CN108563770A - A kind of KPI and various dimensions network data cleaning method based on scene - Google Patents
A kind of KPI and various dimensions network data cleaning method based on scene Download PDFInfo
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Abstract
The invention discloses a kind of KPI based on scene and various dimensions network data cleaning methods, first import the data of acquisition in data structure；Standardize to the data format of importing, resource data is pressed into scene partitioning, the resource data of the same scene merges to obtain resource subdata, and the conflict generated to data value is detected and handles；The data of multiple data sources or file are associated, the unmatched situation of redundancy and pattern to data is judged and handled；Data to cannot directly carry out data mining are handled；The miss rate for checking each attribute is determined processing mode according to miss rate, including abandons and filled up using K NN homing methods；Initial data is backed up in data memory module and stores the data after cleaning；The present invention realizes effective cleaning of data, the technical issues of solving not carrying out going deep into excavating to data caused by the complexity height of data.
Description
Technical field
The invention belongs to data cleansing fields, and in particular to a kind of KPI and various dimensions network data cleaning based on scene
Method.
Background technology
It needs to pay close attention to some KPI Key Performance Indicators (Key Performance in mobile communications network operation management
Indicators, abbreviation KPI), such as cutting off rate, call loss, other than daily maintenance, operator, which wishes to grasp, influences KPI
Factor, obtain the association between KPI and network, distribute and ensure convenient for later stage network optimization task.
Before correlation degree between KPI and network carries out deep analysis mining, need to carry out data effective
Cleaning, reduces the complexity of data.
Invention content
It is an object of the invention to optimize network data, propose that a kind of KPI and various dimensions network data based on scene are clear
Washing method realizes effective cleaning of data, solves not carrying out going deep into excavation to data caused by the complexity height of data
Technical problem.
The present invention adopts the following technical scheme that, a kind of KPI and various dimensions network data cleaning method based on scene, specifically
Steps are as follows：
1) collected initial data is divided into cell basic dimensions data and problem cells inventory, wherein cell is basic
Dimension data is the cell data directly collected, i.e. the data without differentiating by KPI criteria thresholds；Problem cells inventory
For the cell data that obviously deteriorating occurs in a certain item KPI, that is, pass through the data that KPI criteria thresholds differentiate；Mould is imported using data
Block imports data in the data structure of the DataFrame in Pandas packets；
2) standardize to the data format of importing in data processing module, by the money in cell basic dimensions data
Source data presses scene partitioning, and the resource data of the same scene calls concat functions to merge, obtains resource subdata, to merging
The conflict that data value generates in the process is detected and handles；
3) data of multiple data sources or file are associated in data cleansing module, that is, select associated main key
Word will be merged according to major key with other cell basic dimensions data by the resource subdata after scene partitioning, and merged
Middle uniform data pattern；The redundancy of data and the unmatched situation of pattern are judged during merging, delete redundancy category
Property, the form of presentation of unified attributed scheme；Data to cannot directly carry out data mining only retain numerical value or are replaced with numerical value
Generation；
4) miss rate that each attribute is checked in lacking processing module, tentatively fills missing values, is then determined according to miss rate
Processing mode is determined, to miss rate more than 50% attribute, redundant attributes or the attribute discard processing unrelated with analysis theme, to it
The missing values of his attribute are filled up using K-NN homing methods；
5) initial data is backed up in data memory module and stores the data after cleaning.
Preferably, cell basic dimensions data include in the step 1)：Resource data, performance data, work parameter evidence, neighbour
Area's data and measurement data；Problem cells inventory includes：Long term evolution LTE (Long Term Evolution) high load capacity cell,
Fourth generation mobile communication technology 4G zero deliverys cell, long term evolution LTE (Long Term Evolution) high flow capacity problem are serious
Cell, the high call drop cells of high definition voice volte (Voice over LTE), low measurement report MR (Measurement Report)
Coverage rate cell and high definition voice volte (Voice over LTE) high packet loss cell.
Preferably, the data knot for the DataFrame that data are stored in by the Pandas packets of calling Python in the step 1)
Structure.
Preferably, standardized specially the data format of importing to Sino-British present in data in the step 2)
Text and spcial character change format, are uniformly changed to small letter and half width form and removal space.
Preferably, the conflict generated to data value during merging in the step 2) is detected and handles, data value
The conflict of generation includes that the same cell data from different data sources different value or data type occurs, is occurred different
When value, retain the few data of miss rate；When data type difference, character type data is replaced with numeric type data.
Preferably, it selects cgi as major key in the step 3), if cgi is lacked, utilizes public land mobile network
Network PLMN and cell ID eCI are synthesized, and otherwise select cell Chinese as keyword；
It will be by the resource subdata after scene partitioning and the pass in other cell basic dimensions data foundation resource subdata
Key word calls merge functions to be merged using interior connection inner modes, forms the cell basic dimensions subnumber under the scape of branch
According to.
Preferably, judge that the redundancy of data is specially in the step 3)：If attribute can be deduced by other multiple attributes
It obtains, then other the multiple attributes for being used for deduction are redundant attributes, delete redundant attributes, the attribute deduced out is belonged to as a result
Property retain.
Preferably, it fills up, looks for using k neighbours (k-nearest neighbor, K-NN) homing method in the step 4)
K similar samples for going out to lack the sample of attribute, the missing values of sample attribute are replaced with the average value of similar sample.
Preferably, k similar samples for lacking the sample of attribute are k sample of the minimum at a distance from the sample of missing attribute
This, the distance between two samples are measured using Euclidean distance：Two sample point X=(x1,x2,…xn) and Y=(y1,
y2,…yn) between Euclidean distance be：
Wherein, sample refers to cell, the x in samplei、yi、x1、x2、xn、y1、y2And ynIt is right respectively to refer to two cell same alike results
The value answered, m are the distribution numbers for not including attribute missing values in two samples, and n is the attribute number of two samples；If xiIt is equal to
yi, then (xi-yi) it is set as 0, if xiNot equal to yi, then it is (xi-yi) it is set as 1.
Preferably, further include merging basic dimensions data with problem cells listings data, that is, detect basic dimensions data
The cgi of middle cell whether there is in the listings data of problem cells in the cgi lists of cell, and if it exists, then construct new attribute, together
When the cell attribute that is present in basic dimensions data and problem cells listings data set 1, remaining cell attribute is set to 0.
The reached advantageous effect of invention：The present invention is a kind of KPI based on scene and various dimensions network data cleaning side
Method realizes effective cleaning of data, solves the technology that can not carry out going deep into excavation to data caused by the complexity height of data
Problem；The present invention cleans the KPI and various dimensions network data that are collected into, makes data standard, uniformly, is convenient for the phase in later stage
The analysis of closing property.
Description of the drawings
Fig. 1 is the flow chart the present invention is based on the KPI of scene and various dimensions network data cleaning method；
Fig. 2 is the data processing module flow chart of the present invention；
Fig. 3 is the data cleansing module flow chart of the present invention；
Fig. 4 is the missing processing module flow chart of the present invention.
Specific implementation mode
Below according to attached drawing and technical scheme of the present invention is further elaborated in conjunction with the embodiments.
The present invention adopts the following technical scheme that a kind of KPI and various dimensions network data cleaning method based on scene are tested
Partial data of the data from certain operator certain areas, data are provided in a manner of table Excel, and the present invention utilizes
Python realizes that Fig. 1 is the flow chart of KPI and various dimensions network data cleaning method based on scene, is as follows：
1) collected initial data is divided into cell basic dimensions data and problem cells inventory, wherein cell is basic
Dimension data is the cell data directly collected, i.e. the data without differentiating by KPI criteria thresholds；Problem cells inventory
For the cell data that obviously deteriorating occurs in a certain item KPI, that is, pass through the data that KPI criteria thresholds differentiate；Mould is imported using data
Block imports data in the data structure of the DataFrame in Pandas packets.Key Performance Indicator KPI has respective index to want
It asks, as thresholding.For example being successfully established for RRC (radio resource control) connections means that user equipment is established with network
Signaling connection connects into power in RRC>When 95%, it is believed that be satisfactory, if thinking deterioration occurred less than 95%.
Similarly wireless drop rate<3%, wireless percent of call completed>95% etc. is threshold discrimination foundation.
Cell basic dimensions data include：Resource data, performance data, work parameter evidence, neighbor data and measurement data；It asks
Inscribing cell lists includes：Problem cells inventory includes：Long term evolution LTE high load capacities cell, 4G zero deliverys cell, long term evolution
The serious cell of LTE high flow capacity problems, high definition voice volte high call drops cell, low measurement report MR coverage rates cell and high definition language
Sound volte high packet loss cells.
The data of acquisition are imported to the data knot of DataFrame using the Pandas packets of data import modul calling Python
In structure.Pandas is a data analysis bag of python, and DataFrame is most common data structure in Pandas packets, it
It is a kind of two-dimensional Form data structure, the table being similar in database has advantage very much to the processing of Form data.
2) Fig. 2 be the present invention data processing module flow chart, in data processing module to the data format of importing into
Professional etiquette generalized specially changes format to Chinese and English present in data and spcial character, is uniformly changed to small letter and half width form
And removal space；
Resource data in cell basic dimensions data is pressed into scene partitioning, collected resource data is covered into field by it
The Attribute transposition of scape is residential block, colleges and universities, commercial center, the different covering fields such as scenic spot, subway, high ferro, general iron, national highway provincial highway
Scape merges the resource data of same scene concat functions, forms multiple resource subdatas by scene partitioning, above
The problem of can be potentially encountered data value conflict during synthesis resource subdata, the conflict that data value during merging is generated
It is detected and handles；
Same cell data from different data sources may have different value or data type, different values occur
When, retain the few data of miss rate；When data type difference, such as：Support switch attribute in a data source by yes/no
It expresses, and is indicated by 0 and 1 in another data source, but actually refer to identical concept, they may be characters
Type or numeric type replace character type with numeric type.
3) Fig. 3 is the data cleansing module flow chart of the present invention, to multiple data sources or file in data cleansing module
Data be associated, that is, select associated major key, will be by resource subdata and the other basic dimensions after scene partitioning
Data merge according to major key, and the uniform data pattern in merging；
Select associated keyword：It is associated with often through keyword between data and data, keyword pair can be passed through
Data merge, it is however generally that, for data mining, keyword is not of practical significance；
It can pass through key between the various data of cell for the communications field in order to obtain the partial data of same cell
Word cgi or cell Chinese name merge to be associated, and cgi is as major key, and there are correspondences with cell Chinese name；
Cgi full name are Universal Mobile Communication System land radio access web Cell Global Identifiers, in public land
One cell of overall identification in mobile network PLMN, each cell have only one cgi, cgi to be made of PLMN and eCI, example
Such as 460-00-725834-2；Wherein PLMN is made of mobile network code, MNC (MNC) and mobile subscriber belonging country code (MCC),
ECI is cell ID, is made of eNodeB Id and Localcell Id.
It is preferential to select cgi as major key, if cgi is lacked, utilize public land mobile network PLMN and cell mark
Know eCI to be synthesized, otherwise selects cell Chinese as keyword；
By by each resource subdata and the other cell basic dimensions data after scene partitioning, (including performance data, work are joined
Data, neighbor data, measurement data etc.) according to the keyword in resource subdata call merge functions using inner modes into
Row merges, and forms the cell basic dimensions subdata under the scape of branch.
In above-mentioned data merging process, data redundancy and the unmatched problem of pattern are can be potentially encountered, judges data
Redundancy is specially：If attribute can show that other multiple attributes for deduction are redundancy category by other multiple attribute deductions
Property, redundant attributes are deleted, attribute retains as a result by the attribute deduced out；It is unified to belong to when the unmatched situation of appearance pattern
The form of presentation of property；
Such as：Uplink packet loss Q00_ [0,1), uplink packet loss Q01_ [1,2) ... uplink packet loss Q18_ [18,19),
Uplink packet loss Q19_ [19,20), uplink packet loss Q20_ [20,30), uplink packet loss Q21_ [30,40) ... uplink packet loss
Q27_ [90,100] can extrapolate average uplink QCI packet loss for totally 28, and uplink packet loss is probability, and 99, which represent packet loss, is
99%, 100 to represent packet loss be 100%, and packet loss can not possibly reach 20% substantially, in order to data integrality so area herein
Between write [90,100].So uplink packet loss Q00_ [0,1), uplink packet loss Q01_ [1,2) etc. 28 attributes can work as
Redundant attributes deletion is done, attribute retains average uplink QCI packet loss as a result；
Pattern mismatches：The same alike result of the same cell of different data sources may have different statements, than if any number
According to the middle attribute that there may be cgi, is named with Cell Global Identifier in some data, actually refer to same attribute.In view of
This, by such attribute Uniform Name.
Data to cannot directly carry out data mining only retain numerical value or are substituted with numerical value：
Simultaneously comprising Chinese and English and number：Such as the data of the short-period length appearance of DRX are all the formats with SFN, N is
Number, this attribute indicates the short cycle duration that discontinuous reception (Discontinuous Reception, DRX) is taken, with son
Frame is unit, and SFN indicates that short cycle duration (time containing on-duration) is N number of subframe, so only retaining subsequent number N；
Similarly, the macrocyclic length of DRX, DRX duration timers, DRX inactivity timers, DRX are waited for and is retransmitted
The timer of data, lead code maximum transmission times, initially reception target power, acquiescence paging cycle, power climbing step-length etc.
Attribute also uses same processing mode；
Do not have being handled by the method containing only Chinese and English for conditions above；Such as device type (major class), district, is covered districts and cities
Lid type, working frequency range, equipment vendors etc. can use number to replace, if 0 represents ' macro base station ' in device type (major class), 1 generation
Table ' micro-base station '；Working frequency range can replace ' D frequency ranges ' with 0, and 1 represents ' E frequency ranges ', and 2:It represents ' F-band '.
4) Fig. 4 is the missing processing module flow chart of the present invention, and the miss rate of each attribute is checked in lacking processing module,
The miss rate of each KPI and dimension data is calculated, missing values, such as certain attribute are tentatively filled in the presence of scarce by calculation formula
It loses, and this attribute is acquired by other attributes, can be calculated by formula；
Such as in measurement data there is the case where missing in ue transmission powers surplus average value, what power headroom, i.e. UE allowed
Difference between the PUSCH transmission power that maximum transmission power and evaluation obtain.Its unit is dB, range be [-
23dB ,+40dB], it is divided into 0~63 this 64 grade points altogether, an actual dB value corresponds to a grade point.Ue transmission powers
Surplus average value can be counted to obtain one by the sum of power headroom of this 64 grades divided by ue transmission power surplus sampled points
Estimated value.Because power headroom be a range [a, b), take ((a+b))/2 to be used as estimated value, so can in order to ensure data
By property, only to there is the value of missing to fill up.
Then according to miss rate determine processing mode, miss rate more than 50% attribute, redundant attributes or with analysis theme
Unrelated attribute abandons, to reduce the scale of data processing；The missing values of other attributes are filled out using K-NN homing methods
It mends；
It is filled up using K-NN homing methods, k similar samples of the sample of missing attribute is found out, with being averaged for similar sample
Value replaces the missing values of sample attribute.
K similar samples for lacking the sample of attribute are k sample of the minimum at a distance from the sample of missing attribute, are used
Euclidean distance measures the distance between two samples：Two sample point X=(x1,x2,…xn) and Y=(y1,y2,…yn)
Between Euclidean distance be：
Wherein, sample refers to cell, the x in samplei、yi、x1、x2、xn、y1、y2And ynIt is right respectively to refer to two cell same alike results
The value answered, m are the distribution numbers for not including attribute missing values in two samples, and n is the attribute number in two samples；If xiDeng
In yi, then (xi-yi) it is set as 0, if xiNot equal to yi, then it is (xi-yi) it is set as 1.
5) initial data is backed up in data memory module and stores the data after cleaning.
Further include merging basic dimensions data with problem cells listings data, that is, detects cell in basic dimensions data
Cgi whether there is in the listings data of problem cells in the cgi lists of cell, and if it exists, then construct new attribute, exist simultaneously in
The cell attribute in basic dimensions data and problem cells listings data sets 1, remaining cell attribute is set to 0.
By taking LTE (Long Term Evolution, long term evolution) high load capacity cell as an example, detect in basic dimensions data
The cgi of cell whether there is in the cell cgi lists of LTE high load capacity cells；If it does, constructing new attribute：High load capacity is small
Area, the respective cell attribute set 1, indicate that the cell is high load capacity cell；Remaining cell attribute is set to 0, and indicates that the cell is not
High load capacity cell.
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, any
Those familiar with the art in the technical scope disclosed by the present invention, all answer by the change or replacement that can be readily occurred in
Cover within the scope of the present invention.
Claims (10)
1. a kind of KPI and various dimensions network data cleaning method based on scene, which is characterized in that include the following steps：
1) collected initial data is divided into cell basic dimensions data and problem cells inventory, wherein cell basic dimensions
Data are the cell data directly collected, i.e. the data without differentiating by KPI criteria thresholds；Problem cells inventory is certain
There is the cell data obviously deteriorated in one KPI, that is, passes through the data that KPI criteria thresholds differentiate；It will using data import modul
Data import in the data structure of the DataFrame in Pandas packets；
2) standardize to the data format of importing in data processing module, by the number of resources in cell basic dimensions data
It calls concat functions to merge according to by the resource data of scene partitioning, the same scene, resource subdata is obtained, to merging process
The conflict that middle data value generates is detected and handles；
3) data of multiple data sources or file are associated in data cleansing module, that is, select associated major key,
It will be merged according to major key with other cell basic dimensions data by the resource subdata after scene partitioning, and united in merging
One data pattern；The redundancy of data and the unmatched situation of pattern are judged during merging, delete redundant attributes, system
The form of presentation of one attributed scheme；Data to cannot directly carry out data mining only retain numerical value or are substituted with numerical value；
4) miss rate that each attribute is checked in lacking processing module, tentatively fills missing values, then according to miss rate decision at
Reason mode, to miss rate more than 50% attribute, redundant attributes or the attribute discard processing unrelated with analysis theme, to other categories
The missing values of property are filled up using K-NN homing methods；
5) initial data is backed up in data memory module and stores the data after cleaning.
2. a kind of KPI and various dimensions network data cleaning method, feature based on scene according to claim 1 exist
In cell basic dimensions data include in the step 1)：Resource data, performance data, work parameter evidence, neighbor data and measurement
Data；Problem cells inventory includes：Long term evolution LTE high load capacities cell, 4G zero deliverys cell, long term evolution LTE high flow capacities are asked
Serious cell, high definition voice volte high call drops cell, low measurement report MR coverage rates cell and high definition voice volte high is inscribed to lose
Packet rate cell.
3. a kind of KPI and various dimensions network data cleaning method, feature based on scene according to claim 1 exist
In the data structure for the DataFrame that data are stored in by the Pandas packets of calling Python in the step 1).
4. a kind of KPI and various dimensions network data cleaning method, feature based on scene according to claim 1 exist
In, in the step 2) to the data format of importing standardized specially to present in data Chinese and English and spcial character
Format is changed, small letter and half width form and removal space are uniformly changed to.
5. a kind of KPI and various dimensions network data cleaning method, feature based on scene according to claim 1 exist
In the conflict generated to data value during merging in the step 2) is detected and handles, the conflict packet that data value generates
It includes the same cell data from different data sources and different value or data type occurs, when there are different values, retain and lack
The few data of mistake rate；When data type difference, character type data is replaced with numeric type data.
6. a kind of KPI and various dimensions network data cleaning method, feature based on scene according to claim 1 exist
In the step 3) is middle to select cgi as major key, if cgi is lacked, utilizes public land mobile network PLMN and cell
Mark eCI is synthesized, and otherwise selects cell Chinese as keyword；
It will be by the resource subdata after scene partitioning and the keyword in other cell basic dimensions data foundation resource subdata
It calls merge functions to be merged using interior connection inner modes, forms the cell basic dimensions subdata under the scape of branch.
7. a kind of KPI and various dimensions network data cleaning method, feature based on scene according to claim 1 exist
In the step 3) is middle to judge that the redundancy of data is specially：If attribute can be obtained by other multiple attribute deductions, for pushing away
Other the multiple attributes drilled are redundant attributes；Redundant attributes are deleted, attribute retains as a result by the attribute deduced out.
8. a kind of KPI and various dimensions network data cleaning method, feature based on scene according to claim 1 exist
In the middle k similar samples for filling up the sample for specially finding out missing attribute using K-NN homing methods of the step 4) use phase
The attribute missing values of sample are replaced like the average value of sample.
9. a kind of KPI and various dimensions network data cleaning method, feature based on scene according to claim 7 exist
In k similar samples for lacking the sample of attribute are k sample of the minimum at a distance from the sample of missing attribute, several using Europe
The distance between two samples of Reed range measurement：Two sample point X=(x1, x2... xn) and Y=(y1, y2... yn) between
Euclidean distance is：
Wherein, sample refers to cell, the x in samplei、yi、x1、x2、xn、y1、y2And ynIt is corresponding to refer to two cell same alike results
Value, m is the distribution number for not including attribute missing values in two samples, and n is attribute number；If xiEqual to yi, then (xi-yi) set
It is set to 0, if xiNot equal to yi, then it is (xi-yi) it is set as 1.
10. a kind of KPI and various dimensions network data cleaning method, feature based on scene according to claim 1 exist
In, further include merging cell basic dimensions data with problem cells listings data, i.e., it is small in detection cell basic dimensions data
The cgi in area whether there is in the listings data of problem cells in the cgi lists of cell, and if it exists, then constructs new attribute, deposits simultaneously
It is that the attribute of the cell in basic dimensions data and problem cells listings data sets 1, remaining cell attribute is set to 0.
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| CN109782722A (en) * | 2019-01-31 | 2019-05-21 | 吕梁市军民融合协同创新研究院 | One kind is towards the quick processing system of industrial scene large-scale production line multidimensional data |
| CN109783481A (en) * | 2018-12-19 | 2019-05-21 | 新华三大数据技术有限公司 | Data processing method and device |
| CN110097920A (en) * | 2019-04-10 | 2019-08-06 | 大连理工大学 | A kind of metabolism group shortage of data value fill method based on neighbour's stability |
| CN111125194A (en) * | 2019-12-25 | 2020-05-08 | 中国建筑科学研究院有限公司 | Data construction method and device applied to city-level clean heating |
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