CN110399382A - Civil aviaton's master data recognition methods and system based on cloud model and rough set - Google Patents
Civil aviaton's master data recognition methods and system based on cloud model and rough set Download PDFInfo
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Abstract
The invention discloses a kind of civil aviaton's master data recognition methods and system based on cloud model and rough set, belong to civil aviaton's technical field of information processing, it is characterized by comprising following steps: step 1: according to master data feature, most representative distinguishing indexes are selected, grade classification is carried out to master data；Step 2: three parameters of corresponding cloud model are calculated each index different brackets, corresponding cloud model figure is generated；Step 3: the data acquired according to civil aviaton calculate the degree of membership u (x) that each grade corresponds to each index；Step 4: the weight w of each index is determined according to rough set theoryi；Step 5: synthesizing deterministic degree is calculated；Step 6: the grade of master data is determined according to maximum degree of certainty principle.By using above-mentioned technical proposal, the present invention can be objective effectively to identify master data according to each airline's demand.
Description
Technical field
The invention belongs to civil aviaton's technical field of information processing more particularly to a kind of civil aviaton masters based on cloud model and rough set
Data identification method and system.
Background technique
30 years following, data application is increasingly shown especially, this will influence the construction and development of civil aviaton's informationization.With
The popularization of mobile Internet can push the application of some convenience to intelligent terminal, analyze passenger by big data technology
Behavior, understand their focus, with improve user aviation experience.
In terms of global civil aviation development, since market competition is constantly aggravated, Civil Aviation Industry is chronically at meagre profit operation level.
It is continuous worsening with global financial crisis in recent years, so that the survival pressure of airline increasingly increases.What is be increasingly difficult
In market environment, airline wishes the level by the way that passenger facilities are continuously improved, to promote passenger's loyalty, improves company
Profitability and industrial competition.
John's JFK International Airport of USA New York is one of big commercial airport in the U.S. three.The airport establishes one
It is perfect integrate blank pipe, airport, airline's information network information integration platform, issue all kinds of real-time letters to the public
Breath, facilitates the trip of passenger.However, demand of the passenger to information be not single nowadays as the continuous technology of information-based industry develops
Corresponding airline data are singly confined to, and more wish to obtain more perfect non-boat data, airline travel data etc..2015
In the 7th digital civil aviaton trend development summit held May 28, the well-known enterprise such as Chinese South Airways, A-1. Net, Langchao Group
The expert of industry assembles, and with regard to how to use big data, internet, cloud computing generation information technology, promotes the pipe of aircraft industry
The problems such as boat trip personalized with private environment, improvement customer service quality, offer services is managed to be inquired into.
In terms of public platform research and development of taking the initiative in offering a hand, foreign vendor focuses on collecting using the prior art and from other industry
And the data analysis come, the customer experience of Lai Gaishan airline passenger, by crawl passenger entire travelling on the way mostly in
Hold data and analysis assessment, more personalized service is provided for airline client.Middle Air China letter is as domestic unique whole world
Distribution service provider possesses civil aviaton's operation data resource abundant, and Various types of data is handled by different information systems, but
Information cannot effectively be shared, and information asymmetry, process is obstructed, form a large amount of information island.It can be by enterprises
Data resource is effectively integrated with external data resource, is promoted for enterprises service level and industry data standardization is provided with
Power support, becomes the severe challenge put in face of enterprise.Establish that public service platform seeks to be unified for enterprise and Civil Aviation Industry mentions
For complete, consistent data and versatile and flexible, abundant effective service, good basis is provided for data sharing, for service mark
The more perfect application management platform of standardization, specialized offer.
With flourishing for Civil Aviation Industry, the explosion that civil aviaton's data show exponential form increases.It is much in these data
Basis, shared data, i.e., certain data can be reused in multiple departments, system or business.If there is each department
Coding mode is different or some data is situations such as some department has updated, other departments do not update also, it will cause
Information asymmetry, to influence final decision.Therefore how to be identified from the data of these magnanimity this with high value
, the basic, data shared by multiple departments, i.e. master data, become urgent and important.
Summary of the invention
In view of the drawbacks of the prior art, the present invention provides a kind of master data identification side, civil aviaton based on cloud model and rough set
Method and system, the present invention are the randomness and ambiguity eliminated in master data distinguishing indexes, the crucial two o'clock of identification process: 1) are used
In the quantitative description of the qualitative index of master data identification.Positive cloud model method is a kind of based on probability theory and fuzzy number scientific principle
To the transformation model of quantificational expression, it can convert concept connotation (abstract concept in subjective world) to the qualitativing concept of opinion
Concept extension (sample set in objective world).Therefore the present invention is based on this models, realize the quantification of master data distinguishing indexes.
2) the determination problem of index weights.Traditional weight determination almost requires expert estimation, there is subjectivity to a certain degree.Slightly
Rough collection theory can calculate the weight of each index, available relatively objective knot according to the sample data of objective reality
Fruit.
It is of the present invention the specific technical proposal is:
The first invention purpose of this patent is to provide a kind of civil aviaton's master data recognition methods based on cloud model and rough set,
Include the following steps:
Step 1: according to master data feature, selecting most representative distinguishing indexes, carries out grade classification to master data；
Step 2: three parameters of corresponding cloud model are calculated each index different brackets, corresponding cloud model figure is generated；
Step 3: the data acquired according to civil aviaton calculate the degree of membership u (x) that each grade corresponds to each index；
Step 4: the weight w of each index is determined according to rough set theoryi；
Step 5: synthesizing deterministic degree is calculated with following formula；
Step 6: the grade of master data is determined according to maximum degree of certainty principle.
Further, the calculation method of three parameters is as follows:
In above formula, ZmaxAnd ZminThe corresponding maximum value of respectively each grade and minimum value, r are one according to the fuzzy of variable
Spend the fixed value being adjusted.
Further, the corresponding subordinating degree function of data x is as follows in the step 3:
Wherein
Further, the step 4 specifically:
Define 1, in decision table S=(U, A, V, f), wherein U is nonempty finite set, referred to as domain, is denoted as U={ x1,
x2,…,xn}；A=C ∪ D, C are conditional attribute collection, and D is decision kind set, C ∩ D=φ；F:U × A → V is an information letter
Number, V=∪ Va, a ∈ A, VaIndicate the codomain of attribute a；
Define 2, in decision table S=(U, A, V, f), U/D={ D1,D2,…,DnIt is that decision attribute D draws domain U
Point, U/C={ C1,C2,…,CmIt is division of the conditional attribute C to domain U,Positive area of the referred to as C about D
Domain；
3 are defined in decision table S=(U, A, V, f), A=C ∪ D, Criterion Attribute C, U/C={ C1,C2,…,Cm, decision
Attribute D, U/D={ D1,D2,…,Dn, then conditional information entropy of the decision attribute relative to Criterion Attribute are as follows:
4 are defined in decision table S=(U, A, V, f),The then different degree of conditional attribute c
Are as follows:
Wherein a (x)=U/ { a }；
Define 5, decision table S=U, A, V, f) in,The then weight of conditional attribute c are as follows:
Second goal of the invention of this patent is to provide a kind of civil aviaton's master data identifying system based on cloud model and rough set,
Include:
Grade classification module: according to master data feature, selecting most representative distinguishing indexes, carries out grade to master data
It divides；
Cloud model figure generation module: calculating each index different brackets three parameters of corresponding cloud model, generates corresponding
Cloud model figure；
Degree of membership computing module: the data acquired according to civil aviaton calculate the degree of membership u (x) that each grade corresponds to each index；
Weight determination module: the weight w of each index is determined according to rough set theoryi；
Synthesizing deterministic degree computing module: synthesizing deterministic degree is calculated with following formula；
Level determination module: the grade of master data is determined according to maximum degree of certainty principle.
The third goal of the invention of this patent, which is to provide, a kind of realizes above-mentioned civil aviaton's master data based on cloud model and rough set
The computer program of recognition methods.
4th goal of the invention of this patent, which is to provide, a kind of realizes above-mentioned civil aviaton's master data based on cloud model and rough set
The information data processing terminal of recognition methods.
5th goal of the invention of this patent is to provide a kind of computer readable storage medium, including instruction, when it is being calculated
When being run on machine, so that computer executes above-mentioned civil aviaton's master data recognition methods based on cloud model and rough set.
Advantages of the present invention and good effect are as follows:
By using above-mentioned technical proposal, the present invention is had the following technical effect that:
The present invention determines that shortage is objective with uncertain feature and weight for the ambiguity of civil aviaton's master data distinguishing indexes
The problem of property, propose a kind of civil aviaton's master data recognition methods based on cloud model and rough set.Firstly, according to civil aviaton's master data
The characteristics of, it chooses most representative 7 distinguishing indexes and is divided into 5 grades；Secondly, raw using Normal Cloud Generator
It is under the jurisdiction of the synthesis cloud model of each master data grade at each distinguishing indexes, calculates each entity and be under the jurisdiction of being subordinate to for each master data grade
Degree；Finally, introducing the weight that rough set theory calculates each distinguishing indexes, each entity is calculated in conjunction with degree of membership and is under the jurisdiction of each master data
Degree of certainty, the master data grade using maximum degree of certainty as the entity.
Master data divided rank is kept recognition result more accurate by the present invention.And this part is determined in weight and is had
Method is very different, and existing method manual intervention is excessive, that is, is mostly expert estimation, is caused result subjectivity stronger.This
Invention introduces rough set method, calculates weight according to initial data, as a result more objective.The present invention provides for master data identification
One new departure.
Detailed description of the invention
Fig. 1 is the flow chart of the preferred embodiment of the present invention；
Fig. 2 a is the standard cloud atlas of the first index in preferred embodiment of the present invention figure；
Fig. 2 b is the standard cloud atlas of the second index in preferred embodiment of the present invention figure；
Fig. 2 c is the standard cloud atlas of third index in preferred embodiment of the present invention figure；
Fig. 2 d is the standard cloud atlas of four-index in preferred embodiment of the present invention figure；
Fig. 2 e is the five fingers target standard cloud atlas in preferred embodiment of the present invention figure；
Fig. 2 f is the standard cloud atlas of the 6th index in preferred embodiment of the present invention figure；
Fig. 2 g is the standard cloud atlas of the 7th index in preferred embodiment of the present invention figure；
Specific embodiment
In order to further understand the content, features and effects of the present invention, the following examples are hereby given, and cooperate attached drawing
Detailed description are as follows.
Structure of the invention is explained in detail with reference to the accompanying drawing.
Referring to Fig. 1,
A kind of civil aviaton's master data recognition methods based on cloud model and rough set, for the mould of civil aviaton's master data distinguishing indexes
Paste property determines the problem of lacking objectivity with uncertain feature and weight, proposes a kind of people based on cloud model and rough set
Boat master data recognition methods.Method of the present invention is, firstly, choosing most representative the characteristics of according to civil aviaton's master data
7 distinguishing indexes and be divided into 5 grades；It is under the jurisdiction of respectively secondly, generating each distinguishing indexes using Normal Cloud Generator
The synthesis cloud model of master data grade calculates the degree of membership that each entity is under the jurisdiction of each master data grade；Finally, introducing rough set reason
By the weight for calculating each distinguishing indexes, the degree of certainty that each entity is under the jurisdiction of each master data is calculated in conjunction with degree of membership, is determined with maximum
Spend the master data grade as the entity.The present invention can be objective effectively to identify master data according to each companies needs.Due to
Master data have randomness and ambiguity etc. uncertainty feature, if therefore graduation identification is carried out to master data, obtained knot
Fruit can it is more accurate with it is reasonable.So the present invention is by means of cloud models theory, the characteristics of according to master data, selecting most can be qualitative general
The several indexs for including civil aviaton's master data carry out hierarchical identification to master data by these indexs.In identification process, weight is really
Fixed particularly important, the present invention determines the weight of each index using rough set theory, and processing in this way keeps result more objective.Specifically
Process is as follows:
Step 1: according to master data feature, selecting most representative distinguishing indexes, carries out grade classification to master data；
Step 2: three parameters of corresponding cloud model are calculated each index different brackets, corresponding cloud model figure is generated；
Step 3: the data acquired according to civil aviaton calculate the degree of membership u (x) that each grade corresponds to each index；
Step 4: the weight w of each index is determined according to rough set theoryi；
Step 5: synthesizing deterministic degree is calculated with following formula
Step 6: the grade of master data is determined according to maximum degree of certainty principle.
As shown in Figure 1.
The selection of distribution function
There are many forms for the concrete methods of realizing of cloud model, and different clouds can be formed according to different probability distribution, than
Normal Cloud such as based on normal distribution, the Gauss cloud based on Gaussian Profile, the linear cloud based on linear distribution.Wherein, normal state
It is widely distributed to be present in social activities, naturally activity and production technology.The most of chance event encountered in real life is all
Normal distribution or approximate presentation normal distribution is presented.By central-limit theorem it is found that normal cloud model has universality, so
Normal Cloud is selected herein.
The calculation method of parameter in cloud model
The calculation method of three parameters is as follows in cloud model
In above formula, ZmaxAnd ZminThe corresponding maximum value of respectively each grade and minimum value.R is a fixed value, Ke Yigen
It is adjusted according to the fuzziness of variable, is fixed as 0.01 in our current research.
The corresponding subordinating degree function of data x is as shown in formula 3 in identification process step 3, because by being subordinate to letter to all kinds of
Number compares, it is found that other membership function majorities are consistent with normal state membership function.They are largely that normal state membership function is safe
The sum of the low order item for strangling expansion, is the approximate expression of normal state membership function.So normal state membership function has universality, then originally
Selected works select normal state membership function to determine the degree of membership of sample.
Wherein
Weight Determination:
Rough set theory is a kind of data digging method that Polish mathematician Pawlak is proposed, this method is excavated imperfect
Data, find hiding information, it in terms of determining index weights have unique advantage, the shadow of human factor can be eliminated
It rings and its maximum advantage is to overcome the subjectivity of membership function in fuzzy set theory, Attribute Significance, conditional information entropy etc.
It is to be calculated from initial data, people cannot participate in into, so determining that the weight of index is than more objective with it.
1 is defined in decision table S=(U, A, V, f), wherein U is nonempty finite set, referred to as domain, is denoted as U={ x1,
x2,…,xn}；A=C ∪ D, C are conditional attribute collection, and D is decision kind set, C ∩ D=φ；F:U × A → V is an information letter
Number, V=∪ Va, a ∈ A, VaIndicate the codomain of attribute a.
2 are defined in decision table S=(U, A, V, f), U/D={ D1,D2,…,DnIt is that decision attribute D draws domain U
Point, U/C={ C1,C2,…,CmIt is division of the conditional attribute C to domain U,Positive area of the referred to as C about D
Domain.
3 are defined in decision table S=(U, A, V, f), A=C ∪ D, Criterion Attribute C, U/C={ C1,C2,…,Cm, decision
Attribute D, U/D={ D1,D2,…,Dn, then conditional information entropy of the decision attribute relative to Criterion Attribute are as follows:
4 are defined in decision table S=(U, A, V, f),The then different degree of conditional attribute c
Are as follows:
Wherein a (x)=U/ { a }.
Define 5 decision table S=U, A, V, f) in,The then weight of conditional attribute c are as follows:
Experimental situation and data
Experimental situation of the invention is: Intel (R) Core (TM) i5-4590CPU, 8GB memory, operating system are
Windows7 Ultimate is tested under Matlab environment.
Experimental data of the present invention all comes from airline.
Data prediction
Master data is divided into 5 grades, each grade represents a possibility that becoming master data, and concrete meaning is the (pole I
It is high), II (height), III (in), IV (weak), V (extremely weak).It is codetermined by 7 indexs, as shown in table 2.
2 master data classification standard of table
In table 2, data of the Civil aviation information system over 20 months are counted, the meaning of each index value is as follows: according to civil aviaton
Service priority is set 10 grades by the priority rule of information system；The life cycle of statistical data in systems is with the moon
Unit, such as the life cycle of country code in systems is 20 months, then this identification of the life cycle of country code refers to
It is designated as the first estate；The mark action of statistical data, in units of percentage；The accessed system number of the data is inquired, most
Mostly 17 subsystems；The change frequency of statistical data, as unit of the moon；Data in systems accessed in inquiry one day
Number；Judge the basic of data.According to the above analysis, master data classification standard as shown in Table 2 is generated.
Experimentation
Since Normal Cloud has universality, therefore the Normal Cloud Generator of normal distyribution function is used herein.By qualitative description
Distinguishing indexes be converted into the Quantitatively mapping with 3 digital character representations.Mapping process is calculated by formula 2, and it is each to obtain master data
The cloud model parameter (Ex, En, He) of a index, is respectively as follows:
Service priority: I (9.5,0.42,0.01), II (8,0.85,0.01), III (5.5,1.27,0.01), IV (3,
0.85,0.01),Ⅴ(1.5,0.42,0.01)；
Life cycle: I (17.5,2.12,0.01), II (13.5,1.27,0.01), III (9,2.55,0.01), IV
(4.5,1.3,0.01),Ⅴ(1.5,1.27,0.01)；
Uniqueness: I (95,4.25,0.01), II (77.5,10.62,0.01), III (50,12.7,0.01), IV (22.5,
10.6,0.01),Ⅴ(5,4.25,0.01)；
Cross-system uses: I (12,4.25,0.01), II (6,0.85,0.01), III (4,0.85,0.01), IV (2.5,
0.42,0.01),Ⅴ(1,0.85,0.01)；
Change frequency: I (1,0.85,0.01), II (3,0.85,0.01), III (5.5,1.27,0.01), IV (8,0.85,
0.01)Ⅴ(10.5,1.27,0.01)；
Using~: I (150,16.96,0.01), II (115,12.74,0.01), III (75,21.23,0.01), IV (35,
12.7,0.01), V (10,8.49,0.01)；
It is basic: I (8.5,0.42,0.01), II (7.5,1.27,0.01), III (4.5,1.27,0.01), IV (2,
0.85,0.01),Ⅴ(0.5,0.42,0.01)。
Then the standard cloud for generating each index, as shown in Fig. 2 a to Fig. 2 g.
Fig. 2 a to Fig. 2 g is the standard cloud of 7 distinguishing indexes, has 5 grades in each index.Abscissa is each index
Value, ordinate are degree of membership.By taking life cycle as an example, when life cycle takes 16, then I, II, III, IV, V grade
Degree of membership is respectively 0.6,0.3,0.05,0,0.
3 service priority degree of membership of table
Selection for subordinating degree function, linear membership function, Cauchy's membership function, normal state membership function etc., but by
Document 22 is it is found that normal state membership function is with uniformity in many fields and other membership functions, and is widely used in each
Field.Therefore normal state membership function is selected to be substituted into sampled data in Fig. 2 a to Fig. 2 g according to formula 3 by x condition generator herein
Each distinguishing indexes standard cloud, obtains the degree of membership of each distinguishing indexes of each data, this degree of membership has randomness, but is
One random number with steady tendency, therefore carried out 100 times to it herein and calculated and its average is asked to this 100 results,
It is as shown in table 3 to obtain service priority degree of membership therein.In the table, each sample being subordinate under each grade is illustrated
Degree.
The weight that each index is calculated according to formula 4,5 and 6, obtaining each index weights is service priority (0.0084), raw
It orders period (0.0084), uniqueness (0.0105), cross-system uses (0.0105), changes frequency (0.0105), frequency of use
(0.0105), basic (0.0105).
The synthesis degree of certainty that every data is calculated according to formula 1, using maximum degree of certainty as the identification etc. of final master data
Grade.The results are shown in Table 4.
4 recognition result of table
Analysis of experimental results
The recognition result of table 4 is had master data standard with civil aviaton field to compare, country code, city codes, machine
Field code and state or province code this 4 have determined that recognition result of the present invention is I grade centainly for master data, therefore result is closed
Reason is effective.Sample one is identical as expected results to sample four, it was demonstrated that master data recognition methods of the invention is feasible.
A kind of civil aviaton's master data identifying system based on cloud model and rough set, comprising:
Include:
Grade classification module: according to master data feature, selecting most representative distinguishing indexes, carries out grade to master data
It divides；
Cloud model figure generation module: calculating each index different brackets three parameters of corresponding cloud model, generates corresponding
Cloud model figure；
Degree of membership computing module: the data acquired according to civil aviaton calculate the degree of membership u (x) that each grade corresponds to each index；
Weight determination module: the weight w of each index is determined according to rough set theoryi；
Synthesizing deterministic degree computing module: synthesizing deterministic degree is calculated with following formula；
Level determination module: the grade of master data is determined according to maximum degree of certainty principle.
A kind of calculating for realizing civil aviaton's master data recognition methods based on cloud model and rough set in above preferred embodiment
Machine program.
4th goal of the invention of this patent is to provide in a kind of realization above preferred embodiment based on cloud model and rough set
Civil aviaton's master data recognition methods information data processing terminal.
5th goal of the invention of this patent is to provide a kind of computer readable storage medium, including instruction, when it is being calculated
When being run on machine, so that computer executes identifying in above preferred embodiment based on civil aviaton's master data of cloud model and rough set
Method.
The present invention is proposed a kind of based on cloud model and thick by the way that production text snippet task is learnt and studied
Civil aviaton's master data recognition methods of rough collection, for context semantic information in current text summarization generation model using insufficient,
Traditional attention mechanism semantic understanding does not enrich；Generate the problems such as abstract accuracy is not high, amalgamation of global semantic information and part
Semantic information is to improve model language understandability；It is in combination with position insertion, word embedding grammar that part of speech, word frequency rate-is inverse literary
In shelves index, the key fusion term vector character representation of word, understanding of the model to word is improved；Secondly, for word2vec's
Skip-gram model optimizes word embeded matrix by the pairs of inner product loss function with tenth of the twelve Earthly Branches invariance, is current corpus
Best word insertion dimension is selected, the optimum performance that term vector indicates is played；Finally, being obtained by Rouge appraisement system, the present invention
It is proposed that a kind of civil aviaton's master data recognition methods based on cloud model and rough set improves the accuracy and precision of text snippet.
In the above-described embodiments, can come wholly or partly by software, hardware, firmware or any combination thereof real
It is existing.When using entirely or partly realizing in the form of a computer program product, the computer program product include one or
Multiple computer instructions.When loading on computers or executing the computer program instructions, entirely or partly generate according to
Process described in the embodiment of the present invention or function.The computer can be general purpose computer, special purpose computer, computer network
Network or other programmable devices.The computer instruction may be stored in a computer readable storage medium, or from one
Computer readable storage medium is transmitted to another computer readable storage medium, for example, the computer instruction can be from one
A web-site, computer, server or data center pass through wired (such as coaxial cable, optical fiber, Digital Subscriber Line (DSL)
Or wireless (such as infrared, wireless, microwave etc.) mode is carried out to another web-site, computer, server or data center
Transmission).The computer-readable storage medium can be any usable medium or include one that computer can access
The data storage devices such as a or multiple usable mediums integrated server, data center.The usable medium can be magnetic Jie
Matter, (for example, floppy disk, hard disk, tape), optical medium (for example, DVD) or semiconductor medium (such as solid state hard disk Solid
State Disk (SSD)) etc..
The above is only the preferred embodiments of the present invention, and is not intended to limit the present invention in any form,
Any simple modification made to the above embodiment according to the technical essence of the invention, equivalent variations and modification, belong to
In the range of technical solution of the present invention.
Claims (8)
1. a kind of civil aviaton's master data recognition methods based on cloud model and rough set, characterized by the following steps:
Step 1: according to master data feature, selecting most representative distinguishing indexes, carries out grade classification to master data；
Step 2: three parameters of corresponding cloud model are calculated each index different brackets, corresponding cloud model figure is generated；
Step 3: the data acquired according to civil aviaton calculate the degree of membership u (x) that each grade corresponds to each index；
Step 4: the weight w of each index is determined according to rough set theoryi；
Step 5: synthesizing deterministic degree is calculated with following formula；
Step 6: the grade of master data is determined according to maximum degree of certainty principle.
2. civil aviaton's master data recognition methods according to claim 1 based on cloud model and rough set, it is characterised in that: institute
The calculation method for stating three parameters is as follows:
In above formula, ZmaxAnd ZminThe corresponding maximum value of respectively each grade and minimum value, r be a fuzziness according to variable into
The fixed value of row adjustment.
3. civil aviaton's master data recognition methods according to claim 1 based on cloud model and rough set, it is characterised in that: In
In the step 3, the corresponding subordinating degree function of data x is as follows:
Wherein
4. civil aviaton's master data recognition methods according to claim 1 based on cloud model and rough set, it is characterised in that: institute
State step 4 specifically:
Define 1, in decision table S=(U, A, V, f), wherein U is nonempty finite set, referred to as domain, is denoted as U={ x1,
x2,…,xn}；A=C ∪ D, C are conditional attribute collection, and D is decision kind set, C ∩ D=φ；F:U × A → V is an information letter
Number, V=∪ Va, a ∈ A, VaIndicate the codomain of attribute a；
Define 2, in decision table S=(U, A, V, f), U/D={ D1,D2,…,DnIt is division of the decision attribute D to domain U, U/
C={ C1,C2,…,CmIt is division of the conditional attribute C to domain U,Positive region of the referred to as C about D；
3 are defined in decision table S=(U, A, V, f), A=C ∪ D, Criterion Attribute C, U/C={ C1,C2,…,Cm, decision attribute
D, U/D={ D1,D2,…,Dn, then conditional information entropy of the decision attribute relative to Criterion Attribute are as follows:
4 are defined in decision table S=(U, A, V, f),The then different degree of conditional attribute c are as follows:
Wherein a (x)=U/ { a }；
Define 5, decision table S=U, A, V, f) in,The then weight of conditional attribute c are as follows:
5. a kind of civil aviaton's master data identifying system based on cloud model and rough set, it is characterised in that: include:
Grade classification module: according to master data feature, selecting most representative distinguishing indexes, carries out grade to master data and draws
Point；
Cloud model figure generation module: three parameters of corresponding cloud model are calculated each index different brackets, corresponding cloud mould is generated
Type figure；
Degree of membership computing module: the data acquired according to civil aviaton calculate the degree of membership u (x) that each grade corresponds to each index；
Weight determination module: the weight w of each index is determined according to rough set theoryi；
Synthesizing deterministic degree computing module: synthesizing deterministic degree is calculated with following formula；
Level determination module: the grade of master data is determined according to maximum degree of certainty principle.
6. a kind of computer journey for realizing civil aviaton's master data recognition methods based on cloud model and rough set described in claim 1
Sequence.
7. at a kind of information data for realizing civil aviaton's master data recognition methods based on cloud model and rough set described in claim 1
Manage terminal.
8. a kind of computer readable storage medium, including instruction, when run on a computer, so that computer is executed as weighed
Benefit require 1 described in civil aviaton's master data recognition methods based on cloud model and rough set.
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