43results about How to "Optimizing Scheduling Strategy" patented technology

The invention provides a method for the terminal in the wireless LAN entering the leisure mode, it includes following steps: a. the moving terminal sends the requirement for entering the idle mode to the base station BS/ paging agent PA; b. said BS/PA response the requirement sent by the moving terminal, the drop point PC determines to allow the moving terminal entering the leisure mode, and informs the data path to release the link of moving terminal; c. after the link of moving terminal is released, BS/PA send deleted registration message which shows the location is refreshed successfully to the moving terminal; d. BS/PA sends the confirmed messages to the further agent FA, and the FA sends the message to the drop point paging controller PC/location register LR, and saves the context ofmoving terminal to refresh the data. The invention increases the integrality and stability for the moving terminal entering the idle mode.

The invention discloses a method and device of environment and economic dispatching based on a multi-objective hybrid bat algorithm for a power system. The method includes the steps of determining anoptimization target and constraint conditions, and constructing a multi-objective optimization model of the environment and economic dispatching for the power system; solving the multi-objective optimization model using the multi-objective hybrid bat algorithm under the condition that load requirement of current period is met, obtaining a group of non-dominant solutions, and forming a Pareto optimal frontier for the two optimization targets of the environment and economic dispatching; obtaining a global optimal solution as the basis for an optimal dispatching of the power system during the current period using a membership function for the non-dominant solutions. Under the precondition of satisfying electricity load, the method minimizes generation cost of a generating set and minimizes emissions of polluted gas, minimizes use of fossil fuels to a large extent, and provides intelligent decisions for energy saving and synergy of power enterprises.

The invention provides a scheduling method and device, electronic equipment and a readable storage medium, and the method comprises the steps: obtaining a to-be-processed task and to-be-processed data, and obtaining a target learning model needed by the to-be-processed task from a plurality of pre-stored deep learning models according to the correlation between different pre-stored deep learning models and different processing tasks; scheduling the target learning model to the corresponding computing unit based on a pre-trained scheduling model according to the resource utilization rate of each computing unit so as to process the to-be-processed data to obtain an output result. In this way, when the processing task is executed in the end-side equipment, the scheduling model is establishedin advance, and the optimized scheduling strategy is obtained based on the real-time resource utilization rate, so that the overall execution efficiency of the equipment is improved.

The invention discloses a sample scheduling method and device, terminal equipment and a medium, and belongs to the technical field of detection. The method comprises the steps of identifying an item to be detected of a sample in a sample frame; determining at least one piece of matched analysis equipment of the item to be detected according to the acquired detection items supported by each analysis equipment; determining target analysis equipment according to a preset matching rule, wherein the matching rule is set according to any one or any combination of the quantity of cache area sample frames, waiting time and transportation distance of the matched analysis equipment, and the waiting time is the time difference between current time and the receiving time when the matched analysis equipment receives the latest sample frame; and controlling the sample frame to be transmitted to the target analysis equipment. Therefore, the scheduling strategy of the sample frame is optimized, the largest work efficiency can be achieved by using the limited system resources, and the utilization rate of the system resources is improved.

The invention relates to a demand response-based intelligent control method for a home energy management system. The method comprises the following steps that initial parameters are input, and a population is randomly generated by an initialization operator; a target function for spending the least electricity fee is built, constraint conditions are set, and a fitness model is constructed; the fitness model is solved to obtain the equipment states in all the scheduling time periods, and storage battery charging and discharging are conducted according to the equipment states in the scheduling time periods; a selection operation and a crossover operation are conducted in sequence, whether the population meets the constraint conditions or not is judged, and if not, variation is conducted after population repairing; if yes, variation is directly conducted; and whether the population obtained after variation meets the constraint conditions or not is judged, and if not, population repairing is conducted. Compared with the prior art, the method has the advantages of adopting peak-load shifting to transfer part of the distributed energy generating capacity, being more flexible in application, adopting the repairing method to meet the constraint conditions, being more suitable for some complex non-linear integer model problems and the like.

The invention relates to a two-stage scheduling method of real-time extension of the Windows system. According to the method, by means of affinity settings of the Windows system, reallocating the CPU (Central Processing Unit) resources of the system, and specifying a running core of real-time tasks in order to ensure resources needed by the real-time tasks; setting an IOAPIC(I/O Advanced Programmable Interrupt Controller) redirection table to enable external interrupt to point to specific core processing and to not affect the operation of real-time kernel; meanwhile, using a clock interrupt counter of Local APIC (Advanced Programmable Interrupt Controller) to provide task scheduling with high-precision clock signals; using WinDbg to obtain operation function entry addresses of the process and the thread of Windows from NTDLL to be packaged as DLL, and forming a basic interface of task control; locking and mapping kernel task control blocks and task queues to enable the writing and debugging of a scheduling algorithm can be performed in a user mode, and finally linking into the interrupt handling routine of the high-precision clock of the Local APIC so as to complete the real-time kernel scheduling algorithm.

The invention discloses an AVS (Audio Video Standard) intra mode decision device, comprising an intra mode decision scheduling control module, an intra prediction module, a rate-distortion cost calculation and mode decision module and an intra mode decision output module. The intra mode decision scheduling control module is used for controlling a series of processing flows, such as input, command control, function scheduling and data output of the intra prediction module, the rate-distortion cost calculation and mode decision module and the intra mode decision output module; the intra prediction module is used for generating the corresponding prediction data in each mode of each data block so as to enter a rate-distortion optimization flow line, and the intra prediction module performs intra prediction by reconstructed data; the rate-distortion cost calculation and mode decision module is used for calculating the rate-distortion cost of each mode, simultaneously performing intra mode decision and outputting the result to the intra mode decision output module; and the intra mode decision output module is used for outputting the mode information of the optimal mode, the reconstructed data and the entropy coding information. Meanwhile, the invention also discloses an AVS intra mode decision algorithm.

The invention discloses a CDN transparent transmission method adopting an optimized structure, and belongs to the technical field of CDNs. Upgraded CDN lower-level nodes are adopted; when index files and video files pushed by CDN upper-level nodes are received, the index files are upgraded; URL addresses of all video clips in the index files are replaced with addresses of the CDN lower-level nodes, and therefore when requesting each video clip, a client side directly requests services from the CDN lower-level nodes round an SLB. According to the optimized structure, loads of the SLB are reduced, the CDN scheduling policy is optimized, and the user experience is ensured.

The method includes following steps: in term of current emitting power, data buffer and QoS message of transmission service, UE determines an initial expecting bit rate; combines the subscript corresponding to the bit rate and emitting power to form the up stream dispatching message; on the appointed up stream channel, UE encodes and modulates the up stream dispatching message, and then sends it to base station; after NodeB receives the up stream dispatching message, it analyzes the message, and selects a part of UE allowed to transmit at time quantum in next dispatching period; NodeB makes answering to UE through control channel appointed by down stream dispatch; at down stream channel, in term of the received down stream dispatching signaling, selects appropriate current bit rate; UE transmits the data according to the selected bit rate.

The invention provides a method for configuring the queues of a Kubernetes scheduler. The method comprises the following steps of polling all queues to query the task information in each queue in a scheduling period, and calculating an allocated resource value of each queue; calculating the proportion of the resources required by each queue in the resources required by all the queues, and obtaining the residual resource value in a cluster which can be used by the queues based on the proportion; and calculating the ratio of the allocated resource value of the queue to the remaining resource value in the cluster which can be used by the queue, and setting the priority of the queue according to the ratio. According to the method, the cluster resource utilization rate and the performance for deeply learning and training the task are improved, and a system scheduling strategy is optimized.

The invention discloses a method for optimized dispatching of economic benefit of a photovoltaic power generation system. The method comprises the steps of determining a dispatching strategy of the photovoltaic power generation system; building an optimization strategy of the photovoltaic power generation system, wherein the optimization strategy is used for optimizing according to a first allocation proportion when power generation quantity of the photovoltaic power generation system is larger than residual electric quantity of a power utilization load and is used for optimizing according toa second allocation proportion when the power generation quantity of the photovoltaic power generation system is smaller than insufficient electric quantity of the power utilization load; building anoptimized dispatching target function taking benefit of the photovoltaic power generation system within a set period as an optimization target and taking the first allocation proportion and the secondallocation proportion as decision variables; and optimizing the first allocation proportion and the second allocation proportion by employing the dispatching strategy so as to achieve maximum user benefit of the photovoltaic power generation system.

The invention provides a fine-grained cloud storage scheduling method based on erasure codes. Based on a real cloud computing platform, through the analysis of the load information of a large number of storage nodes in a cloud storage system, the performance index which influences data download time delay is determined. Based on the load information, a new cloud storage scheduling algorithm is designed, through using the load information of the storage nodes, the thread scheduling of a proxy node is reversely guided to optimize an existing cloud storage system scheduling scheme. Through a variety of different erasure codes, massive files are subjected to encoding storage. By using an open source project OpenStack, a real cloud computing platform is established, the deployment and verification are carried out on the cloud platform according to real user request data, 15% average time delay can be reduced at most by the scheduling strategy provided by the invention, at the same time the fluctuation of data download time delay can be reduced, the stability of data obtainment is improved, and a better user experience is provided.

The invention discloses a QoE perception service enhancement method for an edge artificial intelligence application, and the method comprises the following steps: A, enabling an IOT terminal to transmit an operation task to a scheduler, and enabling the scheduler to judge whether to transmit an operation request to a local server or upload the operation request to a data center according to the requirements of the operation task; b, establishing a target and a constraint condition of a service enhancement model of perception quality; and C, establishing a two-stage scheduling strategy to reduce the calculation complexity. According to the invention, the defects in the prior art can be solved, and the service experience of users and the task completion rate are improved.

The embodiment of the invention discloses a cloud resource scheduling method and device and a computer storage medium, and belongs to the technical field of cloud computing. The method comprises the steps of: when a resource scheduling request is received, performing scheduling scheme initialization processing according to multiple tasks in the resource scheduling request and the number corresponding to each task in the multiple tasks, and obtaining a reference scheduling scheme set, wherein the reference scheduling scheme set comprises a plurality of scheduling schemes corresponding to each task; performing optimization selection processing on the reference scheduling scheme set to obtain a target scheduling scheme set, the target scheduling scheme set including the scheduling scheme corresponding to each task; and performing cloud resource scheduling on the plurality of tasks according to scheduling schemes in the target scheduling scheme set. According to the embodiment of the invention, the reference scheduling scheme can be optimized, selected and processed, so that the task completion time is shortened, and the cloud resource scheduling efficiency and the cloud service quality are improved.

The invention provides a multi-time scale enhanced interval optimization method for a unified power grid-distributed heat supply network system, and relates to the field of electric heating interconnection system optimization scheduling. By establishing a cost minimum objective function and reasonable unit constraints and considering different current and water flow transmission speeds, a multi-time-scale optimization scheduling strategy is adopted, and the unit output condition is adjusted in real time. Meanwhile, the uncertainty caused by the fact that wind turbine generator set predicted output and electric heating load predicted values are easily influenced by factors such as weather is considered, an enhanced interval optimization method is adopted, the uncertainty is described in an interval number mode, an interval solution of a decision variable and an objective function can be obtained through calculation, an expected value, namely the optimal value of the solution interval, is calculated, and the optimal solution of the enhanced interval optimization model is obtained.

The invention relates to the technical field of job shop scheduling, in particular to a multi-target scheduling method for a job shop constrained by limited transportation capacity, which comprises the following steps: constructing a multi-target scheduling scene of the job shop constrained by the limited transportation capacity according to an actual production shop; determining scheduling parameters and preconditions of a multi-target scheduling scene of the job shop; constructing a multi-target workshop scheduling model with double constraints of transportation resources and processing equipment resources; analyzing the multi-target workshop scheduling model based on a multi-target Moine algorithm; obtaining the optimal minimum maximum completion time and the minimum AGV total transportation time according to the analysis result, and outputting a multi-target scheduling scheme of the job shop according to the optimal minimum maximum completion time and the minimum AGV total transportation time; according to the method, a reasonable joint scheduling scheme is formulated in a limited transportation capacity constraint scene, so that the manufacturing system has better processing efficiency, and the transportation consumption of the AGV can be reduced.

According to the comprehensive energy system operation optimization device considering the user demand response characteristics under the carbon emission cost, based on a computer system, the carbon emission characteristics of a coal-fired power generation unit and a gas-fired boiler are analyzed, a comprehensive energy system carbon dioxide emission model and a stepped carbon emission cost model are constructed, and the user demand response characteristics of the coal-fired power generation unit and the gas-fired boiler are optimized. Calculating the carbon dioxide emission cost of energy consumption at each moment; on the basis, a user demand response model guided by double signals of the carbon emission cost and the energy purchase price is constructed, and an integrated energy system operation scheduling model is constructed with the purpose of minimizing the energy purchase cost and the carbon emission cost of the integrated energy system. The system can fully reflect the environment cost of operation of coal electricity, fuel gas and other equipment, more effectively encourages a user to use more energy in the clean energy output peak period, promotes the consumption of clean energy, reduces the wind and light abandoning conditions of the system, and reduces the degree of dependence on external energy at the same time.

The invention relates to a method and a device for determining a general energy efficiency datum line of an integrated energy system. The method comprises the following steps: acquiring energy consumption and supply data of the integrated energy system in each sampling period and energy consumption and supply data of each subsystem in the integrated energy system in each sampling period; determining the energy efficiency of the comprehensive energy system in each sampling period by utilizing the energy consumption and supply data of the comprehensive energy system; determining the energy efficiency of each subsystem in the comprehensive energy system in each sampling period by utilizing the energy consumption and supply data of each subsystem in the comprehensive energy system; determininga general energy efficiency reference line of the comprehensive energy system based on the energy efficiency of the comprehensive energy system in each sampling period and the energy efficiency of each subsystem in the comprehensive energy system in each sampling period; the reference line is convenient for operators of the comprehensive energy system to perform benchmarking in the operation stage of the system and judge the energy efficiency level of the system in actual operation so as to improve the scheduling strategy.

The invention discloses an updating method, updating equipment, an updating server, equipment and a medium, and the updating method is applied to a client and comprises the following steps: configuring an updating time interval; determining a first random time point between the current first time point and a future second time point as an update time point; wherein the time interval between the first time point and the second time point is equal to the updating time interval; and when the first updating time point is reached, connecting the server side so as to update the application program installed on the client side. Through the above mode, the updating time point when the client obtains the updating resource packet of the server can be adjusted, so that the traffic scheduling strategy is improved, the broadband utilization rate is improved, and the broadband use cost is reduced.

The operation optimization process of the combined cold, heat and power supply microgrids is complex and has the characteristics of uncertainty, nonlinearity, strong coupling and dynamics. Aiming at the problem that the current systems cannot be optimized in real time and are low in energy utilization efficiency and insufficient in optimization process, the invention discloses an ADHDP (Action dependent heuristic dynamic programming)-based optimized operation method for combined cold, heat and power supply type microgrid systems. According to the method, a BP neural network is used to establish a model for a combined cold, heat and power supply microgrid system and the model is used as a model network, and an optimization weight is continuously updated according to a control error and a performance index function until an optimal control strategy is obtained, thereby realizing the optimal control for the operation process of the combined cold, heat and power supply microgrids, improving the energy utilization efficiency and enhancing the optimization effect.