CN105790866B - Base station rankings method and device - Google Patents
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- CN105790866B CN105790866B CN201610201668.1A CN201610201668A CN105790866B CN 105790866 B CN105790866 B CN 105790866B CN 201610201668 A CN201610201668 A CN 201610201668A CN 105790866 B CN105790866 B CN 105790866B
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- 238000012549 training Methods 0.000 claims abstract description 40
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- 238000012360 testing method Methods 0.000 claims description 9
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/391—Modelling the propagation channel
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/16—Threshold monitoring
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W88/00—Devices specially adapted for wireless communication networks, e.g. terminals, base stations or access point devices
- H04W88/08—Access point devices
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Abstract
The embodiment of the present invention provides a kind of base station rankings method and device, and this method includes：Sample data is obtained according to default dimension, and training dataset is obtained from the sample data；The training dataset is screened, obtains the data for being higher than predetermined threshold value with the degree of correlation of each dimension, and Logic Regression Models are established according to the data；The Logic Regression Models are corrected, and the corresponding weight of each dimension is determined according to the Logic Regression Models after correction；According to the scoring of the corresponding weight calculation base station of each dimension, and the base station is classified according to the scoring of the base station.Base station rankings method and device provided in an embodiment of the present invention, can improve the accuracy of base station rankings, be conducive to distributing rationally for base station O＆M resource.
Description
Technical field
The present embodiments relate to field of communication technology more particularly to a kind of base station rankings method and devices.
Background technology
Base station rankings are a kind of modes divided to base station significance level according to base station data.Base station rankings are in base station
Optimization and O＆M in have important direction action.
In the prior art, the grade of base station is divided according to the telephone traffic of base station.But with mobile interchange
The phenomenon that development of net, user application network business, is more and more common, merely can not be accurate according to the index of telephone traffic
The grade of base station is positioned, therefore, is badly in need of a kind of completely new base station rank division method, to be carried out to the grade of base station
It is accurate to divide.
Invention content
The embodiment of the present invention provides a kind of base station rankings method and device, to improve the accuracy rate of base station rankings, realizes
The accurate configuration of base station O＆M resource.
First aspect of the embodiment of the present invention provides a kind of base station rankings method, and this method includes：
Sample data is obtained according to default dimension, and training dataset is obtained from the sample data；
The training dataset is screened, the data for being higher than predetermined threshold value with the degree of correlation of each dimension, and root are obtained
Logic Regression Models are established according to the data；
The Logic Regression Models are corrected, and the corresponding weight of each dimension is determined according to the Logic Regression Models after correction；
According to the scoring of the corresponding weight calculation base station of each dimension, and according to the scoring of the base station to the base station
It is classified.
Second aspect of the embodiment of the present invention provides a kind of base station rankings device, which includes：
First acquisition module for obtaining sample data according to default dimension, and obtains training from the sample data
Data set；
Second acquisition module is obtained with the degree of correlation of each dimension for being screened to the training dataset higher than pre-
If the data of threshold value；
Model building module, for establishing Logic Regression Models according to the data；
Correction module, for correcting the Logic Regression Models；
Determining module, for determining the corresponding weight of each dimension according to the Logic Regression Models after correction；
Diversity module, for the scoring according to the corresponding weight calculation base station of each dimension, and according to the base station
Scoring is classified the base station.
The third aspect of the embodiment of the present invention provides a kind of base station rankings device, which includes：
Processor；
Memory for storing the processor-executable instruction；
Wherein, the processor is configured as：
Sample data is obtained according to default dimension, and training dataset is obtained from the sample data；
The training dataset is screened, the data for being higher than predetermined threshold value with the degree of correlation of each dimension, and root are obtained
Logic Regression Models are established according to the data；
The Logic Regression Models are corrected, and the corresponding weight of each dimension is determined according to the Logic Regression Models after correction；
According to the scoring of the corresponding weight calculation base station of each dimension, and according to the scoring of the base station to the base station
It is classified.
Base station rankings method and device provided in an embodiment of the present invention, by concentrating the phase with each dimension according to training data
Guan Du establishes Logic Regression Models higher than the data of predetermined threshold value, and determines the corresponding power of each dimension by Logic Regression Models
Weight, thus according to the scoring of the corresponding weight calculation base station of each dimension, and base station is classified according to the scoring of base station, it improves
The accuracy rate of base station rankings, is beneficial to the accurate configuration of base station O＆M resource.
Description of the drawings
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technology description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention without having to pay creative labor, may be used also for those of ordinary skill in the art
With obtain other attached drawings according to these attached drawings.
Fig. 1 is the flow diagram for the base station rankings method that one embodiment of the invention provides；
Fig. 2 is the flow diagram for the base station rankings method that another embodiment of the present invention provides；
Fig. 3 is the structural schematic diagram for the base station rankings device that one embodiment of the invention provides；
Fig. 4 is the structural schematic diagram for the base station rankings device that another embodiment of the present invention provides.
Specific implementation mode
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation describes, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
The term " comprising " and " having " of description and claims of this specification and their any deformation, it is intended that
Be to cover it is non-exclusive include, for example, the device of the process or structure that contain series of steps is not necessarily limited to clearly arrange
Those of go out structure or step but may include not listing clearly or for the intrinsic other steps of these processes or device
Rapid or structure.
Fig. 1 is the flow diagram for the base station rankings method that one embodiment of the invention provides, and this method can be by a base station
Grading plant executes, as shown in Figure 1, base station rankings method provided in this embodiment includes the following steps：
Step S101, sample data is obtained according to default dimension, and training dataset is obtained from the sample data.
Default dimension is specifically set according to Practical Project scene.Project scenarios in the present embodiment are the grades of base station
It divides, it preferably may include the basic condition of base station, base station user value, base station user therefore, in the present embodiment to preset dimension
The four dimensions such as stability and base station user business usage amount, wherein：
Base station basic condition includes mainly that base station type (2G/3G/4G), the rate of complaints, geographical location and history use duration
Four aspects give base station basic condition corresponding score value according to the different conditions of this four aspects in conjunction with corresponding weight, should
Score value will be included in the grading of the base station.
Base station user is worth, and includes mainly two aspects of user's current value and the following potential value.Such as user is current
Value may include the amount of money of entering an item of expenditure in the accounts of user, and the following potential value of value-added service amount of money etc., user may include the latent of user
In the level of consumption, consumer loyalty degree and user credit degree etc..Wherein, the potential consumption level of user can be worked as according to user
Preceding terminal type, set meal type etc., the scalable terminal type of determining user and set meal type.According to user's current value
Base station user is given with the two aspects of the following potential value and corresponding weights and is worth corresponding score value, which will be included in
The grading of the base station.
Base station user stability is mainly reflected in user using in the stability of business, it uses every business by user
The indexs such as the frequency, and last usage time constitute, these indexs illustrate whether user has stable business use habit.Root
According to user using indexs and the corresponding weights of indices such as the frequencys of every business and last usage time, base station is given
One corresponding score value of user's stability, the score value will be included in the grading of the base station.
Further, after determining preset dimension, correlation analysis can be carried out to each dimension.It is worth with base station user
For, it can be according to the factor for determining base station user value, it should includes representing user to determine that base station user is worth corresponding data
Enter an item of expenditure in the accounts the amount of money related data and represent the related data of the value-added service amount of money.
After determining the corresponding related data of each dimension, corresponding related data is obtained from the database of base station and forms sample
Notebook data.
It further, can be according to preset strategy acquisition unit score from sample data after obtaining sample data
According to as training dataset, can not also be limited herein by sample data all as training data.Wherein, described pre-
If strategy can be specifically arranged as needed, do not limit equally herein.
Step S102, the training dataset is screened, is obtained with the degree of correlation of each dimension higher than predetermined threshold value
Data, and establish Logic Regression Models according to the data.
In the corresponding related data of each dimension, the data of different attribute (for example, age, gender etc.) are in corresponding dimension
Related data in the ratio that occupies may be different, i.e. the data of some attributes may be to influence the principal element of corresponding dimension,
And the data of some attributes may be to the influence very little of corresponding dimension.Such as in base station user value relevance, different age group
User it is shared in the value-added service of base station ratio it is different.Therefore, if obtain occupy principal element attribute it is corresponding
Data can represent the characteristic of corresponding dimension.
In the present embodiment, determine that each attribute data is related to each dimension in training data by Pearson's coefficient first
Degree, and each attribute data is compared with the degree of correlation of each dimension with preset threshold value, if the degree of correlation is higher than predetermined threshold value,
It is concentrated from training data and obtains corresponding data.Herein it should be noted that the underlying attribute for influencing each dimension may be
One is also likely to be multiple.
Further, after carrying out screening acquisition and each higher data of the dimension degree of correlation to training dataset, you can root
Logic Regression Models are established according to the data of acquisition.
Include the basic condition of base station, base station user value, base station user stability and base station user with default dimension
For business usage amount four dimensions, after obtaining training dataset according to the method for above-mentioned acquisition training dataset, Ke Yigen
Base station user value relevance and the corresponding data of base station user stability dimension is concentrated to carry out training data according to Pearson's coefficient
Controlling UEP, and it is corresponding higher than the data attribute of predetermined threshold value with the degree of correlation of the two dimensions from training dataset acquisition
Data, and establish Logic Regression Models according to the data.
Need to illustrate herein, the method for building up of Logic Regression Models similarly to the prior art, herein no longer
It repeats.
Step S103, the Logic Regression Models are corrected, and each dimension pair is determined according to the Logic Regression Models after correction
The weight answered.
Specifically, the bearing calibration of Logic Regression Models is similar with the bearing calibration of existing model in the present embodiment, at this
In repeat no more.
Further, after calibrating patterns, the corresponding weight of each dimension is determined according to the model after calibration.
Specifically, in the present embodiment, a test data set can be provided previously, which can be from sample
It is obtained in data, can also be to obtain by other means, be not specifically limited herein.Obtaining test data set
Afterwards, which is inputted to calibrated Logic Regression Models, passes through correlation of the Logic Regression Models between each dimension
Property is analyzed, to obtain the corresponding weight of each dimension.
Step S104, according to the scoring of the corresponding weight calculation base station of each dimension, and according to the scoring of the base station
The base station is classified.
Specifically, this implementation preferably passes through formulaThe scoring of calculation base station, wherein Q is the scoring of base station, Xi
For the corresponding assigned results of dimension i, RiFor the corresponding weights of dimension i, dimension i is positive integer.
Calculate obtain base station scoring after, by each base station scoring it is descending be ranked up, according to each base station
It is ordered as base station rankings.
Base station rankings method and device provided in this embodiment, by concentrating the degree of correlation with each dimension according to training data
Data higher than predetermined threshold value establish Logic Regression Models, and determine the corresponding weight of each dimension by Logic Regression Models, from
And base station is classified according to the scoring of the corresponding weight calculation base station of each dimension, and according to the scoring of base station, improve base
Stand classification accuracy rate, be beneficial to the accurate configuration of base station O＆M resource.
Fig. 2 is the flow diagram for the base station rankings method that another embodiment of the present invention provides, and the present embodiment is with default
Dimension includes the basic condition of base station, base station user value, base station user stability and base station user business usage amount four
For dimension, the stage division of base station is illustrated, as shown in Fig. 2, method provided in this embodiment includes the following steps：
Step S201, it obtains from the database of base station and stablizes with base station basic condition, base station user value, base station user
Degree and the relevant data of base station user business usage amount four dimensions form sample data, and according to preset strategy from sample
Training dataset and test data set are obtained in data.
Step S202, correlation analysis, determining and base are carried out to base station user value relevance, base station user stability dimension
User's value relevance and/or the base station user stability dimension degree of correlation of standing are higher than the data attribute of predetermined threshold value, and from training number
The corresponding data of each data attribute are obtained according to concentrating.
Step S203, Logic Regression Models are established according to the corresponding data of each data attribute of acquisition.
Step S204, the Logic Regression Models are corrected, and each dimension pair is determined according to the Logic Regression Models after correction
The weight answered.
Step S205, according to formulaThe scoring of calculation base station, and base station is divided according to the scoring of base station
Grade.Wherein, Q is the scoring of base station, XiFor the corresponding assigned results of dimension i, RiFor the corresponding weights of dimension i, dimension i is just whole
Number.
Base station rankings method and device provided in this embodiment, by concentrating the degree of correlation with each dimension according to training data
Data higher than predetermined threshold value establish Logic Regression Models, and determine the corresponding weight of each dimension by Logic Regression Models, from
And base station is classified according to the scoring of the corresponding weight calculation base station of each dimension, and according to the scoring of base station, improve base
Stand classification accuracy rate, be beneficial to the accurate configuration of base station O＆M resource.
Fig. 3 is the structural schematic diagram for the base station rankings device that one embodiment of the invention provides, which can be used in executing
Method as shown in Figure 1, as shown in figure 3, base station rankings device provided in this embodiment includes：
First acquisition module 10 for obtaining sample data according to default dimension, and obtains instruction from the sample data
Practice data set；
Second acquisition module 20 obtains and is higher than with the degree of correlation of each dimension for being screened to the training dataset
The data of predetermined threshold value；
Model building module 30, for establishing Logic Regression Models according to the data；
Correction module 40, for correcting the Logic Regression Models；
Determining module 50, for determining the corresponding weight of each dimension according to the Logic Regression Models after correction；
Diversity module 60, for the scoring according to the corresponding weight calculation base station of each dimension, and according to the base station
Scoring the base station is classified.
Wherein, first acquisition module 10 may include：
First determination sub-module 101 determines the corresponding correlation of each dimension for carrying out correlation analysis to each default dimension
Data；
First acquisition submodule 102, for obtained from base station database the corresponding related data of each dimension formed it is described
Sample data.
Second acquisition module 20 may include：
Second determination sub-module 201 is related to each dimension for determining that the training data is concentrated using Pearson's coefficient
Data attribute of the degree higher than predetermined threshold value；
Second acquisition submodule 202 obtains the data for being concentrated from the training data.
First acquisition module 10 is additionally operable to obtain test data set from the sample data；
The determining module 50 is specifically used for the test data set inputting each dimension of the Logic Regression Models progress
Between correlation analysis, and the corresponding weight of each dimension is determined according to analysis result.
The diversity module 60 is specifically used for according to formulaThe scoring of calculation base station, and according to the base station
Scoring is classified the base station, wherein Q is the scoring of base station, XiFor the corresponding assigned results of dimension i, RiFor i pairs of dimension
The weight answered, dimension i are positive integer.
Base station rankings device provided in this embodiment can be used in executing method as shown in Figure 1, the specific side of execution
Formula and advantageous effect are similar with embodiment illustrated in fig. 1, repeat no more herein.
Fig. 4 is the structural schematic diagram for the base station rankings device that another embodiment of the present invention provides, as shown in figure 4, this implementation
Example provide base station rankings device include：
Processor；
Memory for storing the processor-executable instruction；
Wherein, the processor is configured as：
Sample data is obtained according to default dimension, and training dataset is obtained from the sample data；
The training dataset is screened, the data for being higher than predetermined threshold value with the degree of correlation of each dimension, and root are obtained
Logic Regression Models are established according to the data；
The Logic Regression Models are corrected, and the corresponding weight of each dimension is determined according to the Logic Regression Models after correction；
According to the scoring of the corresponding weight calculation base station of each dimension, and according to the scoring of the base station to the base station
It is classified.
Base station rankings device provided in this embodiment can be used in executing method as shown in Figure 1, the specific side of execution
Formula and advantageous effect are similar with embodiment illustrated in fig. 1, repeat no more herein.
Finally it should be noted that：The above embodiments are only used to illustrate the technical solution of the present invention., rather than its limitations；To the greatest extent
Present invention has been described in detail with reference to the aforementioned embodiments for pipe, it will be understood by those of ordinary skill in the art that：Its according to
So can with technical scheme described in the above embodiments is modified, either to which part or all technical features into
Row equivalent replacement；And these modifications or replacements, various embodiments of the present invention technology that it does not separate the essence of the corresponding technical solution
The range of scheme.
Claims (9)
1. a kind of base station rankings method, which is characterized in that including：
Sample data is obtained according to default dimension, and training dataset is obtained from the sample data；
The training dataset is screened, obtains the data for being higher than predetermined threshold value with the degree of correlation of each dimension, and according to institute
It states data and establishes Logic Regression Models；
The Logic Regression Models are corrected, and the corresponding weight of each dimension is determined according to the Logic Regression Models after correction；
According to formulaThe scoring of calculation base station, and the base station is classified according to the scoring of the base station,
In, Q is the scoring of base station, XiFor the corresponding assigned results of dimension i, RiFor the corresponding weights of dimension i, dimension i is positive integer.
2. according to the method described in claim 1, it is characterized in that, the basis preset dimension obtain sample data, including：
Correlation analysis is carried out to each default dimension, determines the corresponding related data of each dimension；
The corresponding related data of each dimension is obtained from base station database forms the sample data.
3. according to the method described in claim 1, it is characterized in that, described screen the training dataset, obtain with
The degree of correlation of each dimension is higher than the data of predetermined threshold value, and establishes Logic Regression Models according to the data, including：
Determine that the training data concentrates the attribute higher than the data of predetermined threshold value with each dimension degree of correlation using Pearson's coefficient；
According to the attribute of the data, is concentrated from the training data and obtain corresponding data, and institute is established according to the data
State Logic Regression Models.
4. according to the method described in claim 3, it is characterized in that, the Logic Regression Models according to after correction determine each dimension
Corresponding weight is spent, including：
Test data set is obtained from the sample data；
The test data set is inputted into the Logic Regression Models and carries out correlation analysis between each dimension, and is tied according to analysis
Fruit determines the corresponding weight of each dimension.
5. a kind of base station rankings device, which is characterized in that including：
First acquisition module for obtaining sample data according to default dimension, and obtains training data from the sample data
Collection；
Second acquisition module obtains for being screened to the training dataset and is higher than default threshold with the degree of correlation of each dimension
The data of value；
Model building module, for establishing Logic Regression Models according to the data；
Correction module, for correcting the Logic Regression Models；
Determining module, for determining the corresponding weight of each dimension according to the Logic Regression Models after correction；
Diversity module, for according to formulaThe scoring of calculation base station, and according to the scoring of the base station to the base station
It is classified, wherein Q is the scoring of base station, XiFor the corresponding assigned results of dimension i, RiFor the corresponding weights of dimension i, dimension i
For positive integer.
6. device according to claim 5, which is characterized in that first acquisition module, including：
First determination sub-module determines the corresponding related data of each dimension for carrying out correlation analysis to each default dimension；
First acquisition submodule forms the sample number for obtaining the corresponding related data of each dimension from base station database
According to.
7. device according to claim 5, which is characterized in that second acquisition module, including：
Second determination sub-module, for determining that the training data is concentrated with each dimension degree of correlation higher than pre- using Pearson's coefficient
If the attribute of the data of threshold value；
Second acquisition submodule obtains corresponding data for being concentrated from the training data according to the attribute of the data.
8. device according to claim 7, which is characterized in that first acquisition module is additionally operable to from the sample number
According to middle acquisition test data set；
The determining module carries out phase between each dimension specifically for the test data set is inputted the Logic Regression Models
The analysis of closing property, and the corresponding weight of each dimension is determined according to analysis result.
9. a kind of base station rankings device, which is characterized in that including：
Processor；
Memory for storing the processor-executable instruction；
Wherein, the processor is configured as：
Sample data is obtained according to default dimension, and training dataset is obtained from the sample data；
The training dataset is screened, obtains the data for being higher than predetermined threshold value with the degree of correlation of each dimension, and according to institute
It states data and establishes Logic Regression Models；
The Logic Regression Models are corrected, and the corresponding weight of each dimension is determined according to the Logic Regression Models after correction；
According to formulaThe scoring of calculation base station, and the base station is classified according to the scoring of the base station, wherein
Q is the scoring of base station, XiFor the corresponding assigned results of dimension i, RiFor the corresponding weights of dimension i, dimension i is positive integer.
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| CN110162958B (en) * | 2018-10-18 | 2023-04-18 | 腾讯科技（深圳）有限公司 | Method, apparatus and recording medium for calculating comprehensive credit score of device |
| KR102096558B1 (en) * | 2018-11-26 | 2020-04-02 | 두산중공업 주식회사 | Apparatus for combustion optimization and method therefor |
| CN109766845B (en) * | 2019-01-14 | 2021-09-24 | 首都医科大学宣武医院 | Electroencephalogram signal classification method, device, equipment and medium |
| CN111050344B (en) * | 2019-12-12 | 2023-02-28 | 中国联合网络通信集团有限公司 | Base station operation and maintenance guarantee method and equipment |
| CN116982993B (en) * | 2023-09-27 | 2024-04-02 | 之江实验室 | Electroencephalogram signal classification method and system based on high-dimensional random matrix theory |
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