River water quality monitoring data sequence encryption method

Abstract

The invention provides a river water quality monitoring data sequence encryption method. Firstly a regression equation between a pollutant flux and flow is optimized through an LOADEST model, a water quality sequence encrypted for the first time is pre-estimated through high-frequency flow monitoring data, and a fluctuation characteristic of a flow process is transmitted to a water quality process through the regression equation, so that the deficiency that a linear interpolation method cannot describe a fluctuation characteristic of a water quality change process is made up for; secondly the water quality sequence encrypted for the first time is corrected through a Kalman filtering correction model, and a water quality actual measurement value and a water quality estimation value based on the LOADEST model are subjected to data assimilation, so that an error between the final estimation value and the actual measurement value can be effectively reduced and the deficiency of relatively large error of related model interpolation methods is made up for; and finally the obtained encrypted water quality data sequence can not only describe the fluctuation change characteristic of the water quality process between two actual measurement points but also effectively reduce the error between the estimation value and the actual measurement value, so that the defects of an existing method are overcome.

Description

technical field [0001] The invention relates to the field of water quality monitoring and simulation analysis, in particular to a method for interpolation and encryption of river water quality monitoring data. Background technique [0002] The pollution and destruction of the water environment is one of the main problems facing the world today. Water quality monitoring is the basic means to grasp the quality of the river water environment, but limited by the technical and economic level of the current water quality monitoring means, the frequency of routine water quality monitoring is usually 1 -3 times / month, of which January / time accounts for the vast majority. [0003] The measured water quality data series of 1-3 times / month, due to the large time span and discrete distribution between data points, can only reflect the macroscopic change trend of river water quality, and it is difficult to reflect the real change process of water quality. Therefore, it is necessary to i...

AI Technical Summary

Problems solved by technology

The water quality sequence encrypted by the interpolation method of the correlation model can describe the fluctuation and change characteristics of the water quality process between the two measured points to a certain extent; Inevitably there is an error between the values, and i

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