CN110378481A - Decision-making treatment method, apparatus, computer and storage medium based on rough set - Google Patents
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Abstract
This application involves a kind of decision-making treatment method, apparatus, computer equipment and storage medium based on rough set.Method includes: to obtain the data of the preset term of power distribution network, and the object set composition domain for meeting preset condition is chosen from the data of preset term；Decision kind set is determined according to the collection of preset term, and parsing influences the factor of decision attribute, obtains conditional attribute collection；Knowledge representation model is constructed on the basis of decision kind set and conditional attribute collection；The calculating of correlation and independence is carried out to conditional attribute collection, core is sought in the collection merging after obtaining Reduction of Knowledge；According to the result extraction decision rule for seeking core after Reduction of Knowledge；The performance for carrying out precision and roughness to decision rule parses.It proves that this method tallies with the actual situation with performance evaluation by the parsing to Decision Rules Extraction, is aestivated contacting between project and final solution by describing power distribution network to Decision Rules Extraction, be conducive to mitigate operation burden to relevant staff.
Description
Technical field
This application involves application data deleting technique fields, more particularly to a kind of decision-making treatment based on rough set
Method, apparatus, computer equipment and storage medium.
Background technique
With the continuous development of national economy, power demand is also being continuously increased.Numerous feeder lines and distribution transforming meeting in power distribution network
Heavy-overload phenomenon occurs in electricity peak period, happens occasionally so as to cause failure power blackout situation, customer complaint is also following.
Therefore, power supply enterprise can all carry out summer peak meeting project every year, to alleviate shortage of electric power, improve power supply reliability and client is full
Meaning degree.But project decision of aestivating at present work more relies on expertise, has certain subjectivity.Item number to be formulated
The huge of amount leads to operation management larger workload.
And in recent years, the fast development of power industry industrial information makes electric power data that the same of explosive growth be presented
When, significant challenge and opportunity are brought to the management and decision of power supply enterprise.It can be effectively using suitable data mining technology
It was found that existing incidence relation between data, is of great significance to related service planning and decision.
Summary of the invention
Based on this, it is necessary in view of the above technical problems, provide a kind of decision-making treatment method, apparatus based on rough set,
Computer equipment and storage medium.
A kind of decision-making treatment method based on rough set, which comprises
The data for obtaining the preset term of power distribution network choose pair for meeting preset condition from the data of the preset term
As set constitutes domain；
Decision kind set is determined according to the collection of the preset term, and parsing influences the factor of decision attribute, obtains condition category
Property collection；
Knowledge representation model is constructed on the basis of the decision kind set and the conditional attribute collection；
The calculating of correlation and independence is carried out to the conditional attribute collection, core is sought in the collection merging after obtaining Reduction of Knowledge；
According to the result extraction decision rule for seeking core after Reduction of Knowledge；
The performance for carrying out precision and roughness to the decision rule parses.
The step of performance to decision rule progress precision and roughness parses in one of the embodiments,
Later further include:
Export the result that the performance parsing of precision and roughness is carried out to the decision rule.
The building on the basis of decision kind set and the conditional attribute collection is former in one of the embodiments,
The step of beginning knowledge representation model includes:
According to the decision kind set, the conditional attribute collection and the domain, the conditional attribute collection is divided into
The set of multiple conditional attributes impacted by many pairs of decision rules, wherein the characteristic value of the conditional attribute corresponds to it
The description of dimension is evaluated, and the characteristic value of the decision kind set corresponds to the description evaluation of its dimension, according to the conditional attribute
Characteristic value and the characteristic value of the decision kind set construct the knowledge representation model.
The calculating that correlation and independence are carried out to the conditional attribute collection in one of the embodiments, obtains
Collection after Reduction of Knowledge merges the step of seeking core
Correlation and independence are carried out to the conditional attribute collection on the basis of knowledge representation model of building
Calculate, to delete the conditional attribute of redundancy, set after obtaining Reduction of Knowledge, to the set of each reduction seek common ground operation with
Acquire core.
The step of performance to decision rule progress precision and roughness parses in one of the embodiments,
Include:
The performance parsing of precision is carried out to the decision rule, specific formula for calculation is as follows:
Wherein, U is a domain, and X is a nonvoid subset in domain U, and R is an equivalence relation on U, with season
Card function is the function for seeking set member's number；
The performance parsing of roughness is carried out to the decision rule, specific formula for calculation is as follows:
ρR(X)=1- αR(X)。
A kind of decision-making treatment device based on rough set, described device include:
Domain obtains module, and the data of the preset term for obtaining power distribution network are selected from the data of the preset term
The object set for meeting preset condition is taken to constitute domain；
Conditional attribute collection obtains module, and for determining decision kind set according to the collection of the preset term, parsing influences to determine
The factor of plan attribute obtains conditional attribute collection；
Knowledge representation model constructs module, for constructing on the basis of the decision kind set is with the conditional attribute collection
Knowledge representation model；
Reduction finding core module obtains knowledge about for carrying out the calculating of correlation and independence to the conditional attribute collection
Core is sought in collection merging after letter；
Decision Rules Extraction module, for according to the result extraction decision rule for seeking core after Reduction of Knowledge；
Performance parsing module, the performance for carrying out precision and roughness to the decision rule parse.
It in one of the embodiments, further include output module, for exporting to decision rule progress precision and slightly
The result of the performance parsing of rugosity.
In one of the embodiments, knowledge representation model building module be also used to according to the decision kind set,
The conditional attribute collection is divided into and multiple is impacted by many pairs of decision rules by the conditional attribute collection and the domain
Conditional attribute set, wherein the characteristic value of the conditional attribute corresponds to the description evaluation of its dimension, and the decision attribute
The characteristic value of collection corresponds to the description evaluation of its dimension, according to the feature of the characteristic value of the conditional attribute and the decision kind set
Value constructs the knowledge representation model.
A kind of computer equipment can be run on a memory and on a processor including memory, processor and storage
The step of computer program, the processor realizes method described in any of the above-described embodiment when executing the computer program.
A kind of computer readable storage medium, is stored thereon with computer program, and the computer program is held by processor
The step of any of the above-described method as described in the examples is realized when row.
Above-mentioned decision-making treatment method, apparatus, computer equipment and storage medium based on rough set, first acquisition power distribution network
Preset term data simultaneously therefrom choose the object set composition domain for meeting preset condition；Then it is determined according to the determination of preset term collection
Plan property set simultaneously parses the factor for influencing its decision attribute, structure condition property set；Then in decision kind set and conditional attribute
Original knowledge expression system is constructed on the basis of collection；And then the calculating of correlation and independence is carried out to conditional attribute collection, it acquires
Core is sought in collection merging after Reduction of Knowledge；Attribute is described to extract decision rule according to determining the case where core；Finally to being extracted
Decision rule carry out the performance evaluation of precision and roughness and analyze.It aestivates project with the method for rough set theory to power distribution network
The extraction for carrying out decision rule, proves that this method tallies with the actual situation with performance evaluation by the parsing to Decision Rules Extraction,
It is aestivated contacting between project and final solution, is conducive to related work by describing power distribution network to Decision Rules Extraction
Personnel mitigate operation burden.
Detailed description of the invention
Fig. 1 is the flow diagram of the decision-making treatment method based on rough set in one embodiment；
Fig. 2 is the structural block diagram of the decision-making treatment device based on rough set in one embodiment；
Fig. 3 is the internal structure chart of computer equipment in one embodiment.
Specific embodiment
It is with reference to the accompanying drawings and embodiments, right in order to which the objects, technical solutions and advantages of the application are more clearly understood
The application is further elaborated.It should be appreciated that specific embodiment described herein is only used to explain the application, not
For limiting the application.
In one embodiment, as shown in Figure 1, providing a kind of decision-making treatment method based on rough set, including it is following
Step:
Step 110, the data for obtaining the preset term of power distribution network, selection meets default from the data of the preset term
The object set of condition constitutes domain.
In the present embodiment, which is project of aestivating, which is preset condition, the default item
Part from the data of preset term for screening required data, which is the object for meeting preset condition, to constitute
Domain, in the present embodiment, the domain be the preset term of power distribution network data in meet the data of preset condition.One embodiment
It is that the preset condition is that monitoring personnel is interested.One embodiment is, step 110 is to obtain practical power distribution network to aestivate project
Data simultaneously therefrom choose interested object set composition domain.
Specifically, in this step, first from giving in project table of aestivating the set interested to choosing to constitute domain, relatively
It is not that set all in table has consideration to be worth, such as serial number, project name for the project table of aestivating given
Deng.The set for constituting domain is only to have the set of reference value in table, such as there is problem, task category and solution etc..
Therefore, it is necessary to the data required according to preset condition screening, and then constitute domain.
Step 120, decision kind set is determined according to the collection of the preset term, parsing influences the factor of decision attribute, obtains
Obtain conditional attribute collection.
Specifically, in this step, after decision kind set has been determined, decision kind set is parsed, obtaining influences it
The factor of decision attribute, the set of the factor are conditional attribute collection.
Item Sets of aestivating in one embodiment, according to step 120 determine decision kind set simultaneously its decision category of analyzing influence
The factor of property, structure condition property set.
In the present embodiment, the building of conditional attribute collection is to choose to solve to final according to the actual situation from project table of aestivating
The sets of factors that measure impacts.Including task category, there are problem, treatment measures, project investment, it is estimated solve the time,
Customer complaint situation, load factor, responsible department and out-of-limit number etc..Decision attribute set is with the equivalence of solution in domain
Relationship composition.
Step 130, knowledge representation model is constructed on the basis of the decision kind set and the conditional attribute collection.
Specifically, knowledge representation model is referred to as knowledge-representation system.One embodiment is, in known decision category
Property collection and conditional attribute collection on the basis of construct original knowledge expression system.
In one embodiment, described construct on the basis of the decision kind set is with the conditional attribute collection original is known
The step of knowing expression model includes: according to the decision kind set, the conditional attribute collection and the domain, by the condition
Property set is divided into the set of multiple conditional attributes impacted by many pairs of decision rules, wherein the conditional attribute
Characteristic value corresponds to the description evaluation of its dimension, and the characteristic value of the decision kind set corresponds to the description evaluation of its dimension, according to
The characteristic value of the conditional attribute and the characteristic value of the decision kind set construct the knowledge representation model.
Specifically, by the conditional attribute collection R and decision kind set D and domain U that are determined in above-mentioned steps, by conditional attribute
Collection is divided into the set of the conditional attribute impacted by many pairs of decision rules, as R={ r1, r2, r3 ..., rn }, and
The characteristic value of conditional attribute corresponds to the description evaluation of its dimension.And it is identical, the corresponding characteristic value of decision kind set D is also right for its
Answer the description of dimension to evaluate, for example, solution have capital construction project verification, emergency measure, operation measures, marketing measures and other.It is then right
Answering decision attribute set characteristic value that can be set as D=, { 1 (capital construction project verification), 2 (emergency measures), 3 (operation measures), 4 (marketing is arranged
Apply), 5 (other) }.Knowledge-representation system can be constructed on this basis:
Step 140, the calculating of correlation and independence is carried out to the conditional attribute collection, the set after obtaining Reduction of Knowledge
And seek core.
Specifically, in this step, the calculating of correlation and independence is carried out to conditional attribute collection, after acquiring Reduction of Knowledge
Collection, which merges, seeks core.
Correlation and solely is carried out to conditional attribute set on the basis of what is constituted in above-mentioned steps makes knowledge-representation system
The calculating of vertical property, i.e. Reduction of Knowledge, to leave out the conditional attribute of redundancy.Finally obtain independent about degeneracy to all reduction into
Row seeks common ground operation in the hope of core.
Step 150, according to the result extraction decision rule for seeking core after Reduction of Knowledge.
In this step, attribute is described to extract decision rule according to determining the case where core.
Step 160, the performance for carrying out precision and roughness to the decision rule parses.
Performance parsing is carried out to extracted decision in this step, is based primarily upon two aspects, be precision respectively with it is coarse
Degree.It is parsed by the performance for carrying out precision and roughness to decision rule, the evaluation knot of corresponding decision rule performance can be calculated
Fruit.
In above-described embodiment, with rough set theory method to power distribution network aestivate project carry out decision rule extraction,
Prove that this method tallies with the actual situation with performance evaluation by the parsing to Decision Rules Extraction, by retouching to Decision Rules Extraction
It states power distribution network to aestivate contacting between project and final solution, is conducive to mitigate operation burden to relevant staff.
In one embodiment, after the step of performance to decision rule progress precision and roughness parses
Further include: export the result that the performance parsing of precision and roughness is carried out to the decision rule.
One embodiment is to carry out the knot of the performance parsing of precision and roughness to the decision rule by network output
Fruit.One embodiment is to be exported the result that the performance parsing of precision and roughness is carried out to the decision rule by network
To terminal.One embodiment is parsed by performance of the display screen output to decision rule progress precision and roughness
As a result.
In one embodiment, the calculating that correlation and independence are carried out to the conditional attribute collection, obtains knowledge
The step of core is sought in collection merging after reduction includes: on the basis of the knowledge representation model of building to the conditional attribute collection
The calculating of correlation and independence is carried out, to delete the conditional attribute of redundancy, the set after obtaining Reduction of Knowledge, to each reduction
Set seek common ground operation in the hope of core.Specifically comprise the following steps:
Step 141, classified using conditional attribute r1, r2, r3, r4 as knowledge to domain U according to knowledge-representation system,
Set is obtained to be respectively as follows:
U/r1={ { x3, x7 }, { x1, x2, x8, x10 }, { x5, x6 }, { x4, x9 } }；
U/r2={ { x4, x5, x6, x8, x10 }, { x1, x2, x3, x7, x9 } }；
U/r3={ { x3, x5, x6, x7, x8, x9, x10 }, { x1, x2, x4 } }；
U/r4={ { x1, x2x8, x9 }, { x3, x4, x5, x6, x7, x10 } }；
And its equivalence relation U/IND (R) is asked by intersection operation:
U/IND (R)={ { x3, x7 }, { x1, x2 }, { x5, x6 }, { x8 }, { x9 }, { x10 } }.
Step 142, the calculating that four conditional attribute set in S4.1 are carried out with correlation respectively, by taking r1 as an example, meter
Calculate formula are as follows:
U/IND (R- { r1 })=U/r2 ∩ U/r3 ∩ U/r4=U/IND (R)
If calculated result is U/IND (R- { r1 })=U/IND (R), it is believed that conditional attribute r1 carrys out decision attribute
Say it is unnecessary, as redundancy.Then the correlation of r2, r3 and r4 are calculated, and whether is unnecessary judge to it.
Step 143, required attribute is chosen on the basis of S4.2, it is assumed for example that r4 is indispensable attributes, other are not
Indispensable attributes are then constituted set with this indispensable attributes and other dispensable attributes, make its classification capacity and equivalence relation U/IND
(R) identical.And the calculating of independence is carried out to it, and if meeting the requirement of independence each other, then as one group of reduction.Choosing
For taking set { r1, r2, r4 }, independence calculation formula is as follows:
There is U/IND ({ r1, r2, r4 })=U/IND (R) first；
Next have U/IND ({ r1, r2, r4 }-{ r4 })=U/IND ({ r1, r2 }) ≠
U/IND ({ r1, r2, r4 }), U/IND ({ r1, r2, r4 }-{ r1 })=U/IND ({ r2, r4 }) ≠
U/IND ({ r1, r2, r4 }), U/IND ({ r1, r2, r4 }-{ r2 })=U/IND ({ r1, r4 }) ≠
U/IND({r1,r2,r4})；
Set { r1, r2, r4 } known to then is one group of independent reduction.After similarly carrying out independence calculating to its all reduction
Obtain all independent reduction.
Step 144, intersection operation is carried out to all independent reduction on the basis of S4.3, the result acquired is core.
In one embodiment, described to include: according to the step of asking the result of core to extract decision rule after Reduction of Knowledge
Step 151, if description attribute P={ there are problems, complain situation, task category }, there is equivalence relation U/IND (P)
={ X1, X2, X3, X4, X5, X6, X7 }, wherein X1={ x1, x2 }, X2={ x3, x7 }, X3={ x5, x6 }, X4={ x8 }, X5
={ x9 }, X6={ x4 }, X7={ x10 }.
Step 152, decision attribute D has equivalence relation U/D={ Y1, Y2, Y3, Y4 }, wherein Y1={ x1, x2, x3, x7 },
Y2={ x4 }, Y3={ x5, x6, x10 }, Y4={ x8, x9 }.Then carried out according to description attribute set P and decision attribute set D
The calculating of approximation, lower aprons, positive negative domain and Boundary Region index of correlation has following calculated result:
1)
2)
3)
4)
Step 153, decision rule can be extracted according to description attribute on the basis of required positive domain.Such as Y2,
Its extracting rule is IF (there are problem=instantaneous heavy duties) and (complaint situation=few) and (task category=distribution transforming), THEN
(solution=emergency measure).
In one embodiment, the step of performance to decision rule progress precision and roughness parses is wrapped
Include: the performance for carrying out precision to the decision rule parses, and specific formula for calculation is as follows:
Wherein, U is a domain, and X is a nonvoid subset in domain U, and R is an equivalence relation on U, with season
Card function is the function for seeking set member's number；
The performance parsing of roughness is carried out to the decision rule, specific formula for calculation is as follows:
ρR(X)=1- αR(X)。
It is noted that the decision-making treatment method based on rough set is also referred to as based on rough set theory in each embodiment
Distribution aestivate project decision rule research method.
Here is a specific embodiment:
A kind of distribution based on rough set theory is aestivated project decision rule research method, comprising the following steps:
S1, it obtains practical power distribution network and aestivates and project data and therefrom choose interested object set composition domain；
In the step S1, first from giving in project table of aestivating the set interested to choosing to constitute domain, relatively
It is not that set all in table has consideration value, such as serial number, project name for the project table given.Structure
Set at domain is only to have the set of reference value in table, such as there is problem, task category and solution etc..
S2, the factor that its decision attribute of decision kind set and analyzing influence is determined according to Item Sets of aestivating, structure condition category
Property collection；
In the step S2, the building of conditional attribute set is to be chosen according to the actual situation from project table of aestivating to most
The sets of factors that whole solution impacts.Including task category, there are problem, treatment measures, project investment, estimated solutions
Time, customer complaint situation, load factor, responsible department and out-of-limit number etc..Decision attribute set is with solution in domain
Equivalence relation composition.
S3, original knowledge expression system is constructed on the basis of known decision kind set and conditional attribute collection；
In the step S3, the building process of knowledge-representation system is as follows:
It, will by identified domain U in conditional attribute collection R and decision kind set D and step S1 determining in step S2
Conditional attribute collection is divided into the set of the conditional attribute impacted by many pairs of decision rules, as R=r1, r2, r3 ...,
Rn }, and the characteristic value of conditional attribute corresponds to the description evaluation of its dimension.And identical, the corresponding characteristic value of decision kind set D
For its correspond to dimension description evaluation, such as solution have capital construction project verification, emergency measure, operation measures, marketing measures and its
He.D={ 1 (capital construction project verification), 2 (emergency measures), 3 (operation measures), 4 (battalion can be set as by then corresponding to decision attribute set characteristic value
Pin measure), 5 (other) }.Knowledge representation model can be constructed on this basis:
Core is sought in S4, the calculating that correlation and independence are carried out to conditional attribute collection, the collection merging after acquiring Reduction of Knowledge；
Making of constituting in step s 4 carries out correlation and independence to conditional attribute set on the basis of knowledge-representation system
The calculating of property, i.e. Reduction of Knowledge, to leave out the conditional attribute of redundancy.Independent about degeneracy is finally obtained to carry out all reduction
Operation seek common ground in the hope of core.Specific step is as follows:
S4.1 classifies to domain U using conditional attribute r1, r2, r3, r4 as knowledge according to knowledge-representation system, is collected
Conjunction is respectively as follows:
U/r1={ { x3, x7 }, { x1, x2, x8, x10 }, { x5, x6 }, { x4, x9 } }；
U/r2={ { x4, x5, x6, x8, x10 }, { x1, x2, x3, x7, x9 } }；
U/r3={ { x3, x5, x6, x7, x8, x9, x10 }, { x1, x2, x4 } }；
U/r4={ { x1, x2x8, x9 }, { x3, x4, x5, x6, x7, x10 } }；
And its equivalence relation U/IND (R) is asked by intersection operation:
U/IND (R)={ { x3, x7 }, { x1, x2 }, { x5, x6 }, { x8 }, { x9 }, { x10 } }.
S4.2 carries out the calculating of correlation to four conditional attribute set in S4.1 respectively, by taking r1 as an example, calculates public
Formula are as follows:
U/IND (R- { r1 })=U/r2 ∩ U/r3 ∩ U/r4=U/IND (R)
If calculated result is U/IND (R- { r1 })=U/IND (R), it is believed that conditional attribute r1 carrys out decision attribute
Say it is unnecessary, as redundancy.Then the correlation of r2, r3 and r4 are calculated, and whether is unnecessary judge to it.
S4.3 chooses required attribute on the basis of S4.2, it is assumed for example that r4 is indispensable attributes, other are unnecessary
Attribute is then constituted set with this indispensable attributes and other dispensable attributes, makes its classification capacity and equivalence relation U/IND (R) phase
Together.And the calculating of independence is carried out to it, and if meeting the requirement of independence each other, then as one group of reduction.Choose collection
For closing { r1, r2, r4 }, independence calculation formula is as follows:
There is U/IND ({ r1, r2, r4 })=U/IND (R) first；
Next has U/IND ({ r1, r2, r4 }-{ r4 })=U/IND ({ r1, r2 }) ≠ U/IND ({ r1, r2, r4 }), U/
IND ({ r1, r2, r4 }-{ r1 })=U/IND ({ r2, r4 }) ≠ U/IND ({ r1, r2, r4 }), U/IND ({ r1, r2, r4 }-
{ r2 })=U/IND ({ r1, r4 }) ≠ U/IND ({ r1, r2, r4 })；
Set { r1, r2, r4 } known to then is one group of independent reduction.After similarly carrying out independence calculating to its all reduction
Obtain all independent reduction.
S4.4 carries out intersection operation to all independent reduction on the basis of S4.3, and the result acquired is core.
S5, attribute is described to extract decision rule according to determining the case where core；
Core in step S5 carries out the extraction of decision rule as description attribute, and detailed process is as follows:
S5.1 sets description attribute P={ there are problem, complaining situation, task category }, have equivalence relation U/IND (P)=
{ X1, X2, X3, X4, X5, X6, X7 }, wherein X1={ x1, x2 }, X2={ x3, x7 }, X3={ x5, x6 }, X4={ x8 }, X5=
{ x9 }, X6={ x4 }, X7={ x10 }.
S5.2 decision attribute D has equivalence relation U/D={ Y1, Y2, Y3, Y4 }, wherein Y1={ x1, x2, x3, x7 }, Y2=
{ x4 }, Y3={ x5, x6, x10 }, Y4={ x8, x9 }.It is then carried out according to description attribute set P and decision attribute set D upper close
Seemingly, the calculating of lower aprons, positive negative domain and Boundary Region index of correlation, there is following calculated result:
1)
2)
3)
4)
S5.3 can extract decision rule according to description attribute on the basis of required positive domain.Such as Y2, institute
Extracting rule is IF (there are problem=instantaneous heavy duties) and (complaint situation=few) and (task category=distribution transforming), THEN (solution
Certainly measure=emergency measure).
S6, the performance evaluation analysis that precision and roughness are carried out to extracted decision rule.
Performance analysis and evaluation is carried out to decision extracted in step S6.Two aspects are based primarily upon, are precision respectively
With roughness, specific formula for calculation is as follows:
The above are accuracy computation formula.Wherein U is a domain, and X is a nonvoid subset in domain U, and R is on U
One equivalence relation is the function for seeking set member's number, also referred to as radix or gesture with season card function.
Roughness calculation formula is as follows:
ρR(X)=1- αR(X)
Thus the evaluation result of corresponding decision rule performance can be calculated.
Experimental verification: a kind of distribution based on rough set theory is aestivated the experiment of project decision rule research method
1, practical power distribution network is obtained first aestivate project data and therefrom choose interested object set composition domain；So
The factor of its decision attribute of decision kind set and analyzing influence, structure condition property set are determined according to Item Sets of aestivating afterwards；Then
Original knowledge expression system is constructed on the basis of known decision kind set and conditional attribute collection, such as table 1:
1 knowledge-representation system of table
2, the calculating for and then to conditional attribute collection carrying out correlation and independence, the collection merging after acquiring Reduction of Knowledge are asked
Core:
(1) classified using conditional attribute r1, r2, r3, r4 as knowledge to domain U according to knowledge-representation system, collected
Conjunction is respectively as follows:
U/r1={ { x3, x7 }, { x1, x2, x8, x10 }, { x5, x6 }, { x4, x9 } }；
U/r2={ { x4, x5, x6, x8, x10 }, { x1, x2, x3, x7, x9 } }；
U/r3={ { x3, x5, x6, x7, x8, x9, x10 }, { x1, x2, x4 } }；
U/r4={ { x1, x2x8, x9 }, { x3, x4, x5, x6, x7, x10 } }；
And its equivalence relation U/IND (R) is asked by intersection operation:
U/IND (R)={ { x3, x7 }, { x1, x2 }, { x5, x6 }, { x8 }, { x9 }, { x10 } }.
(2) calculating for carrying out correlation respectively to aforementioned four conditional attribute set, by taking r1 as an example, its calculation formula is:
U/IND (R- { r1 })=U/r2 ∩ U/r3 ∩ U/r4=U/IND (R)
If calculated result is U/IND (R- { r1 })=U/IND (R), it is believed that conditional attribute r1 carrys out decision attribute
Say it is unnecessary, as redundancy.Then the correlation of r2, r3 and r4 are calculated, and whether is unnecessary judge to it.
(3) required attribute is chosen, it is assumed for example that r4 is indispensable attributes, other are dispensable attributes, then with this necessity
Attribute and other dispensable attributes, which are constituted, to be gathered, and keeps its classification capacity identical as equivalence relation U/IND (R).And it is carried out solely
The calculating of vertical property, if meeting the requirement of independence each other, then as one group of reduction.Choosing set { r1, r2, r4 } is
Example, independence calculation formula are as follows:
There is U/IND ({ r1, r2, r4 })=U/IND (R) first；
Next has:
U/IND ({ r1, r2, r4 }-{ r4 })=U/IND ({ r1, r2 }) ≠
U/IND ({ r1, r2, r4 }), U/IND ({ r1, r2, r4 }-{ r1 })=U/IND ({ r2, r4 }) ≠
U/IND ({ r1, r2, r4 }), U/IND ({ r1, r2, r4 }-{ r2 })=U/IND ({ r1, r4 }) ≠
U/IND({r1,r2,r4})；
Set { r1, r2, r4 } known to then is one group of independent reduction.After similarly carrying out independence calculating to its all reduction
Obtain all independent reduction.
(4) intersection operation is carried out to all independent reduction, the result acquired is core.
3, according to description attribute determining the case where core to extract decision rule:
Detailed process is as follows:
If description attribute P={ there are problem, complaining situation, task category }, have equivalence relation U/IND (P)=X1,
X2, X3, X4, X5, X6, X7 }, wherein X1={ x1, x2 }, X2={ x3, x7 }, X3={ x5, x6 }, X4={ x8 }, X5=
{ x9 }, X6={ x4 }, X7={ x10 }.
Decision attribute D has equivalence relation U/D={ Y1, Y2, Y3, Y4 }, wherein Y1={ x1, x2, x3, x7 }, Y2=
{ x4 }, Y3={ x5, x6, x10 }, Y4={ x8, x9 }.It is then carried out according to description attribute set P and decision attribute set D upper close
Seemingly, the calculating of lower aprons, positive negative domain and Boundary Region index of correlation, there is following calculated result:
1)
2)
3)
4)
Decision rule can be extracted according to description attribute on the basis of required positive domain.Such as Y2, extracted
Rule are as follows:
IF (there are problem=instantaneous heavy duties) and (complaint situation=few) and (task category=distribution transforming), THEN (are solved
Measure=emergency measure).
4, the performance evaluation for finally carrying out precision and roughness to extracted decision rule is analyzed:
The decision of extraction carries out performance analysis and evaluation.Two aspects are based primarily upon, are precision and roughness respectively, specifically
Calculation formula is as follows:
The above are accuracy computation formula.Wherein U is a domain, and X is a nonvoid subset in domain U, and R is on U
One equivalence relation is the function for seeking set member's number, also referred to as radix or gesture with season card function.
Roughness calculation formula is as follows:
ρR(X)=1- αR(X)
Thus the parsing result that corresponding decision rule performance can be calculated, then exports the result.
It should be understood that although each step in the flow chart of Fig. 1 is successively shown according to the instruction of arrow, this
A little steps are not that the inevitable sequence according to arrow instruction successively executes.Unless expressly state otherwise herein, these steps
It executes there is no the limitation of stringent sequence, these steps can execute in other order.Moreover, at least part in Fig. 1
Step may include that perhaps these sub-steps of multiple stages or stage are executed in synchronization to multiple sub-steps
It completes, but can execute at different times, the execution sequence in these sub-steps or stage, which is also not necessarily, successively to be carried out,
But it can be executed in turn or alternately at least part of the sub-step or stage of other steps or other steps.
In one embodiment, as shown in Fig. 2, providing a kind of decision-making treatment device based on rough set, comprising:
Domain obtains module 210, the data of the preset term for obtaining power distribution network, from the data of the preset term
Choose the object set composition domain for meeting preset condition；
Conditional attribute collection obtains module 220, and for determining decision kind set according to the collection of the preset term, parsing influences
The factor of decision attribute obtains conditional attribute collection；
Knowledge representation model constructs module 230, on the basis of the decision kind set and the conditional attribute collection
Construct knowledge representation model；
Reduction finding core module 240 obtains knowledge for carrying out the calculating of correlation and independence to the conditional attribute collection
Core is sought in collection merging after reduction；
Decision Rules Extraction module 250, for according to the result extraction decision rule for seeking core after Reduction of Knowledge；
Performance parsing module 260, the performance for carrying out precision and roughness to the decision rule parse.
In one embodiment, the decision-making treatment device based on rough set further includes output module, for exporting to described
Decision rule carries out the result of the performance parsing of precision and roughness.
In one embodiment, knowledge representation model building module is also used to according to the decision kind set, described
The conditional attribute collection is divided into multiple items impacted by many pairs of decision rules by conditional attribute collection and the domain
The set of part attribute, wherein the description that the characteristic value of the conditional attribute corresponds to its dimension is evaluated, and the decision kind set
Characteristic value corresponds to the description evaluation of its dimension, according to the characteristic value structure of the characteristic value of the conditional attribute and the decision kind set
Build the knowledge representation model.
In one embodiment, the reduction finding core module is also used on the basis of the knowledge representation model of building
The calculating of correlation and independence is carried out to the conditional attribute collection, to delete the conditional attribute of redundancy, after obtaining Reduction of Knowledge
Set, seek common ground operation to the set of each reduction in the hope of core.
In one embodiment, the performance parsing module is also used to carry out the decision rule performance solution of precision
Analysis, specific formula for calculation are as follows:
Wherein, U is a domain, and X is a nonvoid subset in domain U, and R is an equivalence relation on U, with season
Card function is the function for seeking set member's number；
The performance parsing of roughness is carried out to the decision rule, specific formula for calculation is as follows:
ρR(X)=1- αR(X)。
Specific restriction about the decision-making treatment device based on rough set may refer to above for based on rough set
The restriction of decision-making treatment method, details are not described herein.Modules in the above-mentioned decision-making treatment device based on rough set can be complete
Portion or part are realized by software, hardware and combinations thereof.Above-mentioned each module can be embedded in the form of hardware or independently of calculating
In processor in machine equipment, it can also be stored in a software form in the memory in computer equipment, in order to processor
It calls and executes the corresponding operation of the above modules.
In one embodiment, a kind of computer equipment is provided, which can be server, internal junction
Composition can be as shown in Figure 3.The computer equipment include by system bus connect processor, memory, network interface and
Database.Wherein, the processor of the computer equipment is for providing calculating and control ability.The memory packet of the computer equipment
Include non-volatile memory medium, built-in storage.The non-volatile memory medium is stored with operating system, computer program and data
Library.The built-in storage provides environment for the operation of operating system and computer program in non-volatile memory medium.The calculating
The database of machine equipment is used to store the data such as the identification information of terminal and the user account of application program.The computer equipment
Network interface be used to communicate with external terminal by network connection.To realize one when the computer program is executed by processor
Decision-making treatment method of the kind based on rough set.
It will be understood by those skilled in the art that structure shown in Fig. 3, only part relevant to application scheme is tied
The block diagram of structure does not constitute the restriction for the computer equipment being applied thereon to application scheme, specific computer equipment
It may include perhaps combining certain components or with different component layouts than more or fewer components as shown in the figure.
In one embodiment, a kind of computer equipment is provided, including memory, processor and storage are on a memory
And the computer program that can be run on a processor, processor perform the steps of when executing computer program
The data for obtaining the preset term of power distribution network choose pair for meeting preset condition from the data of the preset term
As set constitutes domain；
Decision kind set is determined according to the collection of the preset term, and parsing influences the factor of decision attribute, obtains condition category
Property collection；
Knowledge representation model is constructed on the basis of the decision kind set and the conditional attribute collection；
The calculating of correlation and independence is carried out to the conditional attribute collection, core is sought in the collection merging after obtaining Reduction of Knowledge；
According to the result extraction decision rule for seeking core after Reduction of Knowledge；
The performance for carrying out precision and roughness to the decision rule parses.
In one embodiment, it is also performed the steps of when processor executes computer program
Export the result that the performance parsing of precision and roughness is carried out to the decision rule.
In one embodiment, it is also performed the steps of when processor executes computer program
According to the decision kind set, the conditional attribute collection and the domain, the conditional attribute collection is divided into
The set of multiple conditional attributes impacted by many pairs of decision rules, wherein the characteristic value of the conditional attribute corresponds to it
The description of dimension is evaluated, and the characteristic value of the decision kind set corresponds to the description evaluation of its dimension, according to the conditional attribute
Characteristic value and the characteristic value of the decision kind set construct the knowledge representation model.
In one embodiment, it is also performed the steps of when processor executes computer program
Correlation and independence are carried out to the conditional attribute collection on the basis of knowledge representation model of building
Calculate, to delete the conditional attribute of redundancy, set after obtaining Reduction of Knowledge, to the set of each reduction seek common ground operation with
Acquire core.
In one embodiment, it is also performed the steps of when processor executes computer program
The performance parsing of precision is carried out to the decision rule, specific formula for calculation is as follows:
Wherein, U is a domain, and X is a nonvoid subset in domain U, and R is an equivalence relation on U, with season
Card function is the function for seeking set member's number；
The performance parsing of roughness is carried out to the decision rule, specific formula for calculation is as follows:
ρR(X)=1- αR(X)。
In one embodiment, a kind of computer readable storage medium is provided, computer program is stored thereon with, is calculated
Machine program performs the steps of when being executed by processor
The data for obtaining the preset term of power distribution network choose pair for meeting preset condition from the data of the preset term
As set constitutes domain；
Decision kind set is determined according to the collection of the preset term, and parsing influences the factor of decision attribute, obtains condition category
Property collection；
Knowledge representation model is constructed on the basis of the decision kind set and the conditional attribute collection；
The calculating of correlation and independence is carried out to the conditional attribute collection, core is sought in the collection merging after obtaining Reduction of Knowledge；
According to the result extraction decision rule for seeking core after Reduction of Knowledge；
The performance for carrying out precision and roughness to the decision rule parses.
In one embodiment, it is also performed the steps of when computer program is executed by processor
Export the result that the performance parsing of precision and roughness is carried out to the decision rule.
In one embodiment, it is also performed the steps of when computer program is executed by processor
According to the decision kind set, the conditional attribute collection and the domain, the conditional attribute collection is divided into
The set of multiple conditional attributes impacted by many pairs of decision rules, wherein the characteristic value of the conditional attribute corresponds to it
The description of dimension is evaluated, and the characteristic value of the decision kind set corresponds to the description evaluation of its dimension, according to the conditional attribute
Characteristic value and the characteristic value of the decision kind set construct the knowledge representation model.
In one embodiment, it is also performed the steps of when computer program is executed by processor
Correlation and independence are carried out to the conditional attribute collection on the basis of knowledge representation model of building
Calculate, to delete the conditional attribute of redundancy, set after obtaining Reduction of Knowledge, to the set of each reduction seek common ground operation with
Acquire core.
In one embodiment, it is also performed the steps of when computer program is executed by processor
The performance parsing of precision is carried out to the decision rule, specific formula for calculation is as follows:
Wherein, U is a domain, and X is a nonvoid subset in domain U, and R is an equivalence relation on U, with season
Card function is the function for seeking set member's number；
The performance parsing of roughness is carried out to the decision rule, specific formula for calculation is as follows:
ρR(X)=1- αR(X)。
Those of ordinary skill in the art will appreciate that realizing all or part of the process in above-described embodiment method, being can be with
Relevant hardware is instructed to complete by computer program, the computer program can be stored in a non-volatile computer
In read/write memory medium, the computer program is when being executed, it may include such as the process of the embodiment of above-mentioned each method.Wherein,
To any reference of memory, storage, database or other media used in each embodiment provided herein,
Including non-volatile and/or volatile memory.Nonvolatile memory may include read-only memory (ROM), programming ROM
(PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM) or flash memory.Volatile memory may include
Random access memory (RAM) or external cache.By way of illustration and not limitation, RAM is available in many forms,
Such as static state RAM (SRAM), dynamic ram (DRAM), synchronous dram (SDRAM), double data rate sdram (DDRSDRAM), enhancing
Type SDRAM (ESDRAM), synchronization link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM
(RDRAM), direct memory bus dynamic ram (DRDRAM) and memory bus dynamic ram (RDRAM) etc..
Each technical characteristic of above embodiments can be combined arbitrarily, for simplicity of description, not to above-described embodiment
In each technical characteristic it is all possible combination be all described, as long as however, the combination of these technical characteristics be not present lance
Shield all should be considered as described in this specification.
The several embodiments of the application above described embodiment only expresses, the description thereof is more specific and detailed, but simultaneously
It cannot therefore be construed as limiting the scope of the patent.It should be pointed out that coming for those of ordinary skill in the art
It says, without departing from the concept of this application, various modifications and improvements can be made, these belong to the protection of the application
Range.Therefore, the scope of protection shall be subject to the appended claims for the application patent.
Claims (10)
1. a kind of decision-making treatment method based on rough set, which comprises
The data for obtaining the preset term of power distribution network choose the object set for meeting preset condition from the data of the preset term
It closes and constitutes domain；
Decision kind set is determined according to the collection of the preset term, and parsing influences the factor of decision attribute, obtains conditional attribute collection；
Knowledge representation model is constructed on the basis of the decision kind set and the conditional attribute collection；
The calculating of correlation and independence is carried out to the conditional attribute collection, core is sought in the collection merging after obtaining Reduction of Knowledge；
According to the result extraction decision rule for seeking core after Reduction of Knowledge；
The performance for carrying out precision and roughness to the decision rule parses.
2. the method according to claim 1, wherein described carry out precision and roughness to the decision rule
After the step of performance parses further include:
Export the result that the performance parsing of precision and roughness is carried out to the decision rule.
3. the method according to claim 1, wherein described in the decision kind set and the conditional attribute collection
On the basis of construct original knowledge expression model the step of include:
According to the decision kind set, the conditional attribute collection and the domain, the conditional attribute collection is divided into multiple
By the set for the conditional attribute that many pairs of decision rules impact, wherein the characteristic value of the conditional attribute corresponds to its dimension
Description evaluation, and the characteristic value of the decision kind set correspond to its dimension description evaluate, according to the spy of the conditional attribute
Value indicative and the characteristic value of the decision kind set construct the knowledge representation model.
4. the method according to claim 1, wherein described carry out correlation and independence to the conditional attribute collection
Property calculating, collection merging the step of seeking core after obtaining Reduction of Knowledge includes:
The calculating of correlation and independence is carried out to the conditional attribute collection on the basis of knowledge representation model of building,
To delete the conditional attribute of redundancy, set after obtaining Reduction of Knowledge, to the set of each reduction seek common ground operation in the hope of
Core.
5. the method according to claim 1, wherein described carry out precision and roughness to the decision rule
Performance parse the step of include:
The performance parsing of precision is carried out to the decision rule, specific formula for calculation is as follows:
Wherein, U is a domain, and X is a nonvoid subset in domain U, and R is an equivalence relation on U, with season card
Function is the function for seeking set member's number；
The performance parsing of roughness is carried out to the decision rule, specific formula for calculation is as follows:
ρR(X)=1- αR(X)。
6. a kind of decision-making treatment device based on rough set, which is characterized in that described device includes:
Domain obtains module, and the data of the preset term for obtaining power distribution network choose symbol from the data of the preset term
The object set for closing preset condition constitutes domain；
Conditional attribute collection obtains module, and for determining decision kind set according to the collection of the preset term, parsing influences decision category
Property factor, obtain conditional attribute collection；
Knowledge representation model constructs module, for constructing knowledge on the basis of the decision kind set and the conditional attribute collection
Expression model；
Reduction finding core module, for carrying out the calculating of correlation and independence to the conditional attribute collection, after obtaining Reduction of Knowledge
Collection merging seek core；
Decision Rules Extraction module, for according to the result extraction decision rule for seeking core after Reduction of Knowledge；
Performance parsing module, the performance for carrying out precision and roughness to the decision rule parse.
7. device according to claim 6, which is characterized in that further include output module, advised for exporting to the decision
Then carry out the result of the performance parsing of precision and roughness.
8. device according to claim 6, which is characterized in that the knowledge representation model building module is also used to according to institute
Decision kind set, the conditional attribute collection and the domain are stated, the conditional attribute collection is divided into and multiple is fought to the finish by many
The set for the conditional attribute that plan rule impacts, wherein the characteristic value of the conditional attribute corresponds to the description evaluation of its dimension,
And the characteristic value of the decision kind set correspond to its dimension description evaluation, according to the characteristic value of the conditional attribute and it is described certainly
The characteristic value of plan property set constructs the knowledge representation model.
9. a kind of computer equipment including memory, processor and stores the meter that can be run on a memory and on a processor
Calculation machine program, which is characterized in that the processor realizes any one of claims 1 to 5 institute when executing the computer program
The step of stating method.
10. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the computer program
The step of method described in any one of claims 1 to 5 is realized when being executed by processor.
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