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Data flow online abnormity detection method based on integrated learning

An integrated learning and anomaly detection technology, applied in the field of data processing, to achieve the effect of reducing false positive rate and false negative rate, good precision, and improving accuracy

Active Publication Date: 2019-05-31
TECH & ENG CENT FOR SPACE UTILIZATION CHINESE ACAD OF SCI
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Problems solved by technology

[0008] The purpose of the present invention is to provide an online anomaly detection method for data streams based on integrated learning, so as to solve the aforementioned problems in the prior art

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  • Data flow online abnormity detection method based on integrated learning

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Embodiment Construction

[0031] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings. It should be understood that the specific embodiments described here are only used to explain the present invention, and are not intended to limit the present invention.

[0032] Payload downlink data is high-speed, real-time, continuous streaming data with complex correlation and time-varying characteristics. The data distribution characteristics change in an unforeseen way over time, showing obvious concept drift phenomenon, which is high The implementation of detection rate, low false positive rate, and strong interpretation of payload data flow anomaly detection poses severe challenges.

[0033] (1) The payload data flow is real-time, high-speed, and continuous, and the distribution characteristics of the data flow will change unforeseen over time; the tradition...

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Abstract

The invention discloses a data flow online abnormity detection method based on integrated learning, and relates to the technical field of data processing. According to the method, firstly, a Bagging integrated learning framework is applied, a stable LSTM prediction model is obtained through multiple times of iterative training of an LSTM model, and normal-complex scene data flow is achieved; the depth of the abnormal sample is identified; Meanwhile, a payload data flow is used as input; On one hand, real-time test data is provided for a stable LSTM model; secondly, a Bagging integrated learning framework is applied, a plurality of weak learners are integrated to obtain strong learners, a learning device based on a Stacking algorithm is established, an optimal detection result is obtained by combining output results of the weak learners, and the accuracy of data flow online abnormity detection is improved; And an abnormal detection result with better precision is obtained, and the falsealarm rate and the missing alarm rate are reduced. The problem that a traditional anomaly detection method cannot accurately mine the potential anomaly of the effective load in the complex space is solved.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to an online anomaly detection method for data streams based on integrated learning. Background technique [0002] The payload is the scientific instrument and equipment carried by the spacecraft to directly carry out space application tasks, and its healthy and stable operation is the key to ensuring the smooth development of space application tasks. During the on-orbit operation of the payload, the data collected by the sensor is encoded in real time by the information system host and transmitted to the ground through the telemetry channel. Therefore, the downlink data is an important basis for the ground operation and management personnel to carry out payload operation, management, and maintenance. Whether the telemetry data is abnormal or not is closely related to the health of the payload and the execution status of space application tasks. Real-time, scientific and eff...

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Application Information

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IPC IPC(8): G06N3/08G06N3/04
Inventor 宋磊梁浩然郑太生郭丽丽李绪志
Owner TECH & ENG CENT FOR SPACE UTILIZATION CHINESE ACAD OF SCI
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