452 results about "Energy consumption optimization" patented technology

The invention discloses a flexible flow shop energy consumption optimization scheduling method which has two kinds of adjustment time and is sequence dependent. The method includes the steps: (1) establishing a mathematical model in regard to minimization of total energy consumption for a flexible flow shop scheduling problem which has two kinds of adjustment time and is sequence dependent, wherein the two kinds of adjusting time include a first kind of adjusting time related to sequence and a second kind of adjusting time related to workpiece sequence and machines that workpieces are arranged to; (2) according to the established mathematical model, generating an energy consumption cost ordering strategy based on the workpiece adjusting time and processing time to schedule the workpieces so as to obtain scheduling results enabling the total energy consumption to be least.

The invention discloses a building and energy-saving deployment method of a network model based on internet of things and belongs to the field of internet of things communication technologies. The invention provides a three-layer network framework and network energy consumption optimization model based on the internet of things. The energy-saving deployment method includes the steps: firstly, eliminating alternative relay nodes which cannot communicate with any sensor node according to the distance between the sensor nodes and the alternative relay nodes, then choosing i relay nodes in the rest of the relay nodes, equating the sum of sending energy consumption of sending nodes in each section of link and receiving energy consumption of receiving nodes in each section of link with a weight of the section of link when the i relay nodes meet the condition that whether topology which is formed by all standing sensor nodes and a station is a connected graph, obtaining a minimum value of energy consumption and survival time of a network in an improved Dijkstra method, and taking the network node deployment method under the energy consumption as an optical energy-saving deploy method.

The invention discloses a method of target tracking and energy consumption optimization of a dynamic cluster mechanism of a wireless sensor network. Creation of initial clusters is simple and effective, whether relevant nodes are added into a cluster head is decided by relevant nodes according to residual energy and target signal intensity, dynamic adjustment is conducted on a cluster structure according to movement of a target, and the relevant nodes are awakened in real time to continuously track the target. While tracking precision is ensured, energy consumption is reduced and the service life of the network is prolonged. In addition, dynamic cluster member nodes carry out tracking calculation on the position of the target through a method of maximum likelihood, precision is ensured, and detection radiuses of the nodes of a sensor can reduce the energy consumption of the whole network in a classification mode. The algorithm can effectively track the target, due to the fact that algorithms in which the dynamic cluster mechanism and the detection radiuses of the nodes of the sensor are classified are adopted, nodes which participate in the track every single moment are the nodes with the residual energy and the position optimized, and therefore, tracking precision of the target is ensured, energy consumption can be reduced greatly, and the service life of the network can be prolonged remarkably.

The invention relates to an energy consumption optimization method of a UAV mobile edge computing system based on cellular network. The method aims at minimizing data processing and flight energy consumption of UAV nodes, flight condition constraint of UAV nodes themselves and energy consumption constraint of a communication base station of the ground cellular network are taken into consideration,and a model is established to optimize the data distribution amount of the UAV nodes and parameters including the flight path, speed and acceleration of the UAV nodes in a combined way. The method has the advantages including solving problems in energy consumption optimization of the present UAV mobile edge computing system, and reducing the energy consumption of the UAV nodes.

The invention discloses a Lagrange-based energy consumption optimization method for a computing migration terminal. The method takes the minimization of the total computing time and the energy consumption of a mobile terminal as a target, models a computing migration resource partitioning problem of the mobile terminal into a convex optimization problem, and constructs a multi-user MECO system forlong-term evolution application based on time division multiple access and a task migration model which migrates multiple user tasks to an edge cloud base station. In the problem of convex optimization for the multi-user computing migration energy consumption, the migration scheme is designed by taking the optimal computing time as a constraint and taking the minimization of the local energy consumption of the terminal as a target, and finally an optimal terminal resource partitioning strategy is determined. The method achieves the purposes of saving the energy consumption of the terminal andreducing the network delay so as to improve the performance of the mobile terminal to be greatest extent. Experiments show that the migration strategy model can effectively balance the relation between local computing and migration computing, thereby providing a guarantee for executing computing intensive applications in mobile edge computing.

The invention discloses an energy saving illumination control method and system. The energy saving illumination control method and system divide the indoor area according to the indoor environment configuration and the prior information, thus reducing the control area and being able to realize energy saving effect, and can divide all the lights indoors into groups, aiming at one area in a room; therefore, compared with simultaneous control of all the lights indoors, group control of the lights can be more flexible; and compared with that independent control of all the lights indoors respectively, group control of the lights can reduce the complexity of the system and the lights can be controlled more easily. Finally, according to the illumination requirement of the required area, the energy saving illumination control method and system can satisfy the illumination requirement of the area by adjusting the brightness of the light group corresponding to the required area, and can find a light group brightness combination with relatively lowest total energy consumption from the plurality of light group brightness combinations which satisfy the illumination requirement required by the required area and output the light group brightness combination, so the energy is further saved. The energy saving illumination control method and system integrate area division, light grouping and energy consumption optimization control, and can satisfy the illumination requirement of one required area by means of relatively lowest total energy consumption, so as to realize secondary energy saving.

The invention discloses an LEO system DCS signal reconstruction method achieving energy efficiency priority delay tolerance. The method comprises the following steps that 1, a time-varying LEO satellite perception channel model is built; 2, a distributed compressive sensing joint sparsity model is built; 3, signal reconstruction and spectrum detection based on DCS comprises a signal reconstruction stage and a spectrum detection stage; 4, DCS signal reconstruction and detection energy consumption under the condition of a lower signal-to-noise ratio is calculated; 5, a DCS perception signal reconstruction energy consumption optimization scheme under the delay tolerance condition is determined. According to the LEO system DCS signal reconstruction method achieving energy efficiency priority delay tolerance, a good reconstruction property is achieved under the conditions of the low signal-to-noise ratio and low compression ratio, and the reconstruction complexity is obviously reduced when effective judgment is conducted on an LEO spectrum. Meanwhile, for energy efficiency of an L-CR system, the energy consumption of the two methods in signal reconstruction and spectrum detection stage is taken into account, and weighted energy consumption functions of the two stages are constructed.

The invention relates to an energy consumption optimization method for a fixed priority periodic task of a hybrid critical system, the method comprising the following steps: establishing a hybrid critical periodic task model comprising a plurality of hybrid critical periodic tasks; determining priority of hybrid critical periodic tasks by using the critical hierarchy partitioning method; calculating the lowest feasible speed S of hybrid critical period task scheduling; calculating an idle time ST generated by a task of a high critical level in a low mode, and determining an execution speed Siof the processor by using the idle time, wherein the low level critical tasks and high level critical tasks are always executed at speed Si in low mode, and the high critical level tasks are executedat maximum processor speed with extra load in high mode; using dynamic power management technology to reduce processor power consumption. The method of the invention effectively reduces the system energy consumption by utilizing the idle time generated by the high-critical-level tasks and the dynamic power consumption management technology.

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 discloses a method for optimizing cutting consumption and energy consumption of a machine tool based on an adaptive genetic algorithm, comprising the following steps: 1) a step of determining model optimization variables; 2) a step of determining an optimization objective function; 3) a step of determining constraint conditions in the model; 4 ) using an adaptive genetic algorithm to determine the cutting amount. The advantages of the present invention are: because the present invention adopts the self-adaptive genetic algorithm scheme, it is more reasonable in the selection of the cutting amount, effectively improves the utilization efficiency of the machine tool, and reduces energy consumption.

The invention discloses a cloud computing data center based unified resource scheduling energy-saving method. The method comprises the following steps: 1, initializing a network node and a virtual machine queue; 2, storing a virtual machine request in the virtual machine queue; 3, arranging virtual machines in a descending order according to a resource request number of the virtual machines; 4, traversing all network nodes in sequence, and judging whether a network node meets a request requirement of a current virtual machine or not; if yes, taking a network node with lowest energy consumption, requested by the current virtual machine, as a target placement node, and otherwise, looking for a network node with most residual available resources, emigrating one virtual machine on the node, and placing the current virtual machine; 5, sequentially selecting a next virtual machine as a current virtual machine, and making a judgment again; and 6, optimizing system energy consumption again. The method has the advantages that a contradictory relation between a power consumption minimization problem and SLA requirement satisfaction is balanced; the method is an energy consumption optimization oriented resource scheduling algorithm; and the method has higher efficiency in energy consumption optimization.

The invention discloses a heterogeneous multi-core embedded system energy consumption optimization scheduling method based on a reinforcement learning algorithm. In the hardware aspect, a DVFS regulator is loaded on each processor, and the hardware platform matching the software characteristics is dynamically constructed by adjusting the working voltage of each processor and changing the hardwarecharacteristics of each processor. In the aspect of software, aiming at the shortcomings of traditional heuristic algorithm (genetic algorithm, annealing algorithm, etc.), such as insufficient local searching ability or weak global searching ability, this paper makes an exploratory application of Q-Learning algorithm to find the optimal scheduling solution of energy consumption. The Q-Learning algorithm can give consideration to the performance of global search and local search by trial-and-error and interactive feedback with the environment, so as to achieve better search results than the traditional heuristic algorithm. Thousands of experiments show that compared with the traditional GA algorithm, the energy consumption reduction rate of the Q-learning algorithm can reach 6%-32%.

The invention discloses a reinforcement learning based urban rail train energy-saving operation strategy online optimization method which comprises the following steps of firstly, analyzing a train operation process, establishing a multi-target speed adjustment model, and then performing solving on a train energy consumption optimization problem based on a reinforcement learning algorithm. In themethod, an energy-saving strategy can be selected for operation at different planned operation times and under the condition that safe, punctual, comfortable and accurate parking are satisfied by utilizing train speed and position information acquired in real time without a target speed curve, so that energy consumption is effectively lowered; and disturbance in the actual operation process can beresponded on line, and the method is strong in applicability.

The invention discloses a method and system for ball type hot blast furnace sintering process modeling and energy consumption optimization, and can realize purposes of energy saving and cost reduction. The method includes the steps: S1, according to heat transfer and fluid mechanics principles, establishing a regenerative chamber transient heat transfer model of a hot blast furnace in two stages of furnace sintering and air feeding, and providing boundary conditions and initial conditions for model calculation; S2, calculating the regenerative chamber transient heat transfer model, according to the calculated results of the regenerative chamber transient heat transfer model and with combination of a working system of the hot blast furnace, establishing an optimization model taking the coal gas volume as an optimized target and meeting the dome temperature, the exhaust gas temperature, the hot air flow quantity and temperature, the thermal efficiency and other constraint conditions by a method of time-dividing optimization of the coal gas flow quantity; S3, solving the optimization model, to obtain a change curve of the coal gas flow quantity having minimum energy consumption along with the time; and S4, controlling the coal gas flow quantity of the ball type hot blast furnace sintering process according to the change curve of the coal gas flow quantity along with the time.

The invention relates to the field of advanced manufacture and automation, in particular to a low-temperature heat real-time optimization system based on the general algorithm sequential quadratic programming (GA-SQP) mixed optimization strategy, which comprises a real-time data base, a low-temperature heat system soft measurement module, an energy consumption optimization module, a user interface and a real-time information issuing module based on Flash. Compared with the prior art, the low-temperature heat real-time optimization system has the advantages that the flow process simulation technology and the exergy economics are used as the basis for building a low-temperature heat system simulation and optimization model, a mixed solving strategy of combining the classical mathematical programming sequence secondary planning SQP and the novel group intelligent optimization algorithm GA is adopted for solving the nonlinear optimization model, and the real-time data base technology is used for collecting real-time information in the production process, so the real-time monitoring, the off-line emulation, the on-line simulation and the real-time optimization are realized.

The invention discloses a data-oriented processing energy consumption optimization dataset distribution method. The method includes: ranking datasets to be distributed, according to IOPS attributes sequentially from large to small so as to form a dataset queue to be distributed, and ranking data center servers according to IOPS capacities sequentially from large to small so as to form a server queue to be distributed; creating a distributed server queue and a distributed dataset queue of the data center servers, extracting a first dataset to be distributed, from the head of the dataset queue to be distributed, judging whether the distributed server queue is blank or not, and if yes, selecting the first server from the head of the server queue to be distributed, as the current data center server. The data-oriented processing energy consumption optimization dataset distribution method has the advantages that hotspot datasets with high IOPS demands are centrally distributed to the data center servers, placement and transfer problems of virtual machines in the process of processing large-scale data are avoided, data center energy consumption is comprehensively optimized, and server load is balanced.

A system for controlling energy consumption in a building having a heating, ventilation and air-conditioning (HVAC) which includes using an external application to perform HVAC energy consumption optimization algorithms and other external energy control functions and transmit application control data to a building automation system (BAS), which in turn provides hardware level equipment control for the HVAC system. The external application evaluates equipment data received from the HVAC system by way of the BAS and processes these equipment data to provide application control data back to the BAS. The application control data are calculated to achieve a desired operating efficiency for the HVAC system.

The invention provides a railway locomotive running dynamic simulation test device and a simulation method of the railway locomotive running dynamic simulation test device. The railway locomotive running dynamic simulation test device comprises an operation and display interface module, a locomotive model base module, an environment simulation module, a locomotive running energy consumption optimization module, a locomotive running control simulation module and a locomotive running simulation module. According to the technical scheme, the railway locomotive running dynamic simulation test device and the simulation method of the railway locomotive running dynamic simulation test device have the advantages of being capable of effectively testing the effectiveness and reasonability of a proposed locomotive running energy consumption optimization scheme and testing running safety and punctuality of a locomotive.

The invention discloses a container cloud resource distribution method based on stable matching; the traditional stable marriage matching algorithm is improved into a multi-to-one stable matching algorithm; and a common similarity algorithm in machine learning is used as a preference rule of the stable matching algorithm to generate a preference list so as to realize the load balance of a container cloud environment and reduce the energy consumption of a data center. The container cloud resource distribution method based on stable matching belongs to a centralized scheduling algorithm, whereinthe resource utilization rate of the four kinds of virtual machines of each cloud computing node needs to be balanced in a resource allocation process and is matched with a to-be-distributed task, sothat the energy consumption of the overall system can be influenced. The invention provides an optimization algorithm of deploying the allocation of a task-level container to a system-level virtual machine in a cloud computing system in a container virtualization technology, and the resource utilization rate is improved at the level of a server and the virtual machines, so that the problem of energy consumption optimization in the container cloud environment is solved.

The invention relates to an adaptive load balancing energy consumption optimization method and system in a spectrum flexible optical network, which is designed in order to effectively improve the spectrum resource efficiency of the optical network and reduce the energy consumption of the optical network. Different linear rate and modulation format combinations are adopted for each connection request so as to bear bandwidth requirements of the connection request, and the weight of each optical fiber link is dynamically adjusted according to a distance-adaptive load balance adjustment method so as to enable the connection request to select a lightly-loaded optical fiber link to act as a transmission path. According to the invention, some short-distance optical fiber links are avoided from being selected by excessive connection requests to result in congestion, thereby improving the resource efficiency of the spectrum flexible optical network, and realizing high energy consumption efficiency.

In the contemporary data center industry, energy consumption of air conditioning systems accounts for a large proportion of total energy consumption, so how to reduce the total energy consumption of air conditioners and servers is what the industry and the academia focus on. The present invention provides a data center energy consumption optimization resource control algorithm under a server average temperature constraint. With quality of service and a server average temperature constraint ensured, an average temperature-aware power minimization model (called ATPM for short) is established, and a Lyapunov optimization theory is used in an algorithm to approximately solve an ATPM problem. The algorithm does not need to measure statistical information of a workload in advance, and the algorithm is low in complexity and easy for implementation.

The invention relates to a greenhouse environment multifactorial coordination control method based on crop physiology and energy consumption optimization. The method comprises the steps that firstly, initial set values of greenhouse environment factors are obtained; secondly, real-time values of the greenhouse environment factors, real-time values of crop physiology parameters and real-time values of climate parameters are obtained; thirdly, yield prediction and energy consumption prediction are conducted, the initial set values of the greenhouse environment factors are taken as constraints, the maximum economic benefit is set as the objective, the greenhouse environment factors are optimized so as to obtain target values of the greenhouse environment factors; fourthly, the target values of the greenhouse environment factors are input into a greenhouse controlling and executing mechanism for greenhouse environment multifactorial coordination control; in the same growth stage, the second to fourth steps are executed repeatedly, and when crops enter the next growth stage, the first step is executed to obtain the initial set values of the greenhouse environment factors again. Compared with the prior art, the crop physiology information and energy consumption are taken into full consideration so control the multiple greenhouse environment factors, the more appropriate growth environment of the crops is constructed, and high-yield and energy-saving greenhouse running is achieved.

The invention relates to an energy consumption optimization resource scheduling method for a heterogeneous cloud data center. The method comprises the following steps that 1, resource load information of a heterogeneous physical server and resource load information of a virtual machine are collected; 2, the resource load information and energy consumption parameters of the heterogeneous physical server are sent to a resource rescheduling decision maker; 3, the resource rescheduling decision maker obtains a resource rescheduling result with optimal energy consumption; 4, the resource rescheduling decision maker obtains resource rescheduling decision information, and sends the resource rescheduling decision information to a VM rescheduling actuator; 5, the VM rescheduling actuator conducts resource rescheduling according to the rescheduling decision information, namely, whether processing of rescheduling of the physical server is finished is judged, if yes, the step 1 is executed again, and if not, the step 6 is executed; 6, the mode of starting the physical server is executed according to the rescheduling decision information, and the step 5 is executed. The energy consumption optimization resource scheduling method for the heterogeneous cloud data center has the advantages of being capable of saving energy, and reducing the running cost of the cloud data center.

The invention relates to a game resetting method for virtual machines capable of controlling energy consumption. The method includes the first step of placing all physical nodes into three combinations in an ascending sort order according to the number of the virtual machines borne by the physical nodes, the second step of calculating future load values of a CPU, the internal memory and the network on the physical nodes in an R3, the third step of grouping the physical nodes not performing transferring of the virtual machines into three groups according to the future load conditions of the CPU, the internal memory and the network, the fourth step of carrying out pretreatment on selection of destination physical nodes according to the node collection which source physical nodes belong to, the fifth step of calculating the energy consumption variable quantity of each node when each virtual machine to be transferred is placed in a corresponding candidate set, wherein suppose that the maximum physical node corresponding to the virtual machine is Pi, selecting the destination physical nodes corresponding to the virtual machines to be transferred through a game playing algorithm with the overall energy consumption optimization as an objective if the physical nodes with the maximum energy consumption variable quantity corresponding to the virtual machines are the same, and then placing the virtual machines on the corresponding physical nodes again. According to the method, prediction accuracy of the future load can be improved by removing error data.

The invention provides an air compressor cluster system energy consumption optimization method and device based on big data analysis and relates to the technical field of air compressor cluster systems. The method comprises steps of according to time sequence trend characteristics, periodical rules and production scheduling information, predicting flow requirements in the future periods; calculating power responding curves of a volume adjusting machine, further predicting pressure change of an air supplying head pipeline and each pipeline branch, and generating nitrogen utilization quantity prediction data; according power data of the air compressor and the nitrogen utilization quantity prediction data, by combining real-time electricity price and nitrogen unit volume price, calculating cost data of an air compressor cluster system in a certain period; and according to the flow requirement prediction curves and cost data corresponding multiple kinds of use ways of assemblies of the aircompressor, by combining current states of the air compressor cluster and limitations of starting and stopping adjustment frequency, generating a cluster control method. According to the invention, working modes of the air compressor cluster system is effectively optimized and economical benefit is improved.

A system for controlling energy consumption in a building having a heating, ventilation and air-conditioning (HVAC) which includes using an external application to perform HVAC energy consumption optimization algorithms and other external energy control functions and transmit application control data to a building automation system (BAS), which in turn provides hardware level equipment control for the HVAC system. The external application evaluates equipment data received from the HVAC system by way of the BAS and processes these equipment data to provide application control data back to the BAS. The application control data are calculated to achieve a desired operating efficiency for the HVAC system.

The invention relates to a method for optimizing a mobile application by utilizing an access cost model of an API (Application Programming Interface) of an Android system. Firstly, an access cost model which is used for an MSA (Mobile Sensing Application) to access a Sensing API on a specific intelligent mobile phone platform is measured by utilizing a power consumption measurement tool and a Sensing API testing program; then an energy consumption optimization access interface is provided on the basis of the access cost model, is marked as an Sensing API* and is enabled to replace the corresponding Sensing API; and the MSA accesses sensing data by the Sensing API* to reduce energy consumption of the system. By the method for optimizing the mobile application on the basis of the access cost model of the API of the Android system, which is provided by the invention, energy consumption cost of the mobile sensing application can be effectively reduced; and meanwhile, a Sensing API redirection mechanism implemented by code instrumentation enables the method disclosed by the invention to have wider applicability.

The invention discloses a vulcanizing workshop energy consumption optimized dispatching method based on the heuristic rule. The method includes the steps that firstly, a vulcanizing workshop energy consumption optimized dispatching model is established; secondly, three types of heuristic rule algorithms are proposed for a tardiness cost optimization target in the dispatching model; thirdly, a minimum operation energy consumption heuristic algorithm is proposed for an energy consumption cost optimization target; fourthly, a control machine 'off-on' state algorithm is proposed for machine waiting and halts which affect energy consumption cost; fifthly, based on the lean principle that the number of state conversion times is reduced for energy conservation, a BC algorithm based on workpiece batch machining is proposed; sixthly, based on the principle that the performance of a combinational rule is superior to that of a simple dispatching rule theory, four heuristic algorithms based on the combinational rule are proposed; seventhly, a segmentation experimental method is adopted for designing a simulation experiment case and a simulation experiment is conducted; eighthly, an experiment result is analyzed. According to the method, the dispatching performance is good, the optimizing effect on vulcanizing workshop energy consumption is obvious, and the good energy conservation effect is achieved.

The invention discloses a smart phone energy consumption optimization method based on a set optimization algorithm. The method is realized by middleware software of a cross application. The method mainly comprises three assemblies: a monitoring assembly, a mining assembly and a scheduling assembly. Network activity data used by a user when a screen is closed is collected; the importance of an application for the user is predicted through utilization of a decision-making tree; the obtained value of the importance is introduced into the set optimization algorithm; a network request of the application is constrained; network activities after the screen is closed are reduced; and the battery loss resulting from the network activities is reduced. On the premise of ensuring the user experience, the duration and energy saving performance of a mobile phone are maximized.