Economically Feasible Parallel Dynamic Programming Method for Reservoir Group Scheduling Considering Computational Resources

A computing resource, dynamic programming technology, applied in computing, complex mathematical operations, other database retrieval and other directions, can solve problems such as reducing the computing time of DP method

Active Publication Date: 2019-11-08
CHINA INST OF WATER RESOURCES & HYDROPOWER RES
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

(2) Most of the research is based on shared storage or small parallel computing environments, and research applications in distributed storage or high-performance parallel computing environments still need to tap the potential
The parallel strategy of the master-slave mode can only reduce the calculation time of the DP method, ignoring the problem that the DP method may not be executed on a stand-alone or shared storage parallel computer due to excessive computing memory

Method used

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  • Economically Feasible Parallel Dynamic Programming Method for Reservoir Group Scheduling Considering Computational Resources
  • Economically Feasible Parallel Dynamic Programming Method for Reservoir Group Scheduling Considering Computational Resources
  • Economically Feasible Parallel Dynamic Programming Method for Reservoir Group Scheduling Considering Computational Resources

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Experimental program
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Embodiment 1

[0073] This embodiment is a parallel dynamic programming method for reservoir group dispatching that considers computing resources to be economically feasible. The distributed storage parallel computing system used in the method includes: a plurality of computing units connected through a network, and the computing units are set There are multiple physical cores, memory and hard disks, such as figure 1 shown.

[0074] Considering the huge amount of calculation, hundreds or even thousands of processing units are needed in the method described in this implementation to form a distributed storage and parallel computing environment. In order to reduce the footprint of the hardware system, when a large number of processing units are required, hundreds or even thousands of blade servers can be used as processing units and connected together through the network, see figure 1 .

[0075] In a distributed storage parallel computing environment, each processing unit exchanges informati...

Embodiment 2

[0148] This embodiment is an improvement of the first embodiment, and is a refinement of the steps of the first embodiment regarding the calculation resource economy and feasibility analysis. Clock time τ in the steps of computing resource economy and feasibility analysis described in this embodiment K :

[0149] τ K =(τ'+τ"+τ"') / K,

[0150] Where: τ K is the clock time calculated using K peer processes, including the calculation fragment time τ′, τ′=m 2n ×Δτ×T, communication fragmentation time τ″, load unbalanced time loss τ″′;

[0151] Memory RAM under the jurisdiction of a single peer process K :

[0152] RAM k =(m n ×3×Φ) / K,

[0153] In the formula: Φ is the storage space occupied by variables that do not distinguish variable types;

[0154] HDD under the jurisdiction of a single peer process K :

[0155] HDD K =(m n ×T×Φ) / K.

Embodiment 3

[0157] This embodiment is an improvement of the first embodiment, and is a refinement of the steps of the first embodiment related to the execution of the parallel dynamic programming operation. The sub-steps included in the steps performed by the parallel dynamic programming operation described in this embodiment are as follows:

[0158] The parallel dynamic programming operation includes two processes, the first process and the second process:

[0159] The first process, the process is as follows Figure 4 Shown:

[0160] (1) Based on the principle of equal division, allocate computing memory and hard disk space for K peer-to-peer processes.

[0161] This step is a key step to realize distributed memory and hard disk fragmentation and dynamic access. All memory and hard disk space can be rationally utilized through allocation to make them fully functional.

[0162] (2) When t=1, use k to represent any peer-to-peer process, according to F 1 * (·)=0 and C(p 1 ,1), initia...

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Abstract

The invention relates to a reservoir group scheduling parallel dynamic planning method considering computing resource economy and feasibility. The method comprises the steps of starting; computing resource recognition; problem scale recognition; import of known data into a transit process and trial; analysis of computing resource economy and feasibility; judgment of computing resource and problemscale matching; import of the known data into peer processes; execution of parallel dynamic planning operation; export of a reservoir group scheduling result; and stop. According to the method, the "dimension disaster" problem of reservoir group scheduling is solved according to serial dynamic planning, distributed computing is adopted to cope with a "time disaster", distributed storage is adoptedto cope with a "memory disaster", and computing efficiency and availability are improved. From the perspective of economy, distributed memories, hard disk fragments and a dynamic access technology are utilized, hard disks replace memory space, and economical expenditure is substantially saved. From the perspective of feasibility, computing time and memory and hard disk space demands are given, matching between computing resources and the problem scale is prejudged and adjusted, and the situation that overloaded operation causes ineffective computing is avoided.

Description

technical field [0001] The invention relates to a parallel dynamic planning method for reservoir group dispatching in consideration of economically feasible computing resources, a computer processing method for hydrology and water resource data, and a method for reservoir group computer dispatching and parallel computing. Background technique [0002] Reservoir optimal dispatching generally needs to establish a mathematical model of the problem, determine the specific goals of the problem, such as flood control, power generation, water supply, ecological dispatching, etc., and adopt an appropriate optimal solution method, under the constraints of water balance, storage capacity, flow, hydraulic and electric power, etc., Do target extremum calculation or multi-target analysis. Dynamic programming (DP) was proposed by Bellman (1957) for optimizing multi-stage decision process problems. If the return value of each stage of the multi-stage decision-making process problem is ind...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/90G06F17/16
CPCG06F17/16G06Q10/04G06Q10/06313G06Q50/06
Inventor 李想尹冬勤陈智梁司源鲍军朱厚华穆恩良刘荣华范哲刘家宏白音包力皋穆祥鹏崔巍郭丹红
Owner CHINA INST OF WATER RESOURCES & HYDROPOWER RES
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