A big data-based estimation method for parameter pairs of a simulation mathematical model

Abstract

The invention belongs to the technical field of simulation, and discloses a big data-based evaluation method for parameter pairs of a simulation mathematical model, and a big data-based evaluation system for the parameter pairs of the simulation mathematical model comprises a big data collection module, a main control module, a parameter setting module, an optimization module, an estimation module, a data storage module, and a display module. The industrial big data can be ensured to be transmitted safely in real time through the big data acquisition module, so that the pressure of network transmission is relieved, Most distorted data can be filtered through the estimation module, and the unbiased performance of a parameter estimation result is improved. Meanwhile, a row key composed of the measuring point ID and the time sequence is designed through the data storage module to store the industrial big data row by row, so that the data with time correlation and measuring point correlation in service logic are adjacently arranged row by row in physical storage, meanwhile, the read-write performance is optimized, and the balance between the query efficiency and the write-in efficiencyis achieved.

Description

technical field [0001] The invention belongs to the technical field of simulation, and in particular relates to a method for estimating a parameter pair of a simulation mathematical model based on big data. Background technique [0002] When the simulation mathematical model is applied to a specific industrial production device, targeted parameter estimation is required. Simulation, that is, the use of project models to transform uncertainties specific to a specific level into their impact on objectives, which is expressed at the level of project simulation as a whole. Project simulation uses computer models and a specific level of risk estimation, generally using the Monte Carlo method for simulation. Use the model to reproduce the essential process that occurs in the actual system, and study the existing or designed system through the experiment of the system model, also known as simulation. The models referred to here include physical and mathematical, static and dynami...

AI Technical Summary

Problems solved by technology

[0003] To sum up, the problems existing in the existing technology are: the amount of data generated by the existing simulation data sources in real time is huge, the cost of data storage is high, and the data management and scheduling capabilities are not high; the data of industrial control systems or smart sensors is almost second In the case of small data packets, large quantities and high frequency, the pressure on the collection server and transmission network is very high, and the collection efficiency is low; and, industrial production data often contains various noise errors, and some data may be distorted increase the interference of large residual data on parameter calculation, and the calculation results at this time are biased; at the same time, the scale of data in industrial processes is getting larger and larger, and the amount of data is increasing. The collection of massive big data 1. Storage is under enormous pressure. Traditional relational databases or real-time databases can no longer meet the application requirements of industrial big data; the image displayed by the monitor is noisy, and the image texture display effect is not good

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