76 results about "Energy efficient scheduling" patented technology

The invention belongs to the technical field of elevator group control, and relates to an elevator group control energy-saving scheduling method. The method comprises the following steps: (1) determining a traffic flow mode; (2) establishing a virtual simulation environment for elevator operation; (3) carrying out curve fitting on each data array respectively, and establishing the traffic flow mode, namely, an optimal energy-saving point knowledge base; (4) choosing the average elevator waiting time, congestion degree and energy consumption as objective functions of the multi-objective optimization problem study on an elevator group control system, and establishing an evaluation function; (5) generating elevator traffic flow information by simulation or collecting the actual traffic flow information; (6) determining the traffic flow mode, and searching the evaluation function parameters matched with the type of the traffic flow in a parameter library of the knowledge base as well as an evaluation function expected value matched with the traffic flow mode; and (7) solving a optimal elevator scheduling scheme by a discrete particle swarm optimization method. The proposed scheduling method can realize energy saving effectiveness in the elevator group control system.

The invention relates to a self-adaptive threshold-based energy-saving scheduling method in an environment of supporting a heterogeneous distributed system, belonging to the technical field of parallel task scheduling of the heterogeneous distributed system. The method provided by the invention specifically comprises the steps of: reading a parallel task directed acyclic graph (DAG) file; obtaining an initial task scheduling sequence; obtaining an optimal threshold: dynamically obtaining an optimal threshold according to parallel task and system environment; grouping the tasks: controlling task duplication by using the optimal threshold, namely, selectively duplicating optimal precursors of the tasks to balance system performance and energy consumption to obtain approximate optimal grouping; mapping the tasks: scheduling all groups into a processor, wherein the processor is not distributed and has minimum energy consumption; and regulating the voltage of the processor: dynamically regulating the voltage of the processor by using a task idle time to reduce the energy consumption of the processor. According to the invention, requirements on system performance and energy consumption are comprehensively considered. The optical threshold in the method can self-adapt the parallel tasks and the system environment, and is used for controlling the task duplication for balancing the system performance and the energy consumption, so that the energy consumption is reduced as far as possible under the premise that the final scheduling result meets the requirement of the system performance.

The invention relates to a generating method of a universality cost space when an electric grid dispatching mechanism draws up generation plans meeting various security constraints, and provides a 'sequence cost space' construction method required by an optimization algorithm, including information of a generation dispatching loading sequence, a constraint adjustment sequence, and the like by summarizing production rules of dispatch and combining an electric power market with an economics theory of energy-saving dispatch. Cost management is cooperated in the process of solving security-constrained economic dispatch, such that the optimization algorithm can universally meet requirements on various generating modes of just, fair and open dispatch, energy-saving dispatch, cost dispatch, electric power market, and the like, and a new solving way is provided for realizing generation resource optimum distribution and large electric grid security constraint, reducing loss of the electric grid and further improving electric transmission and transformation resource. The invention provides a time-saving, labor-saving and universal cost space generation method without needing of carrying out complex mold construction and solution according to the particular case in the security-constrained dispatch.

The embodiment of the invention discloses a method and a device of online energy-saving dispatching in a cloud computing data center. The method comprises that an application scene which the embodiment aims at is that resources in the data center are dispatched online; that is to say, a dispatching system does not known all the virtual machine requests in the data center, but only can distribute the virtual machines one by one online and in real time. The dispatching system distributes the virtual machines to suitable physic machines based on the specification and service time of the virtual machine task requests and the use condition of the current data center resource. The serial number of the physic servers is regarded as priority when the virtual machine task requests are distributed, and the physic servers with secondary high distribution priority are considered after tasks are sequentially distributed to all physic servers with high priority. Meanwhile, the virtual machine resources on different physic machines are further integrated together in combination with suitable migration every a period of time. Therefore, through reasonable and efficient utilization of resources of the physic machines, the total boot time of the physic servers is reduced and accordingly the purpose of reducing the energy consumption of the data center is reduced.

The invention discloses a clustering-based energy-saving scheduling method in a cluster environment, which comprises the following steps of: reading a directed acyclic graph (DAG) file of a parallel task, constructing a cluster corresponding to each task, merging clusters, and mapping the clusters to complete scheduling from the clusters to a processor. A threshold is set according to performance and energy consumption emphasis degree of a cluster system and characteristics of central processing unit (CPU) power consumption and network power consumption in the system, the increased computing energy consumption and reduced communication energy consumption of a current non-clustered task are weighed in the process of merging the clusters, and whether to delete the clustered task is determined so as to control the system performance, the CPU energy consumption and network communication energy consumption. The requirements on performance and energy consumption of the cluster system are comprehensively considered, the performance, the CPU energy consumption and the network communication energy consumption are balanced by a cluster scheduling method, the total energy consumption of the system is reduced on the premise of guaranteeing the system performance, and the method is suitable for scheduling the parallel task based on energy optimization in the cluster system environment.

The invention relates to an energy-efficient scheduling method and system for information technology (IT) business of a cloud computing center. The method comprises the following steps: acquiring the power of an air conditioner of a computer room, the utilization rate of a central processing unit (CPU) of a task server and the temperature and humidity data of the computer room; judging whether a task shift condition is achieved; performing task shift; and judging whether a no-load server has sleep condition or not. The system comprises a management server which is provided with a built-in task shift module, a unit which is used for detecting the temperature of the CUP of the task server, the load of an internal storage and the rotating speed of a case fan and is in communication connection with the management server, and a unit for detecting the temperature and humidity of the computer room and the power of the air conditioner. The method and system disclosed by the invention combines the detection of the temperature and humidity and the linkage system of the allocation of computing task to perform task scheduling and allocation on the load of each physical server, a server fan and an air supply and return system of the air conditioner in the environment of cloud computing and maximal energy saving is achieved on the premise of safe operation.

The invention discloses an energy-saving scheduling method and an energy-saving scheduling device for a mobile cloud computing terminal. The method comprises the following steps of based on an Agent technology, acquiring mobile application task parameters and mobile equipment status parameters in a preset format; according to the mobile application task parameters, computing to generate local execution energy consumption of the mobile equipment; according to the mobile application task parameters and the mobile equipment status parameters, computing to generate the execution energy consumption of a cloud; comparing whether the execution energy consumption of the cloud is greater than the local execution energy consumption of the mobile equipment or not, if so, enabling a mobile application task to be executed on the mobile equipment, and otherwise, downloading the mobile application task to the cloud for executing. According to the energy-saving scheduling method and the energy-saving scheduling device disclosed by the invention, the Agent technology is integrated into mobile cloud computing, physical difference among mobile equipment is overcome, the communication and the coordination between the equipment and a cloud server are realized in a specified information format, the local execution energy consumption of the mobile equipment and the execution energy consumption of the cloud are computed and compared, and finally an energy-saving scheduling scheme is made, so that the energy consumption of the mobile computing terminal is effectively reduced.

The invention relates to an energy-saving scheduling control method taking equipment maintenance into consideration for a hybrid flow shop. The method comprises the following steps: 1, acquiring a production constraint and solving a target function according to the production characteristics of the hybrid flow shop, introducing an energy consumption target, and establishing a multi-target planningmodel; 2, analyzing the influence of the equipment maintenance on the production scheduling according to maintenance data of equipment, and adding the equipment maintenance as a corresponding constraint condition into the multi-target planning model to form a complete multi-target planning model; 3, performing a coding genetic operation on an actual hybrid flow shop by adopting a GA algorithm toobtain an offspring individual; and 4, performing optimal solving on the complete multi-target planning model by adopting a hybrid algorithm in which a NSGA-II algorithm coordinates with an heuristicalgorithm in conjunction with the offspring individual, and performing scheduling control on the hybrid flow shop according to a finally-obtained optimal solving result. Compared with the prior art, the method has the advantages of high calculating speed, high robustness and the like.

The invention provides a DVFS (Dynamic Voltage and Frequency Scaling)-based energy-saving dispatching method for large-scale parallel tasks, and belongs to the field of distributed computation. The method comprises the following steps: firstly, task mapping stage: setting the original states of all the processors as the states running at the maximal voltage and the maximal frequency, then obtaining the overall execution time of the dispatching result of the directed acyclic graph for the task mapping stage through computation MHEFT; and secondly, task expansion stage: performing expansion optimization of the execution voltage and frequency for the tasks, and lowering energy consumption and cost without affecting the overall performance. The method provided by the invention can remarkably reduce the energy consumption and cost caused by parallel tasks, without affecting the overall execution time of the large-scale parallel tasks.

The invention belongs to the technical field of power system dispatching automation, and in particular relates to an energy-saving power generation method considering security constraint and network loss modification in the power system. The method comprises the following steps of: a, firstly, providing a mathematical model of the network loss modification and economic dispatch taking minimum total consumption of coal of the whole system as an objective function, wherein the mathematical model is min; b, converting the mathematical model of the energy-saving dispatch into the min, and then acquiring an optimized generation schedule; c, linearizing a constraint condition of the energy-saving dispatch; and d, solving the model of the energy-saving dispatch, and then adjusting the consumption of coal of a corresponding automatic generation control unit according to the solved result of the model. The method can obviously reduce the consumption of coal for power generation, and plays an important role in improving economic growth and benefits, reducing environmental pollution, and ensuring sustainable and rapid economic growth.

The invention relates to an optimal scheduling method of a smart power grid. The method comprises the following steps: acquiring power grid information, and performing data preprocessing on the acquired power grid information; preparing scheduling basic data, wherein the scheduling basic data comprises national scheduling basic data, branch scheduling basic data and provincial scheduling basic data; constructing a power grid security constraint energy-saving scheduling model according to the acquired power grid information and scheduling basic data; performing index evaluation on the power grid security constraint energy-saving scheduling model and the scheduling optimization strategy thereof, comparing the power grid security constraint energy-saving scheduling model and the scheduling optimization strategy with an established optimization target, and issuing and executing if the requirements are met; and storing the executed scheduling optimization result into a strategy library. According to the method, a characteristic scene used as required can be formed through flexible configuration of an optimization target and constraint conditions so as to meet different scheduling requirements, so that the large-scale optimization advantage of large-scale power grid scheduling can be fully exerted, the model construction difficulty can be reduced, and the solving efficiency can be improved.

The invention discloses a forming workshop energy-saving dispatching method based on a genetic simulated annealing algorithm. The forming workshop energy-saving dispatching method comprises the following steps that 1, an initial production scheme is generated; 2, an analysis period is set; 3, a production model for the total processing energy consumption and the processing time of the forming workshop is constructed; 4, a corresponding constraint condition is set to form a multi-objective optimization model; 5, relevant information is acquired, and the genetic simulated annealing algorithm isused for solving the multi-objective optimization model according to the collected information to obtain a production scheme of the analysis period; 6, it is judged that whether the production schemeof the analysis period is superior to the production scheme of previous analysis period, if yes, production is arranged according to the production scheme of the analysis period, and if not, production is continued according to the production scheme of the previous analysis period; and 7, a next production period begins and the step 5 is executed. The method can obtain an optimal production schemewith lowest energy consumption and shortest time in real time and perform production and processing, so that productivity is improved, and production cost and energy consumption are reduced.

The invention relates to an energy consumption management method of a data center and discloses an energy-saving scheduling method of the data center. The energy-saving scheduling method comprises the following specific steps: scheduling method generation: randomly generating a plurality of virtual machine scheduling methods, wherein the virtual machine scheduling methods refer to corresponding relationships between virtual machines and a server; scheduling optimization: adjusting the virtual machine scheduling methods through a cross algorithm and a mutation algorithm to obtain a final scheduling result, and executing the final scheduling result, wherein the cross algorithm comprises the step of selecting a higher-fitness virtual machine scheduling method to conduct cross operation; the mutation algorithm comprises the step of selecting a lower-fitness virtual machine scheduling method for conduct cross operation. The energy-saving scheduling method of the data center has the advantages that the problem of difficult energy consumption optimization of the data center is solved, the accuracy is high, the energy-saving effect is good, the calculation steps are fewer and online real-time calculation can be supported.

The invention provides an energy-saving scheduling method for improving reliability of multiple workflows. According to the energy-saving scheduling method for improving the reliability of the multiple workflows, the reliability of the workflows submitted by the multiple users is improved and the energy consumption of a data center is reduced by using a virtual machine frequency modulation technology through two phases of workflow pre-scheduling and task re-scheduling on the basis of the reliability and deadline requirements of the users. According to the multi-workflow energy-saving scheduling algorithm based on the two phases of pre-scheduling and re-scheduling, workflow task sorting and workflow reliability constraint decomposition are optimized in the workflow pre-scheduling phase; and, in a task rescheduling phase, a task reliability target value is obtained according to a pre-scheduling phase, the virtual machine frequency modulation technology is used, the virtual machine meeting the reliability target value and having the minimum energy consumption is reselected for the task, and the mapping relation between the optimized task and the virtual machine is obtained, so that the reliability of multiple workflows is improved, and meanwhile, the operation energy consumption of a data center is reduced.

The invention discloses a product energy-saving scheduling optimization method for a flexible manufacturing system, which belongs to the technical field of production scheduling. The implementation method comprises the steps: workshop product structures and machining and assembling resources are obtained, and a product structure set and a production resource set are constructed; a process set, a process time set and an energy consumption set are constructed; aiming at the assembly process, the process with the maximum start time of each assembly process is selected as an actual completion process to form an assembly constraint, and the assembly constraint is fused into the flexible production scheduling model to construct a flexible manufacturing scheduling model; and the flexible manufacturing scheduling model is optimized through an improved multi-target particle swarm-genetic algorithm. The optimization speed can be guaranteed, the algorithm convergence can be improved, a flexible manufacturing system scheduling scheme with a better target function is obtained, the scheduling scheme can integrally consider the production process and the assembly process of a product and is moresuitable for existing production and assembly working conditions, and therefore the production time of the product is shortened, and energy consumption in the production process of the product is reduced.

The invention discloses a dynamic energy-saving scheduling method for a data center network in an SDN (Software Defined Network) environment, which comprises the following steps that: (1) a switch forwards flow information to the data center network and forwards the flow information to an SDN controller; (2) the SDN controller detects the received flow information, and if the flow information is mouse flow, a static load balancing mechanism is used for scheduling; if the elephant flow is the elephant flow, entering the step (3); (3) scheduling the received elephant streams together with the elephant streams which are not transmitted before, and calculating the scheduling sequence and speed for the elephant streams in the set by adopting a key interval priority algorithm; and (4) the SDN controller issues a flow table according to a scheduling result, and issues the elephant flow and the mouse flow to corresponding links to realize energy-saving scheduling. According to the method, the elephant flow and the mouse flow are reasonably distinguished through the SDN controller, the network resource energy consumption of the data center is reduced, the number of flow entries of dynamic scheduling is greatly reduced, and the calculation efficiency of the SDN controller is improved.

The invention discloses a method for determining the peak shaving capacity of a heat supply unit based on actual measurement performance parameters. According to the method, actual measurement performance data (main engine actual measurement cylinder efficiency, pipeline actual measurement pressure loss, heater actual measurement end difference and the like) based on unit main and auxiliary machine operation characteristics are used, combined with a variable working condition theory of a steam turbine, through unit heat balance correction calculation, refined modeling simulation of a unit heatsupply working condition is carried out, and accurate determination of thermoelectric characteristics under the unit heat supply working condition is realized, so that peak load regulation capabilitycharacteristics under the unit heat supply state are determined, and powerful technical support is provided for power grid peak load regulation and energy-saving scheduling.

The invention relates to the field of big data processing and energy consumption, in particular to a Storm big data energy-saving scheduling method based on energy consumption perception, which comprises the following steps: constructing an improved Storm computing framework-based big data computing energy consumption model, and establishing an energy consumption priority table of tasks and computing resources; after the task is submitted, whether task related information exists in a database of the energy consumption model or not is judged, and if yes, the task is distributed to the low-energy-consumption process according to the energy consumption priority in the database; otherwise, distributing the task to each node in the cluster; Monitoring the energy consumption information on eachprocess, writing the energy consumption information into a database of an energy consumption model, judging the priority of the task according to the energy consumption model, updating a priority list, and re-submitting the task; according to the scheduling strategy algorithm, the operation energy efficiency of the big data program can be improved, meanwhile, the service level protocol is met, andthe cost of the whole data center can be remarkably reduced by improving the energy efficiency of big data calculation of the data center.

The invention relates to a time constraint type parallel machine energy-saving scheduling method based on a collaborative algorithm, and relates to the technical field of production scheduling in themanufacturing industry. The time constraint type parallel machine energy-saving scheduling method based on the collaborative algorithm comprises the following steps: (1) performing formalized description on a time constraint type parallel machine energy-saving scheduling problem by using mathematical symbols and clearly considering performance indexes; (2) establishing a parallel machine scheduling model considering that the maximum completion time constraint is satisfied and the energy consumption is minimized; (3) designing a collaborative algorithm for the scheduling model to solve; and (4)generating a corresponding scheduling Gantt chart according to a scheduling scheme obtained after the algorithm is ended, and recording corresponding energy consumption and maximum completion time. Compared with the prior art, the method has the advantages that the maximum completion time requirement and the energy-saving target can be considered, and meanwhile, the designed collaborative algorithm has good search performance.

The invention relates to an energy-saving scheduling method and a device, and a computer readable storage medium, and relates to the field of cloud computers. The energy-saving scheduling method comprises the following steps: for each created virtual machine, acquiring historical resource consumption of multiple types of resources in each historical time period; and scheduling each to-be-created virtual machine and each created virtual machine in the to-be-scheduled time period according to the service requirements of the user and the historical resource consumption of the multiple types of resources. According to the method, the energy consumption is further reduced.