CN116362577A - Target class membership analysis method, system, device and storage medium - Google Patents
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Abstract
The invention relates to a target class membership analysis method, a system, equipment and a storage medium, which comprise the following steps: analyzing different application scene characteristics and evaluation indexes of the item to be evaluated to obtain an evaluation index set and a scene characteristic set of the item to be evaluated, and respectively marking the evaluation index set and the scene characteristic set as a first variable set and a second variable set; performing membership degree analysis on the probability of each evaluation index in the first variable set and the probability of each scene characteristic in the second variable set under different application scenes to obtain an evaluation index list under different scene characteristics; and determining an evaluation index subset of the item to be evaluated under the preset scene characteristic based on the evaluation index list, and obtaining an evaluation result of the item to be evaluated under the preset scene characteristic based on the evaluation index subset. The invention quantitatively classifies the membership incidence relation of the occurrence of the fuzzy matters in the engineering through membership analysis based on the occurrence probability, and can be widely applied to the field of data mining classification of smart power grids.
Description
Technical Field
The invention relates to the field of data mining classification, in particular to a membership between factors in two types of variable sets with association relations, and particularly relates to a target class membership analysis method, system, equipment and storage medium based on variable association.
Background
At present, in the classification field, methods such as cluster analysis, principal component analysis, expert scoring method, SVM analysis and the like are all used for classifying and analyzing actual data, but at present, in the engineering analysis field, a plurality of events which have relevance but cannot clearly define the relevance relationship exist, for example, the relation between the occurrence frequency of transactions and a plurality of factors, or the membership degree between each evaluation index and the evaluated object class in a certain evaluation index system is defined. The judgment of the association relation or the fuzzy membership relation is usually judged by methods such as expert scoring method and the like, so that the subjectivity is high.
Disclosure of Invention
Aiming at the problems, the invention aims to provide a target class membership analysis method, a system, equipment and a storage medium based on variable association, which are used for quantitatively classifying membership association relations of fuzzy things occurring in engineering through membership analysis based on occurrence probability.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
in a first aspect, the present invention provides a target class membership analysis method, including the steps of:
analyzing different application scene characteristics and evaluation indexes of the item to be evaluated to obtain an evaluation index set and a scene characteristic set of the item to be evaluated, marking the evaluation index set as a first variable set, and marking the scene characteristic set as a second variable set;
performing membership degree analysis on the probability of each evaluation index in the first variable set and the probability of each scene characteristic in the second variable set under different application scenes to obtain an evaluation index list under different scene characteristics;
and determining an evaluation index subset of the item to be evaluated under the preset scene characteristic based on the evaluation index list, and obtaining an evaluation result of the item to be evaluated under the preset scene characteristic based on the evaluation index subset.
Further, the membership degree analysis is performed on the probability of each evaluation index in the first variable set and the probability of each scene characteristic in the second variable set under different application scenes to obtain an evaluation index list under different scene characteristics, and the method comprises the following steps:
constructing a variable probability matrix based on the probability of each variable in the first variable set and the second variable set;
obtaining ideal membership of each evaluation index in the first variable set to ideal scene characteristics based on a fuzzy analysis method according to the variable probability matrix of the first variable set; the ideal membership refers to the probability that all evaluation indexes in the first variable set are clustered in all ideal scene characteristics obtained according to a preset classification rule;
and carrying out deviation correction on the formed ideal membership according to the variable probability matrix of the second variable set and the number of the actual scene characteristics in the second variable set to obtain the actual membership of each evaluation index in the first variable set to the actual scene characteristics in the second variable set, and taking the actual membership as an evaluation index list under different scene characteristics.
Further, the obtaining, based on the fuzzy analysis method, the ideal membership of each evaluation index in the first variable set to the ideal scene characteristic according to the variable probability matrix of the first variable set includes:
determining the number of current clustering centers, classifying the variable probability matrix of the first variable set according to the determined number of the current clustering centers by using a fuzzy K-means algorithm, and calculating to obtain a target function value corresponding to the number of the current clustering centers;
calculating to obtain a cluster center matrix, a membership matrix and a target function value corresponding to all the cluster center numbers;
and selecting the cluster center number with the smallest objective function value as an ideal cluster center number, wherein the corresponding cluster center matrix is an ideal cluster center matrix, and the corresponding membership matrix is used as an ideal membership of each evaluation index in the first variable set to each ideal scene characteristic.
Further, the objective function value calculation formula is:
wherein J (U, W, c) is the objective function value calculated under the conditions of the current membership degree matrix U, the clustering center matrix W and the clustering center number c.
Further, the performing bias correction on the formed ideal membership according to the variable probability matrix of the second variable set and the number of actual scene characteristics in the second variable set includes:
comparing the variable probability matrix of the second variable set with the ideal clustering center matrix to obtain a distance matrix;
normalizing the obtained distance matrix by a fuzzy normalization method to obtain a fuzzy membership matrix;
determining a classification relation according to the number of actual scene characteristics in the second variable set and the comparison result of the ideal clustering center number by combining the fuzzy membership matrix;
and (3) membership of the evaluation indexes in the first variable set belonging to each ideal cluster center to corresponding scene characteristics in the second variable set according to the obtained classification relation, and obtaining the actual membership of each evaluation index in the first variable set to the actual scene characteristics in the second variable set.
Further, the calculation formula of each element in the distance matrix is as follows:
d kl ＝|w k -w’ l |
wherein w is k For probability value, w 'of each actual scene characteristic occurrence in the second variable set' l Is the element value in the ideal cluster center matrix.
Further, the determining the classification relation according to the number of actual scene characteristics in the second variable set and the comparison result of the ideal cluster center number and the fuzzy membership matrix includes:
comparing the quantity q of the actual scene characteristics in the second variable set with the magnitude of the ideal cluster center number C:
if q>C, for each scene characteristic in the second set of variables, select d' kl Ideal cluster center for =0;
if q=c, each scene characteristic in the second variable set corresponds to an ideal cluster center one by one;
if q<C, selecting d for each ideal cluster center l ' k Each scene characteristic in the second set of variables=0 is categorized.
In a second aspect, the present invention provides a target class membership analysis system, including:
the variable set determining module is used for analyzing different application scene characteristics and evaluation indexes of the item to be evaluated to obtain an evaluation index set and a scene characteristic set of the item to be evaluated, which are respectively recorded as a first variable set and a second variable set;
the membership determining module is used for carrying out membership analysis on the probability of each evaluation index in the first variable set and the probability of each scene characteristic in the second variable set under different application scenes to obtain an evaluation index list under different scene characteristics;
the evaluation module is used for determining an evaluation index subset of the item to be evaluated under the preset scene characteristics based on the obtained evaluation index list under the different scene characteristics, and obtaining an evaluation result of the item to be evaluated under the preset scene characteristics based on the evaluation index subset.
In a third aspect, the present invention provides a processing device, at least comprising a processor and a memory, the memory having stored thereon a computer program, the processor executing steps of implementing the target class membership analysis method when running the computer program.
In a fourth aspect, the present invention provides a computer storage medium having stored thereon computer readable instructions executable by a processor to perform the steps of the target class membership analysis method.
Due to the adoption of the technical scheme, the invention has the following advantages: the invention realizes the membership incidence relation of fuzzy transaction occurrence by probability of the occurrence frequency of two types of transactions, then performs membership analysis based on occurrence probability and performs category optimization and correction, and provides an innovative quantitative classification method for a large number of problems in the current engineering.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Like parts are designated with like reference numerals throughout the drawings. In the drawings:
fig. 1 is a flowchart of a target class membership analysis method based on variable association according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which are obtained by a person skilled in the art based on the described embodiments of the invention, fall within the scope of protection of the invention.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the present application. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
In some embodiments of the present invention, a target class membership analysis method is provided, by constructing probability association relationships between variables, determining membership relationships between variable factors by using techniques such as fuzzy cluster analysis, and correcting membership class numbers by using target numbers, and finally quantifying membership relationships between variables according to membership values, so that references and guidance can be provided for quantitative measures of association relationships and relationships between components of any two types of events that may occur simultaneously, and a certain application prospect is provided.
In accordance therewith, in other embodiments of the present invention, a target class membership analysis system, apparatus, and medium are provided.
Example 1
As shown in fig. 1, the target class membership analysis method provided in this embodiment includes the following steps:
s1, analyzing different application scene characteristics and evaluation indexes of an item to be evaluated to obtain an evaluation index set and a scene characteristic set of the item to be evaluated, and respectively marking the evaluation index set and the scene characteristic set as a first variable set and a second variable set;
s2, performing membership degree analysis on the probability of each evaluation index in the first variable set and the probability of each scene characteristic in the variable set B under different application scenes to obtain an evaluation index list under different scene characteristics;
s3, determining an evaluation index subset of the item to be evaluated under the preset scene characteristics based on the obtained evaluation index list under the different scene characteristics, and obtaining an evaluation result of the item to be evaluated under the preset scene characteristics based on the evaluation index subset.
Preferably, in the step S2, membership degree analysis is performed on the probability of occurrence of each evaluation index in the first variable set and the probability of occurrence of each scene characteristic in the second variable set under different application scenes, so as to obtain an evaluation index list under different scene characteristics, including the following steps:
s11, constructing a variable probability matrix based on the occurrence probability of each variable in the first variable set and the second variable set;
s12, obtaining ideal membership of each evaluation index in the first variable set to ideal scene characteristics based on a fuzzy analysis method according to the variable probability matrix of the first variable set; the ideal membership refers to the probability that all evaluation indexes in the first variable set are clustered in all ideal scene characteristics obtained according to a preset classification rule;
s13, carrying out deviation correction on the ideal membership formed in the step S12 according to the variable probability matrix of the second variable set and the number of the actual scene characteristics in the second variable set to obtain the actual membership of each evaluation index in the first variable set to the actual scene characteristics in the second variable set, namely, the evaluation index list under different scene characteristics.
Preferably, in the step S11, the variable probability matrices of the first variable set and the second variable set are obtained by respectively counting the number of occurrences of the variable factors, i.e., the evaluation indexes, in the first variable set and the number of occurrences of the variable factors, i.e., the scene characteristics, in the second variable set, to obtain the probabilities of occurrence of the evaluation indexes, i.e., the scene characteristics, in the first variable set and the scene characteristics, in the second variable set, thereby obtaining two variable probability matrices.
Preferably, in the step S12, the fuzzy analysis method may be a plurality of classification techniques, including but not limited to the fuzzy K-means algorithm based on the improvement. The method for obtaining the ideal membership of each evaluation index in the first variable set to the ideal scene characteristic by adopting the improved fuzzy K-means algorithm comprises the following steps:
s121, determining the current clustering center number, wherein in the embodiment, the clustering center number is determined by adopting an enumeration method, and enumeration is carried out from 2 to a preset value.
S122, classifying the variable probability matrix of the first variable set according to the determined current clustering center number by using a fuzzy K-means algorithm, and calculating to obtain an objective function value corresponding to the current clustering center number.
The calculation formula of the objective function value is as follows:
wherein J (U, W, c) is the objective function value calculated under the conditions of the current membership degree matrix U, the clustering center matrix W and the clustering center number c. Wherein, each matrix calculation formula is:
V＝[v 1 ,...v i ,...,v n ] T ,i＝1,2,...n (2)
W＝[w 1 ,...w j ,...,w c ] T ,j＝1,2,...c (3)
in U L For membership matrices in fuzzy K-means algorithms,
calculating the membership degree of the ith evaluation index to the jth clustering center for the element in the membership degree matrix in the L-th round; w is a clustering center matrix, W j Is an element of a cluster center matrix; c is the number of clustering centers; v is the variable probability matrix of the variable set A, V i Element v representing variable probability matrix in first round of calculation i For element W in clustering center matrix W j And n is the number of evaluation indexes.
S123, repeating the steps S121-S122, and calculating to obtain a clustering center matrix, a membership matrix and an objective function value corresponding to all the clustering center numbers.
S123, selecting the cluster center number with the smallest objective function value as an ideal cluster center number C, wherein the corresponding cluster center matrix is an ideal cluster center matrix, and the corresponding membership matrix is the ideal membership of each evaluation index in the first variable set to each ideal scene characteristic.
Preferably, in the step S13, the method for performing deviation correction on the formed ideal membership includes the following steps:
s131, comparing the variable probability matrix of the second variable set with the ideal clustering center matrix to obtain a distance matrix.
Comparing the variable probability matrix of the second variable set with the ideal cluster center matrix means that the distance between each element value in the variable probability matrix of the second variable set and each element value in the ideal cluster center matrix is calculated, that is, the calculation formula of each element in the distance matrix is as follows:
d kl ＝|w k -w’ l | (5)
wherein w is k For probability value, w 'of each actual scene characteristic occurrence in the second variable set' l Is the element value in the ideal cluster center matrix.
And S132, normalizing the distance matrix obtained in the step S131 by adopting a fuzzy normalization method to obtain a fuzzy membership matrix.
The calculation formula of each value in the fuzzy membership matrix is as follows:
wherein q is the number of variable factors in the second variable set, C is the number of ideal cluster centers, d' kl And d' lk Fuzzy membership degree and d of the first clustering center and the kth scene characteristic lk And d kl All are distances from the k scene characteristics to the l cluster centers.
S133, determining a classification relation according to the number of actual scene characteristics in the second variable set and the comparison result of the ideal clustering center number by combining the fuzzy membership matrix.
Specifically, comparing the number q of actual scene characteristics in the second variable set with the number C of ideal cluster centers:
if q>C, for each scene characteristic in the second set of variables, select d' kl Ideal cluster center for =0;
if q=c, each scene characteristic in the second variable set corresponds to an ideal cluster center one by one;
if q<C, selecting d 'for each ideal cluster center' lk Each scene characteristic in the second variable set=0 is classified, if there is a certain cluster center corresponding to a plurality of scene characteristics (i.e. there is k 1 ≠k 2 So that d' lk1 ＝d’ lk2 =0), at this time, the distances between the evaluation indexes of the first variable set belonging to the cluster center and each scene characteristic are calculated, and each index belongs to the scene characteristic closest to the cluster center.
And S134, membership of the evaluation indexes in the first variable set belonging to each ideal clustering center to corresponding scene characteristics in the second variable set according to the classification relation obtained in the S133, and obtaining the actual membership of each evaluation index in the first variable set to the actual scene characteristics in the second variable set, namely an evaluation index list under different scene characteristics.
Example 2
In this embodiment, the evaluation index of the digitizing system and the target characteristics of the application scene are taken as examples, and the target class membership analysis method provided by the invention is described in detail.
S1, determining an evaluation index set and a scene characteristic set.
For example, for a digitizing system to be applied to a digitizing infrastructure scenario, its evaluation index set a is:
a= [ degree of improvement in work efficiency, degree of cost saving, degree of platform multiplexing, degree of sharing of data storage computing resources, speed of business handling, degree of interface friendliness, ease of use, speed of system response, lean management, level of maintenance, ease of operation, degree of business compliance, rate of application of each system, timeliness of system iteration, reliability of system, security of data, spreading value of digitization mode, availability of storage resources, level of enterprise culture, quality of data ]
For a digitized infrastructure scene, its target features, namely scene characteristic set B, are:
b= [ reliable operation guarantee capability, basic support capability provided, strong applicability compatibility and good user experience ]
S2, determining an evaluation index list under different scene characteristics.
S21, obtaining the probability that each evaluation index is applied to system evaluation through expert investigation as follows:
probability matrix a' = [0.78,0.5357,0.464,0.5,0.5714,0.4286,0.5,0.2143,0.3571,0.2857,0.1429,0.0714,0.5,0.4643,0.1429,0.1786,0.1429,0.1786]
The probability matrix B' = [0.244,0.402,0.084,0.024] obtained by expert investigation
S22, obtaining the membership of each index in the evaluation index set to the ideal category based on the fuzzy analysis technology according to the obtained probability matrix.
In the embodiment, an improved fuzzy K-means algorithm is adopted to determine the membership, and firstly, an enumeration method is applied to enumerate the number of the optimal clustering centers; secondly, after the number of the clustering centers is determined, clustering is carried out on the indexes by adopting a fuzzy clustering method, the distance between the indexes and the target characteristic distance is judged, the membership relation of the indexes is calculated, iteration is carried out, an ideal clustering center is selected, and membership division is carried out according to the current clustering.
Specifically, the method comprises the following steps:
and clustering the n evaluation indexes by adopting an enumeration method to obtain a classification result.
At c=2: and (3) iterating an enumeration-clustering algorithm of m, wherein the number of the clustering centers is c, c clustering is carried out on the n evaluation indexes to obtain c clustering centers, the iteration is carried out by using an enumeration method, and the probability of occurrence of the n evaluation indexes is used as a position point to classify, so as to obtain the classification result of the m clusters.
The method comprises the following specific steps:
a. the initial number of cluster centers is determined to be c=2.
b. An initial membership matrix U (0) is set.
c. And calculating to obtain a clustering center matrix W.
d. And iterating the initial membership matrix according to the obtained clustering center matrix W to obtain a new membership matrix U (L+1).
e. Judging whether a convergence formula is satisfied according to a judging formula (13): if the clustering is converged, determining a clustering center, finishing the clustering, calculating an objective function value, storing a current index membership matrix, the clustering center position and the objective function value, returning to the step a, and taking c=c+1 to continue iteration; otherwise, returning to the step c.
f. And comparing the objective function values under the cluster center numbers, taking the cluster center number with the minimum objective function value as an ideal cluster center number, and taking the corresponding cluster center position as an ideal cluster center position and a membership matrix.
And when the number of the clustering centers is 5, the objective function value is the smallest, and the membership degree matrix and the ideal clustering center position are obtained.
center＝[0.1876 0.5068 0.7822 0.131 0.3117] T
S23, performing deviation correction on the formed membership according to the number of the variable factors in the target feature set to obtain the actual membership of the evaluation index variable factors to the target feature variable factors. The calculation results are shown in tables 1 and 2 below.
TABLE 1 membership of scene Properties to clustering centers
TABLE 2 fuzzy membership degree
Based on the calculation, according to a feature center classification method based on deviation tolerance, the corresponding evaluation indexes in the ideal cluster center matrix are classified into 4 types, indexes belonging to the first cluster and the fourth cluster are obtained after processing and are uniformly placed in the actual fourth type feature center, the second cluster corresponds to the actual second type feature center, the third cluster corresponds to the actual third type feature center, and the fifth cluster corresponds to the actual first type feature center. Finally, the index list distribution of the digital infrastructure scene is obtained as follows:
each evaluation index in the index set can be judged to be respectively subordinate to the [ 32 22 22 22 133 34 42 24 14 1] class according to the obtained membership matrix.
Then, the actual membership of each evaluation index in the variable set a to each target feature in the variable set B is constructed, and a scene-based feature quantization and index extraction list is generated as shown in the following table 3.
Table 3 scene-based feature quantization and index extraction manifest
And S3, evaluating the item to be evaluated under the preset scene characteristic based on the obtained actual membership.
Example 3
In contrast, the embodiment 1 provides a target class membership analysis method, and the present embodiment provides a target class membership analysis system. The system provided in this embodiment may implement the target class membership analysis method of embodiment 1, where the system may be implemented by software, hardware, or a combination of software and hardware. For example, the system may include integrated or separate functional modules or functional units to perform the corresponding steps in the methods of embodiment 1. Since the system of this embodiment is substantially similar to the method embodiment, the description of this embodiment is relatively simple, and the relevant points may be found in part in the description of embodiment 1, which is provided by way of illustration only.
The target class membership analysis system provided in this embodiment includes:
the variable set determining module is used for analyzing different application scene characteristics and evaluation indexes of the item to be evaluated to obtain an evaluation index set and a scene characteristic set of the item to be evaluated, which are respectively recorded as a first variable set and a second variable set;
the membership determining module is used for carrying out membership analysis on the probability of each evaluation index in the first variable set and the probability of each scene characteristic in the second variable set under different application scenes to obtain an evaluation index list under different scene characteristics;
the evaluation module is used for determining an evaluation index subset of the item to be evaluated under the preset scene characteristics based on the obtained evaluation index list under the different scene characteristics, and obtaining an evaluation result of the item to be evaluated under the preset scene characteristics based on the evaluation index subset.
Example 4
The present embodiment provides a processing device corresponding to the target class membership analysis method provided in the present embodiment 1, where the processing device may be a processing device for a client, for example, a mobile phone, a notebook computer, a tablet computer, a desktop computer, or the like, to execute the method of embodiment 1.
The processing device comprises a processor, a memory, a communication interface and a bus, wherein the processor, the memory and the communication interface are connected through the bus so as to complete communication among each other. The memory stores a computer program executable on the processor, and when the processor executes the computer program, the target class membership analysis method provided in embodiment 1 is executed.
In some embodiments, the memory may be a high-speed random access memory (RAM: random Access Memory), and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
In other embodiments, the processor may be a Central Processing Unit (CPU), a Digital Signal Processor (DSP), or other general purpose processor, which is not limited herein.
Example 5
The target class membership analysis method of this embodiment 1 may be embodied as a computer program product, which may include a computer readable storage medium having computer readable program instructions loaded thereon for performing the target class membership analysis method of this embodiment 1.
The computer readable storage medium may be a tangible device that retains and stores instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination of the preceding.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. The object class membership analysis method is characterized by comprising the following steps of:
analyzing different application scene characteristics and evaluation indexes of the item to be evaluated to obtain an evaluation index set and a scene characteristic set of the item to be evaluated, marking the evaluation index set as a first variable set, and marking the scene characteristic set as a second variable set;
performing membership degree analysis on the probability of each evaluation index in the first variable set and the probability of each scene characteristic in the second variable set under different application scenes to obtain an evaluation index list under different scene characteristics;
and determining an evaluation index subset of the item to be evaluated under the preset scene characteristic based on the evaluation index list, and obtaining an evaluation result of the item to be evaluated under the preset scene characteristic based on the evaluation index subset.
2. The method for analyzing membership of a target class according to claim 1, wherein the membership analysis is performed on the probability of occurrence of each evaluation index in the first variable set and the probability of occurrence of each scene characteristic in the second variable set under different application scenes to obtain an evaluation index list under different scene characteristics, and the method comprises the following steps:
constructing a variable probability matrix based on the probability of each variable in the first variable set and the second variable set;
obtaining ideal membership of each evaluation index in the first variable set to ideal scene characteristics based on a fuzzy analysis method according to the variable probability matrix of the first variable set; the ideal membership refers to the probability that all evaluation indexes in the first variable set are clustered in all ideal scene characteristics obtained according to a preset classification rule;
and carrying out deviation correction on the formed ideal membership according to the variable probability matrix of the second variable set and the number of the actual scene characteristics in the second variable set to obtain the actual membership of each evaluation index in the first variable set to the actual scene characteristics in the second variable set, and taking the actual membership as an evaluation index list under different scene characteristics.
3. The method of claim 2, wherein the obtaining, based on the fuzzy analysis method, the ideal membership of each evaluation index in the first variable set to the ideal scene characteristic according to the variable probability matrix of the first variable set comprises:
determining the number of current clustering centers, classifying the variable probability matrix of the first variable set according to the determined number of the current clustering centers by using a fuzzy K-means algorithm, and calculating to obtain a target function value corresponding to the number of the current clustering centers;
calculating to obtain a cluster center matrix, a membership matrix and a target function value corresponding to all the cluster center numbers;
and selecting the cluster center number with the smallest objective function value as an ideal cluster center number, wherein the corresponding cluster center matrix is an ideal cluster center matrix, and the corresponding membership matrix is used as an ideal membership of each evaluation index in the first variable set to each ideal scene characteristic.
4. The method of claim 3, wherein the objective function value calculation formula is:
wherein J (U, W, c) is the objective function value calculated under the conditions of the current membership degree matrix U, the clustering center matrix W and the clustering center number c.
5. The method for analyzing membership of a target class according to claim 3, wherein performing bias correction on the formed ideal membership according to the variable probability matrix of the second variable set and the number of actual scene characteristics in the second variable set includes:
comparing the variable probability matrix of the second variable set with the ideal clustering center matrix to obtain a distance matrix;
normalizing the obtained distance matrix by a fuzzy normalization method to obtain a fuzzy membership matrix;
determining a classification relation according to the number of actual scene characteristics in the second variable set and the comparison result of the ideal clustering center number by combining the fuzzy membership matrix;
and (3) membership of the evaluation indexes in the first variable set belonging to each ideal cluster center to corresponding scene characteristics in the second variable set according to the obtained classification relation, and obtaining the actual membership of each evaluation index in the first variable set to the actual scene characteristics in the second variable set.
6. The method of claim 5, wherein the formula for calculating each element in the distance matrix is as follows:
d kl ＝|w k -w' l |
wherein w is k For probability value, w 'of each actual scene characteristic occurrence in the second variable set' l Is the element value in the ideal cluster center matrix.
7. The method of claim 5, wherein determining the classification relationship according to the comparison result of the number of actual scene characteristics and the number of ideal cluster centers in the second variable set, in combination with the fuzzy membership matrix, includes:
comparing the quantity q of the actual scene characteristics in the second variable set with the magnitude of the ideal cluster center number C:
if q>C, for each scene characteristic in the second set of variables, select d' kl Ideal cluster center for =0;
if q=c, each scene characteristic in the second variable set corresponds to an ideal cluster center one by one;
if q<C, selecting d 'for each ideal cluster center' lk Each scene characteristic in the second set of variables=0 is categorized.
8. A target class membership analysis system, comprising:
the variable set determining module is used for analyzing different application scene characteristics and evaluation indexes of the item to be evaluated to obtain an evaluation index set and a scene characteristic set of the item to be evaluated, which are respectively recorded as a first variable set and a second variable set;
the membership determining module is used for carrying out membership analysis on the probability of each evaluation index in the first variable set and the probability of each scene characteristic in the second variable set under different application scenes to obtain an evaluation index list under different scene characteristics;
the evaluation module is used for determining an evaluation index subset of the item to be evaluated under the preset scene characteristics based on the obtained evaluation index list under the different scene characteristics, and obtaining an evaluation result of the item to be evaluated under the preset scene characteristics based on the evaluation index subset.
9. A processing device comprising at least a processor and a memory, said memory having stored thereon a computer program, characterized in that the processor executes the steps of the object class membership analysis method according to any one of claims 1 to 7 when running said computer program.
10. A computer storage medium having stored thereon computer readable instructions executable by a processor to perform the steps of the target class membership analysis method according to any one of claims 1 to 7.
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