CN104156466A - Grade-based method and device for allocating resources - Google Patents
Grade-based method and device for allocating resources Download PDFInfo
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- CN104156466A CN104156466A CN201410418429.2A CN201410418429A CN104156466A CN 104156466 A CN104156466 A CN 104156466A CN 201410418429 A CN201410418429 A CN 201410418429A CN 104156466 A CN104156466 A CN 104156466A
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- 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
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06313—Resource planning in a project environment
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- 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
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
Abstract
The embodiment of the invention relates to the technical field of data processing, in particular to a grade-based method and device for allocating resources. The method includes the steps of selecting standard dimensionality grade stimulating equations suitable for users from preset standard dimensionality grade stimulating equations according to standard dimensionality values of the users and pre-obtained boundary value vectors of the standard dimensionality; calculating standard dimensionality grade values of the users according to the standard dimensionality values of the users and the selected standard dimensionality grade stimulating equations; calculating multi-dimensionality grade values of the users according to the standard dimensionality grade values of the users and preset multi-dimensionality weight vectors; allocating the resources for the users according to the multi-dimensionality grade values of the users. By means of the method, resource waste can be avoided, and cost is accordingly reduced.
Description
Technical field
The embodiment of the present invention relates to technical field of data processing, relates in particular to a kind of resource allocation methods and device based on grade.
Background technology
Managing customer relation (Customer-Managed Relationship, CMR) is a kind of method, software and internet means of widely used division user gradation.
In the existing resource allocation methods based on grade, mainly adopt the mode providing in CMR to divide user gradation, and be user resource allocation according to user gradation.Owing to there is no clear and definite grading standard, in the time of typing user profile, may arbitrarily divide a grade to user, cause the unreasonable of user gradation.In addition, grading standard is many by the artificial decision of supvr, even if there is clear and definite grading standard, but the work efficiency of artificial definite user gradation is low.
In the process that is user resource allocation according to user gradation, what user gradation was divided is unreasonable unreasonable by what cause resource to be distributed, thus waste resource to a certain degree, and then the cost of increase resource.
Summary of the invention
The object of the invention is to propose a kind of resource allocation methods and device based on grade, with the resource that avoids waste, thus the cost of reduction resource.
On the one hand, the invention provides a kind of resource allocation methods based on grade, comprising:
According to the boundary value vector of user's benchmark dimension values and the benchmark dimension that obtains in advance, from default benchmark dimension grade excitation equation, select to be applicable to user's benchmark dimension grade excitation equation;
According to described user's benchmark dimension values and the benchmark dimension grade excitation equation of selecting, calculate user's benchmark dimension grade point;
According to described user's benchmark dimension grade point and default various dimensions weight vectors, calculate user's various dimensions grade point;
According to described user's various dimensions grade point, it is described user resource allocation.
On the other hand, the invention provides a kind of resource allocation device based on grade, comprising:
Equation selected cell for according to the boundary value vector of user's benchmark dimension values and the benchmark dimension that obtains in advance, selects to be applicable to user's benchmark dimension grade excitation equation from default benchmark dimension grade excitation equation;
Initial grade computing unit, for according to described user's benchmark dimension values and the benchmark dimension grade excitation equation of selecting, calculates user's benchmark dimension grade point;
Final rating calculation unit, for according to described user's benchmark dimension grade point and default various dimensions weight vectors, calculates user's various dimensions grade point;
Resource allocation unit, for according to described user's various dimensions grade point, is described user resource allocation.
The resource allocation methods based on grade and the device that in the embodiment of the present invention, provide, effectively reduce the cost of resource.In the embodiment of the present invention, select according to user's benchmark dimension values the benchmark dimension grade excitation equation that is applicable to user, and calculate user's benchmark dimension grade point according to described user's benchmark dimension values and the benchmark dimension grade excitation equation of selecting; And then, according to user's benchmark dimension grade point and described various dimensions weight vectors, calculate user's various dimensions grade point, thereby determine user's grade.Because this method provides the clear and definite grading standard being determined by described benchmark dimension grade excitation equation, rate range division points vector sum rate range division points vector, therefore can guarantee the rationality of user gradation, and then in the time being user resource allocation according to user gradation, can avoid the waste of resource, thereby reduce resources costs.
Brief description of the drawings
Accompanying drawing described herein is used to provide the further understanding to the embodiment of the present invention, forms a part for the embodiment of the present invention, does not form the restriction to the embodiment of the present invention.In the accompanying drawings:
Fig. 1 is the realization flow figure of the resource allocation methods based on grade that provides in first embodiment of the invention;
Fig. 2 is the schematic diagram of the grade excitation model that provides in the embodiment of the present invention;
Fig. 3 is the realization flow figure of the resource allocation methods based on grade that provides in second embodiment of the invention;
Fig. 4 is the schematic diagram of the grade excitation model that provides in second embodiment of the invention;
Fig. 5 is the schematic diagram of the grade excitation model that provides in second embodiment of the invention;
Fig. 6 is the schematic diagram of the grade excitation model that provides in second embodiment of the invention;
Fig. 7 is the structural representation of the resource allocation device based on grade that provides in third embodiment of the invention.
Embodiment
Below in conjunction with drawings and the specific embodiments, the embodiment of the present invention is carried out more in detail and complete explanation.Be understandable that, specific embodiment described herein is only for explaining the embodiment of the present invention, but not restriction to the embodiment of the present invention.It also should be noted that, for convenience of description, in accompanying drawing, only show the part relevant to the embodiment of the present invention but not full content.
The first embodiment:
Fig. 1 is the realization flow figure of the resource allocation methods based on grade that provides in first embodiment of the invention, the method can be carried out by the resource allocation device based on grade, described device can be realized by software and/or hardware, and a part that can be used as terminal device is built in terminal device inside.As shown in Figure 1, this realization flow comprises:
Step 11, according to the boundary value vector of user's benchmark dimension values and the benchmark dimension that obtains in advance, from default benchmark dimension grade excitation equation, select to be applicable to user's benchmark dimension grade excitation equation.
Divide in equation at the user gradation based on various dimensions, each dimension represents respectively a kind of user's information, the benchmark dimension values using the value of a kind of dimension in various dimensions value as user.The boundary value vector s={s1 of benchmark dimension, s2, s3, s4 ... represent the value range of benchmark dimension corresponding to different user grade, can determine user's benchmark dimension grade according to user's benchmark dimension values and the boundary value vector of described benchmark dimension.
Concrete, with the boundary value vector s={f of benchmark dimension, h, t, p} is example.If user's benchmark dimension values q meets following relation: 0≤q<f, determine that user belongs to the first estate; If user's benchmark dimension values q meets following relation: f≤q < h, determines that user belongs to the second grade; If user's benchmark dimension values q meets following relation: h≤q<t, determine that user belongs to the tertiary gradient; If user's benchmark dimension values q meets following relation: t≤q<p, determine that user belongs to the fourth estate.It is elementary, intermediate, senior and super that the first estate, the second grade, the tertiary gradient and the fourth estate successively can called afters.
Default benchmark dimension grade excitation equation is respectively equation corresponding to each predetermined level.Fig. 2 is the schematic diagram of the grade excitation model that provides in the embodiment of the present invention, and as shown in Figure 2, the first estate excitation equation is y=2x; The second grade excitation equation is y=x+a/2; Tertiary gradient excitation equation is y=x/2+b/2+a/4; Fourth estate excitation equation is y=x/4+c/2+b/4+a/8.Wherein the slope of each grade represents the exitation factor of each grade, and exitation factor less represent grade rise more difficult.
Wherein a, and b, c, d} forms rate range division points vector, according to user's various dimensions grade point and described rate range division points vector, can determine user's grade.
To sum up, according to the boundary value vector of user's benchmark dimension values and the benchmark dimension that obtains in advance, can determine the benchmark dimension rate range under user, and then can select the benchmark dimension grade excitation equation that is applicable to user.It should be noted that, benchmark dimension grade excitation equation corresponding to different benchmark dimension grades can be different, also can be different, and all right each benchmark dimension grade is corresponding to same benchmark dimension grade equation.
Step 12, according to described user's benchmark dimension values and the benchmark dimension grade excitation equation selected, calculate user's benchmark dimension grade point.
By user's benchmark dimension values, what substitution was selected be applicable to user's benchmark dimension grade excitation equation, calculates user's benchmark dimension grade point.For example, user's benchmark dimension values is q, and the user's who selects benchmark dimension grade excitation equation is y=x+a/2, and user's benchmark dimension grade point is: q+a/2.
Step 13, according to described user's benchmark dimension grade point and default various dimensions weight vectors, calculate user's various dimensions grade point.
In user gradation partition process, each component in default various dimensions weight vectors represents respectively the proportion that each dimension is shared.
Wherein, according to described user's benchmark dimension grade point and default various dimensions weight vectors, the various dimensions grade point that calculates user can comprise: according to following formula, calculate user's various dimensions grade point G:
wherein, the benchmark dimension grade point that g is user, v={v1, v2, v3, v4 ..., vk} is various dimensions weight vectors, k is the number of dimensions of various dimensions weight vectors.
For example, v={100%, 110%, 80%, 90%}, and user's benchmark dimension grade point g=q+a/2, user's various dimensions grade point G=(q+a/2) * 0.95.
Step 14, according to described user's various dimensions grade point, be described user resource allocation.
For example,, according to user's various dimensions grade point, while being 500 user assignment shuttlecocks.If described user's various dimensions grade point is larger, characterizing user uses the frequency of shuttlecock higher, can only exceed the user assignment shuttlecock of preset value for various dimensions grade point, and without the shuttlecock that distributes equal number for each user, thereby cost-saving.
It should be noted that, user's benchmark dimension grade point and user's the equal round numbers of various dimensions grade point in first embodiment of the invention, and can adopt the mode rounding up to round.
Wherein, according to described user's various dimensions grade point, be described user resource allocation, can comprise: whether the various dimensions grade point of determining described user is greater than preset value, be if so, described user resource allocation; Otherwise, be not described user resource allocation.
In the embodiment of the present invention, to according to described user's various dimensions grade point, for the concrete grammar of described user resource allocation is not construed as limiting, as long as there is definite distribution principle.
In the resource allocation methods based on grade providing in first embodiment of the invention, a kind of clear and definite grading standard is provided, according to user's benchmark dimension values and the benchmark dimension excitation equation that is applicable to user, and default various dimensions weight vectors, obtain user's various dimensions grade point, and according to described user's various dimensions grade point, be described user resource allocation.Because Ben Fafa provides a kind of clear and definite grading standard, therefore can guarantee the rationality that user gradation is divided, can be reasonably user resource allocation, and then in the time being user resource allocation according to described user's various dimensions grade point, reduce the waste of resource, thus cost-saving.
The second embodiment:
Fig. 3 is the realization flow figure of the resource allocation methods based on grade that provides in second embodiment of the invention, the method can be carried out by the resource allocation device based on grade, described device can be realized by software and/or hardware, and a part that can be used as terminal device is built in terminal device inside.As shown in Figure 3, this realization flow comprises:
Step 21, grade classification dimension vector is set, and according to described grade classification dimension vector, described various dimensions weight vectors is set.
Wherein, grade classification dimension vector n={ n1, n2, n3, n4, n5 ..., various dimensions weight vectors v={v1, v2, v3, v4, v5 ....In grade classification dimension vector, in the number of component and various dimensions weight vectors, the number of component equates, and in grade classification dimension vector, each component all represents a dimension, can get a benchmark dimension in these dimensions.Various dimensions weight vectors represents the weight of each dimension that grade classification dimension vector is corresponding., the shared weight of n1 is v1; The shared weight of n2 is v2; The shared weight of n2 is v3; The shared weight of n4 is v4; The shared weight of n5 is v5.It should be noted that, the weight of benchmark dimension is 100%.
Wherein, described grade classification dimension vector n can be: n={ consumes the amount of money, the last consumption time, consuming frequency }, wherein consuming the amount of money is benchmark dimension.Now, user's the consumption amount of money, the last consumption time and consuming frequency all can obtain by statistics.In addition, because the consumption amount of money is benchmark dimension, therefore the weight of the consumption amount of money is 100%, then arranges the weight of the last consumption time and the weight of consuming frequency according to actual conditions, completed the setting of various dimensions weight vectors., the embodiment of the present invention provides a kind of with the consumption amount of money, the last consumption time, the specific embodiments that consuming frequency is dimension.
Step 22, benchmark dimension grade excitation equation described in described rate range division points vector sum is set, and according to benchmark dimension grade excitation equation described in described rate range division points vector sum, calculates the boundary value vector of described benchmark dimension.
Wherein, according to rate range division points vector r={r1, r2, r3, r4 ... it is user's divided rank.The corresponding default benchmark dimension grade excitation equation of each predetermined level, and the slope of each grade excitation equation represents the exitation factor of this grade, exitation factor is less, and to illustrate that user rises to the difficulty of next grade larger.
By in benchmark dimension grade excitation equation described in each component substitution in described rate range division points vector r, can either calculate the each component in the boundary value vector s of benchmark dimension.
Still encourage model as example taking the benchmark dimension rating calculation providing in Fig. 2, i.e. rate range division points vector r={a, b, c, d}, benchmark dimension grade excitation equation is respectively y=2x, y=x+a/2, y=x/2+b/2+a/4, y=x/4+c/2+b/4+a/8, calculates the boundary value vector s={a/2 of benchmark dimension, b-a/2,2c-b-a/2,4d-2c-b-a/2}.When actual use, by parameter a, b, c and d replace with occurrence.
User's distributed number situation can meet following relation: power user's quantity < naive user quantity < quantity < of advanced level user intermediate users quantity.Be that line segment length can meet following relation: d-c<a<c-b<b-a.
It should be noted that, in the embodiment of the present invention, the type of default benchmark dimension grade excitation equation can convert, and benchmark dimension number of levels also can increase as required or reduce.In addition, in the embodiment of the present invention, the relation between the quantity of each class user is not done to concrete restriction yet.
Wherein, in described benchmark dimension grade excitation equation, benchmark dimension grade corresponding to each benchmark dimension grade encourages the type of equation can comprise respectively at least one in exponential function, linear function, logarithmic function and linear function.
For example, while having four benchmark dimension grades excitation equation, the type of the benchmark dimension grade excitation equation that the first estate is corresponding can be that the type of benchmark dimension grade excitation equation corresponding to exponential function, the second grade can can be linear function for the type of logarithmic function and benchmark dimension grade excitation equation corresponding to the fourth estate for the type of benchmark dimension grade excitation equation corresponding to linear function, the tertiary gradient; While having three benchmark dimension grade excitation equations, the type of the benchmark dimension grade excitation equation that the first estate is corresponding can be that the type of benchmark dimension grade excitation equation corresponding to linear function, the second grade can be exponential function for logarithmic function, benchmark dimension grade corresponding to the tertiary gradient encourage the type of equation.
Concrete, in described benchmark dimension grade excitation equation, benchmark dimension grade excitation equation corresponding to each benchmark dimension grade can be all same exponential function.
Concrete, in described benchmark dimension grade excitation equation, benchmark dimension grade excitation equation corresponding to each benchmark dimension grade can also be same linear function.
Fig. 4-Fig. 6 is respectively the schematic diagram of the grade excitation model that provides in second embodiment of the invention, as shown in Figure 4 and Figure 5, has all embodied the grade more and more difficult trend that rises in these grades excitation models, and the exitation factor of more high-grade correspondence is less.In addition, because Fig. 5 encourages each grade in model to encourage equation identical with the grade described in Fig. 6, these two kinds of grade excitation models judge and just can select the benchmark dimension grade excitation equation that is applicable to user without the benchmark dimension values to user, user gradation is being divided in less demanding situation, can selected this two kinds of models.
Step 23, according to the boundary value vector of user's benchmark dimension values and described benchmark dimension, from described benchmark dimension grade excitation equation, select to be applicable to user's benchmark dimension grade excitation equation.
Obtain user's benchmark dimension values q, and according to the boundary value vector s of user's benchmark dimension values q and described benchmark dimension, from described benchmark dimension grade excitation equation, select to be applicable to user's benchmark dimension grade excitation equation.
Step 24, according to described user's benchmark dimension values and the benchmark dimension grade excitation equation selected, calculate user's benchmark dimension grade point.
Step 25, according to described user's benchmark dimension grade point and default various dimensions weight vectors, calculate user's various dimensions grade point.
Step 26, according to described user's various dimensions grade point, be described user resource allocation.
It should be noted that, in second embodiment of the invention, the particular content of In Grade partition dimension vector, various dimensions weight vectors, benchmark dimension grade excitation equation and rate range division points vector is all not construed as limiting.Be that the resource allocation methods based on grade providing in the embodiment of the present invention is applicable to different application scenarioss.
For example, be preset with grade classification dimension vector n={ sales volume, credibility, liveness, loyalty }, various dimensions weight vectors v={100%, 110%, 80%, 90%}, rate range division points vector r={10,40,60,65}, and calculate the boundary value vector s={100 of benchmark dimension, 300,700,900}.Choosing sales volume is benchmark dimension.The meaning that rate range division points vector r represents be elementary rate range in [0,10], intermediate rate range is in [11,40], senior rate range is in [41,60], and super rate range is in [61,65].The boundary value vector s of benchmark dimension represents the limits corresponding to division points such as each, if limit corresponding to grade 10 division points is 1,000,000.If the user of certain grade to be divided, its benchmark dimension grade point is 11, so its various dimensions grade point G={11* (100%+110%+80%+90%)/4} ≈ 10.
The resource allocation methods based on grade providing in second embodiment of the invention, can realize the intellectuality maintenance of user gradation, thereby guarantee the rationality of user gradation, in the time being the user resource allocation of different brackets, can reduce the waste of resource, thus cost-saving.
Be below device embodiment and the system embodiment of the embodiment of the present invention, the inventive method embodiment, device embodiment and system embodiment belong to same design, the detail content of detailed description not in device embodiment and system embodiment, can be with reference to said method embodiment.
The 3rd embodiment:
Fig. 7 is the structural representation of the resource allocation device based on grade that provides in third embodiment of the invention, as shown in Figure 7, the resource allocation device based on grade described in the present embodiment can comprise: equation selected cell 31, for according to user's benchmark dimension values, from default benchmark dimension grade excitation equation, select to be applicable to user's benchmark dimension grade excitation equation; Initial grade computing unit 32, for according to described user's benchmark dimension values and the benchmark dimension grade excitation equation of selecting, calculates user's benchmark dimension grade point; Final rating calculation unit 33, for according to described user's benchmark dimension grade point and default various dimensions weight vectors, calculates user's various dimensions grade point; Resource allocation unit 34, for according to described user's various dimensions grade point, is described user resource allocation.
Wherein, described resource allocation unit specifically can be for: whether the various dimensions grade point of determining described user is greater than preset value, is if so, described user resource allocation; Otherwise, be not described user resource allocation.
Wherein, final rating calculation unit 33 specifically can be for: according to following formula, calculate user's various dimensions grade point G:
wherein, the benchmark dimension grade point that g is user, v={v1, v2, v3, v4 ..., vk} is various dimensions weight vectors, k is the number of dimensions of various dimensions weight vectors.
Wherein, this device can also comprise: the first setting unit, for grade classification dimension vector is set, and arranges described various dimensions weight vectors according to described grade classification dimension vector; The second setting unit, for benchmark dimension grade excitation equation described in described rate range division points vector sum is set, and according to benchmark dimension grade excitation equation described in described rate range division points vector sum, calculates the boundary value vector of described benchmark dimension.
Wherein, described grade classification dimension vector n can be: n={ consumes the amount of money, the last consumption time, consuming frequency }, wherein consuming the amount of money is benchmark dimension.
Wherein, in described benchmark dimension grade excitation equation, benchmark dimension grade corresponding to each benchmark dimension grade encourages the type of equation can comprise respectively at least one in exponential function, linear function, logarithmic function and linear function.
Wherein, in described benchmark dimension grade excitation equation, benchmark dimension grade excitation equation corresponding to each benchmark dimension grade can be all same exponential function.
The resource allocation device based on grade providing in third embodiment of the invention, characteristic specific as follows: user's benchmark dimension values is calculated user's benchmark dimension grade, determine user's various dimensions grade in conjunction with various dimensions, like this, grade classification more can embody user's comprehensive condition, formulates rewards and punishments means effectively and has realistic meaning; Use different exitation factors to make grade classification there is applicable variability, make inferior grade fast excessively to more high-grade, it is difficult that the rising of each rate range becomes gradually, and the intensity of variation of difficulty embodies by exitation factor, so just can make a large number of users be distributed in middle-and-high-ranking, meet statistical law; The model standard of a grade classification be provided and created implementation process, having made grade classification have model reference and implementation basis, can change model parameter according to reality scene, even model piecewise function, makes grade classification closer to reality situation; Because this programme has detailed model process of establishing and implementation step, make grade classification can robotization with intelligent, no longer need artificial interference.Therefore, this device can be realized the intellectuality of user gradation and safeguard, thereby guarantees the rationality of user gradation, in the time being the user resource allocation of different brackets, can reduce the waste of resource, thereby cost-saving.
The above is only the preferred embodiment of the embodiment of the present invention, is not limited to the embodiment of the present invention, and to those skilled in the art, the embodiment of the present invention can have various changes and variation.Any amendment of doing within all spirit in the embodiment of the present invention and principle, be equal to replacement, improvement etc., within all should being included in the protection domain of the embodiment of the present invention.
Claims (12)
1. the resource allocation methods based on grade, is characterized in that, comprising:
According to the boundary value vector of user's benchmark dimension values and the benchmark dimension that obtains in advance, from default benchmark dimension grade excitation equation, select to be applicable to user's benchmark dimension grade excitation equation;
According to described user's benchmark dimension values and the benchmark dimension grade excitation equation of selecting, calculate user's benchmark dimension grade point;
According to described user's benchmark dimension grade point and default various dimensions weight vectors, calculate user's various dimensions grade point;
According to described user's various dimensions grade point, it is described user resource allocation.
2. method according to claim 1, is characterized in that, according to described user's various dimensions grade point, is described user resource allocation, comprising:
Whether the various dimensions grade point of determining described user is greater than preset value, is if so, described user resource allocation; Otherwise, be not described user resource allocation.
3. method according to claim 1, is characterized in that, according to described user's benchmark dimension values and the benchmark dimension grade excitation equation of selecting, the benchmark dimension grade point that calculates user comprises:
According to following formula, calculate user's various dimensions grade point G:
wherein, the benchmark dimension grade point that g is user, v={v1, v2, v3, v4 ..., vk} is various dimensions weight vectors, k is the number of dimensions of various dimensions weight vectors.
4. method according to claim 1, is characterized in that, before selecting to be applicable to user's benchmark dimension grade excitation equation, also comprises from default benchmark dimension grade excitation equation:
Grade classification dimension vector is set, and according to described grade classification dimension vector, described various dimensions weight vectors is set;
Benchmark dimension grade excitation equation described in described rate range division points vector sum is set, and according to benchmark dimension grade excitation equation described in described rate range division points vector sum, calculates the boundary value vector of described benchmark dimension.
5. method according to claim 4, is characterized in that, described grade classification dimension vector n={ the consumption amount of money, the last consumption time, consuming frequency }, and wherein consuming the amount of money is benchmark dimension.
6. according to the method described in claim 1-5 any one, it is characterized in that, in described benchmark dimension grade excitation equation, benchmark dimension grade corresponding to each benchmark dimension grade encourages the type of equation to comprise respectively at least one in exponential function, linear function, logarithmic function and linear function.
7. according to the method described in claim 1-5 any one, it is characterized in that, in described benchmark dimension grade excitation equation, benchmark dimension grade excitation equation corresponding to each benchmark dimension grade is same exponential function.
8. the resource allocation device based on grade, is characterized in that, comprising:
Equation selected cell for according to the boundary value vector of user's benchmark dimension values and the benchmark dimension that obtains in advance, selects to be applicable to user's benchmark dimension grade excitation equation from default benchmark dimension grade excitation equation;
Initial grade computing unit, for according to described user's benchmark dimension values and the benchmark dimension grade excitation equation of selecting, calculates user's benchmark dimension grade point;
Final rating calculation unit, for according to described user's benchmark dimension grade point and default various dimensions weight vectors, calculates user's various dimensions grade point;
Resource allocation unit, for according to described user's various dimensions grade point, is described user resource allocation.
9. device according to claim 8, is characterized in that, described resource allocation unit specifically for:
Whether the various dimensions grade point of determining described user is greater than preset value, is if so, described user resource allocation; Otherwise, be not described user resource allocation.
10. device according to claim 8, is characterized in that, final rating calculation unit specifically for:
According to following formula, calculate user's various dimensions grade point G:
wherein, the benchmark dimension grade point that g is user, v={v1, v2, v3, v4 ..., vk} is various dimensions weight vectors, k is the number of dimensions of various dimensions weight vectors.
11. devices according to claim 8, is characterized in that, also comprise:
The first setting unit, for grade classification dimension vector is set, and arranges described various dimensions weight vectors according to described grade classification dimension vector;
The second setting unit, for benchmark dimension grade excitation equation described in described rate range division points vector sum is set, and according to benchmark dimension grade excitation equation described in described rate range division points vector sum, calculates the boundary value vector of described benchmark dimension.
12. devices according to claim 11, is characterized in that, described grade classification dimension vector n={ the consumption amount of money, the last consumption time, consuming frequency }, and wherein consuming the amount of money is benchmark dimension.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105391758A (en) * | 2015-10-09 | 2016-03-09 | 天脉聚源（北京）教育科技有限公司 | Method and device for allocating resources in local area network |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6993401B1 (en) * | 2001-11-07 | 2006-01-31 | Autodesk, Inc. | Method and apparatus for simplified determination of a design schedule utilizing computer aided design (CAD) model information |
| CN101477552A (en) * | 2009-02-03 | 2009-07-08 | 辽宁般若网络科技有限公司 | Website user rank division method |
| CN102663101A (en) * | 2012-04-13 | 2012-09-12 | 北京交通大学 | Sina microblog-based user grade sequencing algorithm |
-
2014
- 2014-08-22 CN CN201410418429.2A patent/CN104156466B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6993401B1 (en) * | 2001-11-07 | 2006-01-31 | Autodesk, Inc. | Method and apparatus for simplified determination of a design schedule utilizing computer aided design (CAD) model information |
| CN101477552A (en) * | 2009-02-03 | 2009-07-08 | 辽宁般若网络科技有限公司 | Website user rank division method |
| CN102663101A (en) * | 2012-04-13 | 2012-09-12 | 北京交通大学 | Sina microblog-based user grade sequencing algorithm |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105391758A (en) * | 2015-10-09 | 2016-03-09 | 天脉聚源（北京）教育科技有限公司 | Method and device for allocating resources in local area network |
| CN105391758B (en) * | 2015-10-09 | 2019-07-02 | 天脉聚源（北京）教育科技有限公司 | The method and apparatus of resource allocation in a kind of local area network |
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