CN108197238A - A kind of multi-source heterogeneous data assimilation method of complexity - Google Patents
A kind of multi-source heterogeneous data assimilation method of complexity Download PDFInfo
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- CN108197238A CN108197238A CN201711477201.0A CN201711477201A CN108197238A CN 108197238 A CN108197238 A CN 108197238A CN 201711477201 A CN201711477201 A CN 201711477201A CN 108197238 A CN108197238 A CN 108197238A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/283—Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2468—Fuzzy queries
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/25—Integrating or interfacing systems involving database management systems
- G06F16/258—Data format conversion from or to a database
Abstract
The present invention provides a kind of multi-source heterogeneous data assimilation method of complexity, the multi-source heterogeneous data assimilation method of complexity includes the following steps：First, decision data information is collected, and analyzes the data type feature of the decision data information；2nd, the form of the descriptive language intuitionistic Fuzzy Sets in the data type feature is showed, and interval number is separately converted to the real number in the data type feature；3rd, the interval number is normalized.The beneficial effects of the invention are as follows：The shown multi-source heterogeneous data assimilation method of complexity is directed to complicated multi-source heterogeneous data, realize the digital expression to descriptive matter in which there, the method of processing considers the uncertainty and ambiguity of description content, and analyze the data structure feature of real number, Intuitionistic Fuzzy Numbers and interval number, convert them into same data structure, and standardization processing is carried out, unify module.
Description
Technical field
The present invention relates to a kind of multi-source heterogeneous data assimilation methods of complexity.
Background technology
When carrying out decision, since the difference of decision objective or policymaker often produce the representation difference of decision
Raw blended data information, there are incommensurabilities and paradox between each index when causing decision.Based on multi-source heterogeneous index
Decision be widely present in Comprehensive Assessment, scheme preferably, in the fields such as measures of effectiveness and warfare decision, therefore for multi-source
Isomery index needs to carry out assimilation processing, so as to provide form and the unified achievement data of linear module for decision model.
At present, the existing processing scheme for multi-source heterogeneous data, it is same that the overwhelming majority, which is directed to same index,
A kind of the problem of data structure, data structure is different between different indexs.The form of expression is as shown in table 1, the data structure phase of each column
Together, often capable data structure is different.The decision-making technique of isomeric data is solved to small part limited conditions, i.e., each index is come
Say that data format is consistent, the data format between different indexs is different.As shown in table 2, often the data structure of row and each column is equal
It is different.Text handled by both the above method portrays the Comment gathers of index feature for hardness, is acquired in non-practice decision process
Obtained descriptive text.
The common multi-source heterogeneous index form 1 of table 1
The common multi-source heterogeneous index form 2 of table 2
But in practice decision process, due to decision problem complexity in itself and the incompleteness of data information, make
Decision index system abundance, type are various, linear module also differs.Performance is as shown in table 3, and not only each raw column data form is not
Together, and word is the descriptive text to Index Content.The problem of bringing is as follows：
1st, the descriptive text information being collected into how is effectively utilized, by its digitized processing, and gives expression to decision pair
As the uncertainty of index；
2nd, how complicated multisource data structure is converted into single data structure, and unified metric unit.
The complicated multi-source heterogeneous index form of table 3
Invention content
It is an object of the invention in view of the drawbacks of the prior art or problem, provide a kind of multi-source heterogeneous data assimilation of complexity
Complicated multi-source heterogeneous data can be processed into a kind of data structure form, and be standardized so that respectively refer to by method
Mark unified metric unit.
Technical scheme is as follows：A kind of multi-source heterogeneous data assimilation method of complexity includes the following steps：First, it collects
Decision data information, and analyze the data type feature of the decision data information；2nd, by retouching in the data type feature
The form for stating language intuitionistic Fuzzy Sets shows, and is separately converted to section with the real number in the data type feature
Number；3rd, the interval number is normalized.
Preferably, in step 2, by the form of the descriptive language intuitionistic Fuzzy Sets in the data type feature
The step of showing be specially：It is a nonempty set that definition, which sets X, and it is an intuitionistic Fuzzy Sets to claim F：
F=<X, uF(x), vF(x)>| x ∈ X },
Wherein, 0≤uF(x)+vF(x)≤1,uF：X → [0,1], vF：X → [0,1]；uF(x) and vF(x) respectively
Referred to as element x is to the degree of membership of set F and non-affiliated degree, πF(x)=1-uF(x)-vF(x) it is hesitation degree of the element x to set F,
That is uncertainty degree.
Preferably, in step 2, by the form of the descriptive language intuitionistic Fuzzy Sets in the data type feature
The step of showing, and being separately converted to interval number with the real number in the data type feature be specially：
First, interval of definition number：
Interval of definition number is a, then
A=[aD,aG]
Wherein, aD、aGIt is real number, and aD≤aG, particularly, work as aD=aGWhen, then interval number a refers to real number；
2nd, real number is converted into interval number：
Real number r can be expressed as interval number r=[r, r]；
3rd, Intuitionistic Fuzzy Numbers are converted into interval number：
Note Intuitionistic Fuzzy Numbers are a=[aL, aM, aU], wherein, aL、aM、aUIt is real number, and aL＜ aM＜ aU, then its be subordinate to
Degree function is is expressed as：
The left and right degree of membership that Intuitionistic Fuzzy Numbers a can be obtained is respectively
With
Then acquiring left and right expectation is respectively：
With
Then can Intuitionistic Fuzzy Numbers be converted into interval number according to above formula to be expressed as
Preferably, if two interval number a=[aD,aG] and b=[bD,bG], the distance between interval of definition number a and b
For：
Wherein, P is >=2 positive integer；；
Correspondingly, two Intuitionistic Fuzzy Numbers a=[aL, aM, aU] and b=[bL, bM, bU] the distance between be expressed as：
Preferably, the attribute type of the decision index system of decision model is profit evaluation model and cost type, using range transformation method, according to
According to the normalization method of profit evaluation model index and cost type index, decision index system is normalized respectively；Then in step 3
The step of interval number is normalized be specially：It is Interval Fuzzy to remember decision index system valueSpecification postscript
For
The method of standardization is：
With
Wherein, I1And I2The subscript collection of profit evaluation model index and cost type index is represented respectively.
Technical solution provided by the invention has the advantages that：
In the multi-source heterogeneous data assimilation method of complexity, can not decision directly be carried out to complicated multi-source heterogeneous data and asked
Topic, it is proposed that a kind of homogeneity method can be processed into complicated multi-source heterogeneous data a kind of data structure form, and into rower
Quasi-ization processing so that each index unified metric unit；Descriptive text information can be particularly effectively utilized, is convenient for determining
Plan.
Moreover, in previous multi-source heterogeneous data assimilation processing, the word faced is Comment gathers, and the method for processing is not examined
The uncertainty of index object in itself is considered, the present invention is directed complicated multi-source heterogeneous data, realize to descriptive matter in which there
Digital expression, the method for processing considers the uncertainty of description content, and analyzes real number, Intuitionistic Fuzzy Numbers and section
Several data structure features converts them into same data structure, and carries out standardization processing, has unified module.
Description of the drawings
Fig. 1 is the flow diagram of the multi-source heterogeneous data assimilation method of complexity provided in an embodiment of the present invention.
Specific embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, with reference to the accompanying drawings and embodiments, it is right
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.
The description of specific distinct unless the context otherwise, the present invention in element and component, the shape that quantity both can be single
Formula exists, and form that can also be multiple exists, and the present invention is defined not to this.Although step in the present invention with label into
It has gone arrangement, but is not used to limit the precedence of step, unless expressly stated the order of step or holding for certain step
Based on row needs other steps, otherwise the relative rank of step is adjustable.It is appreciated that used herein
Term "and/or" is related to and covers one of associated Listed Items or one or more of any and all possible group
It closes.
As shown in Figure 1, a kind of multi-source heterogeneous data assimilation method of complexity, includes the following steps：
1st, decision data information is collected, and analyzes the data type feature of the decision data information.
Specifically, in step 1, first choice needs analysis decision object, and determines specific decision index system；Moreover, it is based on
Above-mentioned decision index system collects corresponding decision data information, analyzes the data type feature of the decision data information.
It should be noted that the data type feature includes real number and descriptive text.
For example, it in the last hundred years, is warmed up with temperature increasing and is more highlighted for the Global climate change of important feature, more and more profoundly
People’s lives and society, economic sustainable development are affected, adapts to and reply climate change has become countries in the world concern
Hot spot and decision-making focus.The arctic is the most significant area of Global climate change response, and particularly arctic ice and snow melting band comes
Arctic navigation channel open expection and will bring deep effect to world shipping pattern and big country's Strategic Games, while be also China
Participate in arctic affairs, share arctic equity and expand strategic space provide opportunity, challenge and risk.Due to geographic excellent
Gesture, eight state of the arctic expand game in Arctic, and it is each that content is related to national economy, military affairs, construction condition, the policy of itself etc.
A aspect, it is therefore desirable to which the assessment of objective quantification is carried out to the game risk of eight state of the arctic.Primary work is exactly will be above each
Index does homogeneous processing, and index system is as shown in table 4, and data source is as shown in table 5.
4. Arctic Strategic Games Risk Assessment Index System of table and data source list
5. achievement data source list of table
Wherein, index system is as shown in table 4, and data source is as shown in table 5, comprising database, authoritative report, article, special
The officials sources such as work, authoritative index, 11 item data, tables of data are shown in Table 6 altogether.
6 Arctic Strategic Games risk assessment index firsthand information of table
2nd, the form of the descriptive language intuitionistic Fuzzy Sets in the data type feature is showed, and with it is described
Real number in data type feature is separately converted to interval number.
Specifically, in step 2, it according to the characteristics of real number, descriptive language and interval number, determines to define its mathematical table
It reaches, method for transformation.
Wherein, the step of form of the descriptive language intuitionistic Fuzzy Sets in the data type feature being showed
Specially：It is a nonempty set that definition, which sets X, and it is an intuitionistic Fuzzy Sets to claim F：
F=<X, uF(x), vF(x)>| x ∈ X },
Wherein, 0≤uF(x)+vF(x)≤1,uF：X → [0,1], vF：X → [0,1]；uF(x) and vF(x) respectively
Referred to as element x is to the degree of membership of set F and non-affiliated degree, πF(x)=1-uF(x)-vF(x) it is hesitation degree of the element x to set F,
That is uncertainty degree.
For example, the initial data of table 6 is carried out quantification treatment, the results are shown in Table 7.As a result it is all after quantification treatment
Descriptive matter in which there is converted into the form of Intuitionistic Fuzzy Numbers.
7 Arctic Strategic Games risk assessment quantification of targets table of table
Moreover, in step 2, the form of the descriptive language intuitionistic Fuzzy Sets in the data type feature is represented
Out, the step of and being separately converted to interval number with the real number in the data type feature be specially：
First, interval of definition number：
Interval of definition number is a, then a=[aD,aG]
Wherein, aD、aGIt is real number, and aD≤aG, particularly, work as aD=aGWhen, then interval number a refers to real number；Section
Several algorithms is generally similar with the algorithm of set.If moreover, two interval number a=[aD,aG] and b=[bD,
bG], the distance between interval of definition number a and b are：
Wherein, P is >=2 positive integer；
2nd, real number is converted into interval number：
Real number r can be expressed as interval number r=[r, r]；
3rd, Intuitionistic Fuzzy Numbers are converted into interval number：
Note Intuitionistic Fuzzy Numbers are a=[aL, aM, aU] then its membership function be expressed as
The left and right degree of membership that Intuitionistic Fuzzy Numbers a can be obtained is respectively
With
Then acquiring left and right expectation is respectively
With
Then can Intuitionistic Fuzzy Numbers be converted into interval number according to above formula to be expressed as
Moreover, after being converted more than realizing, Intuitionistic Fuzzy Numbers can come the calculating of participative decision making model, root as interval number
It can be by two Intuitionistic Fuzzy Numbers a=[a according to the distance between interval number formulaL, aM, aU], b=[bL, bM, bU] the distance between table
It is shown as：
3rd, the interval number is normalized.
Specifically, when inputting decision model, not only decision index system structure is consistent, and linear module will also be unified to locate
Reason needs to carry out the dimension of decision index system unification, standardization.There are two types of the attribute types of decision index system, is respectively
Profit evaluation model and cost type, wherein profit evaluation model index are the bigger indexs of index value more Risks, and cost type index is that index value is got over
The smaller index of small risk.It is right respectively according to the normalization method of profit evaluation model index and cost type index using range transformation method
Decision index system is handled.
The step of then interval number is normalized in step 3 be specially：It is section mould to remember decision index system value
PasteSpecification postscript is
The method of standardization is：
With
Wherein [I1,I2] the subscript collection of profit evaluation model and cost type is represented respectively.
For example, as shown in table 8 to the decision index system data after the standardization of 7 final process of table.
8 Arctic Strategic Games risk assessment index standardization processing result of table
It is obvious to a person skilled in the art that the present invention is not limited to the details of above-mentioned exemplary embodiment, Er Qie
In the case of without departing substantially from spirit or essential attributes of the invention, the present invention can be realized in other specific forms.Therefore, no matter
From the point of view of which point, the present embodiments are to be considered as illustrative and not restrictive, and the scope of the present invention is by appended power
Profit requirement rather than above description limit, it is intended that all by what is fallen within the meaning and scope of the equivalent requirements of the claims
Variation is included within the present invention.Any reference numeral in claim should not be considered as to the involved claim of limitation.
In addition, it should be understood that although this specification is described in terms of embodiments, but not each embodiment is only wrapped
Containing an independent technical solution, this description of the specification is merely for the sake of clarity, and those skilled in the art should
It considers the specification as a whole, the technical solutions in each embodiment can also be properly combined, forms those skilled in the art
The other embodiment being appreciated that.
Claims (5)
1. a kind of multi-source heterogeneous data assimilation method of complexity, it is characterised in that：Include the following steps：
First, decision data information is collected, and analyzes the data type feature of the decision data information；
2nd, the form of the descriptive language intuitionistic Fuzzy Sets in the data type feature is showed, and with the number
Interval number is separately converted to according to the real number in type feature；
3rd, the interval number is normalized.
2. a kind of multi-source heterogeneous data assimilation method of complexity according to claim 1, which is characterized in that in step 2,
The step of form of descriptive language intuitionistic Fuzzy Sets in the data type feature is showed be specially：
It is a nonempty set that definition, which sets X, and it is an intuitionistic Fuzzy Sets to claim F：
F=<X, uF(x), vF(x)>| x ∈ X },
Wherein, 0≤uF(x)+vF(x)≤1,uF：X → [0,1], vF：X → [0,1]；uF(x) and vF(x) it is referred to as member
Plain x is to the degree of membership of set F and non-affiliated degree, πF(x)=1-uF(x)-vF(x) it is hesitation degree of the element x to set F, i.e., not really
Qualitative degree.
3. a kind of multi-source heterogeneous data assimilation method of complexity according to claim 2, which is characterized in that in step 2,
The form of descriptive language intuitionistic Fuzzy Sets in the data type feature is showed, and special with the data type
The step of real number in sign is separately converted to interval number be specially：
First, interval of definition number：
Interval of definition number is a, then
A=[aD,aG]
Wherein, aD、aGIt is real number, and aD≤aG, particularly, work as aD=aGWhen, then interval number a refers to real number；
2nd, real number is converted into interval number：
Real number r can be expressed as interval number r=[r, r]；
3rd, Intuitionistic Fuzzy Numbers are converted into interval number：
Note Intuitionistic Fuzzy Numbers are a=[aL, aM, aU] wherein, aL、aM、aUIt is real number, and aL＜ aM＜ aU, then its membership function
It is expressed as：
The left and right degree of membership that Intuitionistic Fuzzy Numbers a can be obtained is respectively
With
Then acquiring left and right expectation is respectively：
With
Then can Intuitionistic Fuzzy Numbers be converted into interval number according to above formula to be expressed as
4. a kind of multi-source heterogeneous data assimilation method of complexity according to claim 3, which is characterized in that if two sections
Number a=[aD,aG] and b=[bD,bG], the distance between interval of definition number a and b are：
Wherein, P is >=2 positive integer；
Correspondingly, two Intuitionistic Fuzzy Numbers a=[aL, aM, aU] b=[bL, bM, bU] the distance between be expressed as：
5. a kind of multi-source heterogeneous data assimilation method of complexity according to claim 1, which is characterized in that decision model is determined
The attribute type of plan index is profit evaluation model and cost type, using range transformation method, according to profit evaluation model index and cost type index
Normalization method is respectively normalized decision index system；Then the interval number is normalized in step 3
The step of be specially：It is Interval Fuzzy to remember decision index system valueSpecification postscript is
The method of standardization is：
With
Wherein, I1And I2The subscript collection of profit evaluation model index and cost type index is represented respectively.
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Application publication date: 20180622 |