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WSN data fusion method based on classification adaptive estimation weighted fusion algorithm

A technology of adaptive estimation and weighted fusion, which is applied in the direction of services, calculations, and computer components based on specific environments, and can solve problems such as slow changes in monitoring information, repetitive monitoring, and low real-time requirements

Inactive Publication Date: 2020-08-14
QINGDAO HUANGHAI UNIV
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

However, in practical applications, monitoring information such as temperature and humidity changes slowly, and most of them are repetitive monitoring. When no special events occur, the real-time requirements are not high

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  • WSN data fusion method based on classification adaptive estimation weighted fusion algorithm
  • WSN data fusion method based on classification adaptive estimation weighted fusion algorithm
  • WSN data fusion method based on classification adaptive estimation weighted fusion algorithm

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Embodiment Construction

[0064] The specific embodiment of the present invention will be further described below in conjunction with accompanying drawing and specific embodiment:

[0065] combine figure 1 , the WSN data fusion method based on the classification adaptive estimation weighted fusion algorithm, first detects and classifies the data collected by the nodes, uses the mutual support matrix between the data to filter out the abnormal data and sends them to the cluster head, and then uses the classification The adaptive estimation weighted fusion algorithm performs estimated weighted fusion on the data, specifically including the following steps:

[0066] Step 1, choose the definition based on distance, that is, the absolute value of the difference between two data, to define and classify abnormal data.

[0067] Let x(t) be the current data collected by the sensor node, and d be the true value of the data, then the abnormal data is defined as:

[0068] Definition 1: For node data x(t), if |x(...

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Abstract

The invention discloses a WSN data fusion method based on a classification adaptive estimation weighted fusion algorithm, which relates to the field of temperature monitoring in medical cold chain warehouses. The method comprises the following steps of classifying abnormal data, screening out validity exception data, introducing a fusion weight and a correction factor to improve a batch estimationalgorithm to calculate a data fusion result, and finally, using MATLAB simulation software for carrying out simulation comparison on the algorithm and other two data fusion methods. Simulation results show that under a temperature detection system model, compared with BEF and MF algorithms, the fusion result of the method is closer to a true value, the mean square error is low, and the requirement for data fusion of the refrigeration house temperature sensor in the medical cold chain is met.

Description

technical field [0001] The invention relates to the field of temperature monitoring in medical cold chain warehouses, in particular to a WSN data fusion method based on classification adaptive estimation weighted fusion algorithm. Background technique [0002] In recent years, relying on its low cost and large sensing area, wireless sensor networks have been widely used in industrial automation, medical and health, environmental monitoring and other fields. In the actual working scene of the medical cold chain, due to the particularity of storing medicines, higher requirements are put forward for temperature monitoring. In the cold storage, multiple sensor nodes are selected to form a sensor network to periodically collect and transmit the temperature. However, in the process of nodes collecting temperature information, the interference factors in the environment or the measurement accuracy of sensors will cause errors in the detection results. Therefore, the node data is fu...

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Application Information

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IPC IPC(8): H04W4/38G06K9/62
CPCH04W4/38G06F18/25
Inventor 刘培学徐辉闫东刘纪新曹爱霞冯飞董丽陈玉杰赵梅莲牛海春
Owner QINGDAO HUANGHAI UNIV