A method, system, device, and storage medium for target category membership analysis

Abstract

This invention relates to a target category membership analysis method, system, device, and storage medium, comprising the following steps: analyzing the characteristics of different application scenarios and evaluation indicators of the project to be evaluated, obtaining a set of evaluation indicators and a set of scenario characteristics for the project to be evaluated, denoted as the first variable set and the second variable set, respectively; performing membership analysis on the probability of occurrence of each evaluation indicator in the first variable set and the probability of occurrence of each scenario characteristic in the second variable set under different application scenarios, obtaining a list of evaluation indicators under different scenario characteristics; determining a subset of evaluation indicators for the project to be evaluated under preset scenario characteristics based on the list of evaluation indicators; and obtaining the evaluation result of the project to be evaluated under preset scenario characteristics based on the subset of evaluation indicators. This invention, through membership analysis based on the probability of occurrence, quantifies and classifies the membership relationships of fuzzy events occurring in engineering, and can be widely applied in the field of smart grid data mining and classification.

Description

Technical Field

[0001] This invention relates to the field of data mining classification, and particularly to the subordinate relationship between factors in two sets of related variables. Specifically, it relates to a method, system, device, and storage medium for analyzing the membership degree of a target category based on variable association. Background Technology

[0002] Currently, in the field of classification, methods such as cluster analysis, principal component analysis, expert scoring, and SVM analysis all classify and analyze data from real-world data. However, in the field of engineering analysis, there are many events that exhibit correlation but whose relationships are not clearly defined. Examples include the relationship between the frequency of event occurrences and various factors, or defining the degree of membership between each evaluation indicator in a given evaluation system and the category of the object being evaluated. Determining such correlations or ambiguous memberships is typically done using methods like expert scoring, which are highly subjective. Summary of the Invention

[0003] To address the aforementioned problems, the purpose of this invention is to provide a target category membership analysis method, system, device, and storage medium based on variable association. Through membership analysis based on occurrence probability, the membership relationships of ambiguous events occurring in engineering can be quantitatively classified.

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] In a first aspect, the present invention provides a target category membership analysis method, comprising the following steps:

[0006] The different application scenario characteristics and evaluation indicators of the project to be evaluated are analyzed to obtain the evaluation indicator set and scenario characteristic set of the project to be evaluated. The evaluation indicator set is denoted as the first variable set and the scenario characteristic set is denoted as the second variable set.

[0007] Membership analysis was performed on the probability of each evaluation index appearing in the first variable set and the probability of each scenario characteristic in the second variable set under different application scenarios to obtain a list of evaluation indicators under different scenario characteristics.

[0008] Based on the list of evaluation indicators, a subset of evaluation indicators for the project to be evaluated under the characteristics of a preset scenario is determined, and the evaluation results of the project to be evaluated under the characteristics of the preset scenario are obtained based on the subset of evaluation indicators.

[0009] Furthermore, the step of performing membership analysis on the probability of each evaluation indicator appearing in the first variable set and the probability of each scenario characteristic in the second variable set under different application scenarios to obtain a list of evaluation indicators under different scenario characteristics includes the following steps:

[0010] Construct a variable probability matrix based on the probability of each variable occurring in the first and second variable sets;

[0011] Based on the variable probability matrix of the first variable set, the ideal membership relationship of each evaluation index in the first variable set to the ideal scenario characteristics is obtained using the fuzzy analysis method; where the ideal membership relationship refers to the probability that each evaluation index in the first variable set is grouped into each ideal scenario characteristic obtained according to the preset classification rules.

[0012] Based on the probability matrix of the variables in the second variable set and the number of actual scenario characteristics in the second variable set, the ideal membership relationship is corrected for deviation, and the actual membership relationship of each evaluation index in the first variable set to the actual scenario characteristics in the second variable set is obtained, which serves as a list of evaluation indicators under different scenario characteristics.

[0013] Furthermore, the step of obtaining the ideal membership relationship between each evaluation index in the first variable set and the characteristics of the ideal scenario based on the variable probability matrix of the first variable set using fuzzy analysis includes:

[0014] Determine the current number of cluster centers, and classify the variable probability matrix of the first variable set using the fuzzy K-means algorithm according to the determined current number of cluster centers, and calculate the objective function value corresponding to the current number of cluster centers;

[0015] The cluster center matrix, membership matrix, and objective function value corresponding to the number of cluster centers are calculated.

[0016] The cluster center number with the smallest objective function value is selected as the ideal cluster center number, and its corresponding cluster center matrix is ​​the ideal cluster center matrix. The corresponding membership matrix is ​​used as the ideal membership relationship of each evaluation index in the first variable set to each ideal scenario characteristic.

[0017] Furthermore, the formula for calculating the objective function value is as follows:

[0018]

[0019] In the formula, J(U,W,c) is the objective function value calculated under the current membership matrix U, cluster center matrix W, and number of cluster centers c.

[0020] Furthermore, the step of correcting the deviation of the formed ideal membership relationship based on the variable probability matrix of the second variable set and the number of actual scene characteristics in the second variable set includes:

[0021] The distance matrix is ​​obtained by comparing the probability matrix of the second set of variables with the ideal cluster center matrix.

[0022] Normalize the obtained distance matrix using the fuzzy normalization method to obtain a fuzzy membership matrix;

[0023] Determine the classification relationship according to the comparison result between the number of actual scenario characteristics in the second variable set and the number of ideal clustering centers, in combination with the fuzzy membership matrix;

[0024] Subordinate the evaluation indicators in the first variable set belonging to each ideal clustering center to the corresponding scenario characteristics in the second variable set according to the obtained classification relationship, and obtain the actual subordination relationship of each evaluation indicator in the first variable set to the actual scenario characteristics in the second variable set.

[0025] Further, the calculation formula for each element in the distance matrix is as follows:

[0026] d kl =|w k -w’ l |

[0027] In the formula, w k is the probability value of the occurrence of each actual scenario characteristic in the second variable set, and w’ l is the element value in the ideal clustering center matrix.

[0028] Further, the determining the classification relationship according to the comparison result between the number of actual scenario characteristics in the second variable set and the number of ideal clustering centers, in combination with the fuzzy membership matrix, includes:

[0029] Compare the size of the number q of actual scenario characteristics in the second variable set with the number C of ideal clustering centers:

[0030] If q > C, for each scenario characteristic in the second variable set, select the ideal clustering center with d' kl =0 for classification;

[0031] If q = C, each scenario characteristic in the second variable set corresponds one-to-one with the ideal clustering center;

[0032] If q < C, then for each ideal clustering center, select each scenario characteristic in the second variable set with d l ' k =0 for classification.

[0033] In the second aspect, the present invention provides a target category membership degree analysis system, including:

[0034] A variable set determination module, configured to analyze different application scenario characteristics and evaluation indicators of the item to be evaluated, and obtain an evaluation indicator set and a scenario characteristic set of the item to be evaluated, denoted as the first variable set and the second variable set respectively;

[0035] The membership determination module is used to perform membership analysis on the probability of each evaluation indicator appearing in the first variable set and the probability of each scenario characteristic in the second variable set under different application scenarios, so as to obtain a list of evaluation indicators under different scenario characteristics.

[0036] The evaluation module is used to determine the subset of evaluation indicators for the project to be evaluated under the preset scenario characteristics based on the obtained list of evaluation indicators under different scenario characteristics, and to obtain the evaluation result of the project to be evaluated under the preset scenario characteristics based on the subset of evaluation indicators.

[0037] Thirdly, the present invention provides a processing device, the processing device including at least a processor and a memory, the memory storing a computer program, and the processor executing the steps of the target category membership analysis method when running the computer program.

[0038] Fourthly, the present invention provides a computer storage medium storing computer-readable instructions thereon, which can be executed by a processor to implement the steps of the target category membership analysis method.

[0039] The present invention has the following advantages due to the adoption of the above technical solutions: The present invention realizes the membership relationship of fuzzy transaction occurrences by probabilizing the frequency of two types of transactions, performing membership degree analysis based on the occurrence probability, and optimizing and correcting the categories. This provides an innovative quantitative classification method for such problems that occur frequently in current engineering. Attached Figure Description

[0040] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. In the drawings:

[0041] Figure 1 This is a flowchart of the target category membership analysis method based on variable association provided in the embodiments of the present invention. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.

[0043] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0044] In some embodiments of the present invention, a target category membership degree analysis method is provided. By constructing probabilistic association relationships between variables, using techniques such as fuzzy clustering analysis to determine the membership relationships between variable factors, and correcting the number of membership relationship categories based on the target quantity, the method quantifies the membership relationships between variables based on the membership degree values. This method can provide reference and guidance for the association relationships between the components of any two possible events that may occur simultaneously, as well as the quantitative measurement of these relationships, and has certain application prospects.

[0045] Correspondingly, in other embodiments of the present invention, a target category membership analysis system, device, and medium are provided.

[0046] Example 1

[0047] like Figure 1 As shown in the figure, this embodiment provides a target category membership analysis method, which includes the following steps:

[0048] S1. Analyze the different application scenario characteristics and evaluation indicators of the project to be evaluated to obtain the set of evaluation indicators and the set of scenario characteristics of the project to be evaluated, which are denoted as the first variable set and the second variable set, respectively.

[0049] S2. Perform membership analysis on the probability of each evaluation index appearing in the first variable set and the probability of each scenario characteristic in the variable set B under different application scenarios to obtain a list of evaluation indicators under different scenario characteristics.

[0050] S3. Based on the list of evaluation indicators under different scenario characteristics, determine the subset of evaluation indicators for the project to be evaluated under the preset scenario characteristics, and obtain the evaluation results of the project to be evaluated under the preset scenario characteristics based on the subset of evaluation indicators.

[0051] Preferably, in step S2 above, a membership analysis is performed on the probability of each evaluation indicator appearing in the first variable set and the probability of each scenario characteristic in the second variable set under different application scenarios to obtain a list of evaluation indicators under different scenario characteristics, including the following steps:

[0052] S11. Construct a variable probability matrix based on the probability of each variable occurring in the first and second variable sets;

[0053] S12. Based on the variable probability matrix of the first variable set, obtain the ideal membership relationship of each evaluation index in the first variable set to the ideal scenario characteristics using fuzzy analysis; where the ideal membership relationship refers to the probability that each evaluation index in the first variable set is grouped into each ideal scenario characteristic obtained according to the preset classification rules.

[0054] S13. Based on the variable probability matrix of the second variable set and the number of actual scenario characteristics in the second variable set, the ideal membership relationship formed in step S12 is corrected to obtain the actual membership relationship of each evaluation index in the first variable set to the actual scenario characteristics in the second variable set, that is, the list of evaluation indicators under different scenario characteristics.

[0055] Preferably, in step S11 above, the probability matrices of the first variable set and the second variable set are obtained by statistically analyzing the number of occurrences of the variable factors (i.e., evaluation indicators) in the first variable set and the number of occurrences of the variable factors (i.e., each scenario characteristic) in the second variable set, respectively, to obtain the probability of occurrence of each evaluation indicator in the first variable set and each scenario characteristic in the second variable set, thereby obtaining two probability matrices.

[0056] Preferably, in step S12 above, the fuzzy analysis method can be a variety of classification techniques, including but not limited to an improved fuzzy K-means algorithm. The method for obtaining the ideal membership relationship between each evaluation index in the first variable set and the characteristics of the ideal scene using the improved fuzzy K-means algorithm includes:

[0057] S121. Determine the current number of cluster centers. In this embodiment, the number of cluster centers is determined by enumeration, from 2 to a preset value.

[0058] S122. Based on the determined number of current cluster centers, classify the variable probability matrix of the first variable set using the fuzzy K-means algorithm, and calculate the objective function value corresponding to the current number of cluster centers.

[0059] The formula for calculating the objective function value is as follows:

[0060]

[0061] In the formula, J(U,W,c) is the objective function value calculated given the current membership matrix U, cluster center matrix W, and number of cluster centers c. The formulas for calculating each matrix are as follows:

[0062] V = [v1,...v] i ,...,v n ] T ,i=1,2,...n (2)

[0063] W = [w1,...wj ,...,w c ] T ,j=1,2,...c (3)

[0064]

[0065]

[0066] In the formula, U L This is the membership matrix in the fuzzy K-means algorithm. Let w be an element in the membership matrix during the Lth round of calculation, representing the membership degree of the i-th evaluation index to the j-th cluster center; W is the cluster center matrix, w j is the element of the cluster center matrix; c is the number of cluster centers; V is the probability matrix of the variable set A, v i v represents the element v of the probability matrix of variables in the l-th round of calculation. i For the elements w in the cluster center matrix W j The membership value is denoted by n, where n is the number of evaluation indicators.

[0067] S123. Repeat steps S121 to S122 to calculate the cluster center matrix, membership matrix, and objective function value corresponding to the number of cluster centers for all clusters.

[0068] S123. Select the cluster center number with the smallest objective function value as the ideal cluster center number C. The corresponding cluster center matrix is ​​the ideal cluster center matrix, and the corresponding membership matrix is ​​the ideal membership relationship of each evaluation index in the first variable set to each ideal scenario characteristic.

[0069] Preferably, the method for correcting the deviation of the formed ideal membership relationship in step S13 above includes the following steps:

[0070] S131. Compare the variable probability matrix of the second variable set with the ideal cluster center matrix to obtain the distance matrix.

[0071] The comparison between the probability matrix of the second set of variables and the ideal cluster center matrix involves calculating the distance between each element in the probability matrix of the second set of variables and the values ​​of each cluster center element in the ideal cluster center matrix. The formula for calculating each element in the distance matrix is ​​as follows:

[0072] d kl =|w k -w' l | (5)

[0073] In the formula, w k w' represents the probability value of each actual scenario characteristic occurring in the second variable set. lis the element value in the ideal clustering center matrix.

[0074] S132. Normalize the distance matrix obtained in step S131 using the fuzzy normalization method to obtain a fuzzy membership matrix.

[0075] Among them, the calculation formula for each value in the fuzzy membership matrix is as follows:

[0076]

[0077]

[0078] In the formula, q is the number of variable factors in the second variable set, C is the number of ideal clustering centers, d’ kl and d’ lk are both the fuzzy membership degrees of the l-th clustering center and the k-th scenario characteristic, d lk and d kl are both the distances between the l-th clustering center and the k-th scenario characteristic.

[0079] S133. Determine the classification relationship based on the comparison result of the number of actual scenario characteristics in the second variable set and the number of ideal clustering centers, in combination with the fuzzy membership matrix.

[0080] Specifically, compare the size of the number q of actual scenario characteristics in the second variable set with the number C of ideal clustering centers:

[0081] If q > C, for each scenario characteristic in the second variable set, select the ideal clustering center with d’ kl = 0 for classification;

[0082] If q = C, then each scenario characteristic in the second variable set corresponds one-to-one with the ideal clustering center;

[0083] If q < C, then for each ideal clustering center, select each scenario characteristic in the second variable set with d’ lk = 0 for classification. If there is a certain clustering center corresponding to multiple scenario characteristics (that is, there exist k1 ≠ k2 such that d’ lk1 = d’ lk2 = 0), at this time, calculate the evaluation index of the first variable set belonging to this clustering center and the distance from each scenario characteristic, and each index belongs to the scenario characteristic with the closest distance.

[0084] S134. Subordinate the evaluation indexes in the first variable set belonging to each ideal clustering center to the corresponding scenario characteristics in the second variable set according to the classification relationship obtained in S133, to obtain the actual membership relationship of each evaluation index in the first variable set to the actual scenario characteristics in the second variable set, that is, the evaluation index list under different scenario characteristics.

[0085] Example 2

[0086] This embodiment uses the evaluation indicators of digital systems and their application scenario target characteristics as examples to provide a detailed introduction to the target category membership analysis method proposed in this invention.

[0087] S1. Determine the set of evaluation indicators and the set of scenario characteristics.

[0088] For example, for a digital system applied to a digital infrastructure scenario, its evaluation index set A is:

[0089] A = [Work efficiency improvement, cost savings, platform reusability, data storage and computing resource sharing, business processing speed, user-friendliness, ease of use, system response speed, lean management, operation and maintenance quality assurance level, operability, business fit, application rate of each system, timeliness of system iteration, system reliability, data security, value of digital model promotion, storage resource utilization, corporate culture level, data quality]

[0090] For digital infrastructure scenarios, the target characteristics, i.e., the set of scenario characteristics B, are:

[0091] B = [Reliable operational support capabilities, provides fundamental support, strong applicability and compatibility, and a good user experience]

[0092] S2. Determine the list of evaluation indicators for different scenario characteristics.

[0093] S21. Based on expert research, the probability of each evaluation indicator being applied to system evaluation is as follows:

[0094] The 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]

[0095] The probability matrix B' obtained through expert research is [0.244, 0.402, 0.084, 0.024].

[0096] S22. Based on the obtained probability matrix, the membership relationship of each index in the evaluation index set to the ideal category is obtained using fuzzy analysis technology.

[0097] This embodiment uses an improved fuzzy K-means algorithm to determine membership relationships. First, an enumeration method is used to enumerate the number of optimal cluster centers. Second, after determining the number of cluster centers, fuzzy clustering is used to cluster the indicators, judge the distance to the target feature, calculate the membership relationship of the indicators, iterate, select the ideal cluster center, and divide the membership degree according to the current cluster.

[0098] Specifically, it includes the following steps:

[0099] The enumeration method is used to cluster the n evaluation indicators to obtain the classification results.

[0100] An enumeration-clustering algorithm with c=2:m is used for iteration, with c cluster centers. The n evaluation indicators are clustered into c clusters to obtain c cluster centers. The enumeration method is used for iteration, and the probability of the occurrence of the n evaluation indicators is used as the location point for classification to obtain the classification result of m clusters.

[0101] The specific steps include:

[0102] a. Determine the initial number of cluster centers as c = 2.

[0103] b. Set the initial membership matrix U(0).

[0104] c. Calculate the cluster center matrix W.

[0105] d. Iterate the initial membership matrix based on the obtained cluster center matrix W to obtain a new membership matrix U(L+1).

[0106]

[0107]

[0108] e. Determine whether the convergence formula is satisfied according to the judgment formula (13): If convergence is achieved, the cluster center is determined, the clustering is completed, the objective function value is calculated, the current index membership matrix and the cluster center position and objective function value are saved, and then return to step a, take c = c + 1 and continue the iteration; otherwise, return to step c.

[0109] f. Compare the objective function values ​​under each cluster center number, and take the cluster center number with the smallest objective function value as the ideal cluster center number. The corresponding cluster center positions are the ideal cluster center positions and membership matrix.

[0110] Analysis revealed that the objective function value is minimized when the number of cluster centers is 5, yielding the membership matrix and the ideal cluster center positions at this point.

[0111] center=[0.1876 0.5068 0.7822 0.131 0.3117]T

[0112] S23. Based on the number of variable factors in the target feature set, the established membership relationships are corrected for bias to obtain the actual membership relationships between the evaluation index variable factors and the target feature variable factors. The calculation results are shown in Tables 1 and 2 below.

[0113] Table 1. Relationship between scene characteristics and cluster centers

[0114]

[0115] Table 2 Fuzzy Membership Degrees

[0116]

[0117]

[0118] Based on the above calculations, and using the bias-tolerant feature center classification method, the evaluation indicators corresponding to the ideal cluster center matrix are divided into four categories. After processing, indicators belonging to the first and fourth clusters are uniformly placed into the actual fourth category of feature centers. The second cluster corresponds to the actual second category of feature centers, the third cluster corresponds to the actual third category of feature centers, and the fifth cluster corresponds to the actual first category of feature centers. The final distribution of the indicator list for the digital infrastructure scenario is shown in the table below.

[0119] The membership matrix obtained can be used to determine that each evaluation index in the index set belongs to the class [3 2 2 2 2 2 2 2 13 3 3 4 4 2 2 4 1 4 1].

[0120] Then, the actual membership relationship between each evaluation index in variable set A and each target feature in variable set B is constructed, and the feature quantification and index extraction list based on the scenario is generated as shown in Table 3 below.

[0121] Table 3. Feature Quantization and Index Extraction List Based on Scenario

[0122]

[0123] S3. Based on the obtained actual affiliation, evaluate the project to be evaluated under the preset scenario characteristics.

[0124] Example 3

[0125] The above-described embodiment 1 provides a target category membership analysis method. Correspondingly, this embodiment provides a target category membership analysis system. The system provided in this embodiment can implement the target category membership analysis method of embodiment 1. The system can be implemented through software, hardware, or a combination of both. For example, the system may include integrated or separate functional modules or units to execute the corresponding steps in the methods of embodiment 1. Since the system in this embodiment is basically similar to the method embodiment, the description process in this embodiment is relatively simple. Relevant details can be found in the description of embodiment 1. The system embodiment provided in this embodiment is merely illustrative.

[0126] The target category membership analysis system provided in this embodiment includes:

[0127] The variable set determination module is used to analyze the different application scenario characteristics and evaluation indicators of the project to be evaluated, and obtain the evaluation indicator set and scenario characteristic set of the project to be evaluated, which are denoted as the first variable set and the second variable set, respectively.

[0128] The membership determination module is used to perform membership analysis on the probability of each evaluation indicator appearing in the first variable set and the probability of each scenario characteristic in the second variable set under different application scenarios, so as to obtain a list of evaluation indicators under different scenario characteristics.

[0129] The evaluation module is used to determine the subset of evaluation indicators for the project to be evaluated under the preset scenario characteristics based on the obtained list of evaluation indicators under different scenario characteristics, and to obtain the evaluation result of the project to be evaluated under the preset scenario characteristics based on the subset of evaluation indicators.

[0130] Example 4

[0131] This embodiment provides a processing device corresponding to the target category membership analysis method provided in Embodiment 1. The processing device can be a client-side processing device, such as a mobile phone, laptop, tablet computer, desktop computer, etc., to execute the method of Embodiment 1.

[0132] The processing device includes a processor, a memory, a communication interface, and a bus. The processor, memory, and communication interface are connected via the bus to enable communication between them. The memory stores a computer program that can run on the processor. When the processor runs the computer program, it executes the target category membership analysis method provided in Embodiment 1.

[0133] In some embodiments, the memory may be high-speed random access memory (RAM), and may also include non-volatile memory, such as at least one disk storage device.

[0134] In other embodiments, the processor can be a general-purpose processor of various types, such as a central processing unit (CPU) or a digital signal processor (DSP), and is not limited thereto.

[0135] Example 5

[0136] The target category membership analysis method of this embodiment 1 can be specifically implemented as a computer program product. The computer program product may include a computer-readable storage medium on which computer-readable program instructions for executing the target category membership analysis method of this embodiment 1 are loaded.

[0137] A computer-readable storage medium can be a tangible device that holds and stores instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof.

[0138] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can 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, etc.) containing computer-usable program code.

[0139] This 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 block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can 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, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0140] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0141] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the functions specified in one or more boxes. Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for analyzing the membership degree of a target category, characterized in that, Includes the following steps: The evaluation process involves analyzing the characteristics of different application scenarios and evaluation indicators of the project to be evaluated, resulting in a set of evaluation indicators and a set of scenario characteristics. The set of evaluation indicators is designated as the first variable set, and the set of scenario characteristics is designated as the second variable set. The project to be evaluated is a digital system, and the application scenario is the digital system applied to digital infrastructure. The first variable set includes [efficiency improvement, cost savings, platform reusability, data storage and computing resource sharing, business processing speed, user-friendliness, ease of use, system response speed, lean management, operation and maintenance quality assurance level, operability, business fit, application rate of each system, system iteration timeliness, system reliability, data security, digital model promotion value, storage resource utilization, corporate culture level, and data quality]. The second variable set includes [reliable operational support capabilities, strong applicability and compatibility, and good user experience]. Membership analysis was performed on the probability of each evaluation index appearing in the first variable set and the probability of each scenario characteristic in the second variable set under different application scenarios to obtain a list of evaluation indicators under different scenario characteristics. Based on the list of evaluation indicators, a subset of evaluation indicators for the project to be evaluated under the characteristics of the preset scenario is determined, and the evaluation results of the project to be evaluated under the characteristics of the preset scenario are obtained based on the subset of evaluation indicators. The step of performing membership analysis on the probability of each evaluation indicator appearing in the first variable set and the probability of each scenario characteristic in the second variable set under different application scenarios to obtain a list of evaluation indicators under different scenario characteristics includes the following steps: Construct a variable probability matrix based on the probability of each variable occurring in the first and second variable sets; Based on the variable probability matrix of the first variable set, the ideal membership relationship of each evaluation index in the first variable set to the ideal scenario characteristics is obtained using the fuzzy analysis method; where the ideal membership relationship refers to the probability that each evaluation index in the first variable set is grouped into each ideal scenario characteristic obtained according to the preset classification rules. Based on the variable probability matrix of the second variable set and the number of actual scenario characteristics in the second variable set, the ideal membership relationship is corrected for deviation, and the actual membership relationship of each evaluation index in the first variable set to the actual scenario characteristics in the second variable set is obtained, which serves as a list of evaluation indicators under different scenario characteristics. The step of correcting the deviation of the formed ideal membership relationship based on the variable probability matrix of the second variable set and the number of actual scene characteristics in the second variable set includes: The distance matrix is ​​obtained by comparing the probability matrix of the second set of variables with the ideal cluster center matrix. The distance matrix is ​​normalized using the fuzzy normalization method to obtain the fuzzy membership matrix. Based on the comparison results of the number of actual scene characteristics in the second variable set and the number of ideal cluster centers, and combined with the fuzzy membership matrix, the classification relationship is determined. The evaluation indicators in the first set of variables belonging to each ideal cluster center are assigned to the corresponding scene characteristics in the second set of variables according to the obtained classification relationship, so as to obtain the actual membership relationship of each evaluation indicator in the first set of variables to the actual scene characteristics in the second set of variables. The formulas for calculating each element in the distance matrix are as follows: In the formula, The probability values ​​of each actual scenario characteristic occurring in the second variable set. These are the element values ​​in the ideal cluster center matrix; The process of determining the classification relationship based on the comparison results of the number of actual scene characteristics in the second variable set and the number of ideal cluster centers, combined with the fuzzy membership matrix, includes: Compare the number of actual scenario characteristics in the second set of variables. The size of the number of cluster centers C compared to the ideal number of cluster centers: like >C, for each scenario characteristic in the second variable set, select The ideal cluster centers are used for classification; If q=C, then each scenario characteristic in the second variable set corresponds one-to-one with the ideal cluster center; If q < C, then for each ideal cluster center, classify each scenario feature in the second variable set of .

2. The target category membership analysis method as described in claim 1, characterized in that, The step of obtaining the ideal membership relationship of each evaluation index in the first variable set to the characteristics of the ideal scenario based on the variable probability matrix of the first variable set and using fuzzy analysis includes: Determine the current number of cluster centers, and classify the variable probability matrix of the first variable set using the fuzzy K-means algorithm according to the determined current number of cluster centers, and calculate the objective function value corresponding to the current number of cluster centers; The cluster center matrix, membership matrix, and objective function value corresponding to the number of cluster centers are calculated. The cluster center number with the smallest objective function value is selected as the ideal cluster center number, and its corresponding cluster center matrix is ​​the ideal cluster center matrix. The corresponding membership matrix is ​​used as the ideal membership relationship of each evaluation index in the first variable set to each ideal scenario characteristic.

3. The target category membership analysis method as described in claim 2, characterized in that, The formula for calculating the objective function value is as follows: In the formula, The current membership matrix Cluster center matrix The objective function value calculated given the number of cluster centers c.

4. A target category membership analysis system, characterized in that, include: The variable set determination module is used to analyze the characteristics of different application scenarios and evaluation indicators of the project to be evaluated, and obtain the evaluation indicator set and scenario characteristic set of the project to be evaluated, which are respectively denoted as the first variable set and the second variable set; wherein, the project to be evaluated is a digital system, and the application scenario is the application of the digital system to digital infrastructure scenarios. The first variable set includes [work efficiency improvement, cost saving, platform reusability, data storage and computing resource sharing, business processing speed, interface friendliness, ease of use, system response speed, lean management, operation and maintenance quality assurance level, operability, business fit, application rate of each system, system iteration timeliness, system reliability, data security, digital model promotion value, storage resource utilization, corporate culture level, and data quality]; the second variable set includes [reliable operation guarantee capability, basic support capability, strong applicability and compatibility, and good user experience]; The membership determination module is used to perform membership analysis on the probability of each evaluation indicator appearing in the first variable set and the probability of each scenario characteristic in the second variable set under different application scenarios, so as to obtain a list of evaluation indicators under different scenario characteristics. The evaluation module is used to determine the subset of evaluation indicators for the project to be evaluated under the preset scenario characteristics based on the list of evaluation indicators obtained under different scenario characteristics, and to obtain the evaluation result of the project to be evaluated under the preset scenario characteristics based on the subset of evaluation indicators. The step of performing membership analysis on the probability of each evaluation indicator appearing in the first variable set and the probability of each scenario characteristic in the second variable set under different application scenarios to obtain a list of evaluation indicators under different scenario characteristics includes the following steps: Construct a variable probability matrix based on the probability of each variable occurring in the first and second variable sets; Based on the variable probability matrix of the first variable set, the ideal membership relationship of each evaluation index in the first variable set to the ideal scenario characteristics is obtained using the fuzzy analysis method; where the ideal membership relationship refers to the probability that each evaluation index in the first variable set is grouped into each ideal scenario characteristic obtained according to the preset classification rules. Based on the variable probability matrix of the second variable set and the number of actual scenario characteristics in the second variable set, the ideal membership relationship is corrected for deviation, and the actual membership relationship of each evaluation index in the first variable set to the actual scenario characteristics in the second variable set is obtained, which serves as a list of evaluation indicators under different scenario characteristics. The step of correcting the deviation of the formed ideal membership relationship based on the variable probability matrix of the second variable set and the number of actual scene characteristics in the second variable set includes: The distance matrix is ​​obtained by comparing the probability matrix of the second set of variables with the ideal cluster center matrix. The distance matrix is ​​normalized using the fuzzy normalization method to obtain the fuzzy membership matrix. Based on the comparison results of the number of actual scene characteristics in the second variable set and the number of ideal cluster centers, and combined with the fuzzy membership matrix, the classification relationship is determined. The evaluation indicators in the first set of variables belonging to each ideal cluster center are assigned to the corresponding scene characteristics in the second set of variables according to the obtained classification relationship, so as to obtain the actual membership relationship of each evaluation indicator in the first set of variables to the actual scene characteristics in the second set of variables. The formulas for calculating each element in the distance matrix are as follows: In the formula, The probability values ​​of each actual scenario characteristic occurring in the second variable set. These are the element values ​​in the ideal cluster center matrix; The process of determining the classification relationship based on the comparison results of the number of actual scene characteristics in the second variable set and the number of ideal cluster centers, combined with the fuzzy membership matrix, includes: Compare the number of actual scenario characteristics in the second set of variables. The size of the number of cluster centers C compared to the ideal number of cluster centers: like >C, for each scenario characteristic in the second variable set, select The ideal cluster centers are used for classification; If q=C, then each scenario characteristic in the second variable set corresponds one-to-one with the ideal cluster center; If q < C, then for each ideal cluster center, classify each scenario feature in the second variable set of .

5. A processing apparatus, the processing apparatus comprising at least a processor and a memory, the memory storing a computer program, characterized in that, When the processor runs the computer program, it performs steps to implement the target category membership analysis method according to any one of claims 1 to 3.

6. A computer storage medium, characterized in that, It stores computer-readable instructions that can be executed by a processor to implement the steps of the target category membership analysis method according to any one of claims 1 to 3.

Images