CN110378481B - Decision processing method and device based on rough set, computer and storage medium - Google Patents

Decision processing method and device based on rough set, computer and storage medium Download PDF

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CN110378481B
CN110378481B CN201910525419.1A CN201910525419A CN110378481B CN 110378481 B CN110378481 B CN 110378481B CN 201910525419 A CN201910525419 A CN 201910525419A CN 110378481 B CN110378481 B CN 110378481B
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洪海生
吴琼
李荣琳
林海
陈菁
刘琦
尚明远
乡立
林茵茵
魏艳霞
余文铖
喻蕾
陈永淑
王伟超
袁玲
周先华
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Guangzhou Power Supply Bureau of Guangdong Power Grid Co Ltd
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Abstract

The application relates to a decision processing method, a decision processing device, computer equipment and a storage medium based on a rough set. The method comprises the following steps: acquiring data of preset projects of the power distribution network, and selecting an object set meeting preset conditions from the data of the preset projects to form a domain; determining a decision attribute set according to a set of preset items, and analyzing factors influencing the decision attribute to obtain a condition attribute set; constructing a knowledge expression model on the basis of the decision attribute set and the condition attribute set; performing correlation and independence calculation on the condition attribute set, acquiring a set after knowledge reduction and solving a core; extracting a decision rule according to a result of the kernel calculation after the knowledge reduction; and performing performance analysis of precision and roughness on the decision rule. The method is proved to be in accordance with the actual situation through analysis and performance evaluation of decision rule extraction, and the connection between the power distribution network summer project and the final solution is extracted and described through the decision rule, so that the operation burden of related workers is favorably reduced.

Description

Decision processing method and device based on rough set, computer and storage medium
Technical Field
The present application relates to the field of application data deletion technologies, and in particular, to a decision processing method and apparatus based on a rough set, a computer device, and a storage medium.
Background
With the continuous development of national economy, the power consumption demand is also continuously increased. A plurality of feeders and distribution transformers in the power distribution network can generate heavy overload phenomenon in the peak period of power utilization, so that fault outage occurs occasionally, and customer complaints follow the situation. Therefore, power supply enterprises can carry out the project of meeting the peak and summer every year so as to relieve the shortage of power utilization and improve the reliability of power supply and the satisfaction of customers. However, at present, the decision-making work of the summer-spending project depends on more expert experience and has certain subjectivity. The huge number of items to be formulated results in a large workload of operation management.
In recent years, the rapid development of industrial informatization of the power industry enables power data to show explosive growth, and meanwhile, significant challenges and opportunities are brought to the management and decision of power supply enterprises. The association relation existing between the data can be effectively found by using a proper data mining technology, and the method has great significance for related business planning and decision making.
Disclosure of Invention
In view of the above, it is necessary to provide a rough set based decision processing method, apparatus, computer device and storage medium for solving the above technical problems.
A method of decision processing based on a coarse set, the method comprising:
acquiring data of preset projects of a power distribution network, and selecting an object set meeting preset conditions from the data of the preset projects to form a domain;
determining a decision attribute set according to the set of preset items, and analyzing factors influencing the decision attribute to obtain a condition attribute set;
constructing a knowledge expression model on the basis of the decision attribute set and the condition attribute set;
performing correlation and independence calculation on the condition attribute set, acquiring a set after knowledge reduction and solving a core;
extracting a decision rule according to a result of the kernel calculation after the knowledge reduction;
and analyzing the performance of precision and roughness of the decision rule.
In one embodiment, the step of performing performance analysis of the accuracy and the roughness on the decision rule further includes:
and outputting a result of performing performance analysis on the precision and the roughness of the decision rule.
In one embodiment, the step of constructing the original knowledge representation model on the basis of the decision attribute set and the condition attribute set comprises:
and dividing the condition attribute set into a plurality of condition attribute sets which are influenced by a plurality of decision rules according to the decision attribute set, the condition attribute set and the domain of discourse, wherein the characteristic value of the condition attribute corresponds to the description evaluation of the dimensionality of the condition attribute set, the characteristic value of the decision attribute set corresponds to the description evaluation of the dimensionality of the condition attribute set, and the knowledge expression model is constructed according to the characteristic value of the condition attribute and the characteristic value of the decision attribute set.
In one embodiment, the step of performing relevance and independence calculation on the condition attribute set, obtaining a reduced knowledge set, and performing kernel calculation includes:
and on the basis of the established knowledge expression model, performing correlation and independence calculation on the condition attribute set to delete redundant condition attributes, obtaining a set after knowledge reduction, and performing intersection operation on the reduced sets to obtain a kernel.
In one embodiment, the performance analysis of the accuracy and the roughness of the decision rule includes:
and performing performance analysis on the accuracy of the decision rule, wherein a specific calculation formula is as follows:
Figure GDA0002179546390000021
wherein, U is a discourse domain, X is a non-empty subset in the discourse domain U, R is an equivalent relation on U, and the card function is a function for solving the membership of a set;
and analyzing the performance of the roughness of the decision rule, wherein a specific calculation formula is as follows:
ρR(X)＝1-αR(X)。
a rough set based decision processing apparatus, the apparatus comprising:
the system comprises a domain acquiring module, a domain selecting module and a domain matching module, wherein the domain acquiring module is used for acquiring data of preset projects of the power distribution network and selecting an object set meeting preset conditions from the data of the preset projects to form a domain;
the conditional attribute set acquisition module is used for determining a decision attribute set according to the preset item set, analyzing factors influencing the decision attribute and acquiring a conditional attribute set;
the knowledge expression model building module is used for building a knowledge expression model on the basis of the decision attribute set and the condition attribute set;
the reduction and core-solving module is used for calculating the relevance and the independence of the condition attribute set, acquiring a set after knowledge reduction and solving a core;
the decision rule extraction module is used for extracting a decision rule according to a result of the kernel calculation after the knowledge reduction;
and the performance analysis module is used for performing performance analysis on the precision and the roughness of the decision rule.
In one embodiment, the system further comprises an output module, configured to output a result of performing performance analysis on the accuracy and the roughness of the decision rule.
In one embodiment, the knowledge expression model building module is further configured to divide the condition attribute set into a plurality of sets of condition attributes influencing a decision rule according to the decision attribute set, the condition attribute set, and the domain, where feature values of the condition attributes correspond to descriptive evaluations of dimensions of the condition attributes, feature values of the decision attribute set correspond to descriptive evaluations of dimensions of the condition attributes, and the knowledge expression model is built according to the feature values of the condition attributes and the feature values of the decision attribute set.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method of any of the above embodiments when executing the computer program.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of any of the above embodiments.
According to the decision processing method, the decision processing device, the computer equipment and the storage medium based on the rough set, firstly, preset project data of the power distribution network are obtained, and an object set meeting preset conditions is selected from the preset project data to form a domain; then determining a decision attribute set according to a preset item set and analyzing factors influencing the decision attribute to form a condition attribute set; then constructing an original knowledge expression system on the basis of the decision attribute set and the condition attribute set; then, the relevance and the independence are calculated for the condition attribute set, and the set after knowledge reduction is solved and the kernel is solved; determining the description attribute according to the condition of the core so as to extract a decision rule; and finally, performing performance evaluation analysis on the precision and the roughness of the extracted decision rule. The decision rule extraction is carried out on the power distribution network summer items by using a rough set theory method, the method is proved to be in accordance with the actual situation through analysis and performance evaluation of the decision rule extraction, and the relation between the power distribution network summer items and the final solution measures is extracted and described through the decision rule, so that the operation burden of related workers is favorably relieved.
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FIG. 1 is a schematic flow chart diagram illustrating a rough set based decision processing method in one embodiment;
FIG. 2 is a block diagram of a rough set based decision processing apparatus in one embodiment;
FIG. 3 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In one embodiment, as shown in fig. 1, there is provided a rough set based decision processing method, including the following steps:
and 110, acquiring data of preset items of the power distribution network, and selecting an object set meeting preset conditions from the data of the preset items to form a domain.
In this embodiment, the preset item is a summer item, the preset condition is a preset condition, the preset condition is used to screen required data from data of the preset item, and the data is an object meeting the preset condition, so as to form a domain of discourse. In one embodiment, the predetermined condition is of interest to a monitoring person. In one embodiment, step 110 forms a domain for acquiring actual power distribution grid summer project data and selecting a set of objects of interest from the data.
Specifically, in this step, the interested sets are first selected from the given summer project table to form the domain, and not all sets in the table are considered valuable, such as the serial number and the project name, with respect to the given summer project table. The set of domains is only the set with reference value in the table, such as problem, task category and solution. Therefore, the required data need to be screened according to preset conditions, and then a domain of discourse is formed.
And 120, determining a decision attribute set according to the preset item set, and analyzing factors influencing the decision attribute to obtain a condition attribute set.
Specifically, in this step, after the decision attribute set is determined, the decision attribute set is analyzed to obtain factors affecting the decision attribute, and the set of the factors is the conditional attribute set.
In one embodiment, step 120 constructs a set of conditional attributes by determining a set of decision attributes from the set of summer items and analyzing factors that affect their decision attributes.
In this embodiment, the condition attribute set is constructed by selecting a factor set that affects a final solution from the charpy item table according to an actual situation. Including task category, problem, treatment, project investment, expected solution time, customer complaints, load rate, department of responsibility, and out-of-limit times, etc. The set of decision attributes consists of equivalence relations of solutions in the domain of discourse.
And 130, constructing a knowledge expression model on the basis of the decision attribute set and the condition attribute set.
In particular, the knowledge expression model may also be referred to as a knowledge expression system. One embodiment is to build the original knowledge expression system on the basis of the known decision attribute set and the condition attribute set.
In one embodiment, the step of building an original knowledge representation model on the basis of the decision attribute set and the condition attribute set comprises: and dividing the condition attribute set into a plurality of condition attribute sets which are influenced by a plurality of decision rules according to the decision attribute set, the condition attribute set and the domain of discourse, wherein the characteristic value of the condition attribute corresponds to the description evaluation of the dimensionality of the condition attribute set, the characteristic value of the decision attribute set corresponds to the description evaluation of the dimensionality of the condition attribute set, and the knowledge expression model is constructed according to the characteristic value of the condition attribute and the characteristic value of the decision attribute set.
Specifically, the condition attribute set is divided into a set of condition attributes affected by a plurality of decision rules, that is, R ═ R1, R2, R3, …, rn }, by the condition attribute set R, the decision attribute set D and the domain of discourse U determined in the above steps, and the feature values of the condition attributes correspond to the description evaluation of the dimensions thereof. Similarly, the characteristic value corresponding to the decision attribute set D is also used for describing and evaluating the corresponding dimension, such as a solution measure including a founding item, an emergency measure, an operation measure, a marketing measure, and the like. The corresponding decision attribute set feature value may be set to D ═ {1 (a base establishment item), 2 (an emergency measure), 3 (an operation measure), 4 (a marketing measure), 5 (another) }. On the basis, a knowledge expression system can be constructed:
Figure GDA0002179546390000061
and 140, performing relevance and independence calculation on the condition attribute set, acquiring a set after knowledge reduction, and solving a core.
Specifically, in this step, the correlation and the independence are calculated for the condition attribute set, and the set after knowledge reduction and the kernel are obtained.
And performing relevance and independence calculation, namely knowledge reduction on the condition attribute set on the basis of the knowledge expression system formed in the step to eliminate redundant condition attributes. Finally, independent reductions are obtained, and intersection operation is carried out on all the reductions to obtain a kernel.
And 150, extracting a decision rule according to the result of the kernel calculation after the knowledge reduction.
In this step, the description attribute is determined according to the condition of the core, thereby extracting the decision rule.
And 160, performing performance analysis of precision and roughness on the decision rule.
In this step, the extracted decision is subjected to performance analysis, which is mainly based on two aspects, namely precision and roughness. And analyzing the performance of the precision and the roughness of the decision rule to calculate the evaluation result of the performance of the corresponding decision rule.
In the embodiment, the decision rule extraction is carried out on the power distribution network summer items by using a rough set theory method, the method is proved to be in accordance with the actual situation through the analysis and the performance evaluation of the decision rule extraction, and the relation between the power distribution network summer items and the final solution measures is extracted and described through the decision rule, so that the operation burden of related workers is favorably relieved.
In one embodiment, the step of performing performance analysis of the accuracy and the roughness on the decision rule further includes: and outputting a result of performing performance analysis on the precision and the roughness of the decision rule.
One embodiment is to output the result of performing the performance analysis of the accuracy and the roughness on the decision rule through a network. In one embodiment, the result of performing the performance analysis of the accuracy and the roughness on the decision rule is output to a terminal through a network. One embodiment is that the result of performing the performance analysis of the accuracy and the roughness on the decision rule is output through a display screen.
In one embodiment, the step of performing relevance and independence calculation on the condition attribute set, obtaining a reduced knowledge set, and performing kernel calculation includes: and on the basis of the established knowledge expression model, performing correlation and independence calculation on the condition attribute set to delete redundant condition attributes, obtaining a set after knowledge reduction, and performing intersection operation on the reduced sets to obtain a kernel. The method specifically comprises the following steps:
step 141, classifying the domain of discourse U by using the condition attributes r1, r2, r3 and r4 as knowledge according to the knowledge expression system, and obtaining sets 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 the equivalent relation U/IND (R) is obtained through intersection operation:
U/IND(R)＝{{x3,x7},{x1,x2},{x5,x6},{x8},{x9},{x10}}。
step 142, performing correlation calculation on the four condition attribute sets in S4.1, taking r1 as an example, and the calculation formula is:
U/IND(R-{r1})＝U/r2∩U/r3∩U/r4＝U/IND(R)
if the calculation result is U/IND (R- { R1}) ═ U/IND (R), then the condition attribute R1 may be considered as unnecessary for the decision attribute, i.e., redundant. Then, the correlations of r2, r3, and r4 are calculated, and a determination is made as to whether it is unnecessary.
Step 143, selecting necessary attributes based on S4.2, for example, assuming that r4 is a necessary attribute and all other attributes are unnecessary attributes, the necessary attribute and other unnecessary attributes form a set, so that the classification capability is the same as the equivalence relation U/ind (r). And calculating the independence of the two parts, namely a set of reduction if the two parts meet the independence requirement. Taking the set { r1, r2, r4} as an example, the formula of the independence calculation is shown as follows:
first, U/IND ({ r1, r2, r4}) is U/IND (r);
next, U/IND ({ r1, r2, r4} - { r4}) - (U/IND ({ r1, r2}) ≠ is present
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})；
Then the set r1, r2, r4 is known as a set of independent reductions. And all independent reductions are obtained after independent calculation is carried out on all the reductions in the same way.
And 144, performing intersection operation on all independent reductions on the basis of S4.3, wherein the obtained result is the kernel.
In one embodiment, the step of extracting a decision rule according to the result of the kernel after the knowledge reduction comprises:
in step 151, let the description attribute P be { problem, complaint, task category }, there is an equivalence relation U/ind (P) { X1, X2, X3, X4, X5, X6, X7}, where X1 ═ X1, X2}, X2 ═ { X3, X7}, X3 ═ X5, X6}, X4 ═ X8}, X5 ═ X9}, X6 ═ X4}, X7 { (X10 }.
In step 152, the decision attribute D has an equivalence relationship U/D ═ { Y1, Y2, Y3, Y4}, where Y1 ═ { x1, x2, x3, x7}, Y2 ═ x4}, Y3 ═ x5, x6, x10}, and Y4 ═ x8, x9 }. Then, according to the description attribute set P and the decision attribute set D, the calculation of the relevant indexes of the upper approximation, the lower approximation, the positive and negative domains and the boundary domain is performed, and the following calculation results are obtained:
1)
Figure GDA0002179546390000089
Figure GDA0002179546390000082
2)
Figure GDA0002179546390000088
Figure GDA0002179546390000084
3)
Figure GDA0002179546390000087
Figure GDA0002179546390000086
4)
Figure GDA0002179546390000094
Figure GDA0002179546390000092
step 153, extracting the decision rule according to the description attribute on the basis of the required domain. For Y2, the extracted rules are IF (instantaneous heavy load) and (few complaints) and (distribution variable task), THEN (emergency measure).
In one embodiment, the performance analysis of the accuracy and the roughness of the decision rule includes: and performing performance analysis on the accuracy of the decision rule, wherein a specific calculation formula is as follows:
Figure GDA0002179546390000093
wherein, U is a discourse domain, X is a non-empty subset in the discourse domain U, R is an equivalent relation on U, and the card function is a function for solving the membership of a set;
and analyzing the performance of the roughness of the decision rule, wherein a specific calculation formula is as follows:
ρR(X)＝1-αR(X)。
it should be noted that, in each embodiment, the decision processing method based on the rough set is also referred to as a distribution network summer project decision rule research method based on the rough set theory.
The following is a specific example:
a distribution network summer item decision rule research method based on a rough set theory comprises the following steps:
s1, acquiring actual power distribution network summer project data and selecting an interested object set from the data to form a domain;
in step S1, the interested sets are first selected from the given item table to form domains, and not all sets in the table are considered valuable, such as sequence numbers, item names, etc., with respect to the given item table. The set of domains is only the set with reference value in the table, such as problem, task category and solution.
S2, determining a decision attribute set according to the summer item set and analyzing factors influencing the decision attribute set to form a condition attribute set;
in step S2, the condition attribute set is constructed by selecting a set of factors that affect the final solution from the table of items in the summer according to actual conditions. Including task category, problem, treatment, project investment, expected solution time, customer complaints, load rate, department of responsibility, and out-of-limit times, etc. The decision attribute set consists of the equivalence relations of the solutions in the domain of discourse.
S3, constructing an original knowledge expression system on the basis of the known decision attribute set and the condition attribute set;
in step S3, the knowledge expression system is constructed as follows:
the condition attribute set R and decision attribute set D determined in step S2 and the domain U determined in step S1 divide the condition attribute set into a set of condition attributes that affect the decision rule, i.e., R { R1, R2, R3, …, rn }, and the feature value of the condition attribute corresponds to the description evaluation of the dimension. Similarly, the characteristic value corresponding to the decision attribute set D is also used for describing and evaluating the corresponding dimension, such as a solution measure including a founding item, an emergency measure, an operation measure, a marketing measure, and the like. The corresponding decision attribute set feature value may be set to D ═ {1 (a base establishment item), 2 (an emergency measure), 3 (an operation measure), 4 (a marketing measure), 5 (another) }. On the basis, a knowledge expression model can be constructed:
Figure GDA0002179546390000101
s4, carrying out correlation and independence calculation on the condition attribute set, solving a set after knowledge reduction and solving a kernel;
the conditional attribute set is subjected to the calculation of the relevance and independence, i.e., the knowledge reduction, based on the knowledge expression system configured in step S4 to eliminate the redundant conditional attributes. Finally, independent reductions are obtained, and intersection operation is carried out on all the reductions to obtain a kernel. The method comprises the following specific steps:
s4.1, classifying the domain U by taking the condition attributes r1, r2, r3 and r4 as knowledge according to a knowledge expression system, and obtaining sets which are respectively:
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 the equivalent relation U/IND (R) is obtained through intersection operation:
U/IND(R)＝{{x3,x7},{x1,x2},{x5,x6},{x8},{x9},{x10}}。
s4.2, respectively carrying out correlation calculation on the four condition attribute sets in S4.1, taking r1 as an example, and the calculation formula is as follows:
U/IND(R-{r1})＝U/r2∩U/r3∩U/r4＝U/IND(R)
if the calculation result is U/IND (R- { R1}) ═ U/IND (R), then the condition attribute R1 may be considered as unnecessary for the decision attribute, i.e., redundant. Then, the correlations of r2, r3, and r4 are calculated, and a determination is made as to whether it is unnecessary.
S4.3 selects necessary attributes on the basis of S4.2, for example, if r4 is a necessary attribute and all other attributes are unnecessary attributes, the necessary attribute and other unnecessary attributes form a set, so that the classification capability is the same as the equivalence relation U/IND (R). And calculating the independence of the two parts, namely a set of reduction if the two parts meet the independence requirement. Taking the set { r1, r2, r4} as an example, the formula of the independence calculation is shown as follows:
first, U/IND ({ r1, r2, r4}) is U/IND (r);
secondly, the number of U/IND ({ r1, r2, r4} - { r4}) is U/IND ({ r1, r2 }). noteq.U/IND ({ r1, r2, r4}), U/IND ({ r1, r2, r4} - { r1}) is U/IND ({ r2, r4 }). noteq.U/IND ({ r1, r2, r4}), U/IND ({ r1, r2, r4} - { r2 }). U/IND ({ r1, r4}) is not equal to U/IND ({ r1, r2, r4 });
then the set r1, r2, r4 is known as a set of independent reductions. And all independent reductions are obtained after independent calculation is carried out on all the reductions in the same way.
And S4.4, performing intersection operation on all independent reductions on the basis of S4.3, wherein the obtained result is the kernel.
S5, determining the description attribute according to the condition of the core so as to extract a decision rule;
the core in step S5 is used as a description attribute to extract a decision rule, and the specific process is as follows:
s5.1 sets the descriptive attribute P to { problem, complaint, task category }, and has an equivalence relation U/ind (P) { X1, X2, X3, X4, X5, X6, X7}, where X1 ═ X1, X2}, X2 ═ X3, X7}, X3 ═ X5, X6}, X4 ═ X8}, X5 ═ X9}, X6 ═ X4}, X7 { (X10 }.
S5.2 decision attribute D has an equivalence relation of U/D ═ Y1, Y2, Y3, Y4, where Y1 ═ { x1, x2, x3, x7}, Y2 ═ x4}, Y3 ═ x5, x6, x10}, and Y4 ═ x8, x9 }. Then, according to the description attribute set P and the decision attribute set D, the calculation of the relevant indexes of the upper approximation, the lower approximation, the positive and negative domains and the boundary domain is performed, and the following calculation results are obtained:
1)
Figure GDA00021795463900001210
Figure GDA0002179546390000122
2)
Figure GDA00021795463900001211
Figure GDA0002179546390000124
3)
Figure GDA00021795463900001212
Figure GDA0002179546390000126
4)
Figure GDA00021795463900001213
Figure GDA0002179546390000128
and S5.3, extracting the decision rule according to the description attribute on the basis of the required domain. For Y2, the extracted rules are IF (instantaneous heavy load) and (few complaints) and (distribution variable task), THEN (emergency measure).
And S6, performing performance evaluation analysis on the precision and the roughness of the extracted decision rule.
And performing performance analysis and evaluation on the decision extracted in the step S6. The method is mainly based on two aspects, namely precision and roughness, and the specific calculation formula is as follows:
Figure GDA0002179546390000129
the above is the precision calculation formula. Wherein U is a domain of discourse, X is a non-empty subset in the domain of discourse U, R is an equivalence relation on U, and the card function is a function for solving the membership of a set, which is also called a cardinality or a potential.
The roughness calculation formula is as follows:
ρR(X)＝1-αR(X)
therefore, the evaluation result of the performance of the corresponding decision rule can be calculated.
And (3) experimental verification: distribution network summer project decision rule research method experiment based on rough set theory
1. Firstly, acquiring actual power distribution network summer project data and selecting an interested object set from the data to form a domain; then determining a decision attribute set according to the summer item set and analyzing factors influencing the decision attribute to form a condition attribute set; then, an original knowledge expression system is constructed on the basis of the known decision attribute set and the condition attribute set, as shown in table 1:
TABLE 1 knowledge expression System
Figure GDA0002179546390000131
2. And further carrying out correlation and independence calculation on the condition attribute set, solving a set after knowledge reduction, and solving a kernel:
(1) classifying the domain U by taking the condition attributes r1, r2, r3 and r4 as knowledge according to a knowledge expression system, and obtaining sets which are respectively:
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 the equivalent relation U/IND (R) is obtained through intersection operation:
U/IND(R)＝{{x3,x7},{x1,x2},{x5,x6},{x8},{x9},{x10}}。
(2) the four condition attribute sets are respectively subjected to correlation calculation, taking r1 as an example, and the calculation formula is as follows:
U/IND(R-{r1})＝U/r2∩U/r3∩U/r4＝U/IND(R)
if the calculation result is U/IND (R- { R1}) ═ U/IND (R), then the condition attribute R1 may be considered as unnecessary for the decision attribute, i.e., redundant. Then, the correlations of r2, r3, and r4 are calculated, and a determination is made as to whether it is unnecessary.
(3) Selecting necessary attributes, for example, assuming that r4 is a necessary attribute, and all others are unnecessary attributes, then using the necessary attribute and other unnecessary attributes to form a set, so that the classification capability is the same as the equivalence relation U/IND (R). And calculating the independence of the two parts, namely a set of reduction if the two parts meet the independence requirement. Taking the set { r1, r2, r4} as an example, the formula of the independence calculation is shown as follows:
first, U/IND ({ r1, r2, r4}) is U/IND (r);
the method comprises the following steps:
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})；
then the set r1, r2, r4 is known as a set of independent reductions. And all independent reductions are obtained after independent calculation is carried out on all the reductions in the same way.
(4) And performing intersection operation on all independent reductions, wherein the obtained result is the kernel.
3. Determining the description attributes according to the conditions of the cores to extract decision rules:
the specific process is as follows:
let the description attribute P be { problem, complaint, task category }, there is an equivalence relationship U/ind (P) { X1, X2, X3, X4, X5, X6, X7}, where X1 is { X1, X2}, X2 is { X3, X7}, X3 is { X5, X6}, X4 is { X8}, X5 is { X9}, X6 is { X4}, and X7 is { X10 }.
The decision attribute D has an equivalence relation of U/D ═ Y1, Y2, Y3, Y4, where Y1 ═ x1, x2, x3, x7, Y2 ═ x4, Y3 ═ x5, x6, x10, and Y4 ═ x8, x 9. Then, according to the description attribute set P and the decision attribute set D, the calculation of the relevant indexes of the upper approximation, the lower approximation, the positive and negative domains and the boundary domain is performed, and the following calculation results are obtained:
1)
Figure GDA0002179546390000148
Figure GDA0002179546390000142
2)
Figure GDA0002179546390000149
Figure GDA0002179546390000144
3)
Figure GDA00021795463900001410
Figure GDA0002179546390000146
4)
Figure GDA00021795463900001411
Figure GDA0002179546390000151
and extracting the decision rule according to the description attribute on the basis of the required domain. For Y2, the extracted rule is:
IF (instantaneous heavy load) and (few complaints) and (distribution of task category), THEN (emergency measure).
4. And finally, performing performance evaluation analysis on the precision and the roughness of the extracted decision rule:
and performing performance analysis and evaluation on the extracted decision. The method is mainly based on two aspects, namely precision and roughness, and the specific calculation formula is as follows:
Figure GDA0002179546390000152
the above is the precision calculation formula. Wherein U is a domain of discourse, X is a non-empty subset in the domain of discourse U, R is an equivalence relation on U, and the card function is a function for solving the membership of a set, which is also called a cardinality or a potential.
The roughness calculation formula is as follows:
ρR(X)＝1-αR(X)
thus, the analysis result of the performance of the corresponding decision rule can be calculated and then output.
It should be understood that, although the steps in the flowchart of fig. 1 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in fig. 1 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 2, there is provided a rough set based decision processing apparatus, including:
the domain acquiring module 210 is configured to acquire data of preset items of the power distribution network, and select an object set meeting preset conditions from the data of the preset items to form a domain;
a conditional attribute set obtaining module 220, configured to determine a decision attribute set according to the set of preset items, and analyze factors affecting the decision attribute to obtain a conditional attribute set;
a knowledge expression model construction module 230, configured to construct a knowledge expression model based on the decision attribute set and the condition attribute set;
a reduction and kernel module 240, configured to perform correlation and independence calculation on the condition attribute set, obtain a set after knowledge reduction, and perform kernel calculation;
a decision rule extraction module 250, configured to extract a decision rule according to a result of the kernel calculation after the knowledge reduction;
and the performance analysis module 260 is used for performing performance analysis on the accuracy and the roughness of the decision rule.
In one embodiment, the decision processing apparatus based on a rough set further includes an output module, configured to output a result of performing performance analysis of precision and roughness on the decision rule.
In one embodiment, the knowledge expression model building module is further configured to divide the condition attribute set into a plurality of sets of condition attributes influencing a decision rule according to the decision attribute set, the condition attribute set, and the domain, where feature values of the condition attributes correspond to descriptive evaluations of dimensions of the condition attributes, feature values of the decision attribute set correspond to descriptive evaluations of dimensions of the condition attributes, and the knowledge expression model is built according to the feature values of the condition attributes and the feature values of the decision attribute set.
In an embodiment, the reduction core module is further configured to perform correlation and independence calculation on the condition attribute set based on the constructed knowledge expression model to delete redundant condition attributes, obtain a set after knowledge reduction, and perform intersection calculation on each reduced set to obtain a core.
In one embodiment, the performance analysis module is further configured to perform performance analysis on the accuracy of the decision rule, where a specific calculation formula is as follows:
Figure GDA0002179546390000161
wherein, U is a discourse domain, X is a non-empty subset in the discourse domain U, R is an equivalent relation on U, and the card function is a function for solving the membership of a set;
and analyzing the performance of the roughness of the decision rule, wherein a specific calculation formula is as follows:
ρR(X)＝1-αR(X)。
for the specific definition of the decision processing device based on rough set, reference may be made to the above definition of the decision processing method based on rough set, and details are not repeated here. The various modules in the rough set based decision processing apparatus described above may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 3. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing data such as identification information of the terminal and a user account of the application program. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a rough set based decision processing method.
Those skilled in the art will appreciate that the architecture shown in fig. 3 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
acquiring data of preset projects of a power distribution network, and selecting an object set meeting preset conditions from the data of the preset projects to form a domain;
determining a decision attribute set according to the set of preset items, and analyzing factors influencing the decision attribute to obtain a condition attribute set;
constructing a knowledge expression model on the basis of the decision attribute set and the condition attribute set;
performing correlation and independence calculation on the condition attribute set, acquiring a set after knowledge reduction and solving a core;
extracting a decision rule according to a result of the kernel calculation after the knowledge reduction;
and analyzing the performance of precision and roughness of the decision rule.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
and outputting a result of performing performance analysis on the precision and the roughness of the decision rule.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
and dividing the condition attribute set into a plurality of condition attribute sets which are influenced by a plurality of decision rules according to the decision attribute set, the condition attribute set and the domain of discourse, wherein the characteristic value of the condition attribute corresponds to the description evaluation of the dimensionality of the condition attribute set, the characteristic value of the decision attribute set corresponds to the description evaluation of the dimensionality of the condition attribute set, and the knowledge expression model is constructed according to the characteristic value of the condition attribute and the characteristic value of the decision attribute set.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
and on the basis of the established knowledge expression model, performing correlation and independence calculation on the condition attribute set to delete redundant condition attributes, obtaining a set after knowledge reduction, and performing intersection operation on the reduced sets to obtain a kernel.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
and performing performance analysis on the accuracy of the decision rule, wherein a specific calculation formula is as follows:
Figure GDA0002179546390000181
wherein, U is a discourse domain, X is a non-empty subset in the discourse domain U, R is an equivalent relation on U, and the card function is a function for solving the membership of a set;
and analyzing the performance of the roughness of the decision rule, wherein a specific calculation formula is as follows:
ρR(X)＝1-αR(X)。
in one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring data of preset projects of a power distribution network, and selecting an object set meeting preset conditions from the data of the preset projects to form a domain;
determining a decision attribute set according to the set of preset items, and analyzing factors influencing the decision attribute to obtain a condition attribute set;
constructing a knowledge expression model on the basis of the decision attribute set and the condition attribute set;
performing correlation and independence calculation on the condition attribute set, acquiring a set after knowledge reduction and solving a core;
extracting a decision rule according to a result of the kernel calculation after the knowledge reduction;
and analyzing the performance of precision and roughness of the decision rule.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and outputting a result of performing performance analysis on the precision and the roughness of the decision rule.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and dividing the condition attribute set into a plurality of condition attribute sets which are influenced by a plurality of decision rules according to the decision attribute set, the condition attribute set and the domain of discourse, wherein the characteristic value of the condition attribute corresponds to the description evaluation of the dimensionality of the condition attribute set, the characteristic value of the decision attribute set corresponds to the description evaluation of the dimensionality of the condition attribute set, and the knowledge expression model is constructed according to the characteristic value of the condition attribute and the characteristic value of the decision attribute set.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and on the basis of the established knowledge expression model, performing correlation and independence calculation on the condition attribute set to delete redundant condition attributes, obtaining a set after knowledge reduction, and performing intersection operation on the reduced sets to obtain a kernel.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and performing performance analysis on the accuracy of the decision rule, wherein a specific calculation formula is as follows:
Figure GDA0002179546390000191
wherein, U is a discourse domain, X is a non-empty subset in the discourse domain U, R is an equivalent relation on U, and the card function is a function for solving the membership of a set;
and analyzing the performance of the roughness of the decision rule, wherein a specific calculation formula is as follows:
ρR(X)＝1-αR(X)。
it will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (10)

1. A method of decision processing based on a coarse set, the method comprising:
acquiring data of preset projects of a power distribution network, and selecting an object set meeting preset conditions from the data of the preset projects to form a domain;
determining a decision attribute set according to the set of preset items, and analyzing factors influencing decision attributes from the set of preset items to obtain a condition attribute set;
constructing a knowledge expression model on the basis of the decision attribute set and the condition attribute set;
performing correlation and independence calculation on the condition attribute set, performing kernel solving on the set after knowledge reduction is obtained, and constructing a description attribute set on the basis;
calculating upper approximation, lower approximation, positive and negative domains and boundary domain related indexes according to the description attribute set and the decision attribute set, and extracting a decision rule according to the description attribute on the basis of the positive domain;
and analyzing the performance of precision and roughness of the decision rule.
2. The method of claim 1, wherein the step of performing a performance analysis of the decision rule for accuracy and coarseness further comprises:
and outputting a result of performing performance analysis on the precision and the roughness of the decision rule.
3. The method of claim 1, wherein the step of building an original knowledge representation model based on the set of decision attributes and the set of condition attributes comprises:
and dividing the condition attribute set into a plurality of condition attribute sets which are influenced by a plurality of decision rules according to the decision attribute set, the condition attribute set and the domain of discourse, wherein the characteristic value of the condition attribute corresponds to the description evaluation of the dimensionality of the condition attribute set, the characteristic value of the decision attribute set corresponds to the description evaluation of the dimensionality of the condition attribute set, and the knowledge expression model is constructed according to the characteristic value of the condition attribute and the characteristic value of the decision attribute set.
4. The method of claim 1, wherein the computing of the relevance and independence of the set of conditional attributes, and wherein the checking the reduced knowledge set comprises:
and on the basis of the established knowledge expression model, performing correlation and independence calculation on the condition attribute set to delete redundant condition attributes, obtaining a set after knowledge reduction, and performing intersection operation on the reduced sets to obtain a kernel.
5. The method of claim 1, wherein the performance analysis of the decision rule for accuracy and coarseness comprises:
and performing performance analysis on the accuracy of the decision rule, wherein a specific calculation formula is as follows:
Figure FDA0003032574220000021
wherein, U is a discourse domain, X is a non-empty subset in the discourse domain U, R is an equivalent relation on U, and the card function is a function for solving the membership of a set;
and analyzing the performance of the roughness of the decision rule, wherein a specific calculation formula is as follows:
ρR(X)＝1-αR(X)。
6. a rough set based decision processing apparatus, the apparatus comprising:
the system comprises a domain acquiring module, a domain selecting module and a domain matching module, wherein the domain acquiring module is used for acquiring data of preset projects of the power distribution network and selecting an object set meeting preset conditions from the data of the preset projects to form a domain;
the conditional attribute set acquisition module is used for determining a decision attribute set according to the set of the preset items and analyzing factors influencing the decision attribute from the set of the preset items to obtain a conditional attribute set;
the knowledge expression model building module is used for building a knowledge expression model on the basis of the decision attribute set and the condition attribute set;
the reduction and calculation core module is used for calculating the relevance and the independence of the condition attribute set, calculating the core of the set after the knowledge reduction is obtained, and constructing a description attribute set on the basis;
the decision rule extraction module is used for calculating relevant indexes of upper approximation, lower approximation, positive and negative domains and boundary domains according to the description attribute set and the decision attribute set and extracting a decision rule according to the description attribute on the basis of the positive domain;
and the performance analysis module is used for performing performance analysis on the precision and the roughness of the decision rule.
7. The apparatus of claim 6, further comprising an output module configured to output a result of performing a performance analysis of the decision rule on the accuracy and the coarseness.
8. The apparatus according to claim 6, wherein the knowledge expression model building module is further configured to divide the condition attribute set into a plurality of sets of condition attributes that affect decision rules according to the decision attribute set, the condition attribute set, and the domain of discourse, wherein a feature value of the condition attribute corresponds to a description evaluation of a dimension thereof, and a feature value of the decision attribute set corresponds to a description evaluation of a dimension thereof, and build the knowledge expression model according to the feature value of the condition attribute and the feature value of the decision attribute set.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of claims 1 to 5 are implemented when the computer program is executed by the processor.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 5.
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