CN107730021A - A kind of operational indicator optimization method and device - Google Patents
A kind of operational indicator optimization method and device Download PDFInfo
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- CN107730021A CN107730021A CN201610652203.8A CN201610652203A CN107730021A CN 107730021 A CN107730021 A CN 107730021A CN 201610652203 A CN201610652203 A CN 201610652203A CN 107730021 A CN107730021 A CN 107730021A
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Abstract
The embodiment of the invention discloses a kind of operational indicator optimization method, including：The first title and the first statistical information of the first operational indicator are obtained respectively, and second operational indicator the second title and the second statistical information, first statistical information is used for the Statistical Criteria for characterizing first operational indicator, and second statistical information is used for the Statistical Criteria for characterizing second operational indicator；First title and second title are analyzed, the first relation between obtaining first title and the second place referred to as；First statistical information and second statistical information are parsed, obtain the second relation between the Statistical Criteria of first operational indicator and the Statistical Criteria of second operational indicator；According to first relation, second relation and default optimisation strategy, first operational indicator and second operational indicator are optimized.The embodiment of the present invention also discloses a kind of operational indicator optimization device simultaneously.
Description
Technical field
The present invention relates to metadata, data relationship and scheduling system optimization technology field, more particularly to a kind of business to refer to
Mark optimization method and device.
Background technology
With the arrival in big data epoch, data decide the future development of enterprise, and over time, people will get over
Recognize importance of the data to enterprise come more.In long-term business process, an enterprise can accumulate substantial amounts of business
Index, these operational indicators can have the problems such as redundancy, therefore the management to these operational indicators is particularly important, so as to more
Management business index well, and then preferably distributors.
The business personnel of experience is the need for the traditional method of operational indicator management at present and technical staff is carried out manually
Comb, specifically, technical staff can be according to the index coding of operational indicator, index name, index unit, index classification, index
The indication informations such as service definition, indicator-specific statistics bore are managed to operational indicator, by manually counting and knowing method for distinguishing reality
The now classification to operational indicator, shave weight and operational indicator combinatory analysis.
Further, since in actual management operational indicator, the amount for the operational indicator that can be related to is bigger, prior art milli
Can spend substantial amounts of manpower without query, and can also bring the procedural depth of management not enough, integrated degree is not high or even the degree of accuracy
The problem of not high.It can be seen that prior art is unable to reach the optimization to operational indicator, therefore certain irrationality be present.
The content of the invention
In order to solve the above technical problems, the embodiment of the present invention it is expected to provide a kind of operational indicator optimization method and device, energy
It is enough that relation between operational indicator is determined by index name and indicator-specific statistics bore, so as to realize the optimization to operational indicator.
The technical proposal of the invention is realized in this way：
In a first aspect, the embodiment of the present invention provides a kind of operational indicator optimization method, methods described includes：Is obtained respectively
The first title and the first statistical information of one operational indicator, and the second title and the second statistical information of the second operational indicator,
First statistical information is used for the Statistical Criteria for characterizing first operational indicator, and second statistical information is used to characterize institute
State the Statistical Criteria of the second operational indicator；First title and second title are analyzed, obtain the first place
Claim and the second place be referred to as between the first relation；First statistical information and second statistical information are parsed,
Obtain the second relation between the Statistical Criteria of first operational indicator and the Statistical Criteria of second operational indicator；According to
First relation, second relation and default optimisation strategy, to first operational indicator and second operational indicator
Optimize.
Second aspect, the embodiment of the present invention provide a kind of operational indicator optimization device, and described device includes：Acquisition module,
For obtaining the first title and the first statistical information of the first operational indicator respectively, and the second title of the second operational indicator and
Second statistical information, first statistical information are used for the Statistical Criteria for characterizing first operational indicator, second statistics
Information is used for the Statistical Criteria for characterizing second operational indicator；Analysis module, for first title and described second
Title is analyzed, the first relation between obtaining first title and the second place referred to as；Parsing module, for described
First statistical information and second statistical information are parsed, and obtain the Statistical Criteria and described the of first operational indicator
The second relation between the Statistical Criteria of two operational indicators；Optimization module, for according to first relation, second relation
With default optimisation strategy, first operational indicator and second operational indicator are optimized.
The embodiments of the invention provide a kind of operational indicator optimization method and device, first, obtains the first business respectively and refers to
The title of target first and the first statistical information, and the second title of the second operational indicator and the second statistical information, the first statistics
Information is used for the Statistical Criteria for characterizing the first operational indicator, and the second statistical information is used for the statistics mouth for characterizing the second operational indicator
Footpath；Then, the first title and the second title are analyzed, obtain the first place title and second place be referred to as between the first relation；Connect
, the first statistical information and the second statistical information are parsed, obtain the Statistical Criteria and the second business of the first operational indicator
The second relation between the Statistical Criteria of index；Afterwards, according to the first relation, the second relation and default optimisation strategy, to first
Operational indicator and the second operational indicator optimize.So, so that it may pass through the title and statistical information (i.e. industry of operational indicator
Business indicator-specific statistics bore) name relation and statistical relationship between operational indicator are determined, can in conjunction with default optimisation strategy
The optimization to operational indicator is realized, substantially increases efficiency and the degree of accuracy of operational indicator optimization, there is provided good user's body
Test.
Brief description of the drawings
Fig. 1 is a kind of index optimization method flow diagram provided in an embodiment of the present invention；
Fig. 2 is a kind of word segmentation result schematic diagram provided in an embodiment of the present invention；
Fig. 3 is another word segmentation result schematic diagram provided in an embodiment of the present invention；
Fig. 4 is a kind of participle comparison schematic diagram provided in an embodiment of the present invention；
Fig. 5 is a kind of determination Statistical Criteria relation flow chart provided in an embodiment of the present invention；
Fig. 6 is another index optimization method flow diagram provided in an embodiment of the present invention；
Fig. 7 is a kind of index optimization apparatus structure schematic diagram provided in an embodiment of the present invention；
Fig. 8 is another index optimization apparatus structure schematic diagram provided in an embodiment of the present invention；
Fig. 9 is other a kind of index optimization apparatus structure schematic diagrams provided in an embodiment of the present invention.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation describes.
Embodiment one
The embodiment of the present invention provides a kind of operational indicator optimization method, applied to the identification device of operational indicator similarity,
The device can be arranged at common server, can also be arranged at the webserver, and the embodiment of the present invention is not specifically limited.
As shown in figure 1, this method includes：
S101, the first title and the first statistical information for obtaining the first operational indicator respectively, and the second operational indicator
Second title and the second statistical information；
Here, the first statistical information is used for the Statistical Criteria for characterizing the first operational indicator, and the second statistical information is used to characterize
The Statistical Criteria of second operational indicator.
In actual applications, the indication information of the operational indicator of a determination includes under normal circumstances：Index coding, index
Title, index unit, index classification, index service definition and indicator-specific statistics bore.Wherein, index coding refers to the unique of index
Mark；Index name refers to the short name of index, such as：Enliven client's number, silence client's number etc.；Index unit refers to index
Measurement unit, such as：Family, ten thousand yuan etc.；Index classification refers to the classification of index, such as：Take in class, rank class, market competition class
Deng；Index service definition refers to enterprise administrator or one section of word description for being directed to index that business personnel is appreciated that；Refer to
Mark Statistical Criteria refers to the calculation formula of the index, belongs to the definition of technological layer, such as：Query statistic script, calculation formula
Deng.
In the present embodiment, the index name and indicator-specific statistics bore the two indication informations of operational indicator are chosen, is come real
Now to the optimization of operational indicator.What deserves to be explained is in practice, all operational indicators can be optimized, the present embodiment
In only by taking the first operational indicator and the second operational indicator as an example, illustrate the optimization process, to except the first operational indicator and the second industry
The optimization process of all operational indicators outside index of being engaged in is complete with the optimization process to the first operational indicator and the second operational indicator
It is exactly the same, just no longer it is described in detail in the present embodiment.
In the first step optimized to the first operational indicator and the second operational indicator, it is necessary to from the storage of operational indicator
The index name and indicator-specific statistics bore of the first operational indicator and the second operational indicator are got in space.
S102, the first title and the second title are analyzed, obtain the first place title and second place be referred to as between first close
System；
Specifically, according to default dictionary, the first title is identified, obtains the first result；According to default dictionary, second place is identified
Claim, obtain the second result；According to the first result, the second result and the first preset rules, the first relation is determined.
Here, presetting dictionary includes dimension dictionary and measurement dictionary, wherein, dimension dictionary be by protection dimension name and
The Composition of contents of dimension, by taking customer status dimension as an example, the dimension dictionary includes：Effective client, invalid client, member (very
Important people, VIP), it is common etc..Measuring dictionary is made up of metric, such as：Client's number, income, flow,
Access times etc..
First, the first title is distinguished into matching dimensionality dictionary and measurement dictionary, determines the first dimension in the first title
Word and the first measurement word；By the second title difference matching dimensionality dictionary and measurement dictionary, the second dimension in the second title is determined
Spend word and the second measurement word；Mark the first dimension word and the first measurement word are the first result；Mark the second dimension word and second degree
Measure word is the second result.
This step can be to the first title matching dimensionality dictionary and measurement dictionary, and then determines the first result；Meanwhile also can
To the second title matching dimensionality dictionary and measurement dictionary, and then determine the second result.The matching process is exactly to the first title
The process of word segmentation processing is carried out with the second title.
Example, exemplified by " moon permissible call client number " and " communicate the moon effective amount " the two titles, illustrate this
With process, i.e. word segmentation processing process, open source projects Luence is used during the word segmentation processing, in combination with Dictionary based segment and
The Chinese word segmentation component of syntax analysis algorithm, choose iteration most fine granularity segmentation algorithm, the algorithm follow Forward Maximum Method and
The principle of word is most segmented, word segmentation processing is carried out to title.Word segmentation result to " moon permissible call client number " is：Time dimension becomes
Amount：Month, customer status dimension variable：Effectively, class of service dimension variable：Voice (call belongs to one kind of voice), measurement become
Amount：Client's number, word segmentation result are as shown in Figure 2；Word segmentation result to " moon communicate effective amount " is：Time dimension variable：Month,
Class of service dimension variable：Voice (call belongs to one kind of voice), customer status dimension variable：Effectively, gauge variable：User
Number, word segmentation result are as shown in Figure 3.
Then, the first result and the second result are compared, obtain comparative result.
Example, exemplified by " moon permissible call client number " and " communicate the moon effective amount ", on the basis of S102, simultaneously
Referring to figs. 2 and 3, the word segmentation result of " moon permissible call client number " and " communicate the moon effective amount " is compared, wherein,
The content compared compares including dimension variable and compared with gauge variable, specifically, be respectively compared " moon permissible call client number " with
Time dimension variable, customer status dimension variable, class of service dimension variable and the gauge variable of " communicate the moon effective amount ",
The schematic diagram compared is as shown in Figure 4.
What deserves to be explained is, on the one hand, the dimension variable and the comparison of gauge variable of the first result and the second result and its
Position is unrelated, such as：" moon silence Fetion number of users " is equivalent to " moon Fetion silent user number "；Second aspect, than
Need to consider alias compared with during, such as：" client's number " and " number of users " are the words for representing the same meaning, between them mutually each other
Mutual alias, therefore, when two gauge variables are incomplete same, but mutually each other mutual alias when, comparative result thinks this
Two gauge variables are consistent.
Afterwards, according to the first preset rules and comparative result, the first relation is determined.
Here, the first relation refer to the first operational indicator the first title and the second operational indicator second place be referred to as between
Name relation, wherein, the first preset rules are：
When the dimension variable of two operational indicators is all consistent with gauge variable, then the name relation of two operational indicators is one
Cause relation；
When the dimension variable of two operational indicators is consistent, and gauge variable is inconsistent, then the title of two operational indicators is closed
It is for dependency relation；
When the gauge variable of two operational indicators is consistent, and the dimension variable of an operational indicator is more than and comprising another
The dimension variable of index, then the name relation of two operational indicators is inclusion relation；
When having located outside above-mentioned three kinds of situations, then the name relation of two operational indicators is unrelated relation.
What deserves to be explained is the preferably judge that above-mentioned first preset rules, which are only this implementation, to be enumerated is regular, this
Embodiment is not limited judging rule.
Here, table 1 below exemplarily illustrates the specifically used of first preset rules.
Table 1
S103, the first statistical information and the second statistical information are parsed, obtain the Statistical Criteria of the first operational indicator
And second operational indicator Statistical Criteria between the second relation；
Specifically, parsed respectively to the first statistical information and the second statistical information, the of the first statistical information is obtained
Second attribute information of one attribute information and the second statistical information；According to the first attribute information, the second attribute information and second
Preset rules, determine the second relation.
Here, the first statistical information and the second statistical information can be parsed respectively, example according to default resolution rules
, it is to be parsed using SQL (Structured Query Language, SQL) resolver in the present embodiment
First statistical information and the second statistical information.Wherein, statistical information includes sql sentences, and sql resolvers can be according to input
Sql sentences, after being parsed by sql resolvers, the statistical form of statistical information is exported, the statistical form is used to record statistical information
Attribute information.Sql sentences are a kind of data base queryings and programming language, for access data and inquiry, renewal and
Administrative relationships Database Systems.What deserves to be explained is the statistical form in the present embodiment is a kind of forms of characterization of attribute information.
Wherein, according to the first attribute information, the second attribute information and the second preset rules, the second relation, including two are determined
Kind feasible program：
Scheme one：
Judge whether the first attribute information is identical with the second attribute information；If the first attribute information and the second attribute information
It is identical, determine the second relation for correlation；If the first attribute information and the second attribute information differ, determine the second relation for not
It is related.
Example, after parsing the first statistical information and the second statistical information using resolver, parse the first statistics
First statistical form of information, and the second statistical form of the second statistical information is parsed, wherein, the first statistical form is the first attribute
A kind of forms of characterization of information, the second statistical form are a kind of forms of characterization of the second attribute information.When the first statistical form and second
When statistical form is same table (i.e. the first attribute information and the second attribute information is identical), determine the second relation for correlation；When
When one statistical form and the second statistical form are not same table (i.e. the first attribute information and the second attribute information differ), is determined
Two relations are uncorrelated.
What deserves to be explained is when the second relation is related, the Statistical Criteria and the second business that show the first operational indicator refer to
Target Statistical Criteria is identical or mutually includes；Second relation for it is uncorrelated when, show the Statistical Criteria of the first operational indicator with
The Statistical Criteria of second operational indicator is not deposited identical at least partially or mutually included.Wherein, correlation specifically include it is consistent and
Mutually include.
Example, as shown in figure 5, when the first attribute information and identical the second attribute information, determine the second relation for correlation
Process include：
S1051, objectification sql sentences.
Here, the difference sql sentences of objectification the first and the 2nd sql sentences, i.e., parse relevant sql pin from sql sentences
This attribute information.
S1052, from the sql sentences after objectification obtain sub- condition.
Specifically, obtaining child node from a kind of TWhere (sql sentences) node, concrete operations are parsed for where, passed through
The parsing, where variables, logical value and fiducial value can be obtained, and where variables, logical value and fiducial value just parse for sql
As a result.
S1053, compare the first sub- condition and the second sub- condition, obtain comparative result.
Specifically, being respectively compared where variables, logical value and the fiducial value of two statistical informations, and then pass through condition pair
Than obtaining logical value relation and fiducial value relation of two statistical informations in the case of where variable identicals.
S1054, according to comparative result, determine the second relation of the first statistical information and the second statistical information.
Second relation of the first statistical information and the second statistical information is in where variable identicals with two statistical informations
In the case of logical value relation and fiducial value relation identical, wherein, two statistical informations are in the case of where variable identicals
Logical value relation include：Concord, inclusion relation and unrelated relation；Two statistical informations are in where variable identical feelings
Fiducial value relation under condition includes：Concord, inclusion relation and unrelated relation；Likewise, the first statistical information and the second system
The statistical relationship of meter information also includes：Concord, inclusion relation and unrelated relation.
Example, the first operational indicator is " moon permissible call client number ", and its corresponding sql sentence is " Selectcount
(1) from user where state=1and telunm>0”；Second operational indicator is the " moon effective caller talk client
Number ", sentence corresponding to its sql is " Select count (1) from user where state=1andtelunm>0and
callnum>0 ", by condition analysis, the where variables, logical value and fiducial value of two statistical informations of com-parison and analysis, obtaining
The second relation between " moon permissible call client number " and " moon effective caller talk client number " is inclusion relation.
Scheme two：
Judge whether the first attribute information is identical with the second attribute information；If the first attribute information and the second attribute information
It is identical, determine the second relation for correlation.If the first attribute information and the second attribute information differ, according to default first blood
Edge relation information finds out the first source information of the first statistical information, and goes out according to default second genetic connection information searching
Second source information of two statistical informations；Judge whether the first source information is identical with the second source information；If the first source information and the
Two source information are identical, determine the second relation for correlation；If the first source information differs with the second source information, the second relation is determined
To be uncorrelated.
Here, the first genetic connection information is used for the source information for storing the first statistical information, and the second genetic connection information is used
In the source information for storing the second statistical information.In actual optimization, can also there are the first attribute information and the second attribute information not
Identical situation, then in this case, the second relation can't be concluded immediately to be uncorrelated, and can believed in the first genetic connection
Each self-corresponding all source information are found out in breath and the second genetic connection, and each self-corresponding all source information are saved in one
In individual array；Finally by array is compared, to determine whether the two statistical informations possess common source information；And then determine
Go out the second relation.Specifically, if the first source information is identical with the second source information, determine the second relation for correlation；If first
Source information differs with the second source information, and second relation that determines is uncorrelated.Wherein, genetic connection is the source of record data table
(i.e. the source of the index technology bore of operational indicator), it is to be calculated by counting to come.Pre-establishing for genetic connection information be
Through gathering after a while, all execution sql scripts of data center are obtained from the execution record of running log or database,
By sql resolvers, the relation of input and the output of data is established.
What deserves to be explained is when the second relation is related, the Statistical Criteria and the second business that show the first operational indicator refer to
Target Statistical Criteria is identical or mutually includes；Second relation for it is uncorrelated when, show the Statistical Criteria of the first operational indicator with
The Statistical Criteria of second operational indicator is not deposited identical at least partially or mutually included.
S104, according to the first relation, the second relation and default optimisation strategy, to the first operational indicator and the second operational indicator
Optimize.
When the first relation is consistent, the second relation is also consistent, then shows that the two operational indicators have no problem, and completely
Unanimously, default optimisation strategy can merge the two operational indicators, that is, delete one of operational indicator；
When the first relation is consistent, the second relation is uncorrelated, then it is of the same name not synonymous to show that the two operational indicators are present
The problem of, default optimisation strategy can be by a title of modifying in the two operational indicators；
When the first relation is uncorrelated, the second relation is consistent, then it is synonymous not of the same name to show that the two operational indicators are present
The problem of, default optimisation strategy will can modify to one in the two operational indicators title or deletes one of business
Index (i.e. offline one of operational indicator)；
When in the first relation and the second relation relation be comprising, and another relation be not comprising, then show this two
There is the problem of index name and index technology bore are inconsistent in individual operational indicator, default optimisation strategy can will be to the two business
One in index index technology bore of modifying.
So, the problem of existing just is eliminated between operational indicator, so as to optimize operational indicator.
So, so that it may pass through the title and statistics sql script informations (i.e. operational indicator Statistical Criteria) of operational indicator
To determine the name relation and Statistical Criteria relation between operational indicator, achieved that in conjunction with default optimisation strategy and business is referred to
Target optimizes, and substantially increases efficiency and the degree of accuracy of operational indicator optimization, there is provided good Consumer's Experience.
Embodiment two
The embodiment of the present invention provides a kind of operational indicator optimization method, applied to the identification device of operational indicator similarity,
Assuming that A, B, C, D and E are 5 operational indicators that a certain enterprise S is run up to, wherein, the problem of of the same name synonymous between A and B be present,
Now illustrate the operational indicator optimization method of the present embodiment, such as Fig. 6 exemplified by being optimized to A, B, C, D and E this 5 operational indicators
Shown, this method includes：
S201, the index name and statistical information for obtaining this 5 operational indicators of A, B, C, D and E.
Here statistical information refers to the Statistical Criteria information of this 5 operational indicators of A, B, C, D and E.
The default dictionary of S202, basis, the recognition result of this 5 index names of identification A, B, C, D and E.
Specifically, it will recognise that the dimension variable and gauge variable of this 5 index names of A, B, C, D and E.
S203, according to recognition result and the first preset rules, determine first relation of this 5 index names.
It can be found that A and B dimension variable and gauge variable are completely the same from 5 recognition results, then just can determine that
The first relation for going out A and B index names is concord, and the first relation between other operational indicator index names is uncorrelated.
S204, the statistical information for parsing this 5 operational indicators of A, B, C, D and E, obtain attribute information.
Specifically, for the Statistical Criteria information of this 5 operational indicators of A, B, C, D and E, parsed using sql resolvers
A, the attribute information of this 5 operational indicators of B, C, D and E.
S205, judge whether the attribute information of this 5 operational indicators of A, B, C, D and E is identical, if so, then performing S206；It is no
Then perform S207.
Because the attribute information of A, B, C, D and E this 5 operational indicators is identical and A, B, C, D and E this 5 operational indicators
Attribute information difference both of these case can correspond to the determination method of different second relations, it is therefore necessary to it is determined that Statistical Criteria is closed
Before system, judge whether the attribute information of this 5 operational indicators of A, B, C, D and E is identical.
S206, determine the second relation for correlation.
According to the present embodiment it is assumed that the second relation being capable of determining that between A and B is correlation, and further, A
The second relation with B is concord.
S207, determine that the second relation is uncorrelated.
According to the present embodiment it is assumed that the second relation in addition to the second relation between A and B between remaining operational indicator is
It is uncorrelated.
S208, according to the first relation, the second bore relation and default optimisation strategy, optimize this 5 operational indicators.
Because the A and B relation of title first and second is concord, illustrates that A and B is of the same name synonymous, redundancy occur,
Therefore A or B can be deleted according to default optimisation strategy.The first relation and the second relation of other operational indicators be it is uncorrelated, i.e., its
His operational indicator is not not of the same name also synonymous, in the absence of any problem, without optimization.
So, so that it may pass through the title and statistics sql script informations (i.e. operational indicator Statistical Criteria) of operational indicator
To determine the name relation and Statistical Criteria relation between operational indicator, achieved that in conjunction with default optimisation strategy and business is referred to
Target optimizes, and substantially increases efficiency and the degree of accuracy of operational indicator optimization, there is provided good Consumer's Experience.
Embodiment three
The embodiment of the present invention provides a kind of operational indicator optimization device 30, as shown in fig. 7, the device 30 includes：Obtain mould
Block 301, for obtaining the first title and the first statistical information of the first operational indicator, and the second of the second operational indicator respectively
Title and the second statistical information, the first statistical information are used for the Statistical Criteria for characterizing the first operational indicator, and the second statistical information is used
In the Statistical Criteria for characterizing the second operational indicator；Analysis module 302, for analyzing the first title and the second title, obtain
Bear the bell title and second place be referred to as between the first relation；Parsing module 303, for believing the first statistical information and the second statistics
Breath is parsed, and obtains the second relation between the Statistical Criteria of the first operational indicator and the Statistical Criteria of the second operational indicator；
Optimization module 304, for according to the first relation, the second relation and default optimisation strategy, to the first operational indicator and the second business
Index optimizes.
In other embodiments of the present invention, analysis module 302 is specifically used for, according to default dictionary, identifying the first title, obtaining
Obtain the first result；According to default dictionary, the second title is identified, obtains the second result；According to the first result, the second result and first
Preset rules, determine the first relation.
In other embodiments of the present invention, as shown in figure 8, parsing module 303 includes：Analyzing sub-module 3031, for dividing
It is other that first statistical information and the second statistical information are parsed, obtain the first attribute information and second of the first statistical information
Second attribute information of statistical information；Determination sub-module 3032, for according to the first attribute information, the second attribute information and second
Preset rules, determine the second relation.
In other embodiments of the present invention, determination sub-module 3032, specifically for judging the first attribute information and the second category
Whether property information is identical；If the first attribute information and the second attribute information are identical, determine the second relation for correlation；If first
Attribute information and the second attribute information differ, and second relation that determines is uncorrelated；
In other embodiments of the present invention, as shown in figure 9, said apparatus 30 also includes：Searching modul 305, judge module
306 and determining module 307；Wherein, searching modul 305, if differed for the first attribute information and the second attribute information,
Go out the first source information of the first statistical information according to default first genetic connection information searching, and according to default second blood relationship
Relation information finds out the second source information of the second statistical information；Judge module 306, for judging the first source information and the second source
Whether information is identical；Determining module 307, if identical with the second source information for the first source information, second relation that determines is phase
Close；If the first source information differs with the second source information, second relation that determines is uncorrelated.
Further, when the second relation is related, the statistics of the Statistical Criteria of the first operational indicator and the second operational indicator
Bore is identical or mutually includes；When second relation is uncorrelated, the Statistical Criteria of the first operational indicator and the second operational indicator
Statistical Criteria not deposit at least a portion identical or mutually include.
In actual applications, above-mentioned acquisition module, analysis module, parsing module, optimization module, searching modul, judge mould
Block, determining module, analyzing sub-module and determination sub-module can be by the central processing units in index optimization device
(Central Processing Unit, CPU), microprocessor (Micro Processor Unit, MPU), Digital Signal Processing
Device (Digital Signal Processor, DSP) or field programmable gate array (Field Programmable Gate
Array, FPGA) etc. realize, the embodiment of the present invention is not specifically limited.
It need to be noted that be：Apparatus above implements the description of item, is similar with above method description, has same
Embodiment of the method identical beneficial effect, therefore do not repeat.For the ins and outs not disclosed in apparatus of the present invention embodiment,
Those skilled in the art refer to the description of the inventive method embodiment and understand, to save length, repeat no more here.
It should be understood by those skilled in the art that, embodiments of the invention can be provided as method, system or computer program
Product.Therefore, the shape of the embodiment in terms of the present invention can use hardware embodiment, software implementation or combination software and hardware
Formula.Moreover, the present invention can use the computer for wherein including computer usable program code in one or more to use storage
The form for the computer program product that medium is implemented on (including but is not limited to magnetic disk storage and optical memory etc.).
The present invention is the flow with reference to method according to embodiments of the present invention, equipment (system) and computer program product
Figure and/or block diagram describe.It should be understood that can be by every first-class in computer program instructions implementation process figure and/or block diagram
Journey and/or the flow in square frame and flow chart and/or block diagram and/or the combination of square frame.These computer programs can be provided
The processors of all-purpose computer, special-purpose computer, Embedded Processor or other programmable data processing devices is instructed to produce
A raw machine so that produced by the instruction of computer or the computing device of other programmable data processing devices for real
The device for the function of being specified in present one flow of flow chart or one square frame of multiple flows and/or block diagram or multiple square frames.
These computer program instructions, which may be alternatively stored in, can guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works so that the instruction being stored in the computer-readable memory, which produces, to be included referring to
Make the manufacture of device, the command device realize in one flow of flow chart or multiple flows and/or one square frame of block diagram or
The function of being specified in multiple square frames.
These computer program instructions can be also loaded into computer or other programmable data processing devices so that counted
Series of operation steps is performed on calculation machine or other programmable devices to produce computer implemented processing, so as in computer or
The instruction performed on other programmable devices is provided for realizing in one flow of flow chart or multiple flows and/or block diagram one
The step of function of being specified in individual square frame or multiple square frames.
The foregoing is only a preferred embodiment of the present invention, is not intended to limit the scope of the present invention.
Claims (11)
1. a kind of operational indicator optimization method, it is characterised in that methods described includes：
Obtain the first title and the first statistical information of the first operational indicator respectively, and the second title of the second operational indicator and
Second statistical information, first statistical information are used for the Statistical Criteria for characterizing first operational indicator, second statistics
Information is used for the Statistical Criteria for characterizing second operational indicator；
First title and second title are analyzed, between obtaining first title and the second place referred to as
First relation；
First statistical information and second statistical information are parsed, obtain the statistics mouth of first operational indicator
The second relation between the Statistical Criteria of footpath and second operational indicator；
According to first relation, second relation and default optimisation strategy, to first operational indicator and described second
Operational indicator optimizes.
2. according to the method for claim 1, it is characterised in that described that first title and second title are carried out
Analysis, the first relation between obtaining first title and the second place referred to as, including：
According to default dictionary, first title is identified, obtains the first result；
According to default dictionary, second title is identified, obtains the second result；
According to first result, second result and the first preset rules, first relation is determined.
3. according to the method for claim 1, it is characterised in that described that first statistical information is counted with described second
Information is parsed, and is obtained between the Statistical Criteria of first operational indicator and the Statistical Criteria of second operational indicator
Second relation, including：
First statistical information and second statistical information are parsed respectively, obtain the of first statistical information
Second attribute information of one attribute information and second statistical information；
According to first attribute information, second attribute information and the second preset rules, second relation is determined.
4. according to the method for claim 3, it is characterised in that it is described according to first attribute information, it is described second category
Property information and the second preset rules, determine second relation, including：
Judge whether first attribute information and second attribute information are identical；
If first attribute information is identical with second attribute information, determine second relation for correlation；
If first attribute information and second attribute information differ, it is uncorrelated to determine second relation.
5. according to the method for claim 4, it is characterised in that judge first attribute information and described second described
After whether attribute information is identical, methods described also includes：
If first attribute information and second attribute information differ, looked into according to default first genetic connection information
The first source information of first statistical information is found out, and second system is gone out according to default second genetic connection information searching
Count the second source information of information；
Judge whether first source information and second source information are identical；
If first source information is identical with second source information, determine second relation for correlation；
If first source information differs with second source information, it is uncorrelated to determine second relation.
6. the method according to claim 4 or 5, it is characterised in that when second relation is related, first business
The Statistical Criteria of index is identical with the Statistical Criteria of second operational indicator or exists identical at least partially；Described second
When relation is uncorrelated, phase is not present in the Statistical Criteria of the Statistical Criteria of first operational indicator and second operational indicator
Same at least a portion.
7. a kind of operational indicator optimizes device, it is characterised in that described device includes：
Acquisition module, for obtaining the first title and the first statistical information of the first operational indicator respectively, and the second business refers to
The title of target second and the second statistical information, first statistical information are used for the statistics mouth for characterizing first operational indicator
Footpath, second statistical information are used for the Statistical Criteria for characterizing second operational indicator；
Analysis module, for analyzing first title and second title, obtain first title and described
Second place be referred to as between the first relation；
Parsing module, for being parsed to first statistical information and second statistical information, obtain first industry
The second relation between the Statistical Criteria for index of being engaged in and the Statistical Criteria of second operational indicator；
Optimization module, for according to first relation, second relation and default optimisation strategy, referring to first business
Mark and second operational indicator optimize.
8. device according to claim 7, it is characterised in that the analysis module, specifically for according to default dictionary, knowing
Not described first title, obtain the first result；According to default dictionary, second title is identified, obtains the second result；According to institute
The first result, second result and the first preset rules are stated, determine first relation.
9. device according to claim 7, it is characterised in that the parsing module, including：
Analyzing sub-module, for being parsed respectively to first statistical information and second statistical information, described in acquisition
Second attribute information of the first attribute information of the first statistical information and second statistical information；
Determination sub-module, for according to first attribute information, second attribute information and the second preset rules, determining institute
State the second relation.
10. device according to claim 9, it is characterised in that the determination sub-module, specifically for judging described first
Whether attribute information and second attribute information are identical；If first attribute information and the second attribute information phase
Together, determine second relation for correlation；If first attribute information and second attribute information differ, institute is determined
It is uncorrelated to state the second relation.
11. device according to claim 10, it is characterised in that described device, in addition to：Searching modul, judge module
And determining module；Wherein,
The searching modul, if differed for first attribute information and second attribute information, according to default
First genetic connection information searching goes out the first source information of first statistical information, and is believed according to default second genetic connection
Breath finds out the second source information of second statistical information；
The judge module, for judging whether first source information and second source information are identical；
The determining module, if identical with second source information for first source information, determine second relation
For correlation；If first source information differs with second source information, it is uncorrelated to determine second relation.
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| CN110516009A (en) * | 2019-08-21 | 2019-11-29 | 北京互金新融科技有限公司 | The method for building up of index system establishes device, storage medium and processor |
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