CN109164351A - Internet of things equipment Analysis on monitoring data method and system based on time series - Google Patents
Internet of things equipment Analysis on monitoring data method and system based on time series Download PDFInfo
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Abstract
The embodiment of the invention discloses a kind of internet of things equipment Analysis on monitoring data method and system based on time series, comprising: obtain the One-dimension Time Series X of preset length n, and according to the element x in One-dimension Time Series XiCalculate the mean value M and variances sigma of One-dimension Time Series X；According to the mean value M and variances sigma of One-dimension Time Series X, center line AVG and standard deviation section are determined；Wherein center line AVG is the mean value M of One-dimension Time Series X；According to the center line AVG of generation and standard deviation section, determine One-dimension Time Series X whether have occurred element cross the border, offset, mid-term offset, long-term trend in short term.
Description
Technical field
The present invention relates to data analysis technique field, especially a kind of internet of things equipment monitoring data based on time series
Analysis method and system.
Background technique
With the rise of technology of Internet of things, a large amount of Internet of things device terminal devices are widely used in all trades and professions, such as supervise
Survey the operating status of power transmission and transforming equipment.Due to the extensive use of internet-of-things terminal equipment, these internet of things equipment generate sea daily
The time series monitoring data of amount.
Since the stationarity of monitoring data reflects the health status of power transmission and transforming equipment, it is therefore desirable to the monitoring number of magnanimity
According to constantly being analyzed to determine whether monitoring data are steady, even if discovery
Judge that the stationarity of internet of things equipment monitoring data seems extremely important.By to internet of things equipment monitoring data
It is non-stationary to be studied, the abnormal operating condition of power transmission and transforming equipment can be found in time.But lack clock synchronization in the prior art
Between sequence data non-stationary analysis method, lead to not in real time analyze data stationarity, can not find defeated change in time
The abnormal operating condition of electric equipment.
Summary of the invention
Aiming at the problems existing in the prior art, the purpose of the embodiment of the present invention is that providing a kind of object based on time series
Networked devices Analysis on monitoring data method and system can rapidly and efficiently carry out time series data analyzing in real time with right
It determines the non-stationary of data, at least partly solves defect in the prior art.
To achieve the goals above, the embodiment of the present invention proposes a kind of internet of things equipment monitoring number based on time series
According to analysis method, comprising:
Step 1, the One-dimension Time Series X for obtaining preset length n, and according to the element x in One-dimension Time Series XiIt calculates
The mean value M and variances sigma of One-dimension Time Series X；
Wherein One-dimension Time Series X={ xi, i=1,2 ..., n in xiFor i-th of element in time series X；
Step 2, mean value M and variances sigma according to One-dimension Time Series X, determine center line AVG and standard deviation section；Wherein
Center line AVG is the mean value M of One-dimension Time Series X, and standard deviation section includes:
UCL3+3 times of standard deviations of expression, i.e. UCL3=3 σ,
UCL2+2 times of standard deviations of expression, i.e. UCL2=2 σ,
UCL1+1 times of standard deviation of expression, i.e. UCL1=σ,
LCL3-3 times of standard deviations of expression, i.e. LCL3=-3 σ,
LCL2-2 times of standard deviations of expression, i.e. LCL2=-2 σ,
LCL1 indicates -1 times of standard deviation；That is LCL1=- σ；
Step 3, the center line AVG according to generation and standard deviation section, determine whether One-dimension Time Series X has occurred member
Element crosses the border, deviates in short term, mid-term deviates, long-term trend.
Further, wherein element cross the border be in One-dimension Time Series X there are an element fall in 3 times of standard deviations it
Outside, i.e., beyond control limit UCL3/LCL3；
The deviation of its middle or short term refers to there are 2 the same sides for falling in center line in continuous 3 elements, and falls in 2 times of marks
Between UCL2/LCL2 and 3 times of standard deviation UCL3/LCL3 of quasi- difference；
Wherein mid-term deviation refers to that at least 4 fall in the same side of center line and fall in 1 times in continuous 5 elements
Except standard deviation UCL1/LCL1；
Wherein long-term trend refer to that there are the same sides that continuous 8 elements fall in center line.
Further, the method also includes:
According to One-dimension Time Series X, center line AVG, standard deviation section, the data profile of One-dimension Time Series X is generated.
Meanwhile the embodiment of the present invention also proposed a kind of internet of things equipment Analysis on monitoring data system based on time series
System, comprising:
Parameter calculating module, for obtaining the One-dimension Time Series X of preset length n, and according in One-dimension Time Series X
Element xiCalculate the mean value M and variances sigma of One-dimension Time Series X；
Wherein One-dimension Time Series X={ xi, i=1,2 ..., n in xiFor i-th of element in time series X；
Section determining module determines center line AVG and standard for the mean value M and variances sigma according to One-dimension Time Series X
Poor section；Wherein center line AVG is the mean value M of One-dimension Time Series X, and standard deviation section includes:
UCL3+3 times of standard deviations of expression, i.e. UCL3=3 σ,
UCL2+2 times of standard deviations of expression, i.e. UCL2=2 σ,
UCL1+1 times of standard deviation of expression, i.e. UCL1=σ,
LCL3-3 times of standard deviations of expression, i.e. LCL3=-3 σ,
LCL2-2 times of standard deviations of expression, i.e. LCL2=-2 σ,
LCL1 indicates -1 times of standard deviation；That is LCL1=- σ；
Riding Quality Analysis module, for according to generation center line AVG and standard deviation section, determine the One-dimension Time Series
X whether have occurred element cross the border, in short term offset, mid-term offset, long-term trend.
Further, wherein element cross the border be in One-dimension Time Series X there are an element fall in 3 times of standard deviations it
Outside, i.e., beyond control limit UCL3/LCL3；
The deviation of its middle or short term refers to there are 2 the same sides for falling in center line in continuous 3 elements, and falls in 2 times of marks
Between UCL2/LCL2 and 3 times of standard deviation UCL3/LCL3 of quasi- difference；
Wherein mid-term deviation refers to that at least 4 fall in the same side of center line and fall in 1 times in continuous 5 elements
Except standard deviation UCL1/LCL1；
Wherein long-term trend refer to that there are the same sides that continuous 8 elements fall in center line.
Further, the system further include:
Data profile generation module, for generating one according to One-dimension Time Series X, center line AVG, standard deviation section
Tie up the data profile of time series X.
The advantageous effects of the above technical solutions of the present invention are as follows: above-mentioned technical proposal proposes one kind based on time series
Internet of things equipment Analysis on monitoring data method and system, the non-stationary progress to internet of things equipment monitoring data can be passed through
Research, can find the abnormal operating condition of power transmission and transforming equipment in time, provide strong branch for the prospective maintenance of power transmission and transforming equipment
Support.
Detailed description of the invention
Fig. 1 a is a kind of time series schematic diagram that element crosses the border；
Fig. 1 b is a kind of time series schematic diagram deviateed in short term；
Fig. 1 c is a kind of time series schematic diagram that mid-term deviates；
Fig. 1 d is a kind of time series schematic diagram of long-term trend；
Fig. 2 is the method flow diagram of the embodiment of the present invention.
Specific embodiment
The present invention is made with reference to the accompanying drawings and detailed description in order to illustrate a kind of base of the invention further detailed
Explanation.
As shown in Figure 2, the embodiment of the present invention proposes a kind of internet of things equipment monitoring data based on time series point
Analysis method, comprising:
Step 1: calculating parameter, for giving the identical One-dimension Time Series X={ x of fixed sample intervali, i=1,2 ...,
N }, its parameter is calculated, parameter includes mean value M and variances sigma:
Wherein, n indicates the number of element in time series X, xiIndicate i-th of element in time series；
Step 2: establishing criterion, the non-stationary logic criterion of Internet of Things monitoring device monitoring data is established；
Step 3: logic judgment is set according to the monitoring data of Internet of Things monitoring device by the monitoring of logic judgment Internet of Things
For the non-stationary of data, and then assess the operation conditions of monitored equipment.
Logic criterion described in step 2 includes: that element crosses the border, deviates in short term, mid-term deviates, long-term trend；
The element, which crosses the border, to be referred to, the acute variation that time series is generated by the effect of certain factor；
The short-term deviation refers to that time series is in shorter continuous sampling interval (at least two continuous sampling interval)
It is acted on by certain factor and generates the variation tendency for deviateing mean value；
The mid-term deviation refers to that time series is in shorter continuous sampling interval (at least four continuous sampling interval)
It is acted on by certain factor and generates the variation tendency for deviateing mean value；
The long-term trend refer to that time series is in longer continuous sampling interval (at least eight continuous sampling interval)
The variation tendency formed by certain basic sexual factor effect；
The particular content of the step 3 includes:
Firstly, drawing control figure, as shown in Figure 1 according to the parameter of time series, in which:
AVG indicates center line (mean value), i.e. AVG=M,
UCL3+3 times of standard deviations of expression, i.e. UCL3=3 σ,
UCL2+2 times of standard deviations of expression, i.e. UCL2=2 σ,
UCL1+1 times of standard deviation of expression, i.e. UCL1=σ,
LCL3-3 times of standard deviations of expression, i.e. LCL3=-3 σ,
LCL2-2 times of standard deviations of expression, i.e. LCL2=-2 σ,
LCL1 indicates -1 times of standard deviation；That is LCL1=- σ；
Secondly, formulating the non-stationary logic criterion of monitoring data, criterion includes: that element crosses the border, offset, mid-term are inclined in short term
It moves, long-term trend；
Finally, determining the non-stationary type of internet of things equipment monitoring data according to logic criterion.
Determine that the non-stationary type of internet of things equipment monitoring data specifically includes:
If there are an elements to fall in except 3 times of standard deviations in time series, i.e., beyond control limit UCL3/LCL3, then sentence
It is set to element to cross the border, as shown in Figure 1a；
If falling in the same side of center line there are at least 2 in continuous 3 elements in time series and falling in 2 times of standards
Between poor UCL2/LCL2 and 3 times of standard deviation UCL3/LCL3, then it is judged to deviateing in short term, as shown in Figure 1 b；
If falling in the same side of center line there are at least 4 in continuous 5 elements in time series and falling in 1 times of standard
Except poor UCL1/LCL1, then it is determined as that mid-term deviates, as illustrated in figure 1 c；
If continuous at least eight element falls in the same side of center line in time series, it is determined as long-term trend, such as Fig. 1 d
It is shown.
The present invention is using internet of things equipment monitoring data as object, by drawing control figure, to the time series representated by it
Carried out it is non-stationary summarize and analyze, and analyzed for each type, be research based on the device predicted of Internet of Things
Property maintenance lay a good foundation.
The above is a preferred embodiment of the present invention, it is noted that for those skilled in the art
For, without departing from the principles of the present invention, it can also make several improvements and retouch, these improvements and modifications
It should be regarded as protection scope of the present invention.
Claims (6)
1. a kind of internet of things equipment Analysis on monitoring data method based on time series characterized by comprising
Step 1, the One-dimension Time Series X for obtaining preset length n, and according to the element x in One-dimension Time Series XiIt is one-dimensional to calculate this
The mean value M and variances sigma of time series X；
Wherein One-dimension Time Series X={ xi, i=1,2 ..., n in xiFor i-th of element in time series X；
Step 2, mean value M and variances sigma according to One-dimension Time Series X, determine center line AVG and standard deviation section；Wherein center
Line AVG is the mean value M of One-dimension Time Series X, and standard deviation section includes:
UCL3+3 times of standard deviations of expression, i.e. UCL3=3 σ,
UCL2+2 times of standard deviations of expression, i.e. UCL2=2 σ,
UCL1+1 times of standard deviation of expression, i.e. UCL1=σ,
LCL3-3 times of standard deviations of expression, i.e. LCL3=-3 σ,
LCL2-2 times of standard deviations of expression, i.e. LCL2=-2 σ,
LCL1 indicates -1 times of standard deviation；That is LCL1=- σ；
Step 3, the center line AVG according to generation and standard deviation section, determine whether One-dimension Time Series X has occurred element and get over
Boundary, short-term offset, mid-term offset, long-term trend.
2. the internet of things equipment Analysis on monitoring data method according to claim 1 based on time series, which is characterized in that
It is there are an elements to fall in except 3 times of standard deviations in One-dimension Time Series X, i.e., beyond control that wherein element, which crosses the border,
Limit UCL3/LCL3；
The deviation of its middle or short term refers to there are 2 the same sides for falling in center line in continuous 3 elements, and falls in 2 times of standard deviations
Between UCL2/LCL2 and 3 times of standard deviation UCL3/LCL3；
Wherein mid-term deviation refers to that at least 4 fall in the same side of center line and fall in 1 times of standard in continuous 5 elements
Except poor UCL1/LCL1；
Wherein long-term trend refer to that there are the same sides that continuous 8 elements fall in center line.
3. the internet of things equipment Analysis on monitoring data method according to claim 1 based on time series, which is characterized in that
The method also includes:
According to One-dimension Time Series X, center line AVG, standard deviation section, the data profile of One-dimension Time Series X is generated.
4. a kind of internet of things equipment Analysis on monitoring data system based on time series characterized by comprising
Parameter calculating module, for obtaining the One-dimension Time Series X of preset length n, and according to the element in One-dimension Time Series X
xiCalculate the mean value M and variances sigma of One-dimension Time Series X；
Wherein One-dimension Time Series X={ xi, i=1,2 ..., n in xiFor i-th of element in time series X；
Section determining module determines center line AVG and standard deviation area for the mean value M and variances sigma according to One-dimension Time Series X
Between；Wherein center line AVG is the mean value M of One-dimension Time Series X, and standard deviation section includes:
UCL3+3 times of standard deviations of expression, i.e. UCL3=3 σ,
UCL2+2 times of standard deviations of expression, i.e. UCL2=2 σ,
UCL1+1 times of standard deviation of expression, i.e. UCL1=σ,
LCL3-3 times of standard deviations of expression, i.e. LCL3=-3 σ,
LCL2-2 times of standard deviations of expression, i.e. LCL2=-2 σ,
LCL1 indicates -1 times of standard deviation；That is LCL1=- σ；
Riding Quality Analysis module, for according to generation center line AVG and standard deviation section, determine that One-dimension Time Series X is
It is no have occurred element cross the border, in short term offset, mid-term offset, long-term trend.
5. the internet of things equipment Analysis on monitoring data system according to claim 4 based on time series, which is characterized in that
It is there are an elements to fall in except 3 times of standard deviations in One-dimension Time Series X, i.e., beyond control that wherein element, which crosses the border,
Limit UCL3/LCL3；
The deviation of its middle or short term refers to there are 2 the same sides for falling in center line in continuous 3 elements, and falls in 2 times of standard deviations
Between UCL2/LCL2 and 3 times of standard deviation UCL3/LCL3；
Wherein mid-term deviation refers to that at least 4 fall in the same side of center line and fall in 1 times of standard in continuous 5 elements
Except poor UCL1/LCL1；
Wherein long-term trend refer to that there are the same sides that continuous 8 elements fall in center line.
6. the internet of things equipment Analysis on monitoring data system according to claim 4 based on time series, which is characterized in that
Further include:
Data profile generation module is used for according to One-dimension Time Series X, center line AVG, standard deviation section, when generating one-dimensional
Between sequence X data profile.
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Application publication date: 20190108 |