A Three-Stage Optimization Method for Concurrent Acquisition of Energy Consumption Data

Abstract

The invention discloses a three-stage optimization method oriented to concurrent acquisition of energy consumption data, which is based on a large-scale energy sensing network, and adopts a plurality of energy nodes and an energy management network, wherein the energy management network comprises an energy main controller, a data acquisition terminal and a time processing system; the data acquisition terminal subdivides a data acquisition task DCJ into a plurality of sub-tasks; the time processing system establishes a time Petri network; the energy main controller proposes a three-stage optimization algorithm 3SOA based on a greedy algorithm and a genetic algorithm, so as to solve a dispatching problem of the DCJ; and the 3SOA comprises a first stage optimization, a second stage optimization and a third stage optimization. The three-stage optimization method adopts the 3SOA for solving the dispatching problem of the data acquisition task, obtains the minimal completion time of the DCJ, optimizes a dispatching scheme of the DCJ, can significantly shortens acquisition completion time by adopting the 3SOA, and improves concurrent efficiency; moreover, the application shows that the 3SOA can shorten a data acquisition cycle from 9.8 seconds to 6 seconds and increase the concurrent efficiency by 34.45%.

Description

technical field [0001] The invention relates to the technical field of data collection and scheduling, in particular to a three-stage optimization method for concurrent collection of energy consumption data. Background technique [0002] Smart energy efficient manufacturing (SEEM) needs to sense parameters such as energy consumption, energy quality, equipment operation, and environmental status. In order to meet the perception requirements, the factory must be equipped with a large sensor network consisting of sensors, network devices and application servers, and realize automatic data collection. The real-time performance of energy data is extremely important for the optimization analysis of SEEM. Many users require data to be obtained within a few seconds. With the increase of the number of sensors, it becomes more and more challenging to meet the requirements of energy data with limited computing resources. . [0003] In the Energy Sensor Network (ESN), RS485 has become...

AI Technical Summary

Problems solved by technology

Therefore, data acquisition scheduling, that is, how to assign acquisition tasks to multiple processors for concurrent execution, so that the completion time is the shortest, has become a concern; and data acquisition scheduling is a type of task-processor mapping problem. Server load balancing is the key to this problem, because an unbalanced processor load will cause the entire system to wait for the processor with the largest processing load; at present, researchers tend to use genetic algorithms and heuristics on the basis of Petri net modeling In the recent 30 years of research on load balancing problems, it is believed that the simple and efficient greedy algorithm is the most popular solution method; but the data acquisition scheduling problem has two characteristics: (1) different RS485 There is no communication constraint between acquisition tasks on the bus; (2) There is no strict sequence constraint between acquisition tasks; therefore, in practical applications, it is usually assumed that the acquisition tasks sharing the same bus are completely serial to simplify the problem The greedy algorithm is used to solve the mapping between the bus and the processor, but if the bus load is unbalanced, it will lead to very low concurrency efficiency. An extreme example is if there is only one bus in the energy network, but there are multiple processors , then only one processor will be utilized during the data collection process, while the other processors are in an idle state, which greatly limits the concurrent collection efficiency of multiple processors

Images