Compressed sensing method based on intelligent vertical network

Abstract

The invention provides a compressed sensing method based on an intelligent vertical network, and the method comprises the steps: enabling an environment information detection signal collected by a vertical network sensor and an original signal f to be sparse signals or to be sparsely expressed in a transformation domain, defining a sparse substrate psi, and enabling the original signal f to be sparsely expressed as f = psi x; performing linear observation on the original signal f according to a compressed sensing theory to obtain a sampling model with a measured value y = phi f, defining a sensing matrix theta, enabling theta to be equal to phi psi, and enabling the sensing matrix theta to meet a constraint equidistant condition; performing a reverse solving process on the sparse signal xaccording to y = theta x to realize reconstruction of the signal in compressed sensing; and extracting data obtained by the well lid, the garbage can and the fire hydrant sensor, and recovering and reconstructing the data by adopting a matrix filling algorithm. The intelligent vertical network data processing system is established based on the compressed sensing and matrix filling technology, thepower consumption of the sensor nodes can be greatly reduced, the data transmission bandwidth is saved, and the network load is reduced.

Description

technical field [0001] The present invention relates to the technical field of compressed sensing, in particular to a method of compressed sensing based on intelligent vertical network. Background technique [0002] In the past ten years, important achievements have been made in the intelligent construction of detection instruments and equipment in the field of meteorology. Intelligent automatic meteorological observation stations have spread all over China's major standard stations, basically replacing manual observations with intelligent automatic observations. However, there are still insufficient distribution of stations and insufficient meteorological data. Accurate, weather forecast rate is not high. And in terms of urban security, although the overall level of domestic security monitoring big data and public safety big data technology has been effectively improved, they still face certain problems in the actual use process. On the one hand, security monitoring big da...

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Problems solved by technology

On the one hand, security monitoring big data has high storage costs and defects in fast retrieval; on the other hand, public security big data has poor early warning and monitoring implementation, insufficient mining of massive data, and difficulty in sharing police data. And problems such as the limited level of mastery of front-line police big data technology

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