CN108959042A - Base-line data calculation method in a kind of baseline management - Google Patents
Base-line data calculation method in a kind of baseline management Download PDFInfo
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Abstract
The invention discloses base-line data calculation method and systems in a kind of baseline management.It include: the History Performance Data obtained in preset time range；It is grouped according to the size of History Performance Data, obtains multiple History Performance Data set；According to the number for the History Performance Data for being included, target histories performance data set is chosen, determines target histories performance data；According to the first preset value and the number of target capabilities data, multiple sample intervals of target histories performance data are determined, and determine the target histories performance data fallen into each sample interval；According to the target histories performance data fallen into each sample interval, the corresponding mean square deviation of each sample interval is determined；According to mean square deviation, target sample section is chosen；According to the target histories performance data fallen into target sample section, base-line data is calculated.Technical solution provided by the invention is able to solve in the prior art can not be to the defect that base-line data is accurately calculated.
Description
Technical field
The present invention relates to baseline management technical fields, more particularly to base-line data calculation method in a kind of baseline management.
Background technique
In IT monitoring field, user often carries out sampling analysis according to the data in the past period, according to analysis
Result according to certain algorithm, calculate the data as reference in following a period of time, to this reference data, we
Also referred to as baseline.
In the prior art, it for the calculating of base-line data, typically for the History Performance Data acquired, then removes
It removes the greatest measure in History Performance Data, after minimum value, is averaged obtained data as following base-line data.
However the data that obtain in this way and not accurate enough, also without good reference significance.
Summary of the invention
The present invention provides base-line data calculation method and system in a kind of baseline management, technical solutions provided by the invention
It can be realized accurate base-line data to be calculated.
The invention discloses base-line data calculation methods in a kind of baseline management, comprising:
The History Performance Data in preset time range is obtained, the preset time range is according to baseline number to be calculated
It is determined according to corresponding date-time；
According to the size of the History Performance Data, the History Performance Data is grouped, obtains multiple histories
It can data acquisition system；
According to the number of History Performance Data included in each History Performance Data set, target histories performance is chosen
Data acquisition system；
According to the target histories performance data set, target histories performance data is determined；
According to the first preset value and the number of the target capabilities data, the more of the target histories performance data are determined
A sample interval, and determine the target histories performance data fallen into each sample interval；
According to the target histories performance data fallen into each sample interval, determine that each sample interval is corresponding square
Difference；
According to the mean square deviation, target sample section is chosen；
According to the target histories performance data fallen into the target sample section, the base-line data is calculated.
Optionally, the History Performance Data is multiple, in multiple History Performance Datas and the preset time range
Multiple date-times are corresponding；
After the History Performance Data in the acquisition preset time range, described according to the History Performance Data
Size, before being grouped to the History Performance Data, further includes:
Judge whether the corresponding date-time of the multiple History Performance Data is continuous；
If discontinuous, History Performance Data corresponding to the date-time adjacent with the date-time of missing is obtained；
According to History Performance Data corresponding to the adjacent date-time, determine that the date-time institute of the missing is right
The History Performance Data answered.
Optionally, in the size according to History Performance Data, before being grouped to the History Performance Data, also
Include:
The multiple History Performance Data is ranked up；
Screen out the forward History Performance Data that sorts；And/or
Screen out the History Performance Data of sequence rearward；
The size according to History Performance Data, is grouped the History Performance Data, obtains multiple histories
Energy data acquisition system, comprising:
According to History Performance Data maximum in the multiple History Performance Data and preset set number, determine each
The range of convergence of a set；
According to the size of the multiple History Performance Data, the multiple History Performance Data is assigned to corresponding set
In range；
History Performance Data in each range of convergence constitutes corresponding History Performance Data set.
Optionally, the number of the History Performance Data according to included in each History Performance Data set is chosen
Target histories performance data set includes:
The largest number of History Performance Data set of History Performance Data are chosen as target histories performance data set；
Alternatively,
Choose the largest number of History Performance Data set of History Performance Data, and with the largest number of histories
One or two adjacent History Performance Data set of energy data acquisition system, as target histories performance data set；
It is described according to the target histories performance data set, determine that target histories performance data includes:
Using the History Performance Data in the target histories performance data set as target histories performance data.
Optionally, described according to the first preset value and the number of the target capabilities data, determine the target histories
Multiple sample intervals of performance data, and determine that the target histories performance data fallen into each sample interval includes:
According to the first preset value and the number of the target capabilities data, the target for including in each sample interval is determined
The number of History Performance Data；
According to the target histories performance number for including in the total number of target histories performance data and a each sample interval
According to number, each sample interval and the target histories in each sample interval for determining the target histories performance data
It can data.
Optionally, described according to the mean square deviation, choosing target sample section includes:
According to the size of the corresponding mean square deviation of each sample interval, the smallest sample interval of mean square deviation is chosen as target sample
This section；
According to the target histories performance data fallen into the target sample section, determine that base-line data includes:
Using the smallest History Performance Data in the target sample section as the value of the lower baseline of the base-line data；
Using the maximum History Performance Data in the target sample section as the value of the upper baseline of the base-line data.
Optionally, after the calculating base-line data, further includes:
The base-line data is calculated with preset tolerance angle value according to the value of the upper baseline of the base-line data
Upper tolerance；
The base-line data is calculated with preset tolerance angle value according to the value of the lower baseline of the base-line data
Lower tolerance.
The invention also discloses base-line data computing systems in a kind of baseline management, comprising:
Acquisition module, for obtaining the History Performance Data in preset time range, the preset time range is basis
The corresponding date-time of base-line data to be calculated determines；
Grouping module is grouped the History Performance Data, obtains for the size according to the History Performance Data
To multiple History Performance Data set；
Screening module chooses mesh according to the number of History Performance Data included in each History Performance Data set
Mark History Performance Data set；According to the target histories performance data set, target histories performance data is determined；
Computing module determines that the target is gone through for the number according to the first preset value and the target capabilities data
Multiple sample intervals of history performance data, and determine the target histories performance data fallen into each sample interval；According to falling into
Target histories performance data in each sample interval determines the corresponding mean square deviation of each sample interval；According to the mean square deviation,
Choose target sample section；According to the target histories performance data fallen into the target sample section, the baseline number is calculated
According to.
Optionally, the History Performance Data is multiple, in multiple History Performance Datas and the preset time range
Multiple date-times are corresponding；System further include:
Judgment module, for judging whether the corresponding date-time of the multiple History Performance Data is continuous；
Acquisition module, for judging that the corresponding date-time of the multiple History Performance Data is discontinuous in judgment module
When, obtain History Performance Data corresponding to the date-time adjacent with the date-time of missing；According to the adjacent date
History Performance Data corresponding to time determines History Performance Data corresponding to the date-time of the missing.
Optionally, the grouping module is also used to be ranked up the multiple History Performance Data；
Screen out the forward History Performance Data that sorts；And/or
Screen out the History Performance Data of sequence rearward；
According to History Performance Data maximum in the multiple History Performance Data and preset set number, determine each
The range of convergence of a set；
According to the size of the multiple History Performance Data, the multiple History Performance Data is assigned to corresponding set
In range；
History Performance Data in each range of convergence constitutes corresponding History Performance Data set.
Optionally, screening module, for choosing the largest number of History Performance Data set of History Performance Data as mesh
Mark History Performance Data set；
Alternatively,
Choose the largest number of History Performance Data set of History Performance Data, and with the largest number of histories
One or two adjacent History Performance Data set of energy data acquisition system, as target histories performance data set；
It is described according to the target histories performance data set, determine that target histories performance data includes:
Using the History Performance Data in the target histories performance data set as target histories performance data.
Optionally, computing module determines each for the number according to the first preset value and the target capabilities data
The number for the target histories performance data for including in sample interval；
According to the target histories performance number for including in the total number of target histories performance data and a each sample interval
According to number, each sample interval and the target histories in each sample interval for determining the target histories performance data
It can data.
Optionally, it according to the size of the corresponding mean square deviation of each sample interval, chooses the smallest sample interval of mean square deviation and makees
For target sample section；
According to the target histories performance data fallen into the target sample section, determine that base-line data includes:
Using the smallest History Performance Data in the target sample section as the value of the lower baseline of the base-line data；
Using the maximum History Performance Data in the target sample section as the value of the upper baseline of the base-line data；
Optionally, computing module is also used to the value of the upper baseline according to the base-line data, with preset tolerance
Value, calculates the upper tolerance of the base-line data；
The base-line data is calculated with preset tolerance angle value according to the value of the lower baseline of the base-line data
Lower tolerance.
In conclusion in the present invention by obtaining the History Performance Data in preset time range, to the history of acquisition
Performance data is smoothed to obtain continuous History Performance Data.Then it is grouped after screening again, obtains target and go through
History performance data set.And then determine target histories performance data.Further according to the first preset value and the target capabilities data
Number, determine multiple sample intervals of the target histories performance data, and determine the target fallen into each sample interval
History Performance Data；And then the corresponding mean square deviation of each sample interval is determined respectively；According to the mean square deviation, target sample is chosen
Section；According to the target histories performance data fallen into the target sample section, the base-line data is calculated.As it can be seen that in root
The History Performance Data of optimal target is determined according to sample interval and corresponding mean square deviation algorithm.Obtaining in turn most can be to be calculated
Base-line data, solve the problems, such as that required base-line data can not be calculated in the prior art, also or be calculated
Base-line data is not accurate enough, without reference to the defect of meaning.
Detailed description of the invention
Fig. 1 is a kind of flow chart of base-line data calculation method in baseline management in the present invention；
Fig. 2 is a kind of detail flowchart of base-line data calculation method in baseline management in the present invention；
Fig. 3 is a kind of structure chart of base-line data computing system in baseline management in the present invention；
Fig. 4 is a kind of detailed structure view of base-line data computing system in baseline management in the present invention.
Specific embodiment
To make the object, technical solutions and advantages of the present invention clearer, below in conjunction with attached drawing to embodiment party of the present invention
Formula is made also to be described in detail.
Fig. 1 is a kind of flow chart of base-line data calculation method in baseline management in the present invention.It participates in shown in Fig. 1, the party
Method includes the following steps:
Step S110, obtains the History Performance Data in preset time range, and the preset time range is according to wait count
The corresponding date-time of the base-line data of calculation determines.
Step S120 is grouped the History Performance Data according to the size of the History Performance Data, obtains more
A History Performance Data set.
Step S130 chooses mesh according to the number of History Performance Data included in each History Performance Data set
Mark History Performance Data set.
Step S140 determines target histories performance data according to the target histories performance data set.
Step S150 determines the target histories according to the first preset value and the number of the target capabilities data
Multiple sample intervals of energy data, and determine the target histories performance data fallen into each sample interval.
Step S160 determines each sample interval pair according to the target histories performance data fallen into each sample interval
The mean square deviation answered.
Step S170 chooses target sample section according to the mean square deviation.
Step S180 calculates the baseline number according to the target histories performance data fallen into the target sample section
According to.
As it can be seen that being grouped screening to the data of acquisition in the application.Screen out interference History Performance Data.According to sieve
Target histories performance data is chosen in History Performance Data after choosing.According to sample interval and corresponding mean square deviation algorithm
Determine the History Performance Data of optimal target.And then obtain base-line data that most can be to be calculated, solve in the prior art without
Method calculates the problem of required base-line data, also or is that the base-line data that is calculated is not accurate enough, without reference to meaning
Defect.
Fig. 2 is a kind of detail flowchart of base-line data calculation method in baseline management in the present invention.It is described referring to fig. 2,
This method comprises the following steps.
Step S210, obtains the History Performance Data in preset time range, and the preset time range is according to wait count
The corresponding date-time of the base-line data of calculation determines.
In the present embodiment, for calculating the base-line data of 03 month 13:00:00 on the 15th in 2015, to skill of the invention
Art scheme is described in detail.
Preset time range refers to a period of time range before the corresponding date-time of base-line data to be calculated.It can be with
It is week age range, can be one month range, can also be one-year age range or even longer time range,
It is not repeating one by one herein.
In other embodiments of the invention, the base-line data that can calculate a certain week can also be and calculate certain January
Base-line data can also be the base-line data for calculating a certain year.Thus, corresponding preset time range can be longer, then may be used
To acquire first N weeks History Performance Data；The History Performance Data of the preceding N month can also be the historical performance number of N before acquiring
According to.It is selected with specific reference to time situation, herein not to calculate the base-line data sometime put limitation in the present embodiment originally
Invention.
In the present embodiment, preset time range is one month.Therefore, the historical performance number in preset time range is obtained
According to referring to the whole 13:00 for taking 2015-3-15 13:00:00 to 2015-4-13 13:00:00 from History Performance Data table
History Performance Data.Base-line data to be calculated on-04-13rd, 2015 13:00:00 of corresponding date-time are obtained,
The previous moon History Performance Data.
In a kind of specific embodiment of the application, the History Performance Data of acquisition is as follows:
Step S220 judges whether the corresponding date-time of the multiple History Performance Data is continuous.If continuous into
Row step S230 enters step S221 if discontinuous.
In this step, in order to enable calculated base-line data is more accurate, then the History Performance Data obtained is required
It is continuous.If the History Performance Data of acquisition is not continuously, calculated base-line data has error, does not have ginseng
Examine value.
Step S221 obtains History Performance Data corresponding to the date-time adjacent with the date-time of missing.
As there is the case where causing History Performance Data corresponding to a certain date-time to lack because of acquisition failure.At this
In step, due to a performance data being acquired at regular intervals, thus, it is possible to get and lack in performance data collection
History Performance Data corresponding to the adjacent date-time of the date-time of mistake.
As the data of 2015-4-6 13:00:00 are not present, it is therefore desirable to when taking the neighbouring date of the date-time
Between corresponding History Performance Data.
Step S222 determines the day of the missing according to History Performance Data corresponding to the adjacent date-time
History Performance Data corresponding to time phase.
In this step, the History Performance Data according to corresponding to adjacent date-time, calculates according to the following formula
History Performance Data corresponding to the date-time of the missing.Specifically:
Value=Va* (1-x)+Vb*x；Wherein, historical performance number corresponding to date-time lacking in Value expression
According to；Adjacent History Performance Data in Va expression, adjacent History Performance Data under Vb expression, lacking in x expression when the date
Between and upper adjacent date-time difference, the ratio between the difference of neighbouring date-time.
Corresponding in above-described embodiment, then Value=85* (1-1/2)+94*1/2=89.
It is as follows to obtain corresponding History Performance Data:
[54,89,75,32,54,51,53,62,35,32,24,36,45,41,41,74,76,78,78,82,84,84,
89,73,95,97,101,102,103,54]
In above-mentioned steps S221 and step S222, by the History Performance Data obtained to sampling, it is smooth to carry out matrix
Processing.Solve because acquisition History Performance Data discontinuously caused by base-line data calculate not accurate enough defect.Also,
In above-mentioned steps, the History Performance Data by the calculated gather disappearance of corresponding formula is more accurate, so that counting
When calculating base-line data, error can be reduced, the accuracy of the base-line data improved.Than the smoothing processing being directly averaging,
It is more accurate to have the advantages that.
Step S230 is ranked up the multiple History Performance Data, screens out interfering History Performance Data.
In this step, after being ranked up to the whole continuous History Performance Datas got, interference is screened out
History Performance Data, so that the base-line data being calculated is more accurate.
It in an embodiment of the present invention, can be by screening out the forward History Performance Data that sorts；And/or it screens out
The History Performance Data of sequence rearward.
In an embodiment of the present invention, it after by screening out interfering History Performance Data, obtains screening out completion
History Performance Data later is as follows:
[32,32,35,36,41,41,45,51,53,54,54,54,62,73,74,75,76,78,78,82,84,84,
89,89,95,97,101,102]
Step S240, according to History Performance Data maximum in the multiple History Performance Data and preset set
Number, determines the range of convergence of each set.
In a specific embodiment of the invention, preset set number can be 5.Then according to maximum history
Energy data 102, section number 5；The range of convergence for obtaining each set is 102/5=21.Then determine the set model of this 5 set
It encloses as follows:
[0~21], [21~42], [42~63], [63~84], [84~105].
The multiple History Performance Data is assigned to by step S250 according to the size of the multiple History Performance Data
In corresponding range of convergence；History Performance Data in each range of convergence constitutes corresponding History Performance Data set.
In this step, 28 numbers in step S230 are assigned in these set determined by step S240 and go to (area
Between minimum value≤N < section maximum value) after are as follows:
Set 1=[]
Set 2=[32,32,35,36,41,41]
Set 3=[45,51,53,54,54,54,62]
Set 4=[73,74,75,76,78,78,82,84,84]
Set 5=[89,89,95,97,101,102]
Step S260 chooses the largest number of History Performance Data set of History Performance Data, and most with the number
One or two adjacent History Performance Data set of more History Performance Data set, as target histories performance data collection
It closes；Using the History Performance Data in the target histories performance data set as target histories performance data.
In step S260, if the largest number of History Performance Data set of History Performance Data it is adjacent only one go through
History performance data set, then only take one.
In the above specific embodiment, the History Performance Data number in section 4 is most, so taking neighboring interval 3 on section 4
With the History Performance Data in its lower neighboring interval 5 as target histories performance data.The target histories performance data taken
It is as follows:
[45,51,53,54,54,54,62,73,74,75,76,78,78,82,84,84,89,89,95,97,101,102]
In other embodiments of the invention, the largest number of History Performance Datas of History Performance Data can also only be chosen
Set is used as target histories performance data set.
Step S270 is determined in each sample interval according to the first preset value and the number of the target capabilities data
The number for the target histories performance data for including；According in the total number of target histories performance data and a each sample interval
The number for the target histories performance data for including determines each sample interval and each sample of the target histories performance data
Target histories performance data in this section.
In a specific embodiment of the invention, the value of the first preset value is 0.8.Thus according to the first preset value and
The number 22 of target capabilities data, the number for calculating the target histories performance data for including in each sample interval is 22*0.8
=17.6, the number 17 for the target histories performance data for including in each sample interval is obtained by being rounded.
In other embodiments of the invention, the value of the first preset value can be set according to the actual situation, herein not
It repeats one by one again.
And then determine that each sample interval of the target histories performance data and the target in each sample interval are gone through
History performance data is as follows:
1~17:[45,51,53,54,54,54,62,73,74,75,76,78,78,82,84,84,89 of sample interval]
2~18:[51,53,54,54,54,62,73,74,75,76,78,78,82,84,84,89,89 of sample interval]
3~19:[53,54,54,54,62,73,74,75,76,78,78,82,84,84,89,89,95 of sample interval]
4~20:[54,54,54,62,73,74,75,76,78,78,82,84,84,89,89,95,97 of sample interval]
5~21:[54,54,62,73,74,75,76,78,78,82,84,84,89,89,95,97,101 of sample interval]
6~22:[54,62,73,74,75,76,78,78,82,84,84,89,89,95,97,101,102 of sample interval]
Step S280 determines the corresponding mean square deviation of each sample interval, according to the corresponding mean square deviation of each sample interval
Size chooses the smallest sample interval of mean square deviation as target sample section；
In the above embodiment of the invention, as follows by calculating separately the corresponding mean square deviation that each sample interval obtains:
In this step, since the value of the corresponding mean square deviation of sample interval 6~21 is minimum, then 6~21 conduct of sample interval
Target sample section.
Step S290, using the smallest History Performance Data in the target sample section as under the base-line data
The value of baseline；Using the maximum History Performance Data in the target sample section as the upper baseline of the base-line data
Value.
In described above, sample interval 6~21:
[54,62,73,74,75,76,78,78,82,84,84,89,89,95,97,101,102] the smallest history in
Performance data is 54, then by 54 value as the lower baseline of base-line data.Maximum History Performance Data is 102, then makees 102
For the value of the upper baseline of the base-line data.
It in a preferred embodiment of the present invention, further include step S291 after step S290.
Step S291 calculates the base with preset tolerance angle value according to the value of the upper baseline of the base-line data
The upper tolerance of line number evidence；The base is calculated with preset tolerance angle value according to the value of the lower baseline of the base-line data
The lower tolerance of line number evidence.
Fig. 3 is a kind of structure chart of base-line data computing system in baseline management in the present invention；Shown in Figure 3, this is
System includes following module:
Acquisition module 301, for obtaining the History Performance Data in preset time range, the preset time range is root
It is determined according to the corresponding date-time of base-line data to be calculated；
Grouping module 302 divides the History Performance Data for the size according to the History Performance Data
Group obtains multiple History Performance Data set；
Screening module 303 is chosen according to the number of History Performance Data included in each History Performance Data set
Target histories performance data set；According to the target histories performance data set, target histories performance data is determined；
Computing module 304 determines the target for the number according to the first preset value and the target capabilities data
Multiple sample intervals of History Performance Data, and determine the target histories performance data fallen into each sample interval；According to falling
Enter the target histories performance data in each sample interval, determines the corresponding mean square deviation of each sample interval；According to described square
Difference chooses target sample section；According to the target histories performance data fallen into the target sample section, the baseline is calculated
Data.
Fig. 4 is a kind of detailed structure view of base-line data computing system in baseline management in the present invention.It is shown in Figure 4:
The History Performance Data be it is multiple, multiple History Performance Datas are opposite with multiple date-times in the preset time range
It answers；On the basis of Fig. 4 is shown in Fig. 3, further includes:
Judgment module 401, for judging whether the corresponding date-time of the multiple History Performance Data is continuous；
Acquisition module 301, for judging that the corresponding date-time of the multiple History Performance Data does not connect in judgment module
When continuous, History Performance Data corresponding to the date-time adjacent with the date-time of missing is obtained；According to the adjacent day
History Performance Data corresponding to time phase determines History Performance Data corresponding to the date-time of the missing.
In an embodiment of the present invention, grouping module 302 are also used to arrange the multiple History Performance Data
Sequence；And after sequence, the forward History Performance Data that sorts is screened out；Or, screening out the History Performance Data of sequence rearward.
In an embodiment of the present invention, grouping module 302 are also used to arrange the multiple History Performance Data
Sequence；And after sequence, the forward History Performance Data that sorts is screened out；The History Performance Data of sequence rearward is screened out simultaneously.
In an embodiment of the present invention, grouping module 302 are gone through according to maximum in the multiple History Performance Data
History performance data and preset set number, determine the range of convergence of each set；According to the multiple History Performance Data
Size, the multiple History Performance Data is assigned in corresponding range of convergence；Historical performance in each range of convergence
Data constitute corresponding History Performance Data set；
In an embodiment of the present invention, screening module 303, for choosing the largest number of history of History Performance Data
Performance data set is as target histories performance data set；By the historical performance number in the target histories performance data set
According to as target histories performance data.
The largest number of History Performance Data set of History Performance Data are chosen in an embodiment of the present invention, and
One or two History Performance Data set adjacent with the largest number of History Performance Data set, as target histories
Performance data set.Using the History Performance Data in the target histories performance data set as target histories performance data.
In an embodiment of the present invention, computing module 304, for according to the first preset value and the target capabilities
The number of data determines the number for the target histories performance data for including in each sample interval；According to target histories performance number
According to total number and a each sample interval in include target histories performance data number, determine the target histories
Each sample interval of energy data and the target histories performance data in each sample interval.
In an embodiment of the present invention, computing module 304, according to the big of the corresponding mean square deviation of each sample interval
It is small, the smallest sample interval of mean square deviation is chosen as target sample section；According to the target fallen into the target sample section
History Performance Data determines that base-line data includes: using the smallest History Performance Data in the target sample section as institute
State the value of the lower baseline of base-line data；Using the maximum History Performance Data in the target sample section as the baseline number
According to upper baseline value；
In an embodiment of the present invention, computing module 304 are also used to the upper baseline according to the base-line data
Value calculate the upper tolerance of the base-line data with preset tolerance angle value；According to the lower baseline of the base-line data
Value calculate the lower tolerance of the base-line data with preset tolerance angle value.
In conclusion in the present invention by obtaining the History Performance Data in preset time range, to the history of acquisition
Performance data is smoothed to obtain continuous History Performance Data.Then it is grouped after screening again, obtains target and go through
History performance data set.And then determine target histories performance data.Further according to the first preset value and the target capabilities data
Number, determine multiple sample intervals of the target histories performance data, and determine the target fallen into each sample interval
History Performance Data；And then the corresponding mean square deviation of each sample interval is determined respectively；According to the mean square deviation, target sample is chosen
Section；According to the target histories performance data fallen into the target sample section, the base-line data is calculated.As it can be seen that in root
The History Performance Data of optimal target is determined according to sample interval and corresponding mean square deviation algorithm.Obtaining in turn most can be to be calculated
Base-line data, solve the problems, such as that required base-line data can not be calculated in the prior art, also or be calculated
Base-line data is not accurate enough, without reference to the defect of meaning.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the scope of the present invention.It is all
Any modification, equivalent replacement, improvement and so within the spirit and principles in the present invention, are all contained in protection scope of the present invention
It is interior.
Claims (10)
1. base-line data calculation method in a kind of baseline management characterized by comprising
The History Performance Data in preset time range is obtained, the preset time range is according to base-line data pair to be calculated
What the date-time answered determined；
According to the size of the History Performance Data, the History Performance Data is grouped, obtains multiple historical performance numbers
According to set；
According to the number of History Performance Data included in each History Performance Data set, target histories performance data is chosen
Set；
According to the target histories performance data set, target histories performance data is determined；
According to the first preset value and the number of the target capabilities data, multiple samples of the target histories performance data are determined
This section, and determine the target histories performance data fallen into each sample interval；
According to the target histories performance data fallen into each sample interval, the corresponding mean square deviation of each sample interval is determined；
According to the mean square deviation, target sample section is chosen；
According to the target histories performance data fallen into the target sample section, the base-line data is calculated.
2. the method according to claim 1, wherein the History Performance Data is multiple, multiple historical performances
Data are corresponding with multiple date-times in the preset time range；
After the History Performance Data in the acquisition preset time range, described according to the big of the History Performance Data
It is small, before being grouped to the History Performance Data, further includes:
Judge whether the corresponding date-time of the multiple History Performance Data is continuous；
If discontinuous, History Performance Data corresponding to the date-time adjacent with the date-time of missing is obtained；
According to History Performance Data corresponding to the adjacent date-time, corresponding to the date-time for determining the missing
History Performance Data.
3. according to the method described in claim 2, it is characterized in that, in the size according to History Performance Data, to described
Before History Performance Data is grouped, further includes:
The multiple History Performance Data is ranked up；
Screen out the forward History Performance Data that sorts；And/or
Screen out the History Performance Data of sequence rearward；
The size according to History Performance Data, is grouped the History Performance Data, obtains multiple historical performance numbers
According to set, comprising:
According to History Performance Data maximum in the multiple History Performance Data and preset set number, each collection is determined
The range of conjunction；
According to the size of the multiple History Performance Data, the multiple History Performance Data is assigned in corresponding set；
History Performance Data in each set constitutes corresponding History Performance Data set.
4. according to the method described in claim 3, it is characterized in that, described according to included in each History Performance Data set
History Performance Data number, choose target histories performance data set include:
The largest number of History Performance Data set of History Performance Data are chosen as target histories performance data set；
Alternatively,
Choose the largest number of History Performance Data set of History Performance Data, and with the largest number of historical performance numbers
According to one or two adjacent History Performance Data set is gathered, as target histories performance data set；
It is described according to the target histories performance data set, determine that target histories performance data includes:
Using the History Performance Data in the target histories performance data set as target histories performance data.
5. the method according to claim 1, wherein described according to the first preset value and the target capabilities number
According to number, determine multiple sample intervals of the target histories performance data, and determine the mesh fallen into each sample interval
Marking History Performance Data includes:
According to the first preset value and the number of the target capabilities data, the target histories for including in each sample interval are determined
The number of performance data；
According to the target histories performance data for including in the total number of target histories performance data and a each sample interval
Number, each sample interval and the target histories performance number in each sample interval for determining the target histories performance data
According to.
6. the method according to claim 1, wherein described according to the mean square deviation, selection target sample section
Include:
According to the size of the corresponding mean square deviation of each sample interval, the smallest sample interval of mean square deviation is chosen as target sample area
Between；
According to the target histories performance data fallen into the target sample section, determine that base-line data includes:
Using the smallest History Performance Data in the target sample section as the value of the lower baseline of the base-line data；By institute
State the value of maximum History Performance Data in target sample section as the upper baseline of the base-line data.
7. according to the method described in claim 6, it is characterized in that, after the calculating base-line data, further includes:
The upper appearance of the base-line data is calculated with preset tolerance angle value according to the value of the upper baseline of the base-line data
Degree of bearing；
The lower appearance of the base-line data is calculated with preset tolerance angle value according to the value of the lower baseline of the base-line data
Degree of bearing.
8. base-line data computing system in a kind of baseline management characterized by comprising
Acquisition module, for obtaining the History Performance Data in preset time range, the preset time range is according to wait count
The corresponding date-time of the base-line data of calculation determines；
Grouping module is grouped the History Performance Data for the size according to the History Performance Data, obtains more
A History Performance Data set；
Screening module is chosen target and is gone through according to the number of History Performance Data included in each History Performance Data set
History performance data set；According to the target histories performance data set, target histories performance data is determined；
Computing module determines the target histories for the number according to the first preset value and the target capabilities data
Multiple sample intervals of energy data, and determine the target histories performance data fallen into each sample interval；It is each according to falling into
Target histories performance data in sample interval determines the corresponding mean square deviation of each sample interval；According to the mean square deviation, choose
Target sample section；According to the target histories performance data fallen into the target sample section, the base-line data is calculated.
9. system according to claim 8, which is characterized in that the History Performance Data is multiple, multiple historical performances
Data are corresponding with multiple date-times in the preset time range；System further include:
Judgment module, for judging whether the corresponding date-time of the multiple History Performance Data is continuous；
Acquisition module, for obtaining when judgment module judges that the corresponding date-time of the multiple History Performance Data is discontinuous
Take History Performance Data corresponding to the date-time adjacent with the date-time of missing；According to the adjacent date-time institute
Corresponding History Performance Data determines History Performance Data corresponding to the date-time of the missing；
The grouping module is also used to be ranked up the multiple History Performance Data；
Screen out the forward History Performance Data that sorts；And/or
Screen out the History Performance Data of sequence rearward；
According to History Performance Data maximum in the multiple History Performance Data and preset set number, each collection is determined
The range of convergence of conjunction；
According to the size of the multiple History Performance Data, the multiple History Performance Data is assigned to corresponding range of convergence
It is interior；
History Performance Data in each range of convergence constitutes corresponding History Performance Data set；
Screening module, for choosing the largest number of History Performance Data set of History Performance Data as target histories performance number
According to set；
Alternatively,
Choose the largest number of History Performance Data set of History Performance Data, and with the largest number of historical performance numbers
According to one or two adjacent History Performance Data set is gathered, as target histories performance data set；
It is described according to the target histories performance data set, determine that target histories performance data includes:
Using the History Performance Data in the target histories performance data set as target histories performance data.
10. system according to claim 9, which is characterized in that
Computing module determines in each sample interval for the number according to the first preset value and the target capabilities data
The number for the target histories performance data for including；
According to the target histories performance data for including in the total number of target histories performance data and a each sample interval
Number, each sample interval and the target histories performance number in each sample interval for determining the target histories performance data
According to；
According to the size of the corresponding mean square deviation of each sample interval, the smallest sample interval of mean square deviation is chosen as target sample area
Between；
According to the target histories performance data fallen into the target sample section, determine that base-line data includes:
Using the smallest History Performance Data in the target sample section as the value of the lower baseline of the base-line data；By institute
State the value of maximum History Performance Data in target sample section as the upper baseline of the base-line data；
Computing module is also used to the value of the upper baseline according to the base-line data, and preset tolerance angle value, described in calculating
The upper tolerance of base-line data；
The lower appearance of the base-line data is calculated with preset tolerance angle value according to the value of the lower baseline of the base-line data
Degree of bearing.
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