problem into two deterministic convex optimization sub-problemsto be solved by utilizing a worst criterion and an alternate iteration method. According to the method, the pressure of executing tasks with large calculation data volume and low time delay request bythe user can be effectively relieved, the service quality of the user is improved, and compared with the prior art, the consumption of executing the calculation tasks can be effectively reduced, theinterruption probability of the system is reduced, and the stubborn performance of the system is improved.

The invention belongs to the technical field of electric vehicles and particularly relates to an energy distribution method of an electric vehicle hybrid energy storage system. The total power required by a whole vehicle is calculated according to various information in the running process of the electric vehicle; according to the calculated total power required by the whole vehicle and a Pontryagin minimum principle, and optimal battery power and a target current value and other relevant parameters of corresponding DCDC converter are calculated; a torque required by a motor and the target current value of the DCDC converter are transmitted to a motor control unit and a DCDC control unit for corresponding control. The optimality of energy distribution between a battery and a super-capacitor can be ensured, the calculated optimal energy distribution result can be also rapidly output in real time, and the energy consumption minimization problem of the hybrid energy storage system and the service life prolonging problem of the battery are further solved. In addition, the energy distribution method is similarly applicable to the situation of distribution of recovered power between the battery and the super-capacitor during recovery and braking of the electric vehicle.

The invention discloses a method for reducing Internet of Things calculation unloading energy consumption. An Internet of Things network architecture based on edge cloud is established by utilizing the concept of SDN in a next generation network and an edge calculation technology; the average time delay of processing the calculation task in the system and the average time delay of locally processing the calculation task are calculated by the IoT equipment by adopting a queuing theory; a global energy consumption minimization problem is modeled based on time delay constraint for the scene of the Internet of Things into a mathematical problem; an unloading calculation optimization algorithm based on a genetic algorithm is designed to solve an unloading decision of each device in the networkand the number of started edge cloud servers, resource allocation is optimized, and energy consumption is reduced.

This invention relates to an energy management method capable of using experienced information to layout sleep time of nodes and optimizing measurement and comunication energies based on the awaken state of nodes including: 1, a central node collects measurement information of each round of radio sensor nodes and computes the current position of a target to forecast its track, 2, when there is not target in a measurement region, the network is at the standby state, after a target enters into the region, a network node makes sure the shortest time the target entering into the region based on its forecast position and enters into a complete sleeping state in the shortest time period, 3, selecting a suitable node from being selected ones to finish actual measurement to minimize energy loss, 4, selecting part of nodes as the transfer nodes for transferring data packets to save communication energy.

The invention relates to a method and a device for adjusting cooling airflow of a motor vehicle, according to the method, when automobile cooling airflow is adjusted, and particularly airflow of an air-conditioner external heat exchanger of an automobile is adjusted, an air-conditioner load parameter and automobile traveling speed are needed to be determined, application of the air-conditioner load parameter and the automobile traveling speed can be used for determining an optimal setting of a flow guide device (14), thereby adjusting cooling airflow to make automobile total energy consumption comprising air-conditioner power consumption be minimized, and the flow guide device (14) controls the cooling airflow according to the determined optimal setting. The invention also relates to a device for adjusting cooling airflow of an automobile.

The invention relates to a multi-objective integrated design and optimization control method for a comprehensive energy system, and belongs to the technical field of new energy power generation and energy storage. A system parameter optimization layer of the invention takes a maximized system electric energy conversion efficiency and a maximized system entropy efficiency as optimization objectives, and takes an output gas temperature and an expansion ratio of a compressed air energy storage system as optimization variables. A system capacity configuration optimization layer takes minimized system economic cost and minimized pollutant emission as the optimization objectives, and takes capacity configuration of a microturbine, a wind power generation system, a photovoltaic power generation system and a compressed air energy storage system as the optimization variables. Besides, a system operation control optimization layer takes minimized system operation cost, minimized SOC in the compressed air energy storage system, and minimized system relative energy consumption as the optimization objectives. A multi-objective genetic algorithm is adopted by the three layers for obtaining a Pareto optimal solution set, and energy efficiency, operation economic efficiency and new energy consumption level of the comprehensive energy system are improved through a fuzzy decision-making method.

The invention discloses an urban rail transit train speed curve and timetable comprehensive energy-saving optimization method. The method comprises the steps of establishing a train interval operation traction energy consumption calculation model based on train operation process stress analysis; establishing a single train speed curve optimization model based on a Pareto multi-objective particle swarm algorithm by taking interval operation traction energy consumption and interval operation time as optimization objectives; establishing a data module for analysis and calculation of the energy-saving time table; establishing a time table optimization model based on a particle swarm algorithm by taking minimization of total traction energy consumption of a full-line train as a target, and obtaining an energy-saving interval operation time and stop time distribution scheme; and outputting an energy-saving optimization result, and determining an energy-saving optimal adjustment strategy of the full-line train. According to the method, the total traction energy consumption of the full-line train can be reduced to the maximum extent under the condition of meeting passenger comfort, train safe operation and normal operation scheduling, and the method has high use value and application prospects.

The invention provides a satellite resource allocation method and device and electronic equipment, and relates to the technical field of satellite resource allocation, and the method comprises the steps: firstly obtaining to-be-allocated resources of a target satellite, and enabling the to-be-allocated resources to comprise calculation task data and / or transmission task data; constructing a calculation model and a transmission model through the to-be-allocated resources, wherein the calculation model is used for calculating the energy consumption of the calculation task data in the allocationprocess, and the transmission model is used for calculating the energy consumption of the transmission task data in the distribution process; determining a target resource allocation scheme accordingto the target mathematical model; and finally, determining a resource allocation mode of the target satellite based on the target resource allocation scheme. According to the method, an energy consumption minimization problem of a satellite system can be decomposed into a transmission energy consumption sub-problem and / or a calculation energy consumption sub-problem under time delay constraint coupling, and a virtual machine resource allocation module and a power resource allocation module are used for carrying out joint iterative optimization, so the energy loss is further reduced.

The invention provides a probability-based task scheduling method for minimizing energy consumption in an embedded system and is applied to an embedded heterogeneous multiprocessor system. The method comprises the following steps of firstly initializing an ASAP (as soon as possible) or ALAP (as later as possible) algorithm to generate CPU (central processing unit) allocation schemes satisfying a data dependence relationship, then, specifying an execution voltage onto a CPU allocated to each task by using a dynamic planning method, and selecting an allocation scheme of minimum energy consumption from the CPU allocation schemes. The method also comprises the following steps of generating disturbance based on a current optimum allocation scheme to generate a set of new CPU allocation schemes satisfying a task dependence relationship, then, allocating a voltage to each CPU allocation scheme by adopting the dynamic planning method, and selecting a final CPU and voltage allocation scheme of the minimum energy consumption. The invention is capable of providing a scheduling scheme which is more effective and energy consumption-saving, is capable of satisfying the time limit of a real-time system by specified probability, and not only is suitable for task scheduling in a soft real-time system but also is suitable for the task scheduling in a hard real-time system.

The invention discloses an intelligent variable frequency control system of an oil extractor, which is provided with a frequency converter connected with the oil extractor, an intelligent controller connected with the frequency converter, an oil extracting rod pressure sensor and an oil extracting rod stroke sensor, wherein outputs of the oil extracting rod pressure sensor and the oil extracting rod stroke sensor are connected with the intelligent controller. The plurality of sensors and the intelligent controller can be used for monitoring the comprehensive situation of down-hole oil depositin a dynamic way, and a corresponding command signal can be sent to the oil extractor in time, so that the actual working state of the oil extractor can be ensured to be matched with the actual osmotic quantity of the down-hole oil deposit, and the aim of rapidly extracting when more oil exists, slowing extracting when less oil exists and not extracting when no oil exists; therefore, the state ofevacuation can be prevented, maximized yield and minimized consumption can be realized, the working efficiency of the oil extractor is effectively improved, and the oil extraction running cost is reduced.

The invention relates to a strategy method for minimizing energy consumption of a mobile edge computing network under a time-sensitive condition, which is characterized by comprising the following steps of: 1, an initialization stage: acquiring basic configuration information of the network by a node in the network at the stage, and initializing related parameters; 2, establishing an energy consumption minimum system optimization model; 3, solving an initial optimal unloading ratio according to constraint conditions; 4, when the constraint condition is met, returning to the step 2, and entering the next round of iterative loop calculation according to the block coordinate descent algorithm; and 5, obtaining the optimal unloading ratio, computing resources and subcarrier allocation expressions of different computing tasks of each user node after reaching the maximum limit frequency in a circulating manner. Under the condition that the QoE requirement of a user for a timeliness calculation task is met, the total energy consumption of the whole mobile edge calculation network is minimized through three strong coupling optimization variables including decoupling optimization, unloadingratio optimization, calculation resource allocation and subcarrier allocation.

The invention discloses an energy efficient fog computing migration method based on deep learning. Firstly, a fog computing migration optimization problem of task completion time minimization is constructed, a fog computing migration decision algorithm based on deep learning is provided for solving the optimization problem, the algorithm has relatively fast convergence performance, and the task completion time in a complex network scene can be reduced to the greatest extent; secondly, in order to further optimize the energy consumption of fog computing migration, a terminal user energy consumption minimization fog computing migration optimization problem is constructed, an optimal transmission power distribution solving algorithm is provided for solving the optimization problem on the basis of an optimal migration decision solved by the migration decision algorithm, and the solving algorithm dynamically distributes transmission power, so that the optimal transmission power, namely theminimum energy consumption, is obtained; finally, the specific implementation of the method verifies the advantages of the fog computing migration method in reducing task completion time and user energy consumption.

The invention discloses a positioning tracking device with the powerful electricity saving function and an electricity saving method of the positioning tracking device with the powerful electricity saving function. The positioning tracking device works with two control modules different in frequency, and the control modules do not require continuous data access and data detection. Only when a sensor module or a battery is abnormal, can the auxiliary control module get into the normal mode, and after position data are sent to the main control module, the auxiliary control module can get into the sleep mode. After being aroused by the auxiliary control module to send the position data, the main control module can get into the sleep mode again. According to the positioning tracking device, the working time of the control modules in the sleep mode is prolonged to the maximum degree, the total energy consumption of the whole positioning tracking device is minimized, and meanwhile the service life of the battery is prolonged.

The invention provides a heat dissipation system of a data center machine room. The heat dissipation system comprises heat collection modules, a gas-liquid separator, a liquid storage tank, a heat dissipation device and a fluid working medium, wherein the heat collection module, the gas-liquid separator, the liquid storage tank and the heat dissipation device form a heat dissipation circulating pipeline, the fluid working medium flows in the heat dissipation circulating pipeline, and the collection module comprises an evaporation section, a fluid inlet pipeline, a fluid outlet pipeline, a control mechanism, an induction mechanism and a steam branch pipeline. According to the invention, the data center machine room automatically switches the heat dissipation mode and the working mode to take away the heat generated by the evaporation end of the heat collection module according to the outdoor temperature condition and the thermal load condition of the machine room by taking energy consumption minimization as a target during operating; the flow is automatically adjusted through the flow self-strain mechanism of the heat collection module so as to solve the problem of heat production difference between the heat collection modules in the cabinet; and the refrigeration cycle, the gravity-driven self-cycle and the pump-driven two-phase flow cycle share a radiator array.

The invention relates to an energy consumption management method for an SLA (service level agreement) based on a heterogeneous MapReduce cluster. First of all, a mixed data copy storage strategy is provided, and the mixed data copy storage strategy allows to close a great number of nodes and ensures the integrity of data and a fault tolerance of the cluster; then a response time prediction method based on historical record is designed, and the response time prediction method accurately estimates the response time of a program according to historical information of quantity and performance parameters and operating time of the server nodes with the relative error being mostly below 6%; and finally, energy consumption minimization is achieved through selectively closing part of the nodes, the real-time performance of the application program is ensured at the same time, and a heuristic node closing strategy is provided. Therefore, the real-time performance of a MapReduce application is ensured at a node closing strategy, and a lot of energy consumption is reduced at the same time.

The invention relates to an M2M communication combined clustering and resource allocation method based on energy consumption optimization and belongs to the technical field of wireless communication.The method comprises the following steps: S1, modeling a cluster head selection variable; S2, modeling an inter-machine M2M equipment and cluster head association variable; S3, modeling the total energy consumption of M2M equipment; S4, modeling the total energy consumption of an intra-cluster communication mode; S5, modeling the total energy consumption of a cellular communication mode; S6, modeling limiting conditions for an M2M equipment clustering and resource allocation demand; and S7, based on minimization of the total energy consumption of the M2M equipment, determining a combined clustering and resource allocation strategy. The method provided by the invention can realize the minimization of energy consumption of equipment by optimizing and designing an equipment association and resource allocation strategy while the situation that the M2M equipment is accessed to a network is effectively guaranteed.

The invention belongs to the technical field of wireless communication, and discloses an unmanned aerial vehicle relay deployment method and system based on a particle swarm optimization algorithm. The unmanned aerial vehicle relay deployment method comprises the steps of: designing and defining a channel model of an unmanned aerial vehicle relay, calculating the large-scale fading path loss and small-scale fading of the unmanned aerial vehicle relay, and acquiring the channel gain and signal-to-interference-and-noise ratio of different equipment during connection; designing and defining an energy consumption model of an unmanned aerial vehicle relay, and defining power consumption compositions of the unmanned aerial vehicle in a hovering state; constructing an optimization target, and converting a constrained mixed 01 integer nonlinear programming problem into an unconstrained optimization problem; and optimizing the transmitting power of the terminal equipment, the candidate deployment position of the unmanned aerial vehicle relay and the association relationship of the terminal equipment, the unmanned aerial vehicle relay and the channel in combination with the improved particleswarm algorithm so as to realize the minimization of the total energy consumption of the system. According to the unmanned aerial vehicle relay deployment method, a standard particle swarm algorithmis improved, the unmanned aerial vehicle relay deployment method is more suitable for solving the optimization problem, and the execution efficiency of the algorithm is improved.

The invention discloses a multi-antenna communication network circulating energy collection method with minimal energy consumption. The method comprises the following steps: a first step of network initialization, a second step of network initial energy collection and information transmission, a third step of optimal distribution of network time and a fourth step of a network transmission phase. The beneficial effects of the multi-antenna communication network circulating energy collection method are that an aim of energy consumption minimization is set while conditions required for information transmission are ensured, a solution for allocating time for information transmission among user nodes and energy collection of a network is effectively optimized, and an aim of improving network user node energy collection efficiency and energy utilization can be attained.