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ARIMA model and user regulation-based dynamic variance real-time alarming method

A dynamic variance and model technology, applied in special data processing applications, instruments, electrical and digital data processing, etc., can solve problems such as inability to achieve accurate early warning, threshold range fluctuations, and unreasonable fixed thresholds, and achieve the effect of improving the accuracy of early warning.

Inactive Publication Date: 2017-04-26
TIANJIN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, it is obviously unreasonable to fix the threshold, because as the environment changes, the threshold range fluctuates, and the fixed threshold obviously cannot achieve the purpose of accurate warning

Method used

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  • ARIMA model and user regulation-based dynamic variance real-time alarming method
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  • ARIMA model and user regulation-based dynamic variance real-time alarming method

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Embodiment 1

[0026] In order to solve the above problems, the embodiment of the present invention uses the ARIMA (p, d, q) prediction model as the cornerstone, and designs a dynamic variance early warning mechanism applicable to various industries, see figure 1 and figure 2 , the method includes the following steps:

[0027] 101: Divide a period into different time periods with the same characteristics according to the data characteristics, and use the data characteristics of each period to determine the p, d, q and other parameters in the forecast model;

[0028] Among them, p is the order of autoregression, q is the order of moving average, and d is the order of difference.

[0029] 102: Establish different forecasting models for each time period;

[0030] This step can reduce the error caused by building a forecast model in all time periods.

[0031] 103: Design the whole early warning mechanism as two small prediction subsystems S1 and S2, and a comparison system C.

[0032] Among...

Embodiment 2

[0039] The scheme in embodiment 1 is further introduced below in conjunction with specific calculation formulas and examples, see the following description for details:

[0040] 201: Divide the data within a period into different periods with the same characteristics;

[0041] 202: Design the prediction subsystem S1, the steps are as follows:

[0042] 1) According to the data of different periods in the current cycle, establish an ARIMA(p,d,q) time prediction model that satisfies each period, including determining the appropriate p,d,q parameters;

[0043] 2) According to the ARIMA(p,d,q) time prediction model established in step 1), predict the forecast data of T sampling periods And record the real data X of the T sampling period at the same time;

[0044] 3) calculate

[0045] 203: Design the prediction subsystem S2, the steps are as follows:

[0046] 1) Calculate the variance of all real data in the same period corresponding to the first n periods of the current per...

Embodiment 3

[0054] The scheme in embodiment 1 and 2 is further introduced below in conjunction with specific calculation formula, example, see the following description for details:

[0055] Prediction subsystem 1: According to the data characteristics of each period, it may be assumed that the data collected in each period can be divided into four time periods A, B, C, and D. The data display form is shown in Table 1:

[0056] Table 1

[0057]

[0058]

[0059] Among them, N in Table 1 A , N B , N C , N D Respectively represent the number of data acquisitions in different periods A, B, C, and D of each cycle; n 1 , n 2 ...n n Indicates the current cycle n 0 The data of the first n cycles of ; x ijkIndicates the data collected for the kth time in the j-th period in the i-th period before the current period; when i=0, it represents the data of the current period; when i=1, it represents the data of the first period before the current period, By analogy, i=n indicates the dat...

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Abstract

The invention discloses an ARIMA model and user regulation-based dynamic variance real-time alarming method. The method comprises the following steps of performing longitudinal prediction to obtain prediction data X' of T data collection cycles, and recording real data X; performing transverse prediction to obtain a variance S<2>' of a corresponding time segment in a current cycle; performing linear combination to obtain a dynamic variance-based dynamic threshold according to weight values, endowed by a user, of all time segments and the variance S<2>'; and when the value of / / X-X' / / is greater than the dynamic threshold, triggering an early warning apparatus. According to the method, a reliable prediction result is provided for a dynamic variance early warning mechanism by adopting an ARIMA time sequence prediction model, so that the early warning accuracy of the early warning mechanism is improved; the early warning error rate caused by normal data fluctuation due to environment change is reduced, namely, the early warning efficiency is not influenced by the outside environment; the problems of non-timely early warning, incorrect early warning and the like caused by a fixed threshold are solved; and the method is suitable for various real-time monitoring systems and production and life environments with relatively high security requirements.

Description

technical field [0001] The invention relates to the field of production safety requirements, in particular to a dynamic variance real-time alarm method based on an ARIMA model and user regulation. Background technique [0002] With the continuous popularization of grating thermal composite instrument in electric power, aerospace, petroleum, medical, life science and other fields, people are more and more optimistic about the application prospect of this instrument in all walks of life. The grating mechanical thermal composite instrument beats the similar products on the market with the characteristics of high stability, high sensitivity, high integration, small footprint and easy modification. [0003] However, in applications such as pressure monitoring in oil barrels, oil temperature monitoring, and strain monitoring in aerospace materials, how to choose a flexible and adjustable threshold has always been a difficult problem for users. If the threshold is too large, the e...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50
CPCG06F30/367G06F2119/12
Inventor 张蕾曾佳刘美光付钊李悦
Owner TIANJIN UNIV
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