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Multivariate time series abnormal mode prediction method and data acquisition monitoring device

A multivariate time series and prediction method technology, applied in the fields of prediction system, abnormal pattern recognition, monitoring system and cloud platform, to achieve the effect of easy real-time monitoring

Active Publication Date: 2020-09-22
UNIV OF SCI & TECH BEIJING
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0009] The technical problem to be solved by the present invention is to provide a multivariate time series abnormal pattern prediction method and a data collection and monitoring device to solve the problem of online abnormal pattern prediction for multivariate time series

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  • Multivariate time series abnormal mode prediction method and data acquisition monitoring device
  • Multivariate time series abnormal mode prediction method and data acquisition monitoring device
  • Multivariate time series abnormal mode prediction method and data acquisition monitoring device

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no. 1 example

[0075] see Figures 1 to 4j, this embodiment provides a multivariate time series abnormal pattern prediction method, a system and device for analyzing multivariate time series abnormal states, and provides real-time, accurate and unified state information through the cloud platform system. Aiming at the problem that the MMOD anomaly detection algorithm for data density estimation based on density peak cannot deal with multivariate time series and needs to manually set parameters, an online density difference anomaly detection algorithm is proposed. And this embodiment verifies the validity of the online multivariate time series abnormal mode prediction algorithm on the multivariate time series abnormal state data set. Finally, a new multivariate time series anomaly model prediction method is proposed based on the whole process, which provides more specific references for the analysis and decision-making of related multivariate time series anomalies. Through the monitoring sys...

no. 2 example

[0209] This embodiment provides a data collection and monitoring system based on cloud platform, such as figure 2 shown; among them,

[0210] The data acquisition system is a support system responsible for multivariate time series abnormal state data acquisition. It uses C++ as the development language and embeds a variety of IEC 60870-5 101, 102, 103, 104, Modbus, CDT, DISA and other data communication protocols. ;Modeling conforms to the requirements of the interface reference model, common information model (CIM) and component interface specification (CIS) in IEC 61970, conforms to international standards, and can be used as middleware to seamlessly integrate with various systems; realize monitoring system, comprehensive energy management and control system, Access to system data such as metering, fault analysis, and alarm push. The system supports the access of multiple devices and has the ability to analyze multiple protocols.

[0211] The monitoring system adopts a tw...

no. 3 example

[0243] This embodiment provides a multiple time series abnormal data collection and monitoring system APP, which mainly realizes query and display of relevant information, online update and modification, and facilitates real-time monitoring by managers. There are mainly user registration and login modules, online query modules, area display, modification modules, and logout and so on. The operation is simple and convenient, and the interface is concise and beautified. It is real-time, and registered users can log in to the system through the mobile APP no matter where they are. The system provides automatic query function and display function, as well as user registration information management function. The system runs stably and safely for a long time.

[0244] The APP is matched with the multiple time series abnormal data collection and monitoring system of the present invention, forming an overall system. Use HBuilderX as a development tool, use HTML5+CSS+JavaScript lan...

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Abstract

The invention provides a multivariate time series abnormal mode prediction method and a data acquisition monitoring device. The method comprises the steps of obtaining an optimal k value of an MMOD algorithm based on historical data according to a natural neighbor principle; carrying out online expansion on the MMOD algorithm to achieve online identification of a multivariate time sequence abnormal mode; and according to an incremental fuzzy adaptive clustering algorithm, achieving conversion from the multivariate time series sub-sequence to the observation sequence, constructing a hidden Markov model based on a Baum-Welch algorithm and all the observation sequences, and achieving online prediction of the multivariate time sequence abnormal mode based on the constructed hidden Markov model. Through the multivariate time series data acquisition system of the cloud platform, related data needing to be mined can be better acquired, and real-time prediction of the abnormal mode of the multivariate time series can be achieved by utilizing an online density difference anomaly detection algorithm and a Markov prediction model algorithm. A monitoring system APP is constructed, so that real-time monitoring is facilitated.

Description

technical field [0001] The present invention relates to the technical field of abnormal pattern recognition, prediction system, cloud platform, and monitoring system, in particular to a multivariate time series abnormal pattern prediction method and a data collection and monitoring device. Background technique [0002] Data mining is a new technology that emerges with the development of artificial intelligence and database technology. It aims to extract hidden hidden information from a large amount of fuzzy and random practical application data, which is unknown to people but has potential Useful information and knowledge of the process. [0003] Anomaly detection is an important subject in data mining, which is widely used in various fields and has always been a research hotspot of scholars. As a type of complex data commonly used in data mining, related research on multivariate time series mainly includes discretization of multivariate time series, similarity measurement ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/2458G06F16/28
CPCG06F16/2465G06F16/285G06F2216/03
Inventor 王玲
Owner UNIV OF SCI & TECH BEIJING
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