Wireless sensor high-dimensional data real-time anomaly detection method based on deep learning

A wireless sensor and anomaly detection technology, applied in wireless communication, instruments, network topology, etc., can solve problems such as increasing data processing burden, unsatisfactory high-dimensional data detection effect, and inability to achieve real-time detection.

Active Publication Date: 2019-10-08
ANHUI AGRICULTURAL UNIVERSITY
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Problems solved by technology

[0009] The purpose of the present invention is to provide a real-time anomaly detection method for high-dimensional data of wireless sensors based on deep learning, so as...

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  • Wireless sensor high-dimensional data real-time anomaly detection method based on deep learning
  • Wireless sensor high-dimensional data real-time anomaly detection method based on deep learning
  • Wireless sensor high-dimensional data real-time anomaly detection method based on deep learning

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Embodiment Construction

[0063] The present invention will be further described below in conjunction with the drawings and embodiments.

[0064] Such as figure 1 As shown, the real-time anomaly detection method of wireless sensor high-dimensional data based on deep learning includes the following steps:

[0065] (101). Obtain sensor data samples in a continuous period of time as historical data X;

[0066] (102), construct a DBN-QSSVM hybrid model, such as figure 2 , image 3 , Figure 4 As shown, the DBN-QSSVM hybrid model is a hybrid model composed of a deep belief network DBN (Deep Belief Network, DBN) and a quarter-sphere support vector machine QSSVM (Quarter-sphere Support Vector Machine, QSSVM);

[0067] (103), such as Figure 5 As shown, the historical data obtained in step (101) is input to the DBN-QSSVM hybrid model to train the DBN-QSSVM hybrid model to obtain an anomaly detection model, where:

[0068] The high-dimensional historical data X is first input to the deep belief network DBN to train the...

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Abstract

The invention discloses a wireless sensor high-dimensional data anomaly detection method based on deep learning. The method comprises the following steps: (101) obtaining historical data; (102) establishing a DBN-QSSVM hybrid model; (103) training the mixed model by using historical data of the sensor; (104) collecting test data of a sensor to be detected; (105) using a trained DBN-QSSVM hybrid model in (103) to perform abnormality detection on sensor test data; (106) outputting abnormal data in sensor test data and (106) outputting abnormal data in the sensor test data. Relevant algorithms and processes in the prior art are improved. The method for achieving online detection technology when the high-dimensional data is processed is provided, the space complexity and time complexity can begreatly reduced under the condition that the accuracy of the data exception detection method is not reduced, and therefore the method is more suitable for large-scale high-dimensional data exceptiondetection.

Description

Technical field [0001] The invention relates to the field of wireless sensor abnormal data detection methods, in particular to a real-time abnormal detection method of wireless sensor high-dimensional data based on deep learning. Background technique [0002] With the rapid development of sensor technology, wireless sensor networks are increasingly used in industries such as industry, agriculture, medical care, and health. Among them, the sensor nodes are scattered in the target area, and the environmental parameters (temperature, humidity, CO 2 Concentration, etc.), can monitor the internal environment changes in the scattered area in real time. In order to detect emergencies in the natural environment timely and accurately, monitor the health of the sensor network, and improve the reliability of the wireless sensor network, it is particularly important to detect anomalies in the data collected by the sensors. [0003] Anomaly detection technology is one of the effective means to...

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Application Information

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IPC IPC(8): G06K9/62H04W84/18
CPCH04W84/18G06F18/2411G06F18/2433G06F18/214
Inventor 乔焰崔信红金鹏焦俊马慧敏王婧苏仕芳
Owner ANHUI AGRICULTURAL UNIVERSITY
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