CN108305013A - The determination method, apparatus and computer equipment of operation project validity - Google Patents
The determination method, apparatus and computer equipment of operation project validity Download PDFInfo
- Publication number
- CN108305013A CN108305013A CN201810146258.0A CN201810146258A CN108305013A CN 108305013 A CN108305013 A CN 108305013A CN 201810146258 A CN201810146258 A CN 201810146258A CN 108305013 A CN108305013 A CN 108305013A
- Authority
- CN
- China
- Prior art keywords
- predetermined operation
- sample data
- result
- predetermined
- characteristic parameter
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 53
- 230000015654 memory Effects 0.000 claims description 24
- 238000004590 computer program Methods 0.000 claims description 18
- 235000013399 edible fruits Nutrition 0.000 claims description 5
- 241001269238 Data Species 0.000 claims description 3
- 230000000052 comparative effect Effects 0.000 claims description 3
- 230000000875 corresponding effect Effects 0.000 description 121
- 238000010586 diagram Methods 0.000 description 13
- 238000007689 inspection Methods 0.000 description 11
- 230000000694 effects Effects 0.000 description 7
- 230000002596 correlated effect Effects 0.000 description 6
- 230000001737 promoting effect Effects 0.000 description 6
- 238000011161 development Methods 0.000 description 4
- 238000012417 linear regression Methods 0.000 description 4
- 238000011002 quantification Methods 0.000 description 4
- 241000208340 Araliaceae Species 0.000 description 3
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 3
- 235000003140 Panax quinquefolius Nutrition 0.000 description 3
- 238000009826 distribution Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 235000008434 ginseng Nutrition 0.000 description 3
- 238000012795 verification Methods 0.000 description 3
- 238000007796 conventional method Methods 0.000 description 2
- 230000001419 dependent effect Effects 0.000 description 2
- 230000014759 maintenance of location Effects 0.000 description 2
- 238000013488 ordinary least square regression Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 238000004891 communication Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 239000004744 fabric Substances 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
- 238000005303 weighing Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
- G06Q10/103—Workflow collaboration or project management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0202—Market predictions or forecasting for commercial activities
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Human Resources & Organizations (AREA)
- Development Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Finance (AREA)
- Accounting & Taxation (AREA)
- Economics (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Theoretical Computer Science (AREA)
- Game Theory and Decision Science (AREA)
- Data Mining & Analysis (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Educational Administration (AREA)
- Complex Calculations (AREA)
Abstract
This application involves a kind of determination method, apparatus, storage medium and the computer equipment of operation project validity, method includes：Obtain feature samples data and its corresponding index sample data, feature samples data include the sample data of the predetermined operation characteristic parameter corresponding to operation project to be determined, and index sample data includes the sample data of the predetermined operation indicator corresponding to operation project；Regression model, feature samples data based on predefined type and index sample data determine the relational model of predetermined operation characteristic parameter and predetermined operation indicator；Statistical check is carried out based on relational model, feature samples data and index sample data, obtains the relevance result between predetermined operation characteristic parameter and predetermined operation indicator；The corresponding validity result of operation project is obtained based on relevance result.Each embodiment of the application can improve efficiency and accuracy.
Description
Technical field
This application involves field of computer technology, more particularly to a kind of determination method, apparatus of operation project validity,
Computer readable storage medium and computer equipment.
Background technology
For internet product, it will usually carry out operation project (user is made to complete the operation condition of operation project) and come
Realize predetermined operation target.By taking online game product as an example, operation condition can be the prize drawing of game store, and making a reservation for operation target can
Think and promotes user activity, retention ratio and payment rate etc..With the rapid development of information technology, market competition is extremely sharp
Strong, the development frequency for runing project is higher and higher, and form is also more and more diversified.In the case, operation project is determined
Effective implementations (that is, determining operation condition to realizing the predetermined effective implementations for runing target) are of great significance.
Traditional determination method is, relevant predetermined by experience pair and predetermined operation target by related practitioner
Operation indicator data carry out subjective analysis, so that it is determined that effective implementations of the operation project.However, due to manually participating in point
Analysis, the efficiency and accuracy of conventional method are relatively low.Also, when between the operation condition of operation project and predetermined operation target
And when without intuitive apparent relationship, manually it is difficult to determine effective implementations of operation project.
Invention content
Based on this, it is necessary to for the relatively low technical problem of efficiency and accuracy in conventional method, provide a kind of fortune
Determination method, apparatus, computer readable storage medium and the computer equipment of battalion's project validity.
A kind of determination method of operation project validity, including step：
Obtain feature samples data and its corresponding index sample data, the feature samples data include fortune to be determined
The sample data of predetermined operation characteristic parameter corresponding to battalion's project, the index sample data include that the operation project institute is right
The sample data for the predetermined operation indicator answered；
Regression model, the feature samples data based on predefined type and the index sample data, determine described pre-
Surely the relational model of characteristic parameter and the predetermined operation indicator is runed；
Statistical check is carried out based on the relational model, the feature samples data and the index sample data, is obtained
Relevance result between the predetermined operation characteristic parameter and the predetermined operation indicator；
The corresponding validity result of the operation project is obtained based on the relevance result.
A kind of determining device of operation project validity, including：
Sample data acquisition module, for obtaining feature samples data and its corresponding index sample data, the feature
Sample data includes the sample data of the predetermined operation characteristic parameter corresponding to operation project to be determined, the index sample number
According to the sample data for including the predetermined operation indicator corresponding to the operation project；
Relational model determining module is used for regression model, the feature samples data and the finger based on predefined type
Standard specimen notebook data determines the relational model of predetermined the operation characteristic parameter and the predetermined operation indicator；
Association results acquisition module, for being based on the relational model, the feature samples data and the index sample
Data carry out statistical check, obtain the relevance result between the predetermined operation characteristic parameter and the predetermined operation indicator；
Validity result obtains module, for obtaining the corresponding validity of the operation project based on the relevance result
As a result.
A kind of computer readable storage medium is stored with computer program, when the computer program is executed by processor
The step of realizing the determination method of operation project validity as described above.
A kind of computer equipment, including memory and processor, the memory are stored with computer program, the processing
Device realizes the step of determination method of operation project validity as described above when executing the computer program.
Determination method, apparatus, computer readable storage medium and the computer equipment of above-mentioned operation project validity, are based on
The correlated samples data of operation project to be determined determine the relational model of predetermined operation characteristic parameter and predetermined operation indicator, then
Statistical check is carried out based on the relational model and sample data, is obtained between predetermined operation characteristic parameter and predetermined operation indicator
Relevance obtain the corresponding validity result of operation project as a result, being based on the relevance result in turn.On the one hand, it is not necessarily to artificial
Incidence relation between the predetermined operation characteristic parameter of analysis and predetermined operation indicator, being capable of raising efficiency and accuracy.Another party
Face carries out statistics verification based on correlated samples data and relational model, then corresponding based on relevance result acquisition operation project
Validity result, the operation condition of the project of operation and predetermined operation target do not have it is intuitive it is apparent between incidence relation when,
Also effective implementations of operation project be can determine.
Description of the drawings
Fig. 1 is the applied environment figure of the determination method of operation project validity in one embodiment；
Fig. 2 is the flow diagram of the determination method of operation project validity in one embodiment；
Fig. 3 is the flow diagram for the method that the first statistics calibrating amount is determined in one embodiment；
Fig. 4 is the flow diagram for the method that the second statistics calibrating amount is determined in one embodiment；
Fig. 5 is to determine that third counts the flow diagram of the method for calibrating amount in one embodiment；
Fig. 6 is the flow diagram for the method that the 4th statistics calibrating amount is determined in one embodiment；
Fig. 7 is to participate in sample of users in one embodiment and have neither part nor lot in the coordinate diagram of the average duration of sample of users；
Fig. 8 is to participate in sample of users in one embodiment and have neither part nor lot in the box figure of the average duration of sample of users；
Fig. 9 is the flow diagram for the method that the 5th statistics calibrating amount is determined in one embodiment；
Figure 10 is the coordinate schematic diagram of the average duration of sample of users before and after participating in Mission Objective in one embodiment；
Figure 11 is the box schematic diagram of the average duration of sample of users before and after participating in Mission Objective in one embodiment；
Figure 12 is the flow diagram of the determination method of operation project validity in another embodiment；
Figure 13 is the structure diagram of the determining device of operation project validity in one embodiment；
Figure 14 is the structure diagram of one embodiment Computer equipment；
Figure 15 is the structure diagram of another embodiment Computer equipment.
Specific implementation mode
It is with reference to the accompanying drawings and embodiments, right in order to make the object, technical solution and advantage of the application be more clearly understood
The application is further elaborated.It should be appreciated that specific embodiment described herein is only used to explain the application, and
It is not used in restriction the application.
The determination method for the operation project validity that each embodiment of the application provides, can should can be used for as shown in Figure 1 answer
Use environment.The application environment is related to user terminal 110 and server 120, user terminal 110 and server 120 by network into
Row communication.Server 120 obtains feature samples data and index sample data corresponding to operation project to be determined, then right
This feature sample data and the index sample data are handled, and obtain the corresponding validity result of operation project, and then will
The validity result is sent to the user terminal 110, and correspondingly, user terminal 110 shows the validity result, for
Family is checked.Wherein, user terminal 110 can be terminal console or mobile terminal, and terminal console may include desktop computer, mobile whole
End may include at least one of mobile phone, tablet computer, laptop, personal digital assistant and Wearable etc..Service
Device 120 can be realized with the server cluster that independent physical server or multiple physical servers are constituted.
It is appreciated that in other application environments, which is related to user terminal 110 shown in FIG. 1.It can also
The feature samples data and index sample data corresponding to operation project to be determined are obtained by the user terminal 110, it is then right
This feature sample data and the index sample data are handled, and obtain the corresponding validity result of operation project, and then right
The validity result is shown, so that user checks.
In one embodiment, as shown in Fig. 2, providing a kind of determination method of operation project validity.In this way
It is illustrated for the server 120 that should can be used in Fig. 1.This method may include following steps S202~S208.
S202, it includes to be determined to obtain feature samples data and its corresponding index sample data, feature samples data
The sample data of predetermined operation characteristic parameter corresponding to operation project, index sample data include pre- corresponding to operation project
Determine the sample data of operation indicator.
Predetermined operation characteristic parameter refers to the parameter of the preset operation condition that can be used for characterizing operation project.
In practical application, predetermined operation characteristic parameter can be set based on the operation condition of operation project.In one embodiment, right
In the operation project of activity class, operation condition can be that user completes the operation project, and correspondingly, making a reservation for operation characteristic parameter can
The characteristic parameter of the operation project is completed including user, such as user completes the number of the operation project.Specifically, it is swum with network
It plays for application scenarios, operation project may include the Mission Objective (for example, a Qualifying) of online game, correspondingly, make a reservation for
Operation characteristic parameter may include that user completes the number of the Mission Objective.
Predetermined operation indicator refers to the index of the preset predetermined operation target that can be used for weighing operation project.
In practical application, runing the predetermined operation target of project can be set based on actual demand, and predetermined operation indicator can be based on should
Predetermined operation target is set.By taking online game application scenarios as an example, predetermined operation target may include promoting user's viscosity, carry
Rise at least one in user activity, promotion user's retention ratio and promotion user charges rate etc..Wherein, for predetermined fortune
Battalion's target is to promote the situation of user's viscosity, and predetermined operation indicator may include the game duration of user, also i other words, can pass through use
The game duration at family weighs the promotion situation of user's viscosity.
In the present embodiment, feature samples data may include that the predetermined operation feature corresponding to operation project to be determined is joined
The sample data of amount.Also, index sample data may include the sample data of the predetermined operation indicator corresponding to the operation project.
In addition, feature samples data are corresponding with index sample data.
In one embodiment, feature samples data may include in scheduled time slot, corresponding to operation project to be determined
Predetermined operation characteristic parameter sample data, correspondingly, index sample data may include in the predetermined period, the operation item
The sample data of predetermined operation indicator corresponding to mesh.Wherein, predetermined period can be set based on actual demand, such as the fortune
In specified 30 days in arbitrary 30 days after the development date of battalion's project or after the development date.
By taking online game application scenarios as an example, operation project to be determined is the Mission Objective of online game, feature samples
Data may include that in scheduled time slot predetermined sample user completes the secondary numerical value of Mission Objective.Index sample data may include
In the predetermined period, the game duration value of predetermined sample user.Show predetermined sample user specified 30 days at certain in table 1
The interior secondary numerical value for completing Mission Objective and game duration value, wherein the number of Mission Objective is 11, the task of each Mission Objective
Mark is respectively T178、T179、T180、T181、T182、T183、T184、T185、T186、T187And T188, and the task of this 11 Mission Objectives
Content is different, such as T178Mobile terminal, T are logged in for user179For league matches, a T180For one-to-one Qualifying etc..
In addition, the number of predetermined sample user is N, the user identifier of each sample of users is respectively U01, U02, U03..., U0N(in table 1 only
U is shown01, U02And U03Sample data).
Feature samples data and index sample data are illustrated below in conjunction with table 1：Feature samples data include sample
User U01, sample of users U02, sample of users U03... and sample of users U0NIt is respectively completed each Mission Objective (T178~T188)
Secondary numerical value.Index sample data includes sample of users U01, sample of users U02, sample of users U03... and sample of users U0NRespectively
From game duration value.Also, sample of users U01It is respectively completed the secondary numerical value and sample of users U of each Mission Objective01Trip
Duration value of playing is corresponding, sample of users U02It is respectively completed the secondary numerical value and sample of users U of each Mission Objective02Game when
Long value is corresponding, sample of users U03, sample of users U04..., sample of users U0NIt is similar, it is not added with and repeats herein.
Table 1
| Sample of users ID | U01 | U02 | U03 | … | U0N |
| User completes T178Secondary numerical value | 2 | 3 | 5 | … | … |
| User completes T179Secondary numerical value | 0 | 12 | 4 | … | … |
| User completes T180Secondary numerical value | 2 | 11 | 4 | … | … |
| User completes T181Secondary numerical value | 2 | 2 | 0 | … | … |
| User completes T182Secondary numerical value | 3 | 0 | 0 | … | … |
| User completes T183Secondary numerical value | 0 | 6 | 2 | … | … |
| User completes T184Secondary numerical value | 4 | 4 | 2 | … | … |
| User completes T185Secondary numerical value | 0 | 2 | 0 | … | … |
| User completes T186Secondary numerical value | 0 | 3 | 0 | … | … |
| User completes T187Secondary numerical value | 0 | 3 | 1 | … | … |
| User completes T188Secondary numerical value | 0 | 0 | 0 | … | … |
| The game duration value (minute) of user | 452672 | 483630 | 149784 | … | … |
S204, the regression model, feature samples data based on predefined type and index sample data determine that predetermined operation is special
Levy the relational model of parameter and predetermined operation indicator.
Regression model refers to the mathematical model that statistical relationship is quantitatively described.In the present embodiment, regression model
Type can be based on predetermined operation characteristic parameter and be determined, such as when predetermined operation characteristic parameter is continuity numerical variable,
The type of regression model can be linear regression model (LRM), on this basis, if the number of predetermined operation characteristic parameter be two with
On, the type of regression model can be multiple linear regression model, be represented by：Y=β0+β1x1+β2x2+···+βmxn.Its
In, y is dependent variable, x1、x2... and xnRespectively each independent variable, β0、β1... and βmRespectively the regression model is each
Regression coefficient.
In the present embodiment, relational model is each with each predetermined operation characteristic parameter using predetermined operation indicator as dependent variable
Independent variable.It is appreciated that feature based sample data and its corresponding index sample data can determine in above-mentioned regression model
The estimated value of each regression coefficient, it is determined that after the estimated value of each regression coefficient, that is, predetermined operation characteristic parameter is determined and makes a reservation for
The relational model of operation indicator.In one embodiment, the estimated value of each regression coefficient can be based on various possible parameter Estimations
Algorithm is determined, such as least square method (Ordinary Least Square, OLS).
S206 carries out statistical check based on relational model, feature samples data and index sample data, obtains predetermined operation
Relevance result between characteristic parameter and predetermined operation indicator.
In the present embodiment, statistical check may include assumed statistical inspection.Assumed statistical inspection refers to according to scheduled vacation
If condition is inferred the overall method of inspection by sample.In one embodiment, relational model, feature samples data can be based on and referred to
Standard specimen notebook data determines corresponding statistics certified value, then based on the corresponding statistics certified value obtain predetermined operation characteristic parameter and
Relevance result between predetermined operation indicator.Wherein, relevance result can be used for characterizing predetermined operation characteristic parameter and make a reservation for
The case where incidence relation between operation indicator (such as whether with corresponding incidence relation and/or associated degree).
In one embodiment, relevance result may include whether association as a result, whether the association result may include it is relevant
Or onrelevant result as a result.Wherein, the relevant result can be used for characterizing predetermined operation characteristic parameter and predetermined operation indicator it
Between there is corresponding incidence relation, also i other words, predetermined operation characteristic parameter has an impact the variation of predetermined operation indicator；Instead
It, which, which can be used for characterizing between predetermined operation characteristic parameter and predetermined operation indicator, does not have corresponding association pass
System, also i other words, making a reservation for that operation characteristic parameter do not influence or influence to the variation for influencing predetermined operation indicator as low as can be with
It ignores.
In another embodiment, relevance result may include correlation degree as a result, the correlation degree result can be used for characterizing
Correlation degree between predetermined operation characteristic parameter and predetermined operation indicator.It is appreciated that correlation degree is higher, predetermined fortune is indicated
It is bigger to the influence degree of the variation of predetermined operation indicator to seek characteristic parameter；Conversely, correlation degree is lower, indicate that predetermined operation is special
It is smaller to the influence degree of the variation of predetermined operation indicator to levy parameter.
In another embodiment, result and correlation degree result whether relevance result can include above-mentioned association simultaneously.
In addition, the number of predetermined operation characteristic parameter can be only one, or more than two.
In one embodiment, when the number of predetermined operation characteristic parameter is more than two, result may include whether association
As a result, result may include being integrated with association results or whole onrelevant result whether entirety is associated with whether whole association.Wherein, should
It is integrated with association results and can be used for characterizing that predetermined operation characteristic parameter is whole has corresponding association between predetermined operation indicator
Relationship, also i other words, the predetermined operation characteristic parameter of at least one in each predetermined operation characteristic parameter and predetermined operation indicator it
Between have corresponding incidence relation；Conversely, the entirety onrelevant result can be used for characterizing predetermined operation characteristic parameter it is whole with it is pre-
Determine do not have corresponding incidence relation between operation indicator, also i other words, each predetermined operation characteristic parameter and predetermined operation indicator
Between do not have corresponding incidence relation.
In one embodiment, when the number of predetermined operation characteristic parameter is more than two, similarly, correlation degree result
It may include whole correlation degree result.The entirety correlation degree result can be used for characterizing predetermined operation characteristic parameter it is whole with it is predetermined
Correlation degree between operation indicator.
In another embodiment, when the number of predetermined operation characteristic parameter is more than two, result can wrap whether association
It includes and result whether each independent association of each predetermined operation characteristic parameter corresponding respectively (corresponding).For any independent pass
As a result, result may include independent relevant result or independent onrelevant result whether the independent association whether connection.Wherein, the independence
Relevant result, which can be used for characterizing between corresponding predetermined operation characteristic parameter and predetermined operation indicator, has corresponding close
Connection relationship, also i other words, which has an impact the variation of predetermined operation indicator；Conversely, this is independent unrelated
It is coupled fruit to can be used for characterizing between corresponding predetermined operation characteristic parameter and predetermined operation indicator without corresponding association
Relationship, also i other words, which does not influence or influence on the variation of predetermined operation indicator as low as can be with
It ignores.
In another embodiment, when the number of predetermined operation characteristic parameter is more than two, similarly, correlation degree knot
Fruit may include each independent association degree result corresponding with each predetermined operation characteristic parameter.For any independent association degree
As a result, the independent association degree result can be used for characterizing between corresponding predetermined operation characteristic parameter and predetermined operation indicator
Correlation degree.
In another embodiment, whether result can include above-mentioned whole association simultaneously whether association result and with it is each predetermined
Result whether runing characteristic parameter corresponding each independent association.In addition, correlation degree result can include above-mentioned entirety simultaneously
Correlation degree result and each independent association degree result corresponding with each predetermined operation characteristic parameter.
S208 obtains the corresponding validity result of operation project to be determined based on relevance result.
Operation project to be determined refers to needing to obtain the operation project of its validity result.Wherein, validity result can
The case where for characterizing validity of the operation project to realizing predetermined operation target.
In one embodiment, the corresponding validity result of operation project may include effectively whether as a result, this effectively whether tie
Fruit includes effective result or null result.Wherein, which can be used for characterizing the operation project to realizing predetermined operation mesh
Effect is indicated, also i other words, which is Effective Operation project；Conversely, the null result can be used for characterizing the operation project
To realizing that predetermined operation target is invalid, also i other words, which is invalid operation project.
In another embodiment, the corresponding validity result of operation project may include effectiveness as a result, the effectiveness
As a result it can be used for characterizing the operation project to realizing the predetermined effectiveness for runing target.It is appreciated that effectiveness is higher, table
Show the operation project to realizing that the contribution degree of predetermined operation target is bigger；Conversely, effectiveness is lower, the operation project pair is indicated
Realize that the contribution degree of predetermined operation target is smaller.
In another embodiment, the corresponding validity result of operation project can include simultaneously it is above-mentioned effectively whether result and have
Effect degree result.
In the present embodiment, relevance result can be based on and obtains the corresponding validity result of operation project.Specifically, work as pass
When result includes association results whether connection, the validity result of acquisition includes effective result.Conversely, result includes whether association
When onrelevant result, the validity result of acquisition includes null result.In addition, also can get effective journey based on correlation degree result
For degree as a result, when for example correlation degree result is 0.86, can get effectiveness result is 86%.
It should be noted that in a determination process, operation project to be determined can only include an operation project,
It may include more than two operation projects.
In one embodiment, when operation project to be determined includes more than two operation projects, and each operation project with
Each scheduled operation characteristic parameter respectively to it is corresponding when association whether result may include whole be associated with whether as a result, correspondingly, effectively
Whether result may include it is whole effectively whether result.Specifically, result includes being integrated with association results or whole whether whole association
Body onrelevant is as a result, result can include correspondingly whole effectively result or whole null result whether this is whole effective.Wherein, should
Whole effectively result can be used for characterizing operation project to be determined integrally to realizing predetermined operation target effective, also i other words, wait for
At least one operation project is to realizing predetermined operation target effective in each operation project in determining operation project.The entirety
Null result can be used for characterizing operation project to be determined integrally to realizing that predetermined operation target is invalid, also i other words, it is to be determined
Operation project in each operation project to realizing that predetermined operation target is invalid.
In one embodiment, when operation project to be determined includes more than two operation projects, and each operation project with
Each scheduled operation characteristic parameter respectively to it is corresponding when similarly, correlation degree result may include whole correlation degree as a result, corresponding
Ground, effectiveness result may include whole effectiveness result.The entirety effectiveness result can be used for characterizing fortune to be determined
Battalion's project is integrally to realizing the predetermined effectiveness for runing target.
In another embodiment, when operation project to be determined includes more than two operation projects, and project is respectively runed
With each scheduled operation characteristic parameter respectively to it is corresponding when association whether result may include it is right respectively with each predetermined operation characteristic parameter
As a result, correspondingly whether each independent association answered, effectively whether result may include each independence corresponding with each operation project
Result whether effectively.Specifically, whether any independent association for result, result may include independence whether the independent association
Relevant result or independent onrelevant as a result, independence corresponding with result whether the independent association effectively whether as a result, then can be with
Include correspondingly independent effectively result and independent null result.Wherein, the independence effectively result can be used for characterizing corresponding
Operation project is to realizing predetermined operation target effective；Conversely, the independent null result can be used for characterizing corresponding operation item
Mesh is invalid to realizing predetermined operation target.
In another embodiment, when operation project to be determined includes more than two operation projects, and project is respectively runed
With each scheduled operation characteristic parameter respectively to it is corresponding when similarly, correlation degree result may include and each scheduled operation feature
The corresponding each independent association degree of parameter is as a result, correspondingly, effectiveness result may include right respectively with each operation project
Each independent effectiveness result answered.It is pre- to realizing that the independent effectiveness result can be used for characterizing corresponding operation project
Surely the effectiveness of target is runed.
In another embodiment, effectively whether result can simultaneously include above-mentioned entirety effectively whether result and each independence it is effective
Whether result.In addition, effectiveness result can include above-mentioned whole effectiveness result and each independent effectiveness result simultaneously.
The determination method of above-mentioned operation project validity is determined in advance based on the correlated samples data of operation project to be determined
Surely the relational model of characteristic parameter and predetermined operation indicator is runed, then statistics inspection is carried out based on the relational model and sample data
It tests, obtains the relevance between predetermined operation characteristic parameter and predetermined operation indicator as a result, being obtained in turn based on the relevance result
Obtain the corresponding validity result of operation project.On the one hand, the predetermined operation characteristic parameter of manual analysis and predetermined operation indicator are not necessarily to
Between incidence relation, being capable of raising efficiency and accuracy.On the other hand, it is united based on correlated samples data and relational model
Meter verification, then the corresponding validity result of operation project is obtained based on relevance result, operation project operation condition with it is pre-
Surely operation target do not have it is intuitive apparent between incidence relation when, also can determine effective implementations of operation project.
In one embodiment, above-mentioned steps S206 may include following steps：Based on relational model, feature samples data and
Index sample data determines the first statistics calibrating amount.Then, the first statistics calibrating amount and predetermined first critical value are subjected to size
Compare, and obtains the relevance result between predetermined operation characteristic parameter and predetermined operation indicator based on comparative result.Wherein, should
Whether relevance result has corresponding incidence relation for characterizing between predetermined operation characteristic parameter and predetermined operation indicator.
As shown in figure 3, in the present embodiment, the method for determination of the first statistics calibrating amount, it may include following steps S302~
S308.S302, be based on relational model, feature samples data and index sample data, obtain regression sum of square, return degree of freedom,
Residual sum of squares (RSS) and residual error degree of freedom.S304 based on regression sum of square and returns degree of freedom, obtains and return mean square deviation.
S306 is based on residual sum of squares (RSS) and residual error degree of freedom, and it is poor to obtain residual mean square (RMS).S308 will return mean square deviation and residual mean square (RMS)
The ratio of difference, is determined as the first statistics calibrating amount.
First statistics calibrating amount, result whether for obtaining previously described whole association.For the first statistics calibrating amount
Determination, regression sum of square can be based on following formula obtain：Wherein, k is characterized sample data and its corresponding
The sample size (by taking table 1 as an example, k=N) of index sample data,For index sample data mean value (by taking table 1 as an example, It (will be with for the estimated value of i-th of predetermined operation indicatorCorresponding feature samples data substitution relational model, which calculate, can be obtainedBy taking table 1 as an example, the 1st predetermined operation refers to
Target estimated value is
Residual sum of squares (RSS) can be based on following formula and obtain：Wherein, yiFor i-th of predetermined operation indicator actual value (with
For table 1,452672) actual value of the 1st predetermined operation indicator is.Recurrence degree of freedom is df1, df1=m, m are relational model
In independent variable total number (by taking table 1 as an example, df1=11), residual error degree of freedom is df2, df2=k-m-1 (by taking table 1 as an example, df2=
N-11-1).Mean square deviation is returned to be regression sum of square divided by return degree of freedom, residual mean square (RMS) difference be residual sum of squares (RSS) divided by residual error from
By spending, the first statistics examines quantification of recurrence mean square deviation divided by residual mean square (RMS) is poor.
In the present embodiment, when carrying out hypothesis statistical check, following null hypothesis and alternative hypothesis can be made.Null hypothesis：Respectively
Do not have corresponding incidence relation, i.e. H0 between predetermined operation characteristic parameter and predetermined operation indicator：β1=β2=...=βm=
0.Alternative hypothesis：Have between the predetermined operation characteristic parameter of at least one in each predetermined operation characteristic parameter and predetermined operation indicator
There are corresponding incidence relation, i.e. H1：β1、β2、…、βmIn at least one be not equal to 0.And it can in advance be set based on actual demand
Determine significance value, which refers to that null hypothesis is practical for probability that is correct but being rejected.
In turn, the first statistics calibrating amount and predetermined first critical value are subjected to size comparison, when the first statistics calibrating amount is big
When predetermined first critical value, refuse null hypothesis, acquisition is integrated with association results, and it is each pre- for characterizing that this is integrated with association results
Surely runing between the predetermined operation characteristic parameter of at least one in characteristic parameter and predetermined operation indicator, there is corresponding association to close
System's (but can not specifically determine the predetermined operation characteristic parameter with corresponding incidence relation between predetermined operation indicator)；Instead
It receives null hypothesis when the first statistics calibrating amount is less than or equal to predetermined first critical value, obtain whole onrelevant as a result,
The entirety onrelevant result does not have corresponding close for characterizing between each predetermined operation characteristic parameter and predetermined operation indicator
Connection relationship.Wherein, make a reservation for the first critical value to be based on returning degree of freedom, residual error degree of freedom, scheduled significance value and F point
Cloth tables of critical values determines.
In one embodiment, when the number of predetermined operation characteristic parameter is more than two, above-mentioned steps S206 can be wrapped
Include following steps：Based on relational model, feature samples data and index sample data, determine and each predetermined operation characteristic parameter point
Not corresponding each second statistics calibrating amount.Then, the absolute value for counting calibrating amount by each second respectively and predetermined second critical value
Size comparison is carried out, and the pass between each predetermined operation characteristic parameter and predetermined operation indicator is obtained based on each comparison result respectively
Connection property result.Wherein the relevance result is for characterizing between its corresponding predetermined operation characteristic parameter and predetermined operation indicator
It is no that there is corresponding incidence relation.
As shown in figure 4, in the present embodiment, determining to count with each predetermined operation characteristic parameter corresponding each second and examine
Quantitative mode, it may include following steps S402~S406.S402 is obtained and each predetermined operation feature respectively based on relational model
The corresponding regression coefficient of parameter.S404 is based on relational model, feature samples data and index sample data, obtains and makes a reservation for each
Run the corresponding regression coefficient standard deviation of characteristic parameter.S406, respectively by the corresponding regression coefficient of each predetermined operation characteristic parameter
With the ratio of regression coefficient standard deviation, it is determined as counting calibrating amount with each predetermined operation characteristic parameter corresponding each second.
Second statistics calibrating amount, result whether for obtaining previously described independent association.For making a reservation for operation with j-th
The determination of the corresponding second statistics calibrating amount of characteristic parameter, regression coefficient mark corresponding with this j-th predetermined operation characteristic parameter
Quasi- difference can be based on following formula and obtain：Wherein, j is less than the total a of the independent variable of relational model
Number, xji(by taking table 1 as an example, the 1st predetermined operation feature is joined for i-th of sample data of j-th of predetermined operation characteristic parameter
Amount --- complete Mission Objective T178Time, x11=2, i.e. sample of users U01Complete Mission Objective T178Secondary numerical value, x12=3, i.e.,
Sample of users U02Complete Mission Objective T178Secondary numerical value, x13=5, i.e. sample of users U03Complete Mission Objective T178Number
Value ...).
In the present embodiment, when the number of predetermined operation characteristic parameter is more than two, when carrying out hypothesis statistical check,
For any predetermined operation characteristic parameter in each predetermined operation characteristic parameter, following null hypothesis and alternative hypothesis can be made.It is former
Assuming that：Do not have corresponding incidence relation, i.e. H0 between the predetermined operation characteristic parameter and predetermined operation indicator：With the predetermined fortune
The actual value for seeking the corresponding regression coefficient of characteristic parameter is equal to 0.Alternative hypothesis：The predetermined operation characteristic parameter refers to predetermined operation
There is corresponding incidence relation, i.e. H1 between mark：The actual value of regression coefficient corresponding with the predetermined operation characteristic parameter differs
In 0.And significance value can be preset based on actual demand.
In turn, by the absolute value of the second statistics calibrating amount corresponding with the predetermined operation characteristic parameter with to make a reservation for second critical
Value carries out size comparison.When the absolute value of the second statistics calibrating amount is greater than or equal to predetermined second critical value, refuse former false
If obtain independence corresponding with the predetermined operation characteristic parameter it is relevant as a result, the relevant result of the independence be used to characterize this it is pre-
Surely runing has corresponding incidence relation between characteristic parameter and predetermined operation indicator；Conversely, when the second statistics calibrating amount
When absolute value is less than predetermined second critical value, receive null hypothesis, obtains that predetermined to run the corresponding independence of characteristic parameter unrelated with this
Connection is as a result, the independent onrelevant result makes a reservation between operation characteristic parameter and predetermined operation indicator for characterizing this without corresponding
Incidence relation.Wherein, make a reservation for the second critical value based on total deviation degree of freedom, scheduled significance value and T distributions to face
Dividing value table determines.In addition, total deviation degree of freedom is (k-1), k is characterized the sample of sample data and its corresponding index sample data
This capacity.
In one embodiment, above-mentioned steps S206 may include following steps：Based on relational model, feature samples data and
Index sample data determines that third counts calibrating amount.Then, the numerical value of calibrating amount is counted based on third, and it is special to obtain predetermined operation
Levy the relevance result between parameter and predetermined operation indicator.Wherein, the relevance result is for characterizing predetermined operation feature ginseng
Correlation degree between amount and predetermined operation indicator.
As shown in figure 5, in the present embodiment, determine the mode of third statistics calibrating amount, it may include following steps S502 and
S504.S502 is based on relational model, feature samples data and index sample data, obtains regression sum of square and total deviation is flat
Fang He.The ratio of regression sum of square and total sum of squares of deviations is determined as third statistics calibrating amount by S504.
Third counts calibrating amount, for obtaining previously described whole correlation degree result.Calibrating amount is counted for third
Determination, the method for determination of regression sum of square with it is described previously identical, be not added with and repeat herein.Total sum of squares of deviations can be based on as follows
Formula obtains：It should be noted that total sum of squares of deviations be regression sum of square and residual sum of squares (RSS) and.And
And third statistics examines quantification of regression sum of square divided by total sum of squares of deviations.In addition, the numerical value of third statistics calibrating amount can indicate
Make a reservation for the operation whole correlation degree between predetermined operation indicator of characteristic parameter, such as the numerical value of third statistics calibrating amount is
0.86, it is 86% to make a reservation for the operation whole correlation degree between predetermined operation indicator of characteristic parameter.
It should be noted that in multiple linear regression equations, the increase of the number of arguments can cause residual sum of squares (RSS)
It reduces, in the case, will increase based on the third statistics calibrating amount that mode shown in Fig. 5 determines.Also i other words, Fig. 5 institutes are based on
Third that the mode of showing determines statistics calibrating amount can be influenced by the number of independent variable in relational model.
The influence that calibrating amount is counted to reject the number of arguments to third in another embodiment, determines that third counts
The mode of calibrating amount, it may include following steps：Based on relational model, feature samples data and index sample data, residual error is obtained
Quadratic sum, residual error degree of freedom, total sum of squares of deviations and total deviation degree of freedom.Residual sum of squares (RSS) divided by residual error degree of freedom are obtained
Residual mean square (RMS) is poor, and total sum of squares of deviations divided by total deviation degree of freedom are obtained total deviation mean square deviation.By residual mean square (RMS) difference divided by
Total deviation mean square deviation obtains quotient, and the quotient is subtracted by 1, and the difference of acquisition, which is determined as third, counts calibrating amount.Wherein, always from
Poor degree of freedom is (k-1), and k is characterized the sample size of sample data and its corresponding index sample data.
In one embodiment, the determination method of above-mentioned operation project validity may also include the steps of：It obtains predetermined
Make a reservation for participate in the first index sample data of project user and predetermined the second index sample number for having neither part nor lot in project user in period
According to, then it is based on the first index sample data and the second index sample data, determine the 4th statistics calibrating amount, and then based on the 4th system
It counts calibrating amount and obtains the corresponding validity result of operation project.
As shown in fig. 6, in the present embodiment, determining the mode of the 4th statistics calibrating amount, it may include following steps S602~
S614.S602 obtains the equal value difference of the first index sample data and the second index sample data.S604, by predetermined participation index
Standard value and the predetermined predetermined indicators standard value work that participates in are poor, obtain standard value difference.S606 makees equal value difference and standard value difference
Difference obtains the first difference.S608 is based on the first index sample data, obtains first sample variance and first sample capacity.
S610 is based on the second index sample data, obtains the second sample variance and the second sample size.S612 is based on first sample side
Difference, first sample capacity, the second sample variance and the second sample size obtain first sample average value standard deviation.S614, by
The ratio of one difference and first sample average value standard deviation is determined as the 4th statistics calibrating amount.
In one embodiment, it can get in predetermined period and make a reservation for participate in project user (hereinafter referred participation sample of users)
The first index sample data and predetermined the second index sample for having neither part nor lot in project user (hereinafter referred has neither part nor lot in sample of users)
Data can determine operation project pair by carrying out across comparison to the first index sample data and the second index sample data
Realize whether predetermined operation target is effective.It by taking online game application scenarios as an example, can get in specified 30 days, participate in game
The mean value of the day game duration (daily game duration) of the predetermined sample user of task and the pre- random sample for having neither part nor lot in Mission Objective
The mean value of the day game duration of this user, it is assumed that such as Fig. 7 and Fig. 8 institutes the case where mean value (average duration) of the game duration of acquisition
Show, carries out across comparison it is found that the mean value for participating in the game duration of the predetermined sample user of Mission Objective, which is higher than, has neither part nor lot in game
The mean value of the game duration of the predetermined sample user of task, is based on this, it may be determined that the Mission Objective is to realizing predetermined operation target
Effectively.
It is to be understood that above-mentioned across comparison, can only illustrate for participating in sample of users and having neither part nor lot in sample of users,
Operation project is to realizing predetermined operation target effective.However, can not illustrate to overall user (all users on operation platform)
For, operation project is to realizing predetermined operation target effective.
Based on this, in the present embodiment, by assuming that statistical check determines for overall user, operation project is pre- to realizing
Surely whether operation target is effective.By taking online game application scenarios as an example, following null hypothesis and alternative hypothesis can be made.Null hypothesis：
The mean value for participating in the game duration of all users of Mission Objective is less than or equal to the trip for all users for having neither part nor lot in Mission Objective
The mean value of play duration.Alternative hypothesis：The mean value for participating in the game duration of all users of Mission Objective is appointed more than game is had neither part nor lot in
The mean value of the game duration of all users of business.And significance value can be preset based on actual demand.
In turn, it is based on the first index sample data and the second index sample data, determines the 4th statistics calibrating amount.Again by
The absolute value of four statistics calibrating amounts carries out size comparison with predetermined third critical value.When the absolute value of the 4th statistics calibrating amount is big
When predetermined third critical value, refuse null hypothesis, obtains effectively as a result, effective result is for characterizing Mission Objective pair
The game duration for promoting user is effective；Conversely, when the absolute value of the 4th statistics calibrating amount is less than predetermined third critical value, connect
By null hypothesis, null result is obtained, it is invalid to the game duration for promoting user which is used to characterize Mission Objective.Its
In, predetermined third critical value is determined based on total deviation degree of freedom, scheduled significance value and T distribution tables of critical values.
It should be noted that in other alternative embodiments, it is also possible to obtain the first inspection corresponding with predetermined third critical value
Test probability.Then, probability is examined to carry out size comparison with significance value by first, when the first inspection probability is less than conspicuousness
When level value, refuses null hypothesis, obtain effective result.Conversely, when first examines probability to be greater than or equal to significance value,
Receive null hypothesis, obtains null result.Wherein, the first inspection probability refers to being obtained and sample phase when it is assumed that null hypothesis is true
Same or more extreme result probability can be recognized when that is, first inspection probability is less than significance value (may generally be 0.05)
It is invalid for null hypothesis.
In addition, the determination for the 4th statistics certified value, equal value difference refers to the mean value and second of the first index sample data
The difference of the mean value of index sample data.The predetermined predetermined operation for participating in the user that indicators standard value refers to participation operation project refers to
Target standard value, predetermined predetermined participation indicators standard value refer to the mark of the predetermined operation indicator for the user for having neither part nor lot in operation project
Quasi- value, the predetermined indicators standard value and the predetermined not predetermined indicators standard value that participates in of participating in can be set based on business experience
It is fixed.First sample variance (is usedIndicate) acquisition of following formula can be based on：Wherein, k1It is first
The first sample capacity of index sample data,For the mean value of the first index sample data, y1iFor in the first index sample data
I-th of sample data.Second sample variance (is usedIndicate) it can be obtained by following formula：Its
In, k2For the second sample size of the second index sample data,For the mean value of the second index sample data, y2iFor the second index
I-th of sample data in sample data.First sample average value standard deviation can be based on following formula and obtain：4th system
Meter examines quantification of first difference divided by first sample average value standard deviation.
In one embodiment, the determination method of above-mentioned operation project validity may also include the steps of：It obtains predetermined
Sample of users is participated in the predetermined third index sample data having neither part nor lot in the project period and predetermined sample user predetermined
Four-index sample data in the project period, then it is based on third index sample data and four-index sample data, determine the
Five statistics calibrating amounts, and then the corresponding validity result of operation project is obtained based on the 5th statistics calibrating amount.
As shown in figure 9, in the present embodiment, determining the mode of the 5th statistics calibrating amount, it may include following steps S902~
S910.Third index sample data and four-index sample data correspondence are subtracted each other, obtain variable quantity sample data by S902.
S904, it is poor that the mean value of variable quantity sample data and predetermined variation amount standard value are made, and obtains the second difference.S906, based on variation
Sample data is measured, sample standard deviation and third sample size are obtained.S908 is based on sample standard deviation and third sample size, obtains
Obtain the second sample average standard deviation.The ratio of second difference and the second sample average standard deviation is determined as the 5th statistics by S910
Calibrating amount.
In one embodiment, it can get and refer in the predetermined third having neither part nor lot in the project period with a collection of predetermined sample user
The four-index sample data of standard specimen notebook data and predetermined sample user in the predetermined participation project period, by this
Third index sample data and four-index sample data carry out longitudinal comparison, determine operation project to realizing predetermined operation target
Whether effectively.By taking online game application scenarios as an example, predetermined sample user is can get in 30 days before participating in Mission Objective
Day play duration equal of the mean value and predetermined sample user of day game duration in 30 days after participating in Mission Objective
Value, it is assumed that the case where mean value (average duration) of the game duration of acquisition, is as shown in Figure 10 and Figure 11, carry out longitudinal comparison it is found that
The mean value of the game duration of predetermined sample user is higher than predetermined sample user before participation Mission Objective after participation Mission Objective
Game duration mean value, be based on this, it may be determined that Mission Objective is to realizing predetermined operation target effective.
It is to be understood that above-mentioned longitudinal comparison, equally can only illustrate for predetermined sample user, operation project is to reality
Now make a reservation for operation target effective.However, can not illustrate for overall user, operation project is to realizing that predetermined operation target has
Effect.
Based on this, in the present embodiment, by assuming that statistical check determines for overall user, operation project is pre- to realizing
Surely target effective is runed.By taking online game application scenarios as an example, following null hypothesis and alternative hypothesis can be made.Null hypothesis：It is each to use
The mean value that family participates in the game duration after Mission Objective is less than or equal to the equal of the game duration before each user participation Mission Objective
Value.Alternative hypothesis：The mean value that each user participates in the game duration after Mission Objective is more than the trip before each user participation Mission Objective
The mean value of play duration.And significance value can be preset based on actual demand.
In turn, it is based on third index sample data and four-index sample data, determines the 5th statistics calibrating amount.Again by
The absolute value of five statistics calibrating amounts carries out size comparison with predetermined 4th critical value.When the absolute value of the 5th statistics calibrating amount is big
When predetermined four critical value, refuse null hypothesis, obtains effectively as a result, effective result is for characterizing Mission Objective pair
The game duration for promoting user is effective；Conversely, when the absolute value of the 5th statistics calibrating amount is less than predetermined four critical value, connect
By null hypothesis, null result is obtained, it is invalid to the game duration for promoting user which is used to characterize Mission Objective.Its
In, make a reservation for the 4th critical value and is determined based on total deviation degree of freedom, scheduled significance value and T distribution tables of critical values.
It should be noted that in other alternative embodiments, it is also possible to obtain with predetermined 4th critical value corresponding second
Examine probability.Then, probability is examined to carry out size comparison with significance value by second, when the second inspection probability is less than notable
Property level value when, refuse null hypothesis, obtain effective result.Conversely, when the second inspection probability is greater than or equal to significance value
When, receive null hypothesis, obtains null result.Wherein, examine probability similar with first, the second inspection probability refers to it is assumed that former false
When being set as true, the probability of the result identical or more extreme with sample is obtained, i.e., second examines probability to be less than significance value
When (may generally be 0.05), it is believed that null hypothesis is invalid.
It should be noted that third index sample data includes several sample datas, in four-index sample data
Include several sample datas corresponding with each sample data in third index sample data.For example, third index sample
Notebook data includes a1、a2And a3, four-index sample data includes b1、b2And b3, wherein a1With b1It is corresponding, a2With b2It is corresponding,
And a3With b3Corresponding, then variable quantity sample data includes (a1-b1)、(a2-b2) and (a3-b3).Wherein, variable quantity sample data table
Sign participates in the case where variable quantity of the mean value of the front and back game duration of Mission Objective.
Determination for the 5th statistics certified value, predetermined variation amount standard value refer to that user plays before and after participating in Mission Objective
The standard value of the variable quantity of the mean value of duration, the predetermined variation amount standard value can be set based on business experience.Sample canonical
Difference (uses sdIndicate) acquisition of following formula can be based on：Wherein, k3For variable quantity sample data
Third sample size,For the mean value of variable quantity sample data, y3iFor i-th of sample data in variable quantity sample data.Second
Sample average standard deviation can be obtained by following formula：5th statistics examines quantification of second difference divided by the second sample standard deviation
It is worth standard deviation.
In one embodiment, operation project to be determined includes the Mission Objective of online game, makes a reservation for operation feature ginseng
Amount includes completing the number of Mission Objective, and predetermined operation indicator includes the game duration of user.
The method of the validity for the determination operation project that each embodiment of the application provides can be applied to network game field, example
Such as a games system, multiple Mission Objectives are provided with (for example, previously described Mission Objective T in the games system178
~T188), then it can determine validity of the games system to promotion user's viscosity based on the method that each embodiment of the application provides
Situation (whether effectively and effectiveness), and determine each Mission Objective in the games system to promoting user's viscosity respectively
Effective implementations.Specifically, it includes number (the completion Mission Objective T for completing each Mission Objective to make a reservation for operation characteristic parameter178's
Number completes Mission Objective T179Number ..., and complete task T188Number), predetermined operation indicator includes the trip of user
Play duration.
As shown in figure 12, in one embodiment, a kind of determination method of operation project validity is provided.This method can wrap
Include following steps S1201~S1211.
S1201, it includes to be determined to obtain feature samples data and its corresponding index sample data, feature samples data
The sample data of predetermined operation characteristic parameter corresponding to operation project, index sample data include pre- corresponding to operation project
Determine the sample data of operation indicator.
S1202, the regression model, feature samples data based on predefined type and index sample data determine predetermined operation
The relational model of characteristic parameter and predetermined operation indicator.
S1203 is based on relational model, feature samples data and index sample data, determines the first statistics calibrating amount.
S1204, judges whether the first statistics calibrating amount is more than predetermined first critical value；If so, obtaining the first relevance
As a result, the first relevance result includes being integrated with association results (not labeled), and the S1205 that gos to step, if it is not, then obtaining
Second relevance is as a result, the second relevance result includes whole onrelevant result (not labeled), and terminates flow.
S1205 is based on relational model, feature samples data and index sample data, determines and joins with each predetermined operation feature
Measure corresponding each second statistics calibrating amount.
S1206 determines one second statistics calibrating amount, as currently waiting sentencing in the second statistics calibrating amount not judged
Disconnected second statistics calibrating amount.
S1207 judges currently to wait judging whether the absolute value of the second statistics calibrating amount is critical more than or equal to predetermined second
Value；If so, the S1207a that gos to step, if it is not, the S1207b that then gos to step.
S1207a obtains the first relevance as a result, the first relevance result further includes currently waiting judging the second system with this
Count the relevant result of the corresponding independence of calibrating amount.
S1207b obtains the first relevance as a result, the first relevance result further includes currently waiting judging the second system with this
Count the corresponding independent onrelevant result of calibrating amount.
S1208 judges whether the number of the do not judged second statistics calibrating amount is zero；If it is not, then going to step
S1206, if so, the S1209 that gos to step.
S1209 is based on relational model, feature samples data and index sample data, determines that third counts calibrating amount.
S1210, the numerical value that calibrating amount is counted based on third obtain the first relevance as a result, the first relevance result is also wrapped
Include whole correlation degree result.
S1211 obtains the corresponding validity result of operation project to be determined based on the first relevance result.
It should be noted that the technical characteristic of each step in the present embodiment can step corresponding with each embodiment above
Rapid technical characteristic is identical, is not added with and repeats herein.
Although should be understood that Fig. 2~6,9 and 12 flow chart in each step according to arrow instruction successively
It has been shown that, but these steps are not the inevitable sequence indicated according to arrow to be executed successively.Unless expressly state otherwise herein,
There is no stringent sequences to limit for the execution of these steps, these steps can execute in other order.Moreover, Fig. 2~6,9
And at least part step in 12 may include multiple sub-steps either these sub-steps of multiple stages or stage be not necessarily
It is so to execute completion in synchronization, but can execute at different times, these sub-steps or stage execute sequence
Also it is not necessarily and carries out successively, but can be with other steps either sub-step of other steps or at least part in stage
It executes in turn or alternately.
As shown in figure 13, in one embodiment, a kind of determining device 1300 of operation project validity is provided.The device
It may include following module 1302~1308.
Sample data acquisition module 1302, for obtaining feature samples data and its corresponding index sample data, feature
Sample data includes the sample data of the predetermined operation characteristic parameter corresponding to operation project to be determined, index sample data packet
Include the sample data of the predetermined operation indicator corresponding to operation project；
Relational model determining module 1304 is used for regression model, feature samples data and index sample based on predefined type
Notebook data determines the relational model of predetermined operation characteristic parameter and predetermined operation indicator；
Association results acquisition module 1306, for being carried out based on relational model, feature samples data and index sample data
Statistical check obtains the relevance result between predetermined operation characteristic parameter and predetermined operation indicator.
Validity result obtains module 1308, and for being obtained based on relevance result, operation project to be determined is corresponding to be had
Effect property result.
The determining device 1300 of above-mentioned operation project validity, the correlated samples data based on operation project to be determined are true
The relational model of fixed predetermined operation characteristic parameter and predetermined operation indicator, then united based on the relational model and sample data
Meter is examined, and obtains the relevance between predetermined operation characteristic parameter and predetermined operation indicator as a result, being based on the relevance knot in turn
Fruit obtains the corresponding validity result of operation project.On the one hand, the predetermined operation characteristic parameter of manual analysis and predetermined operation are not necessarily to
Incidence relation between index, being capable of raising efficiency and accuracy.On the other hand, be based on correlated samples data and relational model into
Row statistics verification, then the corresponding validity result of operation project is obtained based on relevance result, in the operation condition of operation project
With predetermined operation target do not have it is intuitive apparent between incidence relation when, also can determine effective implementations of operation project.
In one embodiment, association results acquisition module 1306 may include such as lower unit：First statistic determines single
Member determines the first statistics calibrating amount for being based on relational model, feature samples data and index sample data.First size ratio
Compared with unit, for the first statistics calibrating amount and predetermined first critical value to be carried out size comparison, and obtain based on comparative result pre-
Surely the relevance between characteristic parameter and predetermined operation indicator is runed as a result, the relevance result is for characterizing predetermined operation feature
Whether there is corresponding incidence relation between parameter and predetermined operation indicator.
Wherein, the first statistics calibrating amount determination unit may include following subelement：First parameter determination subelement is used for base
In relational model, feature samples data and index sample data, obtain regression sum of square, return degree of freedom, residual sum of squares (RSS) with
And residual error degree of freedom.Mean square deviation determination subelement is returned, for returning equal based on regression sum of square and recurrence degree of freedom, acquisition
Variance.It is poor to obtain residual mean square (RMS) for being based on residual sum of squares (RSS) and residual error degree of freedom for residual mean square (RMS) difference determination subelement.The
One statistics calibrating amount determination subelement, the ratio for that will return mean square deviation and residual mean square (RMS) difference are determined as the calibrating of the first statistics
Amount.
In one embodiment, when the number for making a reservation for operation characteristic parameter is more than two, association results acquisition module
1306 may include such as lower unit：Second statistic obtaining unit, for being based on relational model, feature samples data and index sample
Notebook data determines and counts calibrating amount with each predetermined operation characteristic parameter corresponding each second.Second size comparing unit is used
In the absolute value and predetermined second critical value progress size comparison that count calibrating amount by each second respectively, and it is based on each comparison result
The relevance between each predetermined operation characteristic parameter and predetermined operation indicator is obtained respectively as a result, the relevance result is for characterizing
Whether there is corresponding incidence relation between its corresponding predetermined operation characteristic parameter and predetermined operation indicator.
Wherein, the first statistic obtaining unit may include following subelement：Regression coefficient obtains subelement, for being based on
Relational model obtains regression coefficient corresponding with each predetermined operation characteristic parameter respectively.Factor standard difference obtains subelement, is used for
Based on relational model, feature samples data and index sample data, recurrence system corresponding with each predetermined operation characteristic parameter is obtained
Number standard deviation.Second statistic obtains subelement, for respectively will the corresponding regression coefficient of each predetermined operation characteristic parameter with time
The ratio for returning factor standard difference is determined as counting calibrating amount with each predetermined operation characteristic parameter corresponding each second.
In one embodiment, association results acquisition module 1306 may include such as lower unit：Third statistic obtains single
Member determines that third counts calibrating amount for being based on relational model, feature samples data and index sample data.Relevance result
Obtaining unit, the numerical value for being counted calibrating amount based on third are obtained between predetermined operation characteristic parameter and predetermined operation indicator
Relevance be associated with journey as a result, the relevance result is used to characterize between predetermined operation characteristic parameter and predetermined operation indicator
Degree.
Wherein, third statistic obtaining unit may include following subelement：Quadratic sum obtains subelement, for being based on relationship
Model, feature samples data and index sample data obtain regression sum of square and total sum of squares of deviations.Third statistic obtains
Subelement, for by the ratio of regression sum of square and total sum of squares of deviations, being determined as third statistics calibrating amount.
In one embodiment, device 1300 can also include following module：First achievement data acquisition module, for obtaining
Take the first index sample data for making a reservation for participate in project user in predetermined period and predetermined the second index for having neither part nor lot in project user
Sample data.4th statistic obtains module, for being based on the first index sample data and the second index sample data, determines the
Four statistics calibrating amounts.First validity determining module, it is corresponding effectively for obtaining operation project based on the 4th statistics calibrating amount
Property result.
Wherein, it may include such as lower unit that the 4th statistic, which obtains module,：Equal value difference obtaining unit refers to for obtaining first
The equal value difference of standard specimen notebook data and the second index sample data.Standard value difference obtaining unit is used for predetermined participation criterion
Value and the predetermined predetermined indicators standard value work that participates in are poor, obtain standard value difference.First difference obtaining unit, for by equal value difference with
It is poor that standard value difference is made, and obtains the first difference.First sample gain of parameter unit is obtained for being based on the first index sample data
First sample variance and first sample capacity.Second sample parameter obtaining unit is obtained for being based on the second index sample data
Second sample variance and the second sample size.First sample inequality obtaining unit, for based on first sample variance, first sample
Capacity, the second sample variance and the second sample size obtain first sample average value standard deviation.4th statistic obtaining unit,
For by the ratio of the first difference and first sample average value standard deviation, being determined as the 4th statistics calibrating amount.
In one embodiment, device 1300 can also include following module：Second achievement data acquisition module, for obtaining
Take predetermined sample user in the predetermined third index sample data having neither part nor lot in the project period and predetermined sample user pre-
Surely the four-index sample data in the project period is participated in.5th statistic obtains module, for being based on third index sample number
According to four-index sample data, determine the 5th statistics calibrating amount.Second validity determining module, for being examined based on the 5th statistics
It is quantitative to obtain the corresponding validity result of operation project.
Wherein, it may include such as lower unit that the 5th statistic, which obtains module,：Variable quantity data acquiring unit is used for third
Index sample data and four-index sample data correspondence are subtracted each other, and variable quantity sample data is obtained.Second difference obtaining unit is used
It is poor in making the mean value of variable quantity sample data and predetermined variation amount standard value, obtain the second difference.Third sample parameter obtains
Unit obtains sample standard deviation and third sample size for being based on variable quantity sample data.Second sample inequality obtains single
Member obtains the second sample average standard deviation for being based on sample standard deviation and third sample size.5th statistic obtains single
Member, for by the ratio of the second difference and the second sample average standard deviation, being determined as the 5th statistics calibrating amount.
In one embodiment, operation project to be determined includes the Mission Objective of online game, makes a reservation for operation feature ginseng
Amount includes completing the number of Mission Objective, and predetermined operation indicator includes the game duration of user.
The specific restriction of determining device about operation project validity may refer to effective above for operation project
The restriction of the determination method of property, details are not described herein.Modules in the determining device of above-mentioned operation project validity can be complete
Portion or part are realized by software, hardware and combinations thereof.Above-mentioned each module can be in the form of hardware embedded in or independently of calculating
In processor in machine equipment, can also in a software form it be stored in the memory in computer equipment, in order to processor
It calls and executes the corresponding operation of the above modules.
In one embodiment, a kind of computer equipment, including memory and processor are provided, is stored in memory
Computer program, the processor realize the validity for the operation project that the application any embodiment provides when executing computer program
Determine method.
In one embodiment, which can be server 120 shown in Fig. 1, and internal structure chart can
With as shown in figure 14.The computer equipment includes processor, memory and the network interface connected by system bus.Wherein,
The processor is for providing calculating and control ability.The memory includes non-volatile memory medium and built-in storage, this is non-easily
The property lost storage medium is stored with operating system and computer program, which is the operation system in non-volatile memory medium
The operation of system and computer program provides environment, to realize the application any embodiment when which is executed by processor
The validity of the operation project of offer determines method.The network interface is used to communicate by network connection with external terminal.
In another embodiment, which can be user terminal 110 shown in Fig. 1, internal structure
Figure can be as shown in figure 15.The computer equipment includes the processor connected by system bus, memory, network interface, shows
Display screen and input unit.Wherein, the processor is for providing calculating and control ability.The memory includes that non-volatile memories are situated between
Matter and built-in storage, the non-volatile memory medium are stored with operating system and computer program, which is non-volatile
Property storage medium in operating system and computer program operation provide environment, when which is executed by processor with
Realize that the validity for the operation project that the application any embodiment provides determines method.The network interface is used for and external terminal
It is communicated by network connection.The display screen can be liquid crystal display or electric ink display screen.The computer equipment it is defeated
It can be the touch layer covered on display screen to enter device, can also be the button being arranged on computer equipment shell, trace ball or
Trackpad can also be external keyboard, Trackpad or mouse etc..
It is appreciated that Figure 14 and Figure 15 shown in structure, only with the frame of the relevant part-structure of application scheme
Figure, does not constitute the restriction for the computer equipment being applied thereon to application scheme, specific computer equipment can wrap
It includes than more or fewer components as shown in the figure, either combine certain components or is arranged with different components.
In one embodiment, the determining device of operation project validity provided by the present application can be implemented as a kind of calculating
The form of machine program, computer program can be run on the computer equipment as shown in Figure 14 or 15.The storage of computer equipment
The each program module for forming the device can be stored in device.For example, sample data acquisition module 1302, relationship mould shown in Figure 13
Type determining module 1304, association results acquisition module 1306 and validity result obtain module 1308.Each program module is constituted
Computer program make processor execute the operation project validity of each embodiment of the application described in this specification
Determine the step in method.For example, Figure 14 or shown in figure 15 computer equipments can pass through the sample in device as shown in figure 13
Notebook data acquisition module 1302 executes step S202, step S204 is executed by relational model determining module 1304, passes through association
As a result acquisition module 1306, which executes step S206 and obtains module 1308 by validity result, executes step S208 etc..
One of ordinary skill in the art will appreciate that realizing all or part of flow in above-described embodiment method, being can be with
Instruct relevant hardware to complete by computer program, program can be stored in a non-volatile computer storage can be read
In medium, the program is when being executed, it may include such as the flow of the embodiment of above-mentioned each method.Wherein, provided herein each
Any reference to memory, storage, database or other media used in embodiment, may each comprise it is non-volatile and/
Or volatile memory.Nonvolatile memory may include read-only memory (ROM), programming ROM (PROM), electrically programmable
ROM (EPROM), electrically erasable ROM (EEPROM) or flash memory.Volatile memory may include random access memory
(RAM) or external cache.By way of illustration and not limitation, RAM is available in many forms, such as static state RAM
(SRAM), dynamic ram (DRAM), synchronous dram (SDRAM), double data rate sdram (DDRSDRAM), enhanced SDRAM
(ESDRAM), synchronization link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) directly RAM (RDRAM), straight
Connect memory bus dynamic ram (DRDRAM) and memory bus dynamic ram (RDRAM) etc..
Accordingly, in one embodiment, a kind of computer readable storage medium is provided, computer journey is stored thereon with
Sequence realizes the determination side for the operation project validity that the application any embodiment provides when computer program is executed by processor
Method.
Each technical characteristic of above example can be combined arbitrarily, to keep description succinct, not to above-described embodiment
In each technical characteristic it is all possible combination be all described, as long as however, the combination of these technical characteristics be not present lance
Shield is all considered to be the range of this specification record.
Above example only expresses the several embodiments of the application, the description thereof is more specific and detailed, but can not
Therefore it is interpreted as the limitation to the application the scope of the claims.It should be pointed out that for those of ordinary skill in the art,
Under the premise of not departing from the application design, various modifications and improvements can be made, these belong to the protection model of the application
It encloses.Therefore, the protection domain of the application patent should be determined by the appended claims.
Claims (10)
1. a kind of determination method of operation project validity, which is characterized in that including step：
Obtain feature samples data and its corresponding index sample data, the feature samples data include operation item to be determined
The sample data of predetermined operation characteristic parameter corresponding to mesh, the index sample data include corresponding to the operation project
The sample data of predetermined operation indicator；
Regression model, the feature samples data based on predefined type and the index sample data, determine the predetermined fortune
Seek the relational model of characteristic parameter and the predetermined operation indicator；
It is described pre- that statistical check acquisition is carried out based on the relational model, the feature samples data and the index sample data
Surely the relevance result between characteristic parameter and the predetermined operation indicator is runed；
The corresponding validity result of the operation project is obtained based on the relevance result.
2. according to the method described in claim 1, it is characterized in that, described based on the relational model, the feature samples number
According to and the index sample data carry out statistical check, obtain the predetermined operation characteristic parameter and the predetermined operation indicator it
Between relevance as a result, include step：
Based on the relational model, the feature samples data and the index sample data, the first statistics calibrating amount is determined；
Calibrating amount is counted by described first and carries out size comparison with predetermined first critical value, and is obtained based on comparative result described pre-
Surely the relevance between characteristic parameter and the predetermined operation indicator is runed as a result, the relevance result is described pre- for characterizing
Whether run surely between characteristic parameter and the predetermined operation indicator has corresponding incidence relation；
Wherein it is determined that the mode of the first statistics calibrating amount, including step：
Based on the relational model, the feature samples data and the index sample data, obtains regression sum of square, returns certainly
By degree, residual sum of squares (RSS) and residual error degree of freedom；
Based on the regression sum of square and the recurrence degree of freedom, obtains and return mean square deviation；
Based on the residual sum of squares (RSS) and the residual error degree of freedom, it is poor to obtain residual mean square (RMS)；
By the ratio for returning mean square deviation and residual mean square (RMS) difference, it is determined as the first statistics calibrating amount.
3. according to the method described in claim 1, it is characterized in that：
It is described based on the relational model, the feature samples when number of the predetermined operation characteristic parameter is more than two
Data and the index sample data carry out statistical check, obtain the predetermined operation characteristic parameter and the predetermined operation indicator
Between relevance as a result, include step：
Based on the relational model, the feature samples data and the index sample data, determine and each predetermined operation
The corresponding each second statistics calibrating amount of characteristic parameter；
The absolute value of each second statistics calibrating amount is subjected to size comparison with predetermined second critical value respectively, and is based on each ratio
Relatively result obtains the relevance between each predetermined operation characteristic parameter and the predetermined operation indicator as a result, the pass respectively
Whether connection property result is for characterizing between its corresponding predetermined operation characteristic parameter and the predetermined operation indicator with corresponding
Incidence relation；
Wherein it is determined that the mode for counting calibrating amount with each predetermined operation characteristic parameter corresponding each second, packet
Include step：
Obtain regression coefficient corresponding with each predetermined operation characteristic parameter respectively based on the relational model；
Based on the relational model, the feature samples data and the index sample data, obtain and each predetermined operation
The corresponding regression coefficient standard deviation of characteristic parameter；
Respectively by the ratio of each predetermined operation characteristic parameter corresponding regression coefficient and regression coefficient standard deviation, be determined as with
Each predetermined operation characteristic parameter corresponding each described second counts calibrating amount.
4. according to the method described in claim 2, it is characterized in that, described based on the relational model, the feature samples number
According to and the index sample data carry out statistical check, obtain the predetermined operation characteristic parameter and the predetermined operation indicator it
Between relevance as a result, include step：
Based on the relational model, the feature samples data and the index sample data, determine that third counts calibrating amount；
The numerical value of calibrating amount is counted based on the third, obtain the predetermined operation characteristic parameter and the predetermined operation indicator it
Between relevance as a result, the relevance result for characterize the predetermined operation characteristic parameter and the predetermined operation indicator it
Between correlation degree；
Wherein it is determined that the mode of the third statistics calibrating amount, including step：
Based on the relational model, the feature samples data and the index sample data, regression sum of square and total is obtained
Sum of squares of deviations；
By the ratio of the regression sum of square and total sum of squares of deviations, it is determined as the third statistics calibrating amount.
5. according to the method described in claim 1, it is characterized in that, the method further includes：
It obtains the first index sample data for making a reservation for participate in project user in predetermined period and predetermined has neither part nor lot in the of project user
Two index sample datas；
Based on the first index sample data and the second index sample data, the 4th statistics calibrating amount is determined；
The corresponding validity result of the operation project is obtained based on the 4th statistics calibrating amount；
Wherein it is determined that the mode of the 4th statistics calibrating amount, including step：
Obtain the equal value difference of the first index sample data and the second index sample data；
Predetermined participation indicators standard value and the predetermined predetermined indicators standard value work that participates in is poor, obtain standard value difference；
It is poor that the equal value difference and the standard value difference are made, and obtains the first difference；
Based on the first index sample data, first sample variance and first sample capacity are obtained；
Based on the second index sample data, the second sample variance and the second sample size are obtained；
Based on the first sample variance, first sample capacity, second sample variance and second sample size, obtain
Obtain first sample average value standard deviation；
By the ratio of first difference and the first sample average value standard deviation, it is determined as the 4th statistics calibrating amount.
6. according to the method described in claim 1, it is characterized in that, the method further includes：
Predetermined sample user is obtained to use in the predetermined third index sample data having neither part nor lot in the project period and the predetermined sample
Four-index sample data of the family in the predetermined participation project period；
Based on the third index sample data and the four-index sample data, the 5th statistics calibrating amount is determined；
The corresponding validity result of the operation project is obtained based on the 5th statistics calibrating amount；
Wherein it is determined that the mode of the 5th statistics calibrating amount, including step：
The third index sample data and four-index sample data correspondence are subtracted each other, variable quantity sample data is obtained；
It is poor that the mean value of the variable quantity sample data and predetermined variation amount standard value are made, and obtains the second difference；
Based on the variable quantity sample data, sample standard deviation and third sample size are obtained；
Based on the sample standard deviation and third sample size, the second sample average standard deviation is obtained；
By the ratio of second difference and the second sample average standard deviation, it is determined as the 5th statistics calibrating amount.
7. method according to any one of claims 1 to 6, which is characterized in that the operation project to be determined includes net
The Mission Objective of network game, the predetermined operation characteristic parameter includes the number for completing the Mission Objective, the predetermined operation
Index includes the game duration of user.
8. a kind of determining device of operation project validity, which is characterized in that including：
Sample data acquisition module, for obtaining feature samples data and its corresponding index sample data, the feature samples
Data include the sample data of the predetermined operation characteristic parameter corresponding to operation project to be determined, the index sample data packet
Include the sample data of the predetermined operation indicator corresponding to the operation project；
Relational model determining module is used for regression model, the feature samples data and the index sample based on predefined type
Notebook data determines the relational model of predetermined the operation characteristic parameter and the predetermined operation indicator；
Association results acquisition module, for being based on the relational model, the feature samples data and the index sample data
Statistical check is carried out, the relevance result between the predetermined operation characteristic parameter and the predetermined operation indicator is obtained；
Validity result obtains module, for obtaining the corresponding validity knot of the operation project based on the relevance result
Fruit.
9. a kind of computer readable storage medium, is stored with computer program, which is characterized in that the computer program is handled
The step of method described in any one of claim 1 to 7 is realized when device executes.
10. a kind of computer equipment, including memory and processor, the memory are stored with computer program, feature exists
In when the processor executes the computer program the step of any one of realization claim 1 to 7 the method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810146258.0A CN108305013B (en) | 2018-02-12 | 2018-02-12 | Method and device for determining effectiveness of operation project and computer equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810146258.0A CN108305013B (en) | 2018-02-12 | 2018-02-12 | Method and device for determining effectiveness of operation project and computer equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108305013A true CN108305013A (en) | 2018-07-20 |
| CN108305013B CN108305013B (en) | 2022-02-18 |
Family
ID=62865484
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810146258.0A Active CN108305013B (en) | 2018-02-12 | 2018-02-12 | Method and device for determining effectiveness of operation project and computer equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108305013B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6717595B1 (en) * | 2000-12-29 | 2004-04-06 | Sun Microsystems, Inc. | Computer-based list editor |
| CN103246958A (en) * | 2013-05-17 | 2013-08-14 | 国家电网公司 | Production concentrating operation system of provincial power metering center |
| CN104778173A (en) * | 2014-01-10 | 2015-07-15 | 腾讯科技（深圳）有限公司 | Determination method, device and equipment of objective user |
| CN106294182A (en) * | 2016-08-24 | 2017-01-04 | 腾讯科技（深圳）有限公司 | A kind of determine many survey the feedback method of effectiveness, test equipment and system |
-
2018
- 2018-02-12 CN CN201810146258.0A patent/CN108305013B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6717595B1 (en) * | 2000-12-29 | 2004-04-06 | Sun Microsystems, Inc. | Computer-based list editor |
| CN103246958A (en) * | 2013-05-17 | 2013-08-14 | 国家电网公司 | Production concentrating operation system of provincial power metering center |
| CN104778173A (en) * | 2014-01-10 | 2015-07-15 | 腾讯科技（深圳）有限公司 | Determination method, device and equipment of objective user |
| CN106294182A (en) * | 2016-08-24 | 2017-01-04 | 腾讯科技（深圳）有限公司 | A kind of determine many survey the feedback method of effectiveness, test equipment and system |
Non-Patent Citations (1)
| Title |
|---|
| 王明涛: "预测方法的有效性分析", 《预测》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108305013B (en) | 2022-02-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Horiuchi et al. | Measuring voters’ multidimensional policy preferences with conjoint analysis: Application to Japan’s 2014 election | |
| CN110610412A (en) | Credit risk assessment method and device, storage medium and electronic equipment | |
| CN109727070B (en) | Method and device for determining potential active users | |
| CN110223186A (en) | User's similarity determines method and information recommendation method | |
| CN112116245A (en) | Credit risk assessment method, credit risk assessment device, computer equipment and storage medium | |
| Manasis et al. | Between-seasons competitive balance in European football: review of existing and development of specially designed indices | |
| Chen et al. | Real or bogus: Predicting susceptibility to phishing with economic experiments | |
| Kenig et al. | Approximate inference of outcomes in probabilistic elections | |
| CN112237742B (en) | Game recommendation method and device, readable storage medium and computer equipment | |
| CN107067276A (en) | Determine the method and device of object influences power | |
| Li et al. | Bayesian analysis of rank data with covariates and heterogeneous rankers | |
| Dimitrov et al. | Composition of markets with conflicting incentives | |
| WO2011160175A2 (en) | A computer based ballot system and process | |
| Hidayanto et al. | Factors influencing citizen's intention to participate electronically: The perspectives of social cognitive theory and e-government service quality | |
| CN113657930B (en) | Method and device for testing policy effectiveness, electronic equipment and readable storage medium | |
| CN108305013A (en) | The determination method, apparatus and computer equipment of operation project validity | |
| CN117194500A (en) | Data index verification method, device, equipment and storage medium | |
| CN110210884B (en) | Method, device, computer equipment and storage medium for determining user characteristic data | |
| US20140200969A1 (en) | Rating items | |
| Hässelbarth et al. | Uncertainty-based evaluation of certification study data | |
| Blom et al. | Auditing hamiltonian elections | |
| JP2023032293A (en) | Information processing device, program, and information processing method | |
| CN111164633B (en) | Method and device for adjusting scoring card model, server and storage medium | |
| CN112163633B (en) | Test evaluation method, device, electronic equipment and storage medium | |
| CN110188940B (en) | Service processing method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TG01 | Patent term adjustment |