Optimization method of building environmental sensor measurement points based on Gaussian process model

Abstract

The invention relates to the field of building distributed environment monitoring and discloses a building environment sensor measuring point optimization method based on a Gauss process model. The method comprises the steps that a teleoperation moving robot is used for carrying an environment measuring sensor node to be used as a moving measuring platform, and environment parameters and measuring point positions in an unknown building are collected; the environment parameters obtained by collecting are used as data samples, a Gauss process regression model is used for carrying out fitting on continuous probability distribution of the environment parameters, and measured values at unarranged measuring points are predicted; a greedy algorithm is used for solving a group of optimum sensor position set; and the optimum sensor position set and complementary selecting results of the main measuring point positions in a building room are combined, and finally the measuring point distribution results with the limited number and the optimum positions are obtained. According to the method, optimum arranging of environment measuring sensors in a large area of the unknown building can be achieved, the smallest number of sensors are used for reestablishing environment parameter field distribution effectively, and a traditional experience arranging method is replaced.

Description

technical field [0001] The invention relates to the field of distributed environment monitoring of buildings, in particular to a Gaussian process model-based method for optimizing measurement points of building environment sensors. The location selection of distributed sensor measuring points deployed in buildings has been selected by experience for a long time, but there is a lack of effective theoretical basis and guidance. The invention adopts the mobile robot measurement platform to solve the technical problem of sample data collection in the indoor environment of unknown buildings, and introduces methods such as machine learning and optimization solution to fit and predict the continuous probability distribution of environmental parameters (fields), and Combined with the analysis of the geometric characteristics of the environmental map, the optimal selection results of the measuring points are given by solving the solution, which can provide a theoretical basis for the d...

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Problems solved by technology

However, the selection of measuring points for environmental measurement sensors in buildings requires special consideration of two issues: on the one hand, from the perspective of economy and practicability, how to use only a limited number of measuring points to achieve the measurement of environmental parameters ( field) distribution, it is necessary to adopt an optimized calculation method and comprehensively consider the actual geometric characteristics of the environment; on the other hand, due to the large area of ​​the building floor and the unknown environmental map, it is necessary to solve the problem of how to quickly obtain measured data samples

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